diff --git a/.gitignore b/.gitignore index 187847f..fc80316 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,8 @@ -preprocess/dataset/* checkpoints/* +dataset/* + .idea +.DS_Store ### JetBrains template # Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider # Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 diff --git a/00_neural_data_visualization.ipynb b/00_neural_data_visualization.ipynb new file mode 100644 index 0000000..42ce1cb --- /dev/null +++ b/00_neural_data_visualization.ipynb @@ -0,0 +1,641 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Data Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from models import SimpleCNN\n", + "from keras.models import load_model, Model\n", + "from scipy.io import loadmat\n", + "from sklearn.model_selection import train_test_split\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cnn = SimpleCNN(input_shape=(700, 1))\n", + "cnn.model.get_layer(index=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "data = loadmat('./dataset/corn/corn_mositure_split.mat')\n", + "x_train, y_train, x_test, y_test = data['x_train'], data['y_train'], data['x_test'], data['y_test']\n", + "x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.3, random_state=12, shuffle=True)\n", + "x_train, x_val, x_test = x_train[:, np.newaxis, :], x_val[:, np.newaxis, :], x_test[:, np.newaxis, :]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(39, 1, 700)\n" + ] + } + ], + "source": [ + "print(x_train.shape)\n", + "x_train = x_train.transpose(0, 2, 1)\n", + "x_val = x_val.transpose(0, 2, 1)\n", + "# cnn.fit(x=x_train, y=y_train, x_val=x_val, y_val=y_val, epoch=200, batch_size=20000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 700, 1)\n" + ] + } + ], + "source": [ + "test_data = x_train[0, ...]\n", + "test_data = test_data[np.newaxis, ...]\n", + "print(test_data.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, axs = plt.subplots()\n", + "axs.plot(np.linspace(1100, 2498, 700), test_data[0, ...], c=\"black\")\n", + "axs.set_title(\"Input Data\")\n", + "plt.savefig(\"assets/input.png\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-06-03 16:02:38.232092: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n" + ] + } + ], + "source": [ + "conv1 = Model(inputs=cnn.model.input, outputs=cnn.model.get_layer(index=3).output)\n", + "conv1_output = conv1.predict(test_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAbaklEQVR4nO3de3BV5b3/8feX3EAI10SCqAS5h6sQOHZEvFQtogK1xwFrUVvQQ6utZ0adqrT+7Egdq1UPVltESotjken0SI2oJ0VrqQWVBAUlKCHcNIAQBAIhIdfv749s0ogJ2YRczMPnNbMne6317Gd913L5YeXZa62YuyMiIm1fu9YuQEREmoYCXUQkEAp0EZFAKNBFRAKhQBcRCURsa604KSnJU1NTW2v1IiJt0tq1a/e5e3Jdy1ot0FNTU8nOzm6t1YuItElmtqO+ZRpyEREJhAJdRCQQCnQRkUAo0EVEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAqFAFxEJhAJdRCQQUQW6mU00s01mlmdm956g3VgzqzSz/2y6EkVEJBoNBrqZxQDPAFcBacANZpZWT7tfAZlNXaSIiDQsmjP0cUCeu2919zJgKTCljnY/Bv4X2NuE9YmISJSiCfTewGe1pvMj82qYWW/g28D8E3VkZreZWbaZZRcUFJxsrSIicgLRBLrVMc+Pm/4f4KfuXnmijtx9gbunu3t6cnJylCWKiEg0YqNokw+cU2v6bGDXcW3SgaVmBpAETDKzCnf/a1MUKSIiDYsm0LOAAWbWF9gJTAe+W7uBu/c99t7M/ggsV5iLiLSsBgPd3SvM7A6qr16JARa5e46ZzY4sP+G4uYiItIxoztBx99eA146bV2eQu/stp16WiIicLN0pKiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggogp0M5toZpvMLM/M7q1j+RQz+9DM1plZtpmNb/pSRUTkRGIbamBmMcAzwBVAPpBlZhnuvrFWszeBDHd3MxsB/BkY3BwFi4hI3aI5Qx8H5Ln7VncvA5YCU2o3cPcid/fIZEfAERGRFhVNoPcGPqs1nR+Z9yVm9m0z+wR4FfhB05QnIiLRiibQrY55XzkDd/dl7j4YmAo8VGdHZrdFxtizCwoKTqpQERE5sWgCPR84p9b02cCu+hq7+z+BfmaWVMeyBe6e7u7pycnJJ12siIjUL5pAzwIGmFlfM4sHpgMZtRuYWX8zs8j70UA88EVTFysiIvVr8CoXd68wszuATCAGWOTuOWY2O7J8PvAd4CYzKwdKgGm1viQVEZEWYK2Vu+np6Z6dnd0q6xYRaavMbK27p9e1THeKiogEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCCiCnQzm2hmm8wsz8zurWP5jWb2YeS12sxGNn2pIiJyIg0GupnFAM8AVwFpwA1mlnZcs23Axe4+AngIWNDUhYqIyIlFc4Y+Dshz963uXgYsBabUbuDuq939QGTyXeDspi1TREQaEk2g9wY+qzWdH5lXn5nA63UtMLPbzCzbzLILCgqir1JERBoUTaBbHfO8zoZml1Id6D+ta7m7L3D3dHdPT05Ojr5KERFpUGwUbfKBc2pNnw3sOr6RmY0AFgJXufsXTVOeiIhEK5oz9CxggJn1NbN4YDqQUbuBmZ0LvATMcPfcpi9TREQa0uAZurtXmNkdQCYQAyxy9xwzmx1ZPh94AOgB/NbMACrcPb35yhYRkeOZe53D4c0uPT3ds7OzW2XdIiJtlZmtre+EWXeKiogEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCCiCnQzm2hmm8wsz8zurWP5YDN7x8xKzezupi9TREQaEttQAzOLAZ4BrgDygSwzy3D3jbWa7Qd+AkxtjiJFRKRh0ZyhjwPy3H2ru5cBS4EptRu4+153zwLKm6FGERGJQjSB3hv4rNZ0fmTeSTOz28ws28yyCwoKGtOFiIjUI5pAtzrmeWNW5u4L3D3d3dOTk5Mb04WIiNQjmkDPB86pNX02sKt5yhERkcaKJtCzgAFm1tfM4oHpQEbzliUiIierwatc3L3CzO4AMoEYYJG755jZ7Mjy+WaWAmQDnYEqM/tvIM3dDzVf6SIiUluDgQ7g7q8Brx03b36t959TPRQjIiKtRHeKiogEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4g0sYqKCvbt28eRI0dadL1RPW1RRETqV1xczNtvv82KFSt44403yMnJoaKiAoBzzz2XCRMmcM011/Ctb32Lrl27NlsdCnQRkUaorKwkMzOT+fPnk5mZSVlZGfHx8Vx44YXcfffd9OrViyNHjrB+/Xpef/11XnjhBdq3b8+GDRvo169fs9SkQBcROQl79+5l0aJFPPvss2zfvp2ePXty++23c+WVVzJhwgTOOOOMr3ymsrKS559/nh/84Ads27ZNgS4Sgi1btvC3v/2NlJQULrvsMrp06dLaJUkU3J3Vq1fz29/+lr/85S+UlZVxySWX8Ktf/YqpU6cSHx9/ws/HxMQwatQoAIqKipqtTgW6SAv5/e9/z6xZs2qmu3Xrxq9//Wu+//3vY2atWFnbUFlZyfvvv8+2bduYPHky7du3b5H1rlq1ijlz5rBy5Uq6dOnC7NmzmT17NkOGDDmpfjp16gQ0b6DrKheRFvD2228ze/ZsrrzySnJzc1m5ciXDhg1j5syZ/PKXv2zt8r7W3J2XXnqJYcOGMW7cOKZNm8aoUaP48MMPa9qUlpaSkZFBRkYGR48ebZL15ubmMnnyZMaPH88nn3zCU089xc6dO5k3b95JhzlAYmIiAIcPH26S+urk7q3yGjNmjIucDiorK3348OF+3nnn+cGDB780f8aMGQ74c88914oVfn1t2rTJL7jgAgd88ODBvnjxYl+2bJmfddZZnpSU5O+8844vW7bMe/bs6YAD3qVLF7/nnnt8w4YNXlVVddLr3LNnj991110eGxvriYmJ/vDDD3tRUdEpb8vhw4cd8EcfffSU+gGyvZ5c1ZCLSDN7+eWX+eijj3jhhRe+NGberl07Fi1axJ49e7jjjjsYPXo0o0ePbrY6Pv/8c1avXk1ubi6ff/45e/fupbCwkKKiIkpLS+nYsSOdOnWiZ8+e9OvXj/79+zN27FjOOeecVhkSyszM5PrrrychIYGFCxdy8803ExtbHVlDhw5lwoQJfOMb3wBg1KhR/PGPfyQmJoaFCxfy+OOP89hjj9GzZ08GDRpE+/btiY+Pp1+/fowaNYoePXqQl5dHTk4O+/btIyYmBndn586drF27lqqqKmbOnMncuXPp2bNnk2zPsS9Lm3PIxaoDv+Wlp6d7dnZ2q6xbpKV49W+jFBUVsXHjxppAqq2goIDRo0cTHx/P2rVrm/Q6ZXdn+fLlPPnkk/zjH//g2P/vx4K7a9eudOrUifj4eIqLizl8+DC7d++moKCgpo/evXtz0UUXcdVVVzFx4kTOPPPMJquvPn//+9+ZNGkSQ4YMISMjg3POOecrbXbu3Mkbb7xBRUUF3/ve90hISKhZtnv3bjIyMnjnnXfYtm0bpaWllJSUsHnzZkpKSmraJScnk5KSUnOGm5KSwtixY7npppsaNazSkMTERG699VaeeOKJRvdhZmvdPb3OhfWdujf3S0Mucjp46623ohpSWb16tcfGxvrkyZO9vLy8Sdadk5Pj48ePd8D79OnjDz74oL/77rt++PDhBj9bWFjoWVlZ/pvf/ManT59eM6RhZj5u3Dh/8MEHfc2aNV5ZWdkktda2atUq79ixow8bNsz37dvXpH2Xl5d7bm6ur1692vfu3dukfUcjJSXFb7311lPqgxMMuSjQRZrRlClTvEePHl5cXNxg23nz5jngkydP9sLCwkav8+jRoz5nzhyPi4vz7t27+4IFC7ysrKzR/blXj/evXbvWH3roIb/gggvczBzw5ORkv+mmm3zp0qW+f//+U1qHu/vKlSu9c+fOPnDgQN+9e/cp9/d1079/f7/hhhtOqQ8Fukgr2Lx5s5uZ33///VF/5umnn3Yz8zPPPNMfffRRz8/PP6l1ZmVleVpamgN+0003NdtZaEFBgb/wwgv+3e9+17t3715z9j5kyBC/+eab/ZlnnvGsrCwvLS2Nqr/KykqfP3++JyQk+ODBg/3TTz9tlrpb2/nnn+/XXHPNKfVxokDXGHpANm/ezP79+zn33HPp1atXa5dz2ps5cyZ/+tOf2LZt20n998jOzubuu+9m5cqVmBmXXHIJU6dO5dprr6Vv3751fuaLL77gkUce4cknnyQlJYWFCxcyceLEptqUE6qsrGTNmjW8+eabrFmzhvfee4+9e/cCEB8fz9ChQxkxYgRpaWn07t2b3r17061bN9q1a8cXX3zB+++/z/PPP8/69eu5/PLLWbp0KT169GiR2lvahAkTiImJ4a233mp0HycaQ1egByArK4tZs2Z96brc888/nylTpjBlyhRGjhypG1da2LZt2xg4cCA//OEPeeqppxrVx+bNm1myZAlLly7lk08+ASAtLY1JkyYxcuRIOnfuzJ49e1i5ciXLli2jpKSEW265hSeeeKJZHwDVEHfns88+Y82aNWRlZbFu3TrWr1/Pnj176v3M6NGjufPOO5kxY0bQx+qkSZMoKCggKyur0X0o0AP20ksvMX36dHr16sU999xD37592bBhQ803/O5Onz59uPbaaxk3bhwjR45k8ODBDd6qLKdm2rRp/PWvf2XLli2cffbZp9xfXl4ey5cv55VXXuGf//xnzZP8oPqO0+uvv56f/OQnDB069JTX1VwOHTrErl272LlzJ4WFhVRVVdG1a1eGDBlC7969W7u8FjFt2jTWr19f8w90YyjQA/X+++8zfvx4Ro4cyauvvkr37t2/tHzPnj0sX76cl19+mRUrVtTcQRcXF8fQoUMZPXo0Y8aMYcyYMYwYMYIOHTq0xmYEZ9myZVx33XXMnTuXOXPmNHn/ZWVlbN26leLiYrp160afPn1o1043fbcFM2fOJDMzk/z8/Eb3oUAP0O7duxk7diwxMTGsWbOmwZsfKioqyM3NZf369axfv55169axdu1a9u3bB1Tf5NKvXz/S0tLo27cvKSkpJCcnExcXV9OHu1NeXk5FRQXl5eVUVVURHx9PQkIC3bt3JzU1ldTU1GZ94JS7k5OTw4YNG+jZsydjx46teUbGiZSVlREXF9fsv85v3LiR8ePH06dPH9asWfOl/Sdy5513snjxYg4ePNjoPk4U6LpTtA0qKSlh6tSpHDx4kFWrVkV1J1tsbCxpaWmkpaVxww03AP8e68zOzmbdunV8/PHH5OTk8Oabb57S3Wz9+/fnwgsv5KKLLuKb3/wmqampje6rts2bN/PjH/+YzMzMmnkJCQlcdtllDB8+nNjYWEpKSjh69CglJSWUlJSQm5vLpk2bKC4uJiYmhq5du9KlSxe6du1a8+v+hAkTmDhxIp07dz6l+j744AMmT55MQkICL730ksJcviIxMZHDhw9XX5HSDCcXOkNvY9ydG2+8kRdffJFly5YxderUZllPUVERBQUFVFVVfWl+XFwccXFxxMbG0q5dO8rKyigtLWXfvn1s376dvLw83nvvPVatWlVzt2G/fv24/PLLufjiixk9ejT9+/cnJiYm6lr279/PE088wWOPPUZCQgIPPPAAV1xxBbt37yYzM5MVK1aQm5uLu9O+fXs6dOhA+/btSUhIIDU1leHDh9O9e3eKi4spLCzk4MGDFBYWsn//fj766COKioqIi4vj0ksvZfz48fTv378m+I+9kpKS6n2634EDB2rqS0pK4tVXX2XkyJGN3/kSrEceeYT77ruP4uLiRg9x6gw9ID/72c948cUXefjhh5stzKH61vBohjKOSU1NJT3938eYu/PJJ5/wxhtvsGLFCpYsWcKzzz4LVD/T4rzzzqNPnz706dOHc889lx49etClSxcSEhIoKiqquQX9gw8+4M033+TIkSPccMMNPP744zWXAI4cObLm0rzGnvFUVlby7rvv8vLLL7N8+XIeeOCBOtsdG5IaOnQow4YN46yzzuLgwYOsWbOG119/ndLSUqZNm8bTTz9NUlLSSdchp4faj9Btju+sdIbeRrg7c+fO5YEHHuDWW2/l2WefbVOXd5WXl7Nx40Y++OAD1q9fz9atW/n000/ZsWMHBw4cqPMzZlZzdv+jH/2I4cOHN3udx67EOHjwIAcOHODQoUMUFhayc+dOcnJyyMnJYfPmzVRWVgLV/5Bde+21zJo1ixEjRjR7fdK2LV68mFtuuYUtW7Zw3nnnNaoPnaG3cbt27eLuu+/mxRdfZMaMGfzud79rU2EO1UM1I0eOrHMooqioiAMHDlBYWMjRo0fp1KkTiYmJJCcnt/jllZ07d25wLL20tJQDBw5wxhlnnPK4u5xemvuPXCjQv0aqqqrYsmULmzdvJjc3l61bt/LRRx/xzjvvUFlZyUMPPcScOXPaXJg35NjwTl1P1Ps6SkhIICUlpbXLkDZIgR44d+df//oXf/jDH3jllVdqLiOE6m/EBw0axO23387tt9/e6F/RROTrobn/alHwge7u5Ofnk5uby759+9i/fz87duxg48aNbNq0ifLyclJSUhg3bhxXX301l156aZ3PrG5qO3fu5Pnnn2fRokXk5eWRmJjI5MmTufTSSxkyZAgDBgwgKSkpuLNxkdOZztAbae/evcybN48lS5awffv2Ly2Li4tj0KBBjBgxgvbt27Njxw4WLFjAvHnz6N27NzNnzmTWrFlNPgSwY8cOXn31VTIyMlixYgVVVVVcfPHF/PznP+c73/kOHTt2bNL1icjXy9ci0M1sIjAPiAEWuvsjxy23yPJJQDFwi7u/38S1RmXPnj08+uijzJ8/n5KSEiZOnMhdd93F0KFDOfPMM+nRowdJSUlfOQsvKSnh9ddf57nnnuOhhx5i7ty5TJw4kfHjx9OtWzeqqqooLy+vuerh4MGDHDlyhHbt2hETE0OHDh1qvlBLTEykqqqKw4cPc+DAATZt2kROTg6fffYZUH1d9n333cctt9xC//79W2M3iUgraPUhFzOLAZ4BrgDygSwzy3D3jbWaXQUMiLz+A/hd5GeL2b9/P4899hhPPfUUpaWl3Hjjjdx///0MGjQoqs936NCB6667juuuu47t27ezcOFClixZwmuvvfaVtmeccQZdunShU6dOVFVVUVFRwdGjRzl06NCX/rwVQMeOHRk4cCATJkxgzJgxXH311QwcOLBJtllE2pavwxn6OCDP3bcCmNlSYApQO9CnAM9HHr7+rpl1NbNe7r67qQtetmwZM2bMoKqqCnev+Xns6XPTp0/nF7/4BQMGDGj0OlJTU5k7dy5z587l0KFDFBcX15yJd+7c+YS3dFdUVHDo0CHatWtHp06dWmQ8XkTahvbt29OuXbtmO0Nv8MYiM/tPYKK7z4pMzwD+w93vqNVmOfCIu/8rMv0m8FN3zz6ur9uA2yKTg4BNjaw7CdjXYKvwaT9oH4D2wTGny37o4+7JdS2I5vSxrsssjv9XIJo2uPsCYEEU6zxxQWbZ9d0pdTrRftA+AO2DY7QfIJqHKOcDtS/3OBvY1Yg2IiLSjKIJ9CxggJn1NbN4YDqQcVybDOAmq3YBUNgc4+ciIlK/Bodc3L3CzO4AMqm+bHGRu+eY2ezI8vnAa1RfsphH9WWL32++koEmGLYJhPaD9gFoHxxz2u+HVnvaooiINC39IUIRkUAo0EVEAtHmAt3MJprZJjPLM7N7W7uelmJm283sIzNbZ2bZkXndzWyFmW2O/OzW2nU2NTNbZGZ7zWxDrXn1breZ3Rc5NjaZ2bdap+qmVc8+eNDMdkaOh3VmNqnWshD3wTlm9paZfWxmOWZ2Z2T+aXUsNMjd28yL6i9ltwDnAfHAeiCttetqoW3fDiQdN+9R4N7I+3uBX7V2nc2w3ROA0cCGhrYbSIscEwlA38ixEtPa29BM++BB4O462oa6D3oBoyPvE4HcyLaeVsdCQ6+2doZe8xgCdy8Djj2G4HQ1BVgceb8YmNp6pTQPd/8nsP+42fVt9xRgqbuXuvs2qq+6GtcSdTanevZBfULdB7s98sA/dz8MfAz05jQ7FhrS1gK9N/BZren8yLzTgQN/M7O1kUcoAPT0yPX+kZ9ntlp1Lau+7T7djo87zOzDyJDMsaGG4PeBmaUC5wPvoWPhS9paoEf1iIFAXejuo6l+suXtZjahtQv6Gjqdjo/fAf2AUcBu4PHI/KD3gZl1Av4X+G93P3SipnXMC2Y/1KetBfpp+4gBd98V+bkXWEb1r497zKwXQOTn3tarsEXVt92nzfHh7nvcvdLdq4Dn+PdwQrD7wMziqA7zP7n7S5HZp/2xUFtbC/RoHkMQHDPraGaJx94DVwIbqN72myPNbgZebp0KW1x9250BTDezBDPrS/Xz+de0Qn3N7liIRXyb6uMBAt0HkT+i83vgY3d/otai0/5YqK1NPazb63kMQSuX1RJ6Assif180Flji7v9nZlnAn81sJvApcH0r1tgszOxF4BIgyczygf8HPEId2+3Vj6T4M9XP6q8Abnf3ylYpvAnVsw8uMbNRVA8jbAf+C8LdB8CFwAzgIzNbF5l3P6fZsdAQ3fovIhKItjbkIiIi9VCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhKI/w9D1wFHUtbuGAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAMtElEQVR4nO3cUYxc51mH8eePzfYiFEHwUiLbwaZYslZWKNFikIpKuSgkuXEiQDigtkAlE6mBNFKlOlxAFd+kkUAIKdQyYFEkwIrSBiwIBISQelEavEZpGse4OGnAW5dm20QUC1THycvFjpOJ2fGMnV1v9t3nJ612zjmfZ775dPRoPDtzUlVIkta+b1vtCUiSlodBl6QmDLokNWHQJakJgy5JTWxcrQfetGlTbdu2bbUeXpLWpOPHj3+9qqaXOrZqQd+2bRtzc3Or9fCStCYl+fdRx3zLRZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU1MFPQktyQ5leR0kv2XGfcjSV5J8rPLN0VJ0iTGBj3JBuAh4FZgBrgzycyIcZ8AHl/uSUqSxpvkFfpu4HRVPVdV54EjwJ4lxv0a8GnghWWcnyRpQpMEfTNwZmh7frDvNUk2A3cABy93R0n2JZlLMrewsHClc5UkXcYkQc8S++qS7d8FPlZVr1zujqrqUFXNVtXs9PT0hFOUJE1i4wRj5oGtQ9tbgLOXjJkFjiQB2ATcluRCVf3FckxSkjTeJEE/BuxIsh34CrAX+IXhAVW1/eLtJH8M/JUxl6Rra2zQq+pCkrtZ/PTKBuBwVZ1Ictfg+GXfN5ckXRuTvEKnqh4DHrtk35Ihr6pfevPTkiRdKb8pKklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmJgp6kluSnEpyOsn+JY7vSfJUkieTzCX58eWfqiTpcjaOG5BkA/AQ8D5gHjiW5GhVPTM07B+Ao1VVSW4CHgZ2rsSEJUlLm+QV+m7gdFU9V1XngSPAnuEBVXWuqmqweR1QSJKuqUmCvhk4M7Q9P9j3BknuSPKvwF8Dv7I805MkTWqSoGeJff/vFXhVPVpVO4HbgQNL3lGyb/Ae+9zCwsIVTVSSdHmTBH0e2Dq0vQU4O2pwVX0WeGeSTUscO1RVs1U1Oz09fcWTlSSNNknQjwE7kmxPMgXsBY4OD0jyg0kyuH0zMAV8Y7knK0kabeynXKrqQpK7gceBDcDhqjqR5K7B8YPAzwAfSPIy8L/Azw/9kVSSdA1ktbo7Oztbc3Nzq/LYkrRWJTleVbNLHfObopLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMTBT3JLUlOJTmdZP8Sx38xyVODn88l+aHln6ok6XLGBj3JBuAh4FZgBrgzycwlw74M/ERV3QQcAA4t90QlSZc3ySv03cDpqnquqs4DR4A9wwOq6nNV9dJg8/PAluWdpiRpnEmCvhk4M7Q9P9g3yoeAv1nqQJJ9SeaSzC0sLEw+S0nSWJMEPUvsqyUHJj/JYtA/ttTxqjpUVbNVNTs9PT35LCVJY22cYMw8sHVoewtw9tJBSW4C/hC4taq+sTzTkyRNapJX6MeAHUm2J5kC9gJHhwckuRH4DPD+qvrS8k9TkjTO2FfoVXUhyd3A48AG4HBVnUhy1+D4QeA3ge8Bfj8JwIWqml25aUuSLpWqJd8OX3Gzs7M1Nze3Ko8tSWtVkuOjXjD7TVFJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJamJiYKe5JYkp5KcTrJ/ieM7k/xTkm8l+ejyT1OSNM7GcQOSbAAeAt4HzAPHkhytqmeGhr0I/Dpw+0pMUpI03iSv0HcDp6vquao6DxwB9gwPqKoXquoY8PIKzFGSNIFJgr4ZODO0PT/Yd8WS7Esyl2RuYWHhau5CkjTCJEHPEvvqah6sqg5V1WxVzU5PT1/NXUiSRpgk6PPA1qHtLcDZlZmOJOlqTRL0Y8COJNuTTAF7gaMrOy1J0pUa+ymXqrqQ5G7gcWADcLiqTiS5a3D8YJLvA+aA7wReTfIRYKaqvrlyU5ckDRsbdICqegx47JJ9B4du/yeLb8VIklaJ3xSVpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKamCjoSW5JcirJ6ST7lzieJL83OP5UkpuXf6qvu3DhAqdOneLkyZOcOXOGl156iZdffnklH1KS3vLGBj3JBuAh4FZgBrgzycwlw24Fdgx+9gGfXOZ5vuaRRx7huuuuY+fOnczMzHDjjTdy/fXXMzU1xa5du7jvvvt49tlnefXVV1dqCpL0lrRxgjG7gdNV9RxAkiPAHuCZoTF7gD+pqgI+n+S7ktxQVV9d7gnPzMxwzz33sGvXLqampjh37hznzp3jxRdf5IknnuDBBx/kgQceYGpqiqmpqeV+eEl60+69917uv//+Zb/fSYK+GTgztD0P/OgEYzYDbwh6kn0svoIHOJfk1BXN9nWbgK9fbsD58+c5f/78Vd79mjF2HdYB18A1uGjNrMOBAwc4cODA1f7z7x91YJKgZ4l9dRVjqKpDwKEJHvPyE0rmqmr2zd7PWuc6uAbgGlzkOkz2R9F5YOvQ9hbg7FWMkSStoEmCfgzYkWR7kilgL3D0kjFHgQ8MPu3yY8B/rcT755Kk0ca+5VJVF5LcDTwObAAOV9WJJHcNjh8EHgNuA04D/wP88spNGViGt22acB1cA3ANLlr365DFD6ZIktY6vykqSU0YdElqYs0FfdxlCLpK8nySLyZ5MsncYN/1Sf4+yb8Nfn/3as9zuSU5nOSFJE8P7Rv5vJPcNzg3TiX56dWZ9fIasQYfT/KVwfnwZJLbho51XIOtSf4xyckkJ5LcM9i/rs6Fsapqzfyw+EfZZ4EfAKaALwAzqz2va/Tcnwc2XbLvQWD/4PZ+4BOrPc8VeN7vAW4Gnh73vFm8NMUXgLcB2wfnyobVfg4rtAYfBz66xNiua3ADcPPg9tuBLw2e67o6F8b9rLVX6K9dhqCqzgMXL0OwXu0BPjW4/Sng9tWbysqoqs8CL16ye9Tz3gMcqapvVdWXWfzU1e5rMc+VNGINRum6Bl+tqn8Z3P5v4CSL30ZfV+fCOGst6KMuMbAeFPB3SY4PLqEA8I4afN5/8Pt7V21219ao573ezo+7B1c3PTz0VkP7NUiyDfhh4Ak8F95grQV9oksMNPXuqrqZxStbfjjJe1Z7Qm9B6+n8+CTwTuBdLF4z6bcH+1uvQZLvAD4NfKSqvnm5oUvsa7MOo6y1oK/bSwxU1dnB7xeAR1n87+PXktwAMPj9wurN8Joa9bzXzflRVV+rqleq6lXgD3j97YS2a5Dk21mM+Z9W1WcGu9f9uTBsrQV9kssQtJPkuiRvv3gb+CngaRaf+wcHwz4I/OXqzPCaG/W8jwJ7k7wtyXYWr8//z6swvxV3MWIDd7B4PkDTNUgS4I+Ak1X1O0OH1v25MGySqy2+ZdSIyxCs8rSuhXcAjy6e02wE/qyq/jbJMeDhJB8C/gP4uVWc44pI8ufAe4FNSeaB3wIeYInnXYuXpHiYxWv1XwA+XFWvrMrEl9GINXhvknex+DbC88CvQt81AN4NvB/4YpInB/t+g3V2LozjV/8lqYm19paLJGkEgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCb+D+r2EmyZa6c9AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "for i in range(conv1_output.shape[-1]):\n", + " plt.plot(conv1_output[0, :, i], color=\"black\")\n", + " plt.ylim(conv1_output.min(), conv1_output.max())\n", + " plt.savefig(f\"assets/conv1/{i}.png\")\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD4CAYAAAAAczaOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAArR0lEQVR4nO3deVyVdfr/8dcFCrmhKYqTuKWGS7ni9tNcKk1tXPo2pi3aON9y1JzUVrWsrLFtyrLcp9S01JrU1FLQVv2OjgiBO7gkilpKOpqKiMD1+4OjnRDkAAfOgXM9H4/z4Nz3/fnc9/ugXNx87k1UFWOMMb7Bz9MBjDHGFB8r+sYY40Os6BtjjA+xom+MMT7Eir4xxviQMp4OkJPg4GCtV6+ep2MYY0yJERMT84uqVs+rnVcW/Xr16hEdHe3pGMYYU2KIyCFX2rk0vCMivUQkQUT2i8j4a7RrKyIZIvKn/PY1xhhT9PIs+iLiD8wAegNNgftEpGku7V4HIvPb1xhjTPFwZU+/HbBfVX9U1TRgKdA/h3Z/A5YBJwrQ1xhjTDFwpejXApKcpo845l0hIrWAu4HZ+e3rtI7hIhItItHJyckuxDLGGJNfrhR9yWFe9hv2vAM8o6oZBeibNVN1rqqGq2p49ep5HoA2xhhTAK6cvXMEqO00HQocy9YmHFgqIgDBQB8RSXexrzHGmGLiStHfCjQSkfrAUWAwcL9zA1Wtf/m9iCwAvlDVz0WkTF59jTHGFJ88i76qpovIaLLOyvEH5qnqLhEZ4ViefRw/z77uiW6MMSa/xBvvpx8eHq52cZYxxrhORGJUNTyvdnbvHWOM8SFW9I0xxodY0TfGGB9iRd8YY3yIFX1jjPEhVvSNMcaHWNE3xhgfYkXfGGN8iBV9Y4zxIVb0jTHGh1jRN8YYH2JF3xhjfIgVfWOM8SFW9I0xxodY0TfGGB9iRd8YY3yIFX1jjPEhVvSNMcaHuFT0RaSXiCSIyH4RGZ/D8v4isl1E4kQkWkQ6Oy1LFJEdl5e5M7wxxpj8yfPB6CLiD8wAegBHgK0iskpVdzs1+xpYpaoqIs2BT4HGTsu7q+ovbsxtjDGmAFzZ028H7FfVH1U1DVgK9HduoKrn9LcnrFcAvO9p68YYY1wq+rWAJKfpI455vyMid4tIPPAl8BenRQqsE5EYERme20ZEZLhjaCg6OTnZtfTGGGPyxZWiLznMu2pPXlVXqGpjYADwstOiTqraGugNPCoiXXLaiKrOVdVwVQ2vXr26C7GMMcbklytF/whQ22k6FDiWW2NV3QA0EJFgx/Qxx9cTwAqyhouMMcZ4gCtFfyvQSETqi0gAMBhY5dxARBqKiDjetwYCgJMiUkFEKjnmVwB6Ajvd+QGMMca4Ls+zd1Q1XURGA5GAPzBPVXeJyAjH8tnAPcBQEbkEXAAGOc7kCQFWOH4flAEWq2pEEX0WY4wxeZDfTrrxHuHh4Rodbaf0G2OMq0QkRlXD82pnV+QaY4wPsaJvjDE+xIq+Mcb4ECv6xhjjQ6zoG2OMD7Gib4wxPsSKvjHG+BAr+sYY40Os6BtjjA+xom+MMT7Eir4xxvgQK/rGGONDrOgbY4wPsaJvjDE+xIq+Mcb4ECv6xhjjQ6zoG2OMD3Gp6ItILxFJEJH9IjI+h+X9RWS7iMSJSLSIdHa1rzHGmOKTZ9EXEX9gBtAbaArcJyJNszX7Gmihqi2BvwDv56OvMcaYYuLKnn47YL+q/qiqacBSoL9zA1U9p789bLcCoK72NcYYU3xcKfq1gCSn6SOOeb8jIneLSDzwJVl7+y73dfQf7hgaik5OTnYluzHGmHxypehLDvP0qhmqK1S1MTAAeDk/fR3956pquKqGV69e3YVYxhhj8suVon8EqO00HQocy62xqm4AGohIcH77GmOMKVquFP2tQCMRqS8iAcBgYJVzAxFpKCLieN8aCABOutLXGGNM8SmTVwNVTReR0UAk4A/MU9VdIjLCsXw2cA8wVEQuAReAQY4Duzn2LaLPYowxJg/y20k33iM8PFyjo6M9HcMYY0oMEYlR1fC82tkVucYY4wVOnjzJhQsXinw7VvSNMcYLvPjii9xwww1kZmYW6Xas6BtjjBeIioqiRYsW+PkVbVm2om+MMR528eJF4uLiaNeuXZFvy4q+McZ42LZt20hLS6N9+/ZFvi0r+sYY42FRUVEAtqdvjDG+ICoqipo1axIaGlrk27Kib4wxHrZlyxbat2+P48YGRcqKvjHGeNB///tf9u7dWyxDO2BF3xhjPOry3Qes6BtjjA/YsmULAG3bti2W7VnRN8YYD4qKiqJx48ZUrly5WLZnRd8YYzxEVYmKiiq2oR2wom+MMR5z+PBhjh8/XiwXZV1mRd8YYzykOC/KusyKvjHGeEhUVBSBgYE0b9682LZpRd8YYzwkKiqKVq1aERAQUGzbtKJvjDEekJ6eTnR0dLEO7YCLRV9EeolIgojsF5HxOSx/QES2O16bRKSF07JEEdkhInEiYs9ANMYYYPfu3aSkpBR70c/zwegi4g/MAHoAR4CtIrJKVXc7NTsIdFXV/4pIb2Au4Hw4uruq/uLG3MYYU6JdPohbnGfugGt7+u2A/ar6o6qmAUuB/s4NVHWTqv7XMfkfoOhvFWeMMSVUSkoK77zzDrVq1aJBgwbFum1Xin4tIMlp+ohjXm7+F1jrNK3AOhGJEZHhuXUSkeEiEi0i0cnJyS7EMsaYkmnMmDHs2rWLefPmFcudNZ3lObwD5JRIc2wo0p2sot/ZaXYnVT0mIjWA9SISr6obrlqh6lyyhoUIDw/Pcf3GGFPSLV26lPfff5/x48fTs2fPYt++K3v6R4DaTtOhwLHsjUSkOfA+0F9VT16er6rHHF9PACvIGi4yxhifs3//foYPH07Hjh156aWXPJLBlaK/FWgkIvVFJAAYDKxybiAidYDlwBBV3es0v4KIVLr8HugJ7HRXeGOMKSkuXrzI4MGDKVOmDEuWLKFs2bIeyZHn8I6qpovIaCAS8AfmqeouERnhWD4beB6oBsx0jE+lq2o4EAKscMwrAyxW1Ygi+STGGOPF/v73vxMTE8OKFSuoW7eux3KIqvcNn4eHh+vlBwsYY0xJd/jwYcLCwvif//kfPv744yLZhojEOHa2r8muyDXGmCI2YcIEAF599VUPJ7Gib4wxRWrLli0sXryYJ554gjp16ng6jhV9Y4wpKqrK448/Ts2aNRk//qo72HiEK+fpG2OMKYB//etfbNq0iffff5+KFSt6Og5gB3KNMaZIpKam0qRJEypXrkxMTAz+/v5Fuj1XD+Tanr4xxhSB9957j8TERL766qsiL/j5YWP6xhjjZidPnmTKlCn06dOH22+/3dNxfseKvjHGuNkrr7zC2bNnef311z0d5SpW9I0xxo0OHjzI9OnTGTZsGDfffLOn41zFir4xxrjRc889h7+/P5MnT/Z0lBxZ0TfGGDf54YcfWLx4MePGjaNWrWs9dsRzrOgbY4wbqCpPPfUUwcHBPP30056Okys7ZdMYY9xg7dq1fPPNN7z77rtUrlzZ03FyZRdnGWNMIaWkpHDzzTcTGBjItm3bCAgIKPYMdnGWMcYUk5dffpmDBw/y/fffe6Tg54eN6RtjTCHs2LGDN998k2HDhtGlSxdPx8mTFX1jjCmgzMxM/vrXv1KlShX+8Y9/eDqOS1wq+iLSS0QSRGS/iFx1f1AReUBEtjtem0Skhat9jTGmpPrnP//J5s2beeutt6hWrZqn47gkz6IvIv7ADKA30BS4T0SaZmt2EOiqqs2Bl4G5+ehrjDFFLj09nXXr1vHDDz+QlpZW6PUdOXKEZ555hu7duzNkyBA3JCweruzptwP2q+qPqpoGLAX6OzdQ1U2q+l/H5H+AUFf7GmNMUVJVvvjiC1q0aMGdd95JmzZtqFSpEm3btmXkyJF88803ZGZm5mud0dHRtG/fnvT0dGbNmoWIFFF693Ol6NcCkpymjzjm5eZ/gbUF7GuMMW4TFRVFt27d6Nu3L5cuXWLx4sV88sknjBkzhqCgID7++GNuv/12GjZsyJQpUzh69Gie61yyZAm33norZcuWZdOmTYSFhRXDJ3EfV07ZzOlXWI4n94tId7KKfucC9B0ODAe84jmSxpiSKzMzkylTpvDCCy9QvXp1ZsyYwSOPPELZsmUBuPfeewG4cOECK1as4P333+e5555j0qRJNG/enK5du9KlSxfat28PwLlz5zh79izLli3j9ddf59Zbb2XZsmVUr17dY5+xwFT1mi+gIxDpND0BmJBDu+bAAeCm/PbN/mrTpo0aY0xBnDp1Su+66y4F9IEHHtBff/3VpX779u3Tl156SW+//XYtV66ckrWDetXrkUce0YsXLxbxp8g/IFrzqK2qmvcVuSJSBtgL3A4cBbYC96vqLqc2dYBvgKGquik/fXNiV+QaYwoiNjaWe+65hyNHjvD2228zatSoAo23p6WlERMTww8//EBAQAAVK1akUqVKhISEEB4e7pVj+G67IldV00VkNBAJ+APzVHWXiIxwLJ8NPA9UA2Y6vhnpqhqeW98CfypjjMnFqVOn6Nq1K5UrV2bDhg106NChwOsKCAigY8eOdOzY0Y0JvYNLt2FQ1TXAmmzzZju9fxh42NW+xhjXnDlzhmeffZbRo0fTuHFjj2bJyMhg8eLFbNiwgapVq1K9enWqV69Oy5YtadGiRd4rKGILFy7k7NmzbNy40SvyeCu7944xXiozM5OHHnqIlStXsmnTJrZs2XLlQGRxUlW+/PJLJkyYwM6dO6latSrnzp27cq67iDB27FimTJlCuXLlij3f5Yxz5syhQ4cOVvDzYLdhMMZLvfHGG6xcuZK7776b2NhY3nzzzWLPEB8fT5cuXejbty+pqal88sknJCcnk5qaypkzZ9i3bx+jRo3i7bffplWrVmzZsqXYMwJs3LiR+Ph4hg8f7pHtlyiuHO0t7pedvWN83VdffaV+fn46aNAgzczM1IEDB2pAQIDu3r272DIkJibqDTfcoMHBwTpr1ixNS0vLte369eu1du3a6ufnpy+++KJmZmYWW05V1QceeEArV66s58+fL9btehNcPHvH4wU+p5cVfePLDh8+rMHBwdq0aVM9e/asqqr+/PPPWq1aNe3QoYOmp6cXeYbk5GQNCwvTKlWq6I4dO1zqc/r0aX3wwQcV0GeeeabYCv8vv/yigYGB+uijjxbL9ryVFX1jSqCMjAzt0KGDVqpUSffs2fO7ZR9//LECOnXq1CLNcO7cOW3fvr0GBgbqhg0b8tU3MzNTR4wYoYA+99xzRZTw99566y0FdPv27cWyPW9lRd+YEmj79u0K6IwZM65alpmZqX379tVy5crpgQMHimT7aWlp2rt3b/Xz89MVK1YUaB0ZGRn68MMPK6CTJ092b8BsMjMzNSwsTDt27Fik2ykJXC36diDXGC8SExMDwG233XbVMhFh1qxZ+Pn5MXHixCLZ/osvvsjatWuZNWsWAwYMKNA6/Pz8mDNnDn/+85954YUXePXVV90b0smGDRtISEiwA7j54cpvhuJ+2Z6+8VWjR4/WihUrakZGRq5tJk2apIBGR0e7ddvx8fFatmxZHTp0qFvWl56erg888IACOnPmTLesM7v77rvP5w/gXobt6RtT8sTExNCqVSv8/HL/0XzyyScJDg5m/Hj3PZNIVXnssccoX748b7zxhlvW6e/vz/z58+nbty+PPvooS5Yscct6L0tOTmbZsmUMGTKE8uXLu3XdpZkVfWO8RHp6OnFxcbRp0+aa7YKCgnj22Wf56quvWL9+vVu2vXz5ctatW8fLL79MSEiIW9YJULZsWT755BNuvfVWhg4dytq1a/Pu5KJ58+aRlpbGyJEj3bZOX5DnDdc8wW64ZnzRzp07ueWWW1i0aBEPPvjgNdtevHiRsLAwqlWrxtatW6/5l0Fezp8/T5MmTahatSrR0dGUKeP+C/XPnDlD9+7diY+PZ/369XTq1KlQ68vIyKBhw4bUq1ePb7/91k0pSzZXb7hme/rGeInLB3Hz2tMHCAwM5OWXX+aHH37g008/LdR2p0yZQlJSEjNmzCiSgg9QuXJlIiIiqF27NnfddRdxcXGFWl9kZCSJiYm2l18AVvSN8RLR0dFUrFiRm266yaX2999/P7fccgvPPfdcgZ/5mpCQwJtvvslDDz1U6L3vvNSoUYP169cTFBREz5492bt3b4HXNXPmTGrWrFngM4x8mRV9Y7zE5YO4/v7+LrX39/fntdde48CBA0ybNq1A2xw3bhzlypXj9ddfL1D//KpTp86V4xB33HEHhw8fzvc6Dh48yJo1a3jkkUcICAhwd8RSz4q+MV7A1YO42fXu3Zu+ffvy4osvcujQoXz1XbNmDWvXruWFF15w68HbvISFhbFu3Tp+/fVX7rjjDo4fP56v/nPmzMHPz8/OzS8gK/rGeIH4+HguXLiQ76IvIrz33nsA/O1vf8PVEzPS0tIYN24cYWFhjB49Ot95C6tly5Z8+eWXHD16lNtuu82lB5IDpKam8sEHH9CvXz9CQ0OLOGXpZEXfGC+Qn4O42dWtW5fJkyezevVqPv/8c5f6vPfee+zdu5e3337bY0MknTp14ssvvyQpKYlOnTqxb9++PPt89tln/PLLL4waNaoYEpZOdsqmMV7gscceY968eZw5c8blMX1nly5dIjw8nJMnT7Jnzx4qVaqUa9vjx49z00030blzZ7788svCxHaLmJgYevXqhZ+fHxEREbRq1SrHdufOnaNr166cPXuW+Pj4Qp2mWhq59ZRNEeklIgkisl9ErroMUEQai8hmEbkoIk9mW5YoIjtEJE5ErJIbk4P8HsTNrmzZssyZM4djx47x/PPPX7Ptc889R0pKClOnTi3QttytTZs2bNy4kcDAQLp168aqVauuGqbau3cv7du3Jy4ujpdeeskKfmHkdZ8Gsh5ofgC4EQgAtgFNs7WpAbQFpgBPZluWCAS7ck+Iyy+7947xJenp6Vq+fHkdM2ZModc1YsQI9fPz0/nz5+e4fOXKlSoi+vjjjxd6W+526NAhbdKkiQLaunVr/eyzzzQjI0M///xzDQoK0mrVqulXX33l6ZheC3fdWhnoCEQ6TU8AJuTS9kUr+sbkz86dOxXQhQsXFnpdp0+f1u7duyugw4YNu3IjsvPnz+uoUaMU0ObNm+vp06cLva2ikJqaqv/85z+1YcOGCmjdunUV0DZt2mhiYqKn43k1V4u+K38j1QKSnKaPOOa5/McEsE5EYkQk13OsRGS4iESLSHRycnI+Vm9MyXb5+FVBDuJmV7lyZdavX8+kSZNYsGAB7du357PPPqN169bMnDmTxx9/nC1btlC5cuVCb6soBAYG8vDDDxMfH8/SpUsJCQnhr3/9K//3f/9H3bp1PR2vVHDlmmvJYV5+jv52UtVjIlIDWC8i8aq64aoVqs4F5kLWgdx8rN+YEi0mJoYKFSoQFhbmlvX5+/vz0ksv0blzZx544AEGDhxIaGgoX3/9dY736fdG/v7+DBo0iEGDBnk6SqnjStE/AtR2mg4Fjrm6AVU95vh6QkRWAO2Aq4q+Mb4qJiaGli1bFvggbm569uxJXFwcS5cu5S9/+QvXX3+9W9dvSiZXhne2Ao1EpL6IBACDgVWurFxEKohIpcvvgZ7AzoKGNaa0ycjIIC4ujvDwPM+0K5BatWrxxBNPWME3V+S5p6+q6SIyGogk60yeeaq6S0RGOJbPFpGaQDQQBGSKyFigKRAMrBCRy9tarKoRRfJJjCmB9u7dS0pKCq1bt/Z0FOMjXLqPqqquAdZkmzfb6f3PZA37ZPcr0KIwAY0pzWJjYwFyvSDJGHezKxyM8aDY2FgCAwNp3Lixp6MYH2FF3xgPio2N5eabb6Zs2bKejmJ8hBV9YzxEVYmNjbWhHVOsrOgb4yFJSUmcOnXKir4pVlb0jfEQO4hrPMGKvjEeEhsbi4jQvHlzT0cxPsSKvjEeEhsbS1hYGBUqVPB0FONDrOgbzp49y9dff83OnTtdftyeKby4uDgb2jHFzoq+j9q6dStPPPEEbdu2pUqVKtxxxx3ccsstNGjQgLFjx/LNN9+Qnp7u6Zil1smTJzl8+DAtW7b0dBTjY6zo+6Bdu3bRpUsXZsyYQYUKFXj22WeJiIhg7ty5NGvWjDlz5nD77bfTvHlz1q5d6+m4pVJcXBxgB3FN8XPpNgym9Lhw4QKDBg0iKCiIbdu2UbNmzd8tf+SRRzh//jyrV69m0qRJ9OnThzvvvJO33nqLZs2aeSh16WNn7hhPsT19H/P444+za9cuFi5ceFXBv6xChQoMHjyYXbt2MXXqVLZs2ULz5s0ZPnw4R48eLebEpVNsbCyhoaEEBwd7OorxMVb0fciyZcuYPXs2Tz75JHfeeWee7QMCAhg3bhz79+9n9OjRLFiwgIYNG/LMM89w6tSpYkhcetmVuMZTrOj7iEOHDvHwww/Ttm1bpkyZkq++1apVY9q0aezdu5d7772Xf/zjHzRo0ICRI0eyZs0aUlNTiyh16ZSSkkJCQoIVfeMRVvR9xMiRI8nIyGDJkiUEBAQUaB316tXjww8/ZNu2bdx5550sWrSIu+66i2rVqtG/f38mT57M4sWL2bp1K6dPn3bvByhFtm/fTmZmphV94xF2INcHnDx5ksjISCZMmECDBg0Kvb5bbrmFpUuXkpqayvfff8/q1auJiIhg9erVvzvPv1GjRnTu3JnOnTtz66230qhRo0JvuzSwg7jGk6zo+4A1a9aQmZnJgAED3Lre6667jjvvvPPK8YHU1FQOHDjAvn37iI+PZ/PmzaxcuZL58+cDMGDAAKZOnUr9+vXdmqOkiY2N5frrr6dOnTqejmJ8kBV9H7By5UpuuOGGIn8k33XXXUezZs1+d2pnZmYmCQkJfPbZZ7z22ms0bdqUp59+mmeeeYby5csXaR5vFRcXR8uWLXE8RtSYYuXSmL6I9BKRBBHZLyLjc1jeWEQ2i8hFEXkyP31N0UpNTSUiIoK+ffvi51f8h3D8/Pxo0qQJkyZNIiEhgQEDBvDSSy/RtGlToqKiij2Pp50+fZrt27fbM3GNx+S5py8i/sAMoAdwBNgqIqtUdbdTs1PAY8CAAvQ1Rei7777j/Pnz9OvXz9NRCA0NZcmSJYwYMYJhw4bRrVs3li5d6rFsmZmZxMbGsm7dOhISEjh//jwpKSmkpKRQt25dRo4cSbt27dy6Rz579mwuXrzIkCFD3LZOY/LDlV2/dsB+Vf1RVdOApUB/5waqekJVtwKX8tvXFK1Vq1ZRvnx5brvtNk9HuaJr165s3ryZZs2acffddzNz5sxi2e6ZM2fYuHEj06dP57777iMkJITw8HAmTpzIN998w65duzh+/Djp6eksX76cDh060K5dOz788EO3nJZ68eJF3n33XXr06EGLFi3c8ImMKQBVveYL+BPwvtP0EGB6Lm1fBJ4sYN/hQDQQXadOHTWFl5mZqbVq1dK7777b01FydO7cOf3jH/+ogD799NOakZHh1vWfOnVKly5dqkOHDtX69esrcOUVEhKiQ4YM0UWLFunPP/98Vd9ff/1VZ8yYoU2aNFFA69SpoytXrixUnnnz5imgkZGRhVqPMTkBojWPeq6qLhX9gTkU7vdyaZu96Lvc1/nVpk2bov3u+IiYmBgFdP78+Z6OkqtLly7pyJEjFdCBAwdqSkpKodf53XffaefOndXf318BrVatmg4cOFBfeeUV/eKLLzQpKUkzMzNdWldmZqZGRkZqs2bNFND+/fvroUOH8p0pIyNDmzZtqi1atHB528bkh6tF35Wzd44AtZ2mQ4Fjrv0dUai+ppBWrlyJn58fd911l6ej5KpMmTLMmDGDBg0a8NRTT5GUlMTKlSupUaNGgdb3/fff07t3b2rWrMmECRPo06cP7dq1w9/fv0DrExF69uxJbGwsb7/9NpMnT6ZJkyZMnDiRsWPHuvwAlLVr17J7924WLVpkZ+0Yz8rrtwJZB3t/BOoDAcA2oFkubV/k93v6Lvd1ftmevnu0bNlSO3fu7OkYLlu+fLmWK1dO69Wrp7t27cp3/3//+99aoUIFbdKkiR4/frwIEqomJibq3XffrYDWqFFDp02bpqmpqXn269q1q9auXVvT0tKKJJcxuGt4J2td9AH2AgeAZx3zRgAjHO9rkrVX/ytw2vE+KLe+eb2s6BfeoUOHFNA33njD01HyJSoqSkNCQjQoKEgXLlzo8lBIVFSUBgUFaaNGjfTYsWNFnFJ106ZN2q1btyvj/R988IFeunQp12yAvvXWW0Wey/gutxb94n5Z0S+86dOnK6Dx8fGejpJviYmJ2rFjRwW0b9++eRbxb7/9VqtUqaL169fXpKSkYkqZNd6/fv16bdu2rQLauHFjXbZs2ZVfVBkZGbpjxw7t2bOnBgUF6ZkzZ4otm/E9VvR9XM+ePbVRo0aejlFg6enp+tZbb+l1112nVapU0Q8++EDPnTv3uzZHjx7V+++/XwG98cYb9eDBgx7JmpmZqcuXL79ypk/btm11wIABWq1atStnC73yyiseyWZ8h6tFX7Laepfw8HCNjo72dIwSKyUlhapVqzJq1CimTp3q6TiFsnfvXoYNG8amTZsoW7YsnTt3pkePHvj5+TFlyhTS0tJ4+umnGT9+vMdv65Cens6iRYuYMmUKqkrXrl3p0qULXbp04cYbb/RoNlP6iUiMqobn2c6Kfumzdu1a+vTpQ0REhEsPS/F2GRkZfPvtt6xbt45169axbds2APr06cO0adNo2LChhxMa43lW9H3YmDFjmDt3LqdOnaJcuXKejuN2x48f5+eff6Z58+Z2+qMxDq4WfbvLZikUGRlJt27dSmXBBwgJCSEkJMTTMYwpkezJWaXMwYMHSUhIoFevXp6OYozxQlb0S5nIyEgAK/rGmBxZ0S9lIiMjqVu3LjfddJOnoxhjvJAV/VIkLS2Nr7/+ml69etkBTmNMjqzolyKbN2/m7NmzNrRjjMmVFf1SJCIigjJlynjVA1OMMd7Fin4pEhkZSadOnQgKCvJ0FGOMl7KiX0r8/PPPxMbG2tCOMeaarOiXEuvWrQMoFbddMMYUHSv6pcTq1asJCQmxB24bY67Jin4psG/fPpYvX86DDz6In5/9kxpjcmcVohT4+9//TmBgIE899ZSnoxhjvJxLRV9EeolIgojsF5HxOSwXEXnXsXy7iLR2WpYoIjtEJE5E7NaZbrZv3z4++ugjRo0aZTchM8bkKc+7bIqIPzAD6EHWs2+3isgqVd3t1Kw30Mjxag/Mcny9rLuq/uK21OYK28s3xuSHK3v67YD9qvqjqqYBS4H+2dr0BxY6ntr1H6CKiPzBzVlNNraXb4zJL1eKfi0gyWn6iGOeq20UWCciMSIyPLeNiMhwEYkWkejk5GQXYhnbyzfG5JcrRT+nO3dlf9zWtdp0UtXWZA0BPSoiXXLaiKrOVdVwVQ2vXr26C7F8m+3lG2MKwpWifwSo7TQdChxztY2qXv56AlhB1nCRKYSMjAzGjh1re/nGmHxzpehvBRqJSH0RCQAGA6uytVkFDHWcxdMBOKOqP4lIBRGpBCAiFYCewE435vc5qsqYMWNYs2YNb7zxhu3lG2PyJc+zd1Q1XURGA5GAPzBPVXeJyAjH8tnAGqAPsB9IAYY5uocAKxz3di8DLFbVCLd/Ch/y5ptvMmPGDJ588klGjx7t6TjGmBJGVLMPz3teeHi4RkfbKf3ZLVmyhPvvv59BgwaxePFiu/rWGHOFiMSoanhe7axqlBDfffcdDz30EF27duXDDz+0gm+MKRCrHCXAoUOH+NOf/kTDhg1ZsWIFgYGBno5kjCmhrOh7uQsXLnDPPfdw6dIlPv/8c66//npPRzLGlGB5Hsg1nqOqjBo1ipiYGFauXMlNN93k6UjGmBLO9vSv4eTJk2RmZnps+3PmzGHBggVMmjSJfv36eSyHMab0sKKfg3PnzjFy5EiCg4OpUaMG9957L3PnziUxMbHYMmzatInHHnuM3r1788ILLxTbdo0xpZsN72SzadMmhg4dyo8//siIESNITU1l/fr1/Otf/wJg7NixvPbaa0V6MHXnzp307duXOnXq8NFHH+Hv719k2zLG+Bbb03fIyMhg4sSJ3HrrrWRkZPDdd98xa9Ys5s+fT1JSEvHx8Tz66KO88847dOzYkb179xZJjh9//JGePXsSGBjIunXrqFq1apFsxxjjm6zok3XAdNy4cbz66qv8+c9/Ztu2bXTp8tt94USEsLAwpk+fzsqVKzl06BCtW7dmwYIFbh3zP3bsGHfccQcXL15k/fr13HjjjW5btzHGgBV9IOvWBu+99x7jxo3jgw8+ICgoKNe2/fr1Y9u2bbRp04Zhw4Zx44038sILL3Dw4MFCZThx4gQ9evQgOTmZiIgImjVrVqj1GWNMTnz+NgwfffQRQ4YM4d5772XJkiUuX+makZHBp59+yvz58/nqq69QVTp16kSbNm1o3LgxTZo0oXHjxoSEhOC491COjh07xtSpU5k9ezbp6emsXbuW7t27u+vjGWN8hKu3YfDpor9+/Xr69OlD586diYiIKPDB2aSkJBYuXMjnn3/Onj17OH/+/JVl1apV45ZbbuHmm2+mYcOGlClTBhFBRIiLi2PBggWkp6dz3333MXHiRJo2bequj2eM8SFW9PNw4MABWrVqRb169diwYQNVqlRxy3pVlaNHj7Jnzx52797Nrl272LFjBzt37uTcuXO/axsQEMBf/vIXnnrqKRu/N8YUiqtF3ydP2czIyGDYsGGICKtXr3ZbwYesg76hoaGEhobSo0ePK/NV9crFXqqKqlKxYkUqVqzotm0bY0xefLLoT5s2jY0bN7JgwQLq1q1bLNsUEYKDg4tlW8YYkxufO3tnz549TJw4kX79+jF06FBPxzHGmGLlU0U/PT2doUOHUrFiRebMmXPNs2qMMaY08qnhnVdffZXo6Gg+/fRTatas6ek4xhhT7Fza0xeRXiKSICL7RWR8DstFRN51LN8uIq1d7VvUVJWNGzdy11138fzzzzN48GAGDhxY3DGMMcYr5LmnLyL+wAygB3AE2Coiq1R1t1Oz3kAjx6s9MAto72Jft7p48SI//fQTP/30EwcOHGDmzJls3ryZ4OBgXn75ZcaNG1dUmzbGGK/nyvBOO2C/qv4IICJLgf6Ac+HuDyzUrJP+/yMiVUTkD0A9F/q6hapSs2ZNTpw48bv59erVY/r06QwbNozy5cu7e7PGGFOiuFL0awFJTtNHyNqbz6tNLRf7AiAiw4HhjslzIpLgQracBAO/XJ5ITExk9OjRjB49uoCrc6vfZfNC3pzPm7OBd+fz5mzg3fm8ORv8Pp9L55+7UvRzOsUl+2W8ubVxpW/WTNW5wFwX8lyTiES7clWaJ3hzNvDufN6cDbw7nzdnA+/O583ZoGD5XCn6R4DaTtOhwDEX2wS40NcYY0wxceXsna1AIxGpLyIBwGBgVbY2q4ChjrN4OgBnVPUnF/saY4wpJnnu6atquoiMBiIBf2Cequ4SkRGO5bOBNUAfYD+QAgy7Vt8i+SS/KfQQURHy5mzg3fm8ORt4dz5vzgbenc+bs0EB8nnlXTaNMcYUDZ+6DYMxxvg6K/rGGONDSk3R9/TtHnLIM09ETojITqd5VUVkvYjsc3y93kPZaovItyKyR0R2icgYL8t3nYhEicg2R77J3pTPkcVfRGJF5AsvzJYoIjtEJE5Eor0pn+PCzc9EJN7x/6+jF2ULc3zPLr9+FZGxXpRvnOPnYaeILHH8nOQ7W6ko+k63e+gNNAXuExFPP3dwAdAr27zxwNeq2gj42jHtCenAE6raBOgAPOr4fnlLvovAbaraAmgJ9HKcFeYt+QDGAHucpr0pG0B3VW3pdA63t+SbBkSoamOgBVnfQ6/IpqoJju9ZS6ANWSelrPCGfCJSC3gMCFfVm8k6MWZwgbJdfopTSX4BHYFIp+kJwAQvyFUP2Ok0nQD8wfH+D0CCpzM6sqwk6/5IXpcPKA/8QNaV3F6Rj6zrTb4GbgO+8LZ/WyARCM42z+P5gCDgII4TSLwpWw5ZewL/9pZ8/HZ3g6pknXX5hSNjvrOVij19cr8NhLcJ0azrF3B8reHhPIhIPaAVsAUvyucYPokDTgDrVdWb8r0DPA1kOs3zlmyQddX7OhGJcdzeBLwj341AMjDfMTT2vohU8JJs2Q0Gljjeezyfqh4F3gQOAz+RdS3UuoJkKy1F3+XbPZjfiEhFYBkwVlV/9XQeZ6qaoVl/ZocC7UTkZg9HAkBE/gicUNUYT2e5hk6q2pqs4c5HRaSLpwM5lAFaA7NUtRVwHs8Pg13FcSFpP+Bfns5ymWOsvj9QH7gBqCAiDxZkXaWl6LtyqwhvcFyy7j6K4+uJPNoXGREpS1bB/1hVl3tbvstU9TTwHVnHR7whXyegn4gkAkuB20TkIy/JBoCqHnN8PUHWmHQ7L8l3BDji+KsN4DOyfgl4QzZnvYEfVPW4Y9ob8t0BHFTVZFW9BCwH/l9BspWWol9SbvewCnjI8f4hssbSi52ICPABsEdVpzot8pZ81UWkiuN9ObL+w8d7Qz5VnaCqoapaj6z/Z9+o6oPekA1ARCqISKXL78ka993pDflU9WcgSUTCHLNuJ+s26x7Pls19/Da0A96R7zDQQUTKO35+byfrIHj+s3n6gIkbD3T0AfYCB4BnvSDPErLG3i6RtYfzv0A1sg4A7nN8reqhbJ3JGv7aDsQ5Xn28KF9zINaRbyfwvGO+V+RzytmN3w7kekU2ssbNtzleuy7/LHhRvpZAtOPf9nPgem/J5shXHjgJVHaa5xX5gMlk7fzsBBYBgQXJZrdhMMYYH1JahneMMca4wIq+Mcb4ECv6xhjjQ6zoG2OMD7Gib4wxPsSKvjHG+BAr+sYY40P+Pw/dNJeJPmhwAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "conv2 = Model(inputs=cnn.model.get_layer(index=3).input, outputs=cnn.model.get_layer(index=6).output)\n", + "conv2_output = conv2.predict(conv1_output)\n", + "for i in range(conv2_output.shape[-1]):\n", + " plt.plot(conv2_output[0, :, i], c='black')\n", + " plt.ylim(conv2_output.min(), conv2_output.max())\n", + " plt.savefig(f\"assets/conv2/{i}.png\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD4CAYAAAAAczaOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAmnElEQVR4nO3deXhU5d3G8e+TEFmigOxRkUVEGyWoBFAoKogiWsFX8GVTUASkVdG0FbGKILZX7WvFDaNQUCSUYhEsiAVkEWVVgiZh1yAqO1GWhD0hv/ePDBoxmElIck5m7s91zZXMzFnuYblz8sw5zzgzQ0REwkOE1wFERKTsqPRFRMKISl9EJIyo9EVEwohKX0QkjFTwOkBBatWqZQ0bNvQ6hohIubF69ervzKx2Ycv5svQbNmxIcnKy1zFERMoN59w3wSyn4R0RkTCi0hcRCSMqfRGRMKLSFxEJIyp9EZEwotIXEQkjKn0RkTCi0hcRCSMqfRGRMKLSFxEJIyp9EZEwotIXEQkjKn0RkTCi0hcRCSMqfRGRMKLSFxEJIyp9EZEwotIXEQkjKn0RkTCi0hcRCSMqfRGRMBJU6TvnbnbObXLOpTvnhhXwfB/nXFrgttw51zzYdUVEpOwUWvrOuUjgVaAzEAv0cs7FnrLYFuA6M4sDngHGFWFdEREpI8Ec6bcC0s3sKzM7DkwFuuZfwMyWm9m+wN2VwAXBrisiImUnmNI/H9ia7/62wGOncx8wp6jrOucGOeeSnXPJGRkZQcQSEZGiCqb0XQGPWYELOteevNJ/rKjrmtk4M4s3s/jatWsHEUtERIqqQhDLbAPq57t/AbDj1IWcc3HAeKCzmX1flHVFRKRsBHOkvwq42DnXyDl3FtATmJV/AefchcAM4G4z+6Io64qISNkp9EjfzHKccw8C84BI4A0zW+ecGxx4/nXgKaAmkOicA8gJDNUUuG4pvRYRESmEMytwiN1T8fHxlpyc7HUMEZFywzm32sziC1tOV+SKiIQRlb6ISBhR6YuIhBGVvohIGFHpi4iEEZW+iEgYUemLiIQRlb6ISBhR6YuIhBGVvohIGFHpi4iEEZW+iEgYUemLiIQRlb6ISBhR6YuIhBGVvohIGFHpi4iEEZW+iEgYUemLiIQRlb6ISBip4HUAEZFwtmfPHtLS0khLSyMzM5ORI0eW6v5U+iIiHpg+fToPPPAAu3fv/uGxxo0bM2LECJxzpbZflb6IiAemTp1Kbm4uo0ePJi4ujmbNmlGnTp1S369KX0TEA6mpqbRr146EhIQy3a/eyBURKWOHDh0iPT2d5s2bl/m+VfoiImVs7dq1mBlxcXFlvm+VvohIGUtNTQXQkb6ISDhIS0vjnHPOoUGDBmW+b5W+iEgZS01NJS4ujoiIsq9glb6ISBkyM9LS0jwZzweVvohImfrmm2/IzMz0ZDwfVPoiImUqLS0N8OZNXFDpi4iUqdTUVJxzXH755Z7sX6UvIlKGUlNTueiiizj77LM92b9KX0SkDHn5Ji6o9EVEyoyX0y+cpNIXESkjXk6/cJJKX0SkjHg5/cJJKn0RkTLi5fQLJwVV+s65m51zm5xz6c65YQU8f6lzboVz7phz7o+nPPe1c26Ncy7FOZdcUsFFRMobL6dfOKnQPTvnIoFXgc5ALNDLORd7ymJ7gSHA30+zmfZmdoWZxZ9JWBGR8urk9AteDu1AcEf6rYB0M/vKzI4DU4Gu+Rcwsz1mtgrILoWMIiLl3snpF7x8ExeCK/3zga357m8LPBYsAz5wzq12zg063ULOuUHOuWTnXHJGRkYRNi8i4n9+eBMXgiv9gj6W3Yqwj7ZmdhV5w0MPOOeuLWghMxtnZvFmFl+7du0ibF5ExP+WLl3q6fQLJwVT+tuA+vnuXwDsCHYHZrYj8HUP8C55w0UiImFj7969jB07ljvvvNOz6RdOCqb0VwEXO+caOefOAnoCs4LZuHMu2jl3zsnvgZuAtcUNKyJSHr3wwgtkZWUxfPhwr6NQobAFzCzHOfcgMA+IBN4ws3XOucGB5193ztUDkoGqQK5z7hHyzvSpBbzrnDu5rylmNrdUXomIiA/t3buXl156ie7du3s+tANBlD6Amf0X+O8pj72e7/td5A37nCoT8PZdCxERD7344otkZWXx1FNPeR0F0BW5IiKlZt++fbz00kt069aNZs2aeR0HUOmLiJSaF198kczMTN8c5YNKX0SkVOzfv5+XXnqJO+64w/MLsvJT6YuIlILnn3+eAwcO+OooH1T6IiIlbufOnYwePZoePXp4fgXuqVT6IiIlbOTIkWRnZ/OXv/zF6yg/o9IXESlBGzduZMKECQwePJiLLrrI6zg/o9IXESlBjz/+OFWqVPHF1bcFUemLiJSQZcuW8Z///IehQ4fi14kjVfoiIiXAzBg6dCgxMTEkJCR4Hee0gpqGQUREftmUKVNYvnw5Y8eOJTo62us4p6UjfRGRM/TJJ58wYMAA2rRpQ//+/b2O84tU+iIiZ+Cbb76ha9euxMTE8J///IcKFfw9gKLSF5GwlJ2dzfr168nIyMCsKB8G+KPMzExuu+02jhw5wuzZs3375m1+/v6RJCJSgpYtW8b8+fNZsmQJK1eu5PDhwwCcc845NGnShKZNm3Lbbbdx++23Fzouf/z4cXr16sX69euZM2cOsbGxZfESzphKX0TCwuuvv85vf/tbIiIiaN68Offddx8tW7Zk3759pKens3nzZpYuXcrbb7/N2WefzR133EGvXr2IjY0lJiaGqKgozIyVK1eSlJTE22+/zd69e3nttde48cYbvX55QVPpi0jIe/fdd3nggQe49dZbmTJlClWrVi1wudzcXJYsWcLkyZOZNm0akyZNAsA5R7169YiMjGTbtm1UqlSJ22+/nXvvvZebbrqpLF/KGXPFHcsqTfHx8ZacnOx1DBEJAUuXLqVjx45cccUVLFy4MOjTKY8ePcrHH3/Mt99+y7Zt29i2bRuZmZnceuut/M///M9pf3B4xTm32sziC1tOR/oiErLWrVvHbbfdRoMGDZg9e3aRzp+vVKlSuTuKD4bO3hGRkJSVlUXnzp2pVKkS8+bNo1atWl5H8gUd6YtISEpMTGTr1q0sW7aMhg0beh3HN1T6IlIsu3btYuLEiWzdupUOHTpwww03UL16da9jAXDo0CGef/55OnXqRJs2bbyO4ysqfREJWm5uLgsXLmTs2LHMnDmTnJwcoqOjSUxMJDIykquvvpq+ffsycOBAnHOe5Rw3bhwZGRm+nd7YSxrTF5Gg5OTk0LNnT2666SYWL17Mww8/zMaNG9m3bx9Llixh2LBhHDx4kPvvv58BAwZw/PhxT3IePXqU5557jvbt29O2bVtPMviamfnu1qJFCxORn5s4caLFxMTY+++/X6b7zc3Ntf79+xtgf/7zn+3o0aMFLnfixAkbPny4Ada+fXv7/vvvyzSnmdmYMWMMsEWLFpX5vr0EJFsQ/ep5wRd0U+mL/NSRI0dswIABBlhUVJSdd955tn///jLZd25uriUkJBhgI0aMCGqdpKQkO+uss6xp06b2xRdflG7AfI4dO2b169e3tm3bWm5ubpnt1w9U+iIhYvPmzXbllVcaYI8//rgtX77cIiIi7P777y+T/Y8aNcoAGzJkSJGKdMmSJVazZk2rV6+effPNN6WY8Efjxo0zwObOnVsm+/MTlb5ICPj++++tRo0aVr16dZs1a9YPj5888v7oo49Kdf8TJ040wPr162cnTpwo8vpr1qyxqlWrWrNmzezAgQOlkPBHOTk51qhRI2vZsmXYHeWbqfRFQsKMGTMMsIULF/7k8YMHD1qjRo2sadOmduTIkVLZ93fffWc1atSwa6+91rKzs4u9nQ8++MAiIyPt5ptvPqPtFOa///2vATZt2rRS24efBVv6OntHxMeWLVtGxYoVf3YWSnR0NOPGjeOLL75g1KhRpbLvP/3pTxw4cIBXX331jD4Y5MYbb+S1115j7ty5DBkyJO9osxS88cYb1KpViy5dupTK9kNGMD8ZyvqmI32RPFdffbW1bdv2tM/fc889FhkZaWlpaSW631WrVplzzhISEkpsm0OHDjXARo8eXWLbPCkjI8OioqJKNG95g4Z3RMq3w4cPW1RUlA0dOvS0y3z33XdWrVo169q1a4nt98SJE9aqVSurV69eiY7Dnzhxwrp162YRERE2Z86cEtuumdmLL75ogK1Zs6ZEt1ueBFv6Gt4R8ank5GSys7N/8QKjmjVr8vvf/56ZM2dSUtORv/HGG3z66ac899xzJTp9cEREBG+99RbNmjWjZ8+ebNq0qUS2a2ZMmDCBli1bcvnll5fINkOZSl/Ep5YtWwZQ6NwxjzzyCDVq1CiRKQf27t3LsGHDaNeuHX369Dnj7Z0qOjqamTNnctZZZ9G1a1f2799/xttcvXo1a9asoX///mceMAyo9EV8atmyZVxyySWFTglctWpVhg4dyty5c3/4QVFcTz/9NPv27WPMmDGlNndOgwYNmD59Ops3b6ZXr16cOHHijLb3xhtvUKlSJXr27FlCCUObSl/Eh3Jzc1m+fHnQc8c8+OCD1KlT54yO9tPT00lMTGTgwIHExcUVezvBaNeuHYmJicydO5dHH3202Ns5cuQIU6ZMoVu3br6Z4dPvVPoiPrRp0yb27t0bdOlHR0fzpz/9iQ8//JBFixYVa59PPPEEFStWZOTIkcVav6gGDhzIkCFDeOGFFxg9enSxtjFjxgwOHDjAfffdV8LpQlgw7/aW9U1n70i4OzmdwMaNG4Ne58iRI3b++edbmzZtinxF6sqVK4s0t05JycnJse7duxtg//znP4u8fvv27a1Ro0bFulo41KCzd0TKr2XLllGrVi2aNm0a9DqVKlXiySefZPny5bz99ttBr2dmDB06lLp16/KHP/yhOHGLLTIykqSkJK6//nruuece5s+fH/S606ZN48MPP2TQoEFERKjKghbMTwbgZmATkA4MK+D5S4EVwDHgj0VZt6CbjvQl3DVp0sS6dOlS5PWys7OtdevWdu6559q2bduCWmfWrFkGWGJiYpH3V1L2799vcXFxdvbZZ9unn35a6PKbN2+2qlWrWuvWre3YsWNlkND/KKkjfedcJPAq0BmIBXo552JPWWwvMAT4ezHWFZF8du/eTXp6Or/+9a+LvG6FChWYNGkSR48e5b777jt54HVaOTk5PPbYYzRt2pQBAwYUN/IZq1atGnPmzKFmzZpcd911JCUlnXbZ48eP07NnT5xzTJ06lbPOOqsMk5Z/wfxO1ApIN7OvzOw4MBXomn8BM9tjZquA7KKuKyI/tXz5coBif+pT06ZN+fvf/868efN47bXXfnHZp59+mg0bNvDss88SFRVVrP2VlPPOO4+VK1fSqlUr+vbty+DBgzl69OjPlnv88cdZtWoVEyZM0AeeF0dhvwoA3YHx+e7fDYw5zbIjyTe8U8R1BwHJQPKFF15Yer8DifjcH/7wB6tYseJpP50qGLm5udapUyerXLmybdq0qcBl/vKXvxhg9957r6+mIs7OzrbHHnvMAGvRooW98847tmDBAvv0008tKSnJAPvd737ndUzfoaTm3gHuLKC4XznNsqeWftDr5r9pTF/CWWGTrAVr+/btdu6551qrVq3s66+//slzf/vb3wywu+66y3Jycs54X6Vh5syZVr16dQN+cmvevHmpTSddngVb+sHMl7oNqJ/v/gXAjiB/kTiTdUXCztGjR1m9ejUJCQlnvK3zzjuPcePG0aNHDxo2bMj1119Pv3792L17N8OGDaNnz568+eabREZGlkDyktelSxe+/vprtmzZQmZmJpmZmRw6dIiOHTtSqVIlr+OVW8GU/irgYudcI2A70BPoHeT2z2RdkbCzZs0asrOzadWqVYlsr3v37mzevJmkpCQmTZrEvffeC0C3bt1ISko6o3nyy0K1atW44oorvI4RUgr9GzezHOfcg8A8IBJ4w8zWOecGB55/3TlXj7zx+KpArnPuESDWzDILWreUXotIuZeSkgLAlVdeWWLbbNiwIcOHD+fJJ59kxYoVfP755wwaNMj3hS+lw1khp3R5IT4+3kpqmliR8uSBBx5g8uTJ7Nu3TxccSZE451abWXxhy+lflYiPpKSk0Lx5cxW+lBr9yxLxidzcXFJTUzWGLaVKpS/iE5s3b+bQoUMqfSlVKn0Rn/j8888BVPpSqlT6Ij6RkpJChQoVuOyyy7yOIiFMpS/iEykpKcTGxlKxYkWvo0gIU+mL+ERKSoqGdqTUqfRFfGD37t3s3LlTpS+lTqUv4gOpqamA3sSV0qfSF/GBk9MvNG/e3NsgEvJU+iI+kJKSwoUXXkiNGjW8jiIhTqUv4gOff/65hnakTKj0RTx26NAhNm3apNKXMqHSF/HY2rVrMbMSnU5Z5HQ0obYAkJmZyWeffcbGjRvZsGEDX375JbGxsfTv35/Y2Fiv44W0k2/i6khfyoJKX9i1axctWrRgx468T7KMjo6mcePGzJ8/n+eff57WrVvTv39/+vTpQ3R0tMdpQ09KSgrVqlWjQYMGXkeRMKDhnTB34sQJevfuzf79+3n33Xf59ttvycrKIi0tje3btzN69GgOHjzI/fffz69+9SumT5+OHz94pzw7eSWuc87rKBIGVPph7plnnuHDDz8kMTGR22+/nfr16/9QPnXq1CEhIYE1a9awePFiatSoQffu3enUqRObNm3yOHloyMnJIS0tTUM7UmZU+mFs4cKFjBo1in79+tGvX7/TLuec47rrriM5OZlXXnmFTz/9lGbNmpGQkEBGRkYZJg49K1as4PDhw7Rt29brKBImVPphateuXfTp04dLL72UV199Nah1KlSowIMPPsimTZvo27cvL7/8Mo0bN2bEiBFkZmaWcuLQ9N577xEVFUWnTp28jiJhQqUfpgYMGEBmZibTpk0r8puzdevWZfz48axfv57OnTszatQoGjVqREJCAsuXLyc3N7eUUoeeWbNmcf3111O1alWvo0iYUOmHoe3bt/P+++8zdOjQM/rAjksuuYR///vfJCcnc91115GYmEjbtm1p0KABCQkJzJkzR78B/IIvvviCTZs2cdttt3kdRcKISj8MTZ8+HYCePXuWyPZatGjBjBkz2LNnD0lJSVx11VUkJiZyyy23cO655xIfH88f//jHHz4OUPK89957ACp9KVPOj6ffxcfHW3JystcxQla7du3Yv38/a9asKbV9HD58mBUrVvDRRx/x8ccfs2LFCo4fP06rVq0YPHgwPXr0oEqVKqW2//Lg+uuvZ+/evaSlpXkdRUKAc261mcUXtpyO9MPMjh07WLZsGXfeeWep7qdKlSrccMMNjBo1isWLF7N7925efvllDh48SP/+/Tn//PNJSkoq1Qx+tnfvXpYuXaqjfClzKv0wc/LiqtIu/VNVr16dhx56iLVr1/Lxxx/TrFkz+vbtS//+/Tl8+HCZZvGDOXPmcOLECbp06eJ1FAkzKv0wM23aNC677DJ+9atfebJ/5xzt2rVj0aJFPPnkk0ycOJGWLVuyfv16T/J45b333qNu3bq0bNnS6ygSZjT3ThjZuXMnS5cuZcSIEV5HoUKFCjzzzDNce+213HXXXbRs2ZJp06Zxyy23lHkWM2PHjh2sX7+edevWsW7dOg4fPky9evV+uLVu3ZqmTZuWyP6OHz/O3Llz6d69OxEROu6SsqXSDyMzZszwZGjnl9x4442kpKTwm9/8httvv52pU6dyxx13lOo+zYz169fz0Ucf/fBG865du354vmbNmlStWpVdu3Zx5MgRACIiIrj77rsZOXIkDRs2PKP9L1myhAMHDmg8X7xhZr67tWjRwqTkXXfddRYbG+t1jALt27fP2rRpY5GRkZaUlFQq+0hPT7ennnrKGjVqZIABdv7551vv3r3tlVdesQ8//NB27979w/K5ubmWmZlpGzZssN///vdWsWJFi4qKsoceesh27dpV7BwPP/ywVaxY0Q4ePFgSL0vEzMyAZAuiXz0v+IJuKv2St3PnTnPO2YgRI7yOclpZWVnWoUMHc87Z2LFjS2y7CxcutLZt2xpgzjnr2LGjjR8/3jZv3my5ublBb2fr1q02cOBAi4yMtGrVqlliYqKdOHGiSFmOHDliDRo0sFtvvbWoL0PkF6n05SdeffVVA2zNmjVeR/lFhw8ftltvvdUAGzVqVJFKuSBjxoyxyMhIa9Sokf31r3+1rVu3nnHGDRs2WIcOHQyw1q1bW0pKSlDrZWdn2+23326Avffee2ecQyQ/lb78RIcOHezSSy894xItC8eOHbO+ffsaYAMGDLDjx48XeRvZ2dn2u9/9zgD7zW9+Y5mZmSWaMTc315KSkqx27doWGRlpDz300E+GhgpafsCAAQbYSy+9VKJZRMxU+pJPVlaWRUVF2aOPPup1lKDl5uba8OHDDbBOnToVqbT37t1rHTt2NMAeffRRy8nJKbWc33//vQ0ePNgiIyMtOjrannrqKTtw4MDPlnviiScMsCeeeKLUskh4U+nLD95//30D7IMPPvA6SpH94x//sMjISIuLi7MVK1YUunxqaqpddNFFFhUVZRMmTCiDhHk2btxod955pwFWs2ZN69Onjw0ZMsRGjhxpDz30kAE2cODAcvGblpRPKn35QUJCglWsWNEOHz7sdZRimTNnjtWpU8cA69atm23cuLHA5SZPnmyVK1e2mJgYW7ZsWRmnzLNq1Sq77bbbrHHjxlatWrUfzhL63//931L9jUMk2NLXhGthoFmzZtSrV4/58+d7HaXYDh48yPPPP89zzz3H0aNH6d27N7GxscTExBATE8Ps2bN55ZVXaNeuHf/+97+pV6+e15GBvI9DzMrK4txzz/U6ioS4YCdcU+mHuF27dhETE8Ozzz7LY4895nWcM7Z7926eeeYZJk+ezIEDB37y3COPPML//d//ERUV5VE6Ee8EW/q6IjfELViwAMi78jUU1K1blzFjxjBmzBgOHTrEzp072blzJ5UrVyY+vtB/7yJhL6jSd87dDLwERALjzezZU553gedvAQ4D95jZZ4HnvgaygBNATjA/iaTkzJ8/n5o1a3LFFVd4HaXERUdH06RJE5o0aeJ1FJFyo9DSd85FAq8CNwLbgFXOuVlmln9axM7AxYFba+C1wNeT2pvZdyWWWoJiZixYsIAbbrhBE3uJCBDc1MqtgHQz+8rMjgNTga6nLNMVmBR4E3klUN05F1PCWaWINmzYwI4dO0JmaEdEzlwwpX8+sDXf/W2Bx4JdxoAPnHOrnXODTrcT59wg51yycy45IyMjiFhSmJNn63Ts2NHjJCLiF8GUvivgsVNP+fmlZdqa2VXkDQE94Jy7tqCdmNk4M4s3s/jatWsHEUsKs2DBApo0aXLGUwGLSOgIpvS3AfXz3b8A2BHsMmZ28use4F3yhouklGVnZ7N48WId5YvITwRT+quAi51zjZxzZwE9gVmnLDML6OvyXA0cMLOdzrlo59w5AM65aOAmYG0J5pfTWLlyJQcPHtR4voj8RKFn75hZjnPuQWAeeadsvmFm65xzgwPPvw78l7zTNdPJO2Xz3sDqdYF3887opAIwxczmlvirkJ9ZsGABERERtG/f3usoIuIjuiI3RLVs2ZKIiAg++eQTr6OISBkI9opcnbwdgjZs2EBycjI9evTwOoqI+IxKPwS99dZbREZG0rt3b6+jiIjPqPRDzIkTJ5g8eTKdOnXyzUyTIuIfKv0Qs2jRIrZv306/fv28jiIiPqTSDzFvvfUW1atXp0uXLl5HEREfUumHkMzMTGbMmEGPHj2oVKmS13FExIdU+iHknXfe4ciRIxraEZHTUumHkEmTJnHxxRdz9dVXex1FRHxKpR8itmzZwkcffUTfvn0JXAEtIvIzKv0QkZSUBMDdd9/tcRIR8TOVfgjYt28fL7/8MjfddBMNGjTwOo6I+JhKPwQ8/fTT7N27l7/97W9eRxERn1Ppl3Pr169nzJgxDBw4MCQ//FxESpZKvxwzMxISEjj77LP585//7HUcESkHCp1PX/xr9uzZfPDBB7zwwgvoIyZFJBiaT7+cOnbsGJdffjkVKlQgLS2NqKgoryOJiIeCnU9fR/rl1Isvvkh6ejpz585V4YtI0DSmXw59+eWXjBw5kq5du9KpUyev44hIOaLSL2dyc3O57777qFSpEomJiV7HEZFyRsM75UxiYiJLlizhzTff5LzzzvM6joiUMzrSL0e2bNnCsGHD6NSpk2bSFJFiUemXE2bGgAEDiIiIYNy4cZpUTUSKRcM75cTYsWNZtGgRr7/+OhdeeKHXcUSknNKRfjmQnJzMI488wo033sigQYO8jiMi5ZhKPwhmxty5cxk4cCCTJk3i4MGDZbbvjIwM7rjjDurWrcuUKVM0rCMiZ0Sl/wtyc3OZPn068fHxdO7cmaSkJPr160fdunW5++67WbhwIaV5RXNOTg69evViz549TJ8+nVq1apXavkQkPKj0T+Orr74iLi6O7t27k5WVxYQJEzhw4ABLly7lrrvuYvbs2XTs2JEuXbqwffv2UsnwxBNPsHDhQl577TXi4wu9ulpEpHBm5rtbixYtzEu7d++2Jk2aWI0aNWzq1KmWk5Pzs2WOHDlio0ePtsqVK1vVqlVt3LhxlpubW2IZ/vGPfxhggwcPLrFtikjoApItiH71vOALunlZ+llZWdayZUurXLmyLV++vNDl09PTrX379gZY+/btbc6cOQX+kAjW/v37rU+fPgbYDTfcYEePHi32tkQkfARb+hreySc7O5s777yT1atXM3XqVK655ppC17noootYsGABY8eOZc2aNXTu3JlGjRoxcuRItmzZUqT9r1ixgiuvvJKpU6cyatQo5s2bR8WKFYv7ckREfkZTKweYGf3792fixImMGzeOgQMHFnkbx44dY9asWYwfP5758+djZtSpU4e4uDiaN2/OZZddRt26dalduzZ16tQB4PPPP+ezzz5j9erVzJs3j/r16/PPf/6TNm3alPRLFJEQFuzUyir9gJdffpmHH36YESNGMHLkyDPe3tdff83MmTNJTU0lNTWVdevWcezYsQKXjYiIIDY2lvbt2/PMM89QrVq1M96/iIQXlX4RfPbZZ1xzzTXcdNNNzJo1q1TOhc/JyeGbb74hIyODPXv2kJGRQU5ODs2bNycuLo4qVaqU+D5FJHyo9IOUlZXFVVddxZEjR0hJSdG58CJSLumTs4JgZgwePJivvvqKxYsXq/BFJOSF1Nk7ubm5RVr+zTffZMqUKTz99NO0a9eulFKJiPhHyBzpmxkXXXQR9erVo1WrVrRu3ZqWLVvSuHFjIiMjf1ju6NGjvP/++/zrX/9i1qxZdOjQgccff9zD5CIiZSdkSv/48eN0796dTz75hPHjx/Pyyy8DEBkZSUxMDBdccAE1atRg6dKlZGZmUrduXX77298yfPjwn/xQEBEJZSH5Rm5OTg7r169n1apVbNmyhW3btrFt2zZ27dpFy5Yt6d27N+3bt6dChZD5mSciYa5E38h1zt0MvAREAuPN7NlTnneB528BDgP3mNlnwaxbGipUqEBcXBxxcXGlvSsRkXKl0DdynXORwKtAZyAW6OWciz1lsc7AxYHbIOC1IqwrIiJlJJizd1oB6Wb2lZkdB6YCXU9ZpiswKTDvz0qgunMuJsh1RUSkjAQzvHM+sDXf/W1A6yCWOT/IdQFwzg0i77cEgIPOuU1BZCtILeC7Yq5b2vycDfydz8/ZwN/5/JwN/J3Pz9ngp/kaBLNCMKVf0JwEp777e7plglk370GzccC4IPL8IudccjBvZnjBz9nA3/n8nA38nc/P2cDf+fycDYqXL5jS3wbUz3f/AmBHkMucFcS6IiJSRoIZ018FXOyca+ScOwvoCcw6ZZlZQF+X52rggJntDHJdEREpI4Ue6ZtZjnPuQWAeeaddvmFm65xzgwPPvw78l7zTNdPJO2Xz3l9at1ReyY/OeIioFPk5G/g7n5+zgb/z+Tkb+Dufn7NBMfL58uIsEREpHSE14ZqIiPwylb6ISBgJmdJ3zt3snNvknEt3zg3zQZ43nHN7nHNr8z1Wwzk33zn3ZeDruR5lq++c+9A5t8E5t84597DP8lVyzn3qnEsN5HvaT/kCWSKdc58752b7MNvXzrk1zrkU51yyn/I556o7595xzm0M/Pu7xkfZLgn8mZ28ZTrnHvFRvoTA/4e1zrl/Bf6fFDlbSJS+T6d7mAjcfMpjw4CFZnYxsDBw3ws5wB/M7FfA1cADgT8vv+Q7BnQws+bAFcDNgbPC/JIP4GFgQ777fsoG0N7Mrsh3Drdf8r0EzDWzS4Hm5P0Z+iKbmW0K/JldAbQg76SUd/2Qzzl3PjAEiDezy8k7MaZnsbKZWbm/AdcA8/Ldfxx43Ae5GgJr893fBMQEvo8BNnmdMZBlJnCjH/MBVYDPyLuS2xf5yLveZCHQAZjtt79b4Gug1imPeZ4PqApsIXACiZ+yFZD1JmCZX/Lx4+wGNcg763J2IGORs4XEkT6nnwbCb+pa3vULBL7W8TgPzrmGwJXAJ/goX2D4JAXYA8w3Mz/lexEYCuT/qDa/ZIO8q94/cM6tDkxvAv7I1xjIAN4MDI2Nd85F+yTbqXoC/wp873k+M9sO/B34FthJ3rVQHxQnW6iUftDTPciPnHNnA9OBR8ws0+s8+ZnZCcv7NfsCoJVz7nKPIwHgnPsNsMfMVnud5Re0NbOryBvufMA5d63XgQIqAFcBr5nZlcAhvB8G+5nAhaRdgGleZzkpMFbfFWgEnAdEO+fuKs62QqX0g5kqwg92B2YfJfB1j1dBnHNR5BX+P81sht/ynWRm+4HF5L0/4od8bYEuzrmvyZs1toNzbrJPsgFgZjsCX/eQNybdyif5tgHbAr+1AbxD3g8BP2TLrzPwmZntDtz3Q76OwBYzyzCzbGAG0KY42UKl9MvLdA+zgH6B7/uRN5Ze5pxzDpgAbDCz0fme8ku+2s656oHvK5P3D36jH/KZ2eNmdoGZNSTv39kiM7vLD9kAnHPRzrlzTn5P3rjvWj/kM7NdwFbn3CWBh24A1vsh2yl68ePQDvgj37fA1c65KoH/vzeQ9yZ40bN5/YZJCb7RcQvwBbAZeMIHef5F3thbNnlHOPcBNcl7A/DLwNcaHmX7NXnDX2lASuB2i4/yxQGfB/KtBZ4KPO6LfPlyXs+Pb+T6Iht54+apgdu6k/8XfJTvCiA58Hf7H+Bcv2QL5KsCfA9Uy/eYL/IBT5N38LMWSAIqFiebpmEQEQkjoTK8IyIiQVDpi4iEEZW+iEgYUemLiIQRlb6ISBhR6YuIhBGVvohIGPl/2pNf1kOIDfMAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "conv3 = Model(inputs=cnn.model.get_layer(index=6).input, outputs=cnn.model.get_layer(index=9).output)\n", + "conv3_output = conv3.predict(conv2_output)\n", + "for i in range(conv3_output.shape[-1]):\n", + " plt.plot(conv3_output[0, :, i], c='black')\n", + " plt.ylim(conv3_output.min(), conv3_output.max())\n", + " plt.savefig(f\"assets/conv3/{i}.png\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAT7UlEQVR4nO3df4zddZ3v8deLaeuVQovKIHXatagN3kpF4QSL3Gw3wN5ty83tWq8NRGQvidYmVGHDuhRjoqZZbl0r4ZLwwy6ikNsrEX9ca1NuIVjDXV1ITysWS291ZFk6dFaGkEJZfnSGee0f5+jOjqcz3w5neuinz0dyMvP9fj6f7/f94Rte853PfM+pkwgAUK4TOl0AAGByEfQAUDiCHgAKR9ADQOEIegAo3JROF9DKqaeemrlz53a6DAA4ZuzYsePZJN2t2t6QQT937lzV6/VOlwEAxwzb/3S4NpZuAKBwBD0AFI6gB4DCEfQAUDiCHgAKR9ADQOEIegAoHEEPAIUj6AGgcAQ9ABSOoAeAwhH0AFA4gh4ACkfQA0DhCHoAKBxBDwCFI+gBoHAEPQAUrlLQ215se6/tXttrWrS/1/Y/2H7V9l+N2D/H9jbbe2zvtn11O4sHAIxv3H8z1naXpFsk/amkPknbbW9K8viIbs9J+qykPx81fEjStUl22j5Z0g7bD4waCwCYRFXu6M+T1JvkiSSHJN0jadnIDkmeSbJd0uCo/f1Jdja/Pyhpj6SetlQOAKikStD3SNo3YrtPEwhr23MlfVDSI0c6FgAwcVWC3i325UhOYvskSd+TdE2SFw7TZ6Xtuu36wMDAkRweADCGKkHfJ2nOiO3ZkvZXPYHtqWqE/MYk3z9cvyQbktSS1Lq7u6seHgAwjipBv13SPNtn2J4m6VJJm6oc3LYlfUPSniQ3TrxMAMBEjfvUTZIh26slbZXUJenOJLttr2q23277dEl1STMkDdu+RtJ8Se+X9AlJj9l+tHnIzyfZ0vaZAABaGjfoJakZzFtG7bt9xPf/rMaSzmh/r9Zr/ACAo4R3xgJA4Qh6ACgcQQ8AhSPoAaBwBD0AFI6gB4DCEfQAUDiCHgAKR9ADQOEIegAoHEEPAIUj6AGgcAQ9ABSOoAeAwhH0AFA4gh4ACkfQA0DhCHoAKBxBDwCFI+gBoHAEPQAUjqAHgMIR9ABQOIIeAApXKehtL7a913av7TUt2t9r+x9sv2r7r45kLABgco0b9La7JN0iaYmk+ZIusz1/VLfnJH1W0voJjAUATKIqd/TnSepN8kSSQ5LukbRsZIckzyTZLmnwSMcCACZXlaDvkbRvxHZfc18VlcfaXmm7brs+MDBQ8fAAgPFUCXq32JeKx688NsmGJLUkte7u7oqHBwCMp0rQ90maM2J7tqT9FY//esYCANqgStBvlzTP9hm2p0m6VNKmisd/PWMBAG0wZbwOSYZsr5a0VVKXpDuT7La9qtl+u+3TJdUlzZA0bPsaSfOTvNBq7CTNBQDQgpOqy+1HT61WS71e73QZAHDMsL0jSa1VG++MBYDCEfQAUDiCHgAKR9ADQOEIegAoHEEPAIUj6AGgcAQ9ABSOoAeAwhH0AFA4gh4ACkfQA0DhCHoAKBxBDwCFI+gBoHAEPQAUjqAHgMIR9ABQOIIeAApH0ANA4Qh6ACgcQQ8AhSPoAaBwlYLe9mLbe2332l7Tot22b26277J9zoi2v7S92/YvbX/b9n9o5wQAAGMbN+htd0m6RdISSfMlXWZ7/qhuSyTNa75WSrqtObZH0mcl1ZKcJalL0qVtqx4AMK4qd/TnSepN8kSSQ5LukbRsVJ9lku5Ow8OSTrE9q9k2RdKbbU+RdKKk/W2qHQBQQZWg75G0b8R2X3PfuH2SPC1pvaSnJPVLej7J/a1OYnul7brt+sDAQNX6AQDjqBL0brEvVfrYfosad/tnSHqHpOm2L291kiQbktSS1Lq7uyuUBQCookrQ90maM2J7tv5w+eVwfS6W9I9JBpIMSvq+pA9PvFwAwJGqEvTbJc2zfYbtaWr8MXXTqD6bJF3RfPpmoRpLNP1qLNkstH2ibUu6SNKeNtYPABjHlPE6JBmyvVrSVjWemrkzyW7bq5rtt0vaImmppF5JL0m6stn2iO3vStopaUjSzyVtmIyJAABaczJ6ub3zarVa6vV6p8sAgGOG7R1Jaq3aeGcsABSOoAeAwhH0AFA4gh4ACkfQA0DhCHoAKBxBDwCFI+gBoHAEPQAUjqAHgMIR9ABQOIIeAApH0ANA4Qh6ACgcQQ8AhSPoAaBwBD0AFI6gB4DCEfQAUDiCHgAKR9ADQOEIegAoHEEPAIUj6AGgcJWC3vZi23tt99pe06Ldtm9utu+yfc6ItlNsf9f2/7e9x/b57ZwAAGBs4wa97S5Jt0haImm+pMtszx/VbYmkec3XSkm3jWj7n5L+b5L3Sjpb0p421A0AqKjKHf15knqTPJHkkKR7JC0b1WeZpLvT8LCkU2zPsj1D0h9L+oYkJTmU5ED7ygcAjKdK0PdI2jdiu6+5r0qfd0kakPRN2z+3fYft6a1OYnul7brt+sDAQOUJAADGViXo3WJfKvaZIukcSbcl+aCkf5H0B2v8kpRkQ5Jaklp3d3eFsgAAVVQJ+j5Jc0Zsz5a0v2KfPkl9SR5p7v+uGsEPADhKqgT9dknzbJ9he5qkSyVtGtVnk6Qrmk/fLJT0fJL+JP8saZ/tM5v9LpL0eLuKBwCMb8p4HZIM2V4taaukLkl3Jtlte1Wz/XZJWyQtldQr6SVJV444xGckbWz+kHhiVBsAYJI5Gb3c3nm1Wi31er3TZQDAMcP2jiS1Vm28MxYACkfQA0DhCHoAKBxBDwCFI+gBoHAEPQAUjqAHgMIR9ABQOIIeAApH0ANA4Qh6ACgcQQ8AhSPoAaBwBD0AFI6gB4DCEfQAUDiCHgAKR9ADQOEIegAoHEEPAIUj6AGgcAQ9ABSOoAeAwlUKetuLbe+13Wt7TYt227652b7L9jmj2rts/9z25nYVDgCoZtygt90l6RZJSyTNl3SZ7fmjui2RNK/5WinptlHtV0va87qrBQAcsSp39OdJ6k3yRJJDku6RtGxUn2WS7k7Dw5JOsT1LkmzPlnSJpDvaWDcAoKIqQd8jad+I7b7mvqp9bpL015KGxzqJ7ZW267brAwMDFcoCAFRRJejdYl+q9LH9XyQ9k2THeCdJsiFJLUmtu7u7QlkAgCqqBH2fpDkjtmdL2l+xzwWS/qvtJ9VY8rnQ9v+acLUAgCNWJei3S5pn+wzb0yRdKmnTqD6bJF3RfPpmoaTnk/QnuT7J7CRzm+N+nOTydk4AADC2KeN1SDJke7WkrZK6JN2ZZLftVc322yVtkbRUUq+klyRdOXklAwCOhJPRy+2dV6vVUq/XO10GABwzbO9IUmvVxjtjAaBwBD0AFI6gB4DCEfQAUDiCHgAKR9ADQOEIegAoHEEPAIUj6AGgcAQ9ABSOoAeAwhH0AFA4gh4ACkfQA0DhCHoAKBxBDwCFI+gBoHAEPQAUjqAHgMIR9ABQOIIeAApH0ANA4Qh6AChcpaC3vdj2Xtu9tte0aLftm5vtu2yf09w/x/Y223ts77Z9dbsnAAAY27hBb7tL0i2SlkiaL+ky2/NHdVsiaV7ztVLSbc39Q5KuTfIfJS2UdFWLsQCASVTljv48Sb1JnkhySNI9kpaN6rNM0t1peFjSKbZnJelPslOSkhyUtEdSTxvrBwCMo0rQ90jaN2K7T38Y1uP2sT1X0gclPdLqJLZX2q7brg8MDFQoCwBQRZWgd4t9OZI+tk+S9D1J1yR5odVJkmxIUktS6+7urlAWAKCKKkHfJ2nOiO3ZkvZX7WN7qhohvzHJ9ydeKgBgIqoE/XZJ82yfYXuapEslbRrVZ5OkK5pP3yyU9HySftuW9A1Je5Lc2NbKAQCVTBmvQ5Ih26slbZXUJenOJLttr2q23y5pi6SlknolvSTpyubwCyR9QtJjth9t7vt8ki1tnQUA4LCcjF5u77xarZZ6vd7pMgDgmGF7R5JaqzbeGQsAhSPoAaBwBD0AFI6gB4DCEfQAUDiCHgAKR9ADQOEIegAoHEEPAIUj6AGgcAQ9ABSOoAeAwhH0AFA4gh4ACkfQA0DhCPo3kIceekhXXnmlfv3rX3e6FAAFIejfAPbv36/LL79cixYt0re+9S19+MMf1sMPP9zpsgAUgqDvoMHBQa1fv15nnnmm7r33Xn3hC1/QL37xC82cOVMXXnihfvjDH3a6RAAFIOg75MEHH9TZZ5+tz33uc1q0aJF2796ttWvX6v3vf79+9rOfacGCBVq+fLluvfXWTpcK4BhH0B9l+/bt04oVK3TxxRfr1Vdf1Y9+9CNt3rxZ73nPe37f57TTTtOPf/xjXXLJJbrqqqu0Zs0aDQ8Pd7BqHM7LL7+sG2+8UWvXrtWBAwc6XQ7QWpI33Ovcc89NaV555ZXccMMNOfHEE/PmN785a9euzcsvvzzmmMHBwaxatSqS8vGPfzyvvvrqUaoW43nttddy1113Zfbs2ZEUSXnrW9+am266ieuEjpBUz2EyteOh3uo10aBft25d7r///gwNDU1o/GS57777Mm/evEjK8uXL8+STT1YeOzw8nBtuuCGScuGFF+bAgQOTWCmqeOCBB/KBD3wgknLuuedm27Zt2blzZy666KJIyrvf/e585zvfyfDwcKdLxXHkuAj6gwcP5m1ve1skpaenJ2vWrMmePXuO+DjtcujQoWzbti3Lli2LpJx55pnZunXrhI939913Z8qUKVmwYEH27dvXxkpR1WOPPZYlS5ZEUt75zndm48aNee21137fPjw8nC1btuSss86KpJx//vn56U9/2sGKcTw5LoI+aSyP3HvvvbnkkkvS1dUVSfnQhz6UW2+9Nc8999yEjnkknn766dxxxx1Zvnx5ZsyYEUmZPn161q1b15Zf5x944IGcfPLJ6enpya5du9pQMarYv39/PvnJT+aEE07IzJkz89WvfnXMZbehoaHccccdmTVrViTlox/9aH71q18dxYpxPHrdQS9psaS9knolrWnRbkk3N9t3STqn6thWr3as0ff39+drX/taFixYEEmZNm1aPvaxj2Xz5s0ZHBx83cdPGmvoDz30UK6//vqcffbZv1+rnT17dj71qU/lBz/4QV544YW2nOt3Hn300bzjHe/IjBkz8uCDD7b12Pj3Dh48mC9+8Ys58cQTM3Xq1FxzzTV59tlnK49/8cUX8+UvfznTp0/PlClT8pnPfCYDAwOTWDGOZ68r6CV1SfqNpHdJmibpF5Lmj+qzVNJ9zcBfKOmRqmNbvdr5x9jh4eHs3LkzV199dU499dRIytvf/vZce+21E7or7u/vzze/+c2sWLEip5xySiSlq6srixYtyle+8pXs2rVr0tdmn3rqqbzvfe/L1KlTs3Hjxkk91/FocHAwX//613P66adHUlasWJHe3t4JH6+/vz+f/vSnc8IJJ2TGjBlZt25dXnrppTZWDIwd9G60H57t8yV9KcmfNbevbz6t8z9G9Pm6pJ8k+XZze6+kP5E0d7yxrdRqtdTr9THrmohDhw7pvvvu01133aXNmzdrcHBQ06dPl+3Kx3jxxRclSbNmzdKSJUu0dOlSXXzxxZo5c2bb6x3LgQMH9JGPfEQ/+clPdNJJJx3Vc5duaGhIr7zyii644AKtX79eCxcubMtxH3/8cV133XXavHmz3vSmN2nq1KltOS7Kcdppp+k3v/nNhMba3pGk1rKtQtD/N0mLk3yyuf0JSR9KsnpEn82S1iX5++b2g5KuUyPoxxw74hgrJa1sbp6pxnLPRJwq6dkJjj1WMefyHW/zlZjzkXpnku5WDVMqDG51uzv6p8Ph+lQZ29iZbJC0oUI9Y7JdP9xPtVIx5/Idb/OVmHM7VQn6PklzRmzPlrS/Yp9pFcYCACZRlY9A2C5pnu0zbE+TdKmkTaP6bJJ0hRsWSno+SX/FsQCASTTuHX2SIdurJW1V4ymaO5Pstr2q2X67pC1qPHnTK+klSVeONXZSZvJvXvfyzzGIOZfveJuvxJzbZtw/xgIAjm18eiUAFI6gB4DCFRP0thfb3mu71/aaTtdzNNh+0vZjth+13f53mL0B2L7T9jO2fzli31ttP2D7182vb+lkje12mDl/yfbTzWv9qO2lnayx3WzPsb3N9h7bu21f3dxf7LUeY85tv9ZFrNHb7pL0K0l/qsajntslXZbk8Y4WNslsPymplqTYN5XY/mNJL0q6O8lZzX1/K+m5JOuaP9TfkuS6TtbZToeZ85ckvZhkfSdrmyy2Z0malWSn7ZMl7ZD055L+uwq91mPMeYXafK1LuaM/T1JvkieSHJJ0j6RlHa4JbZDkIUnPjdq9TNJdze/vUuN/jmIcZs5FS9KfZGfz+4OS9kjqUcHXeow5t10pQd8jad+I7T5N0n+wN5hIut/2juZHSBwv3t58n4aaX0/rcD1Hy2rbu5pLO8UsYYxme66kD0p6RMfJtR41Z6nN17qUoK/8UQuFuSDJOZKWSLqq+Ss/ynSbpHdL+oCkfklf62g1k8T2SZK+J+maJC90up6jocWc236tSwn6Kh/TUJwk+5tfn5H0AzWWsI4Hv22ub/5unfOZDtcz6ZL8NslrSYYl/Z0KvNa2p6oReBuTfL+5u+hr3WrOk3GtSwn64+6jFmxPb/4BR7anS/rPkn459qhibJL0F83v/0LSDztYy1Hxu7Br+ogKu9ZufFb4NyTtSXLjiKZir/Xh5jwZ17qIp24kqfkI0k36t49a+JvOVjS5bL9Ljbt4qfFRFv+7xDnb/rYa/7bBqZJ+K+mLkv6PpO9I+iNJT0n6WJJi/nh5mDn/iRq/ykfSk5I+/bu16xLY/k+S/p+kxyQNN3d/Xo016yKv9RhzvkxtvtbFBD0AoLVSlm4AAIdB0ANA4Qh6ACgcQQ8AhSPoAaBwBD0AFI6gB4DC/SsEawajog8CWAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAARa0lEQVR4nO3df6xfd13H8efL2zZzsDFkFxxttYM0YgMoy02dIGaISFsJxSxAm/3A/VOXMGFGo5N/9B8TY5DokqVNhRrWW9YAm3JDGquJI0iyLb0dy2ip05uJ9trK7iBuzMK6srd/3O/05vpt72n3/d6v/fT5SG56z/l8Pue8Pznpq9/7ueecpqqQJLXrR0ZdgCRpuAx6SWqcQS9JjTPoJalxBr0kNW7FqAvo5+qrr65169aNugxJumgcPnz46aoa79f2/zLo161bx/T09KjLkKSLRpJ/PVubSzeS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhrXKeiTbEryRJKZJHf1aX9TkoeSPJ/kdxbsX5vkwSTHkhxN8vFBFi9JWtqS/2dskjHgHuA9wCxwKMlUVX1zQbfvAh8DPrBo+Bngt6vq0SRXAIeT/N2isZKkIeryiX4jMFNVT1bVaWA/sHVhh6p6qqoOAS8s2n+yqh7tff894BiweiCVS5I66RL0q4HjC7ZnuYCwTrIOeBvwyPmOlSRduC5Bnz776nxOkuSVwP3AnVX17Fn67EgynWR6bm7ufA4vSTqHLkE/C6xdsL0GONH1BElWMh/y+6rqgbP1q6rdVTVRVRPj4+NdDy9JWkKXoD8ErE9ybZJVwDZgqsvBkwT4DHCsqj514WVKki7UknfdVNWZJHcAB4ExYE9VHU1ye699V5IfB6aBK4EXk9wJbADeCtwCfCPJY71DfqKqDgx8JpKkvpYMeoBeMB9YtG/Xgu//g/klncW+Rv81fknSMvHJWElqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuM6BX2STUmeSDKT5K4+7W9K8lCS55P8zvmMlSQN15JBn2QMuAfYDGwAtifZsKjbd4GPAZ+8gLGSpCHq8ol+IzBTVU9W1WlgP7B1YYeqeqqqDgEvnO9YSdJwdQn61cDxBduzvX1ddB6bZEeS6STTc3NzHQ8vSVpKl6BPn33V8fidx1bV7qqaqKqJ8fHxjoeXJC2lS9DPAmsXbK8BTnQ8/ssZK0kagC5BfwhYn+TaJKuAbcBUx+O/nLGSpAFYsVSHqjqT5A7gIDAG7Kmqo0lu77XvSvLjwDRwJfBikjuBDVX1bL+xQ5qLJKmPVHVdbl8+ExMTNT09PeoyJOmikeRwVU30a/PJWElqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIa1ynok2xK8kSSmSR39WlPkrt77Y8nuW5B228lOZrkSJL7klw2yAlIks5tyaBPMgbcA2wGNgDbk2xY1G0zsL73tQPY2Ru7GvgYMFFVbwbGgG0Dq16StKQun+g3AjNV9WRVnQb2A1sX9dkK3FvzHgauSnJNr20F8KNJVgCXAycGVLskqYMuQb8aOL5ge7a3b8k+VfXvwCeBfwNOAs9U1d/2O0mSHUmmk0zPzc11rV+StIQuQZ8++6pLnySvZv7T/rXA64FXJLm530mqandVTVTVxPj4eIeyJElddAn6WWDtgu01/N/ll7P1+WXgX6pqrqpeAB4A3n7h5UqSzleXoD8ErE9ybZJVzP8ydWpRnyng1t7dN9czv0Rzkvklm+uTXJ4kwLuBYwOsX5K0hBVLdaiqM0nuAA4yf9fMnqo6muT2Xvsu4ACwBZgBTgG39doeSfJF4FHgDPB1YPcwJiJJ6i9Vi5fbR29iYqKmp6dHXYYkXTSSHK6qiX5tPhkrSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqXKegT7IpyRNJZpLc1ac9Se7utT+e5LoFbVcl+WKSf0xyLMnPD3ICkqRzWzLok4wB9wCbgQ3A9iQbFnXbDKzvfe0Adi5o+3Pgb6rqTcDPAMcGULckqaMun+g3AjNV9WRVnQb2A1sX9dkK3FvzHgauSnJNkiuBXwQ+A1BVp6vqPwdXviRpKV2CfjVwfMH2bG9flz5vAOaAv0zy9SSfTvKKfidJsiPJdJLpubm5zhOQJJ1bl6BPn33Vsc8K4DpgZ1W9Dfgv4P+s8QNU1e6qmqiqifHx8Q5lSZK66BL0s8DaBdtrgBMd+8wCs1X1SG//F5kPfknSMukS9IeA9UmuTbIK2AZMLeozBdzau/vmeuCZqjpZVf8BHE/yU71+7wa+OajiJUlLW7FUh6o6k+QO4CAwBuypqqNJbu+17wIOAFuAGeAUcNuCQ/wmsK/3j8STi9okSUOWqsXL7aM3MTFR09PToy5Dki4aSQ5X1US/Np+MlaTGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxnYI+yaYkTySZSXJXn/YkubvX/niS6xa1jyX5epIvD6pwSVI3SwZ9kjHgHmAzsAHYnmTDom6bgfW9rx3AzkXtHweOvexqJUnnrcsn+o3ATFU9WVWngf3A1kV9tgL31ryHgauSXAOQZA3wq8CnB1i3JKmjLkG/Gji+YHu2t69rnz8Dfhd48VwnSbIjyXSS6bm5uQ5lSZK66BL06bOvuvRJ8j7gqao6vNRJqmp3VU1U1cT4+HiHsiRJXXQJ+llg7YLtNcCJjn3eAbw/ybeYX/L5pSSTF1ytJOm8dQn6Q8D6JNcmWQVsA6YW9ZkCbu3dfXM98ExVnayq36+qNVW1rjfu76vq5kFOQJJ0biuW6lBVZ5LcARwExoA9VXU0ye299l3AAWALMAOcAm4bXsmSpPORqsXL7aM3MTFR09PToy5Dki4aSQ5X1US/Np+MlaTGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxnYI+yaYkTySZSXJXn/YkubvX/niS63r71yZ5MMmxJEeTfHzQE5AknduSQZ9kDLgH2AxsALYn2bCo22Zgfe9rB7Czt/8M8NtV9dPA9cBH+4yVpEvezp07ueWWWzh9+vTAj72iQ5+NwExVPQmQZD+wFfjmgj5bgXurqoCHk1yV5JqqOgmcBKiq7yU5BqxeNFaSLnm7d+9m1apVrFq1auDH7rJ0sxo4vmB7trfvvPokWQe8DXik30mS7EgynWR6bm6uQ1mS1IYjR47w2GOPcfPNNw/l+F2CPn321fn0SfJK4H7gzqp6tt9Jqmp3VU1U1cT4+HiHsiSpDZOTk4yNjfHhD394KMfvEvSzwNoF22uAE137JFnJfMjvq6oHLrxUSWrPiy++yL59+9i0aROvfe1rh3KOLkF/CFif5Nokq4BtwNSiPlPArb27b64Hnqmqk0kCfAY4VlWfGmjlktSAr371q8zOzg5t2QY6/DK2qs4kuQM4CIwBe6rqaJLbe+27gAPAFmAGOAXc1hv+DuAW4BtJHuvt+0RVHRjoLCTpIjU5OckVV1zB+9///qGdo8tdN/SC+cCifbsWfF/AR/uM+xr91+8l6ZL3/e9/ny984QvceOONXH755UM7j0/GStKIfPnLX+bZZ58d6rINGPSSNDKTk5O8/vWv54YbbhjqeQx6SRqBp59+mgMHDnDTTTcxNjY21HMZ9JI0Ap///Oc5c+bM0JdtwKCXpJGYnJzkLW95C29961uHfi6DXpKW2czMDA899BC33HLLspzPoJekZbZv3z6SsH379mU5n0EvScuoqpicnORd73oXa9asWZZzGvSStIweeeQRZmZmluWXsC8x6CVpGU1OTnLZZZdx4403Lts5DXpJWiYvvPAC+/fvZ+vWrVx55ZXLdl6DXpKWycGDB/nOd76zrMs2YNBL0rLZu3cvV199Ne9973uX9bwGvSQtg2eeeYapqSm2bdvGypUrl/XcBr0kLYMHHniAH/zgB8u+bAMGvSQti71797J+/Xo2bty47Oc26CVpyI4fP85XvvIVbr75Zub/h9XlZdBL0pDdd999VBU33XTTSM5v0EvSEFUVe/fu5e1vfztvfOMbR1KDQS9JQ/T4449z5MiRkfwS9iUGvSQN0eTkJCtXruRDH/rQyGow6CVpSH74wx/yuc99ji1btvCa17xmZHUY9JI0JA8++CAnTpwY6bINGPSSNDSTk5O86lWv4n3ve99I6+gU9Ek2JXkiyUySu/q0J8ndvfbHk1zXdawktejUqVPcf//9fPCDH+Syyy4baS1LBn2SMeAeYDOwAdieZMOibpuB9b2vHcDO8xgrSc350pe+xHPPPTfyZRuAFR36bARmqupJgCT7ga3ANxf02QrcW1UFPJzkqiTXAOs6jB2Y173udZw6dWoYh5ak8/L888+zdu1a3vnOd466lE5Bvxo4vmB7Fvi5Dn1WdxwLQJIdzP80APBckic61NbP1cDTFzj2YuWc23epzRcamPPx48cZGxs7nyEvZ84/ebaGLkHf78UM1bFPl7HzO6t2A7s71HNOSaarauLlHudi4pzbd6nNF5zzIHUJ+llg7YLtNcCJjn1WdRgrSRqiLnfdHALWJ7k2ySpgGzC1qM8UcGvv7pvrgWeq6mTHsZKkIVryE31VnUlyB3AQGAP2VNXRJLf32ncBB4AtwAxwCrjtXGOHMpP/9bKXfy5Czrl9l9p8wTkPTOZvlJEktconYyWpcQa9JDWumaC/FF+1kORbSb6R5LEk06OuZxiS7EnyVJIjC/b9WJK/S/LPvT9fPcoaB+0sc/7DJP/eu9aPJdkyyhoHLcnaJA8mOZbkaJKP9/Y3e63PMeeBX+sm1uh7r1r4J+A9zN/qeQjYXlVDeQL3/4sk3wImquqifqjkXJL8IvAc809ev7m370+A71bVH/f+UX91Vf3eKOscpLPM+Q+B56rqk6OsbVh6T9JfU1WPJrkCOAx8APh1Gr3W55jzhxjwtW7lE/3/vKahqk4DL71qQRe5qvoq8N1Fu7cCn+19/1nm/3I04yxzblpVnayqR3vffw84xvyT9c1e63PMeeBaCfqzvYKhdQX8bZLDvVdIXCpe13tOg96frx1xPcvljt7bYfe0tISxWJJ1wNuAR7hErvWiOcOAr3UrQd/5VQuNeUdVXcf820E/2vuRX23aCbwR+FngJPCnI61mSJK8ErgfuLOqnh11Pcuhz5wHfq1bCfour2loTlWd6P35FPBXzC9hXQq+3VvffGmd86kR1zN0VfXtqvphVb0I/AUNXuskK5kPvH1V9UBvd9PXut+ch3GtWwn6S+5VC0le0fsFDkleAfwKcOTco5oxBXyk9/1HgC+NsJZl8VLY9fwajV3rJAE+Axyrqk8taGr2Wp9tzsO41k3cdQPQuwXpz/jfVy380WgrGq4kb2D+UzzMv8ricy3OOcl9wA3Mv77128AfAH8NfB74CeDfgA9WVTO/vDzLnG9g/kf5Ar4F/MZLa9ctSPILwD8A3wBe7O3+BPNr1k1e63PMeTsDvtbNBL0kqb9Wlm4kSWdh0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TG/TdoYV2BMR8wxAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "conv4 = Model(inputs=cnn.model.get_layer(index=9).input, outputs=cnn.model.get_layer(index=12).output)\n", + "conv4_output = conv4.predict(conv3_output)\n", + "for i in range(conv4_output.shape[-1]):\n", + " plt.plot(conv4_output[0, :, i], c='black')\n", + " plt.ylim(conv4_output.min(), conv4_output.max())\n", + " plt.savefig(f\"assets/conv4/{i}.png\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "7f619fc91ee8bdab81d49e7c14228037474662e3f2d607687ae505108922fa06" + }, + "kernelspec": { + "display_name": "Python 3.9.7 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/preprocess.ipynb b/01_preprocess.ipynb similarity index 100% rename from preprocess.ipynb rename to 01_preprocess.ipynb diff --git a/model_training.ipynb b/02_model_training.ipynb similarity index 85% rename from model_training.ipynb rename to 02_model_training.ipynb index 1e0aecd..3a282cc 100644 --- a/model_training.ipynb +++ b/02_model_training.ipynb @@ -66,7 +66,18 @@ "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of data:\n", + "x_train: (5728, 1, 102), y_train: (5728, 1),\n", + "x_val: (2455, 1, 102), y_val: (2455, 1)\n", + "x_test: (3508, 1, 102), y_test: (3508, 1)\n" + ] + } + ], "source": [ "import random\n", "from numpy.random import seed\n", @@ -75,7 +86,7 @@ "seed(4750)\n", "tensorflow.random.set_seed(4750)\n", "time1 = time.time()\n", - "data = loadmat('./preprocess/dataset/mango/mango_dm_split.mat')\n", + "data = loadmat('dataset/mango/mango_dm_split.mat')\n", "x_train, y_train, x_test, y_test = data['x_train'], data['y_train'], data['x_test'], data['y_test']\n", "x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.3, random_state=12, shuffle=True)\n", "x_train, x_val, x_test = x_train[:, np.newaxis, :], x_val[:, np.newaxis, :], x_test[:, np.newaxis, :]\n", @@ -98,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "pycharm": { "name": "#%%\n" @@ -111,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "pycharm": { "name": "#%%\n" @@ -122,7 +133,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2022-05-10 17:09:33.906952: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n" + "2022-06-12 20:45:05.812551: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n" ] }, { @@ -136,7 +147,7 @@ "Epoch 3/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0127 - val_loss: 0.0301 - lr: 0.0025\n", "Epoch 4/1024\n", - "90/90 [==============================] - 0s 3ms/step - loss: 0.0088 - val_loss: 0.0315 - lr: 0.0025\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0088 - val_loss: 0.0315 - lr: 0.0025\n", "Epoch 5/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0081 - val_loss: 0.0813 - lr: 0.0025\n", "Epoch 6/1024\n", @@ -168,7 +179,7 @@ "Epoch 19/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0043 - val_loss: 0.3292 - lr: 0.0025\n", "Epoch 20/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0042 - val_loss: 0.0732 - lr: 0.0025\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0042 - val_loss: 0.0732 - lr: 0.0025\n", "Epoch 21/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0041 - val_loss: 0.0785 - lr: 0.0025\n", "Epoch 22/1024\n", @@ -188,13 +199,13 @@ "Epoch 29/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0039 - val_loss: 0.2374 - lr: 0.0012\n", "Epoch 30/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0038 - val_loss: 0.1049 - lr: 0.0012\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0038 - val_loss: 0.1049 - lr: 0.0012\n", "Epoch 31/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0041 - val_loss: 0.0522 - lr: 0.0012\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0041 - val_loss: 0.0522 - lr: 0.0012\n", "Epoch 32/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0039 - val_loss: 0.2504 - lr: 0.0012\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0039 - val_loss: 0.2504 - lr: 0.0012\n", "Epoch 33/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0040 - val_loss: 0.1557 - lr: 0.0012\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0040 - val_loss: 0.1557 - lr: 0.0012\n", "Epoch 34/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0037 - val_loss: 0.0697 - lr: 0.0012\n", "Epoch 35/1024\n", @@ -218,17 +229,17 @@ "Epoch 44/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0038 - val_loss: 0.0897 - lr: 0.0012\n", "Epoch 45/1024\n", - "90/90 [==============================] - 0s 3ms/step - loss: 0.0038 - val_loss: 0.3225 - lr: 0.0012\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0038 - val_loss: 0.3225 - lr: 0.0012\n", "Epoch 46/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0038 - val_loss: 0.3101 - lr: 0.0012\n", "Epoch 47/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0038 - val_loss: 0.3089 - lr: 0.0012\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0038 - val_loss: 0.3089 - lr: 0.0012\n", "Epoch 48/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0037 - val_loss: 0.0290 - lr: 0.0012\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0037 - val_loss: 0.0290 - lr: 0.0012\n", "Epoch 49/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0038 - val_loss: 0.0591 - lr: 0.0012\n", "Epoch 50/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0036 - val_loss: 0.1132 - lr: 0.0012\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0036 - val_loss: 0.1132 - lr: 0.0012\n", "Epoch 51/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0038 - val_loss: 0.3202 - lr: 0.0012\n", "Epoch 52/1024\n", @@ -238,9 +249,9 @@ "Epoch 54/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.1351 - lr: 6.2500e-04\n", "Epoch 55/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.1235 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.1235 - lr: 6.2500e-04\n", "Epoch 56/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0036 - val_loss: 0.2803 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0036 - val_loss: 0.2803 - lr: 6.2500e-04\n", "Epoch 57/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.0929 - lr: 6.2500e-04\n", "Epoch 58/1024\n", @@ -248,37 +259,37 @@ "Epoch 59/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.0590 - lr: 6.2500e-04\n", "Epoch 60/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0036 - val_loss: 0.3023 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0036 - val_loss: 0.3023 - lr: 6.2500e-04\n", "Epoch 61/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.1735 - lr: 6.2500e-04\n", "Epoch 62/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0036 - val_loss: 0.1487 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0036 - val_loss: 0.1487 - lr: 6.2500e-04\n", "Epoch 63/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.1859 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.1859 - lr: 6.2500e-04\n", "Epoch 64/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.3022 - lr: 6.2500e-04\n", "Epoch 65/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.2015 - lr: 6.2500e-04\n", "Epoch 66/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0036 - val_loss: 0.3198 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0036 - val_loss: 0.3198 - lr: 6.2500e-04\n", "Epoch 67/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.2997 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.2997 - lr: 6.2500e-04\n", "Epoch 68/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.1298 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.1298 - lr: 6.2500e-04\n", "Epoch 69/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.1149 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.1149 - lr: 6.2500e-04\n", "Epoch 70/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.1068 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.1068 - lr: 6.2500e-04\n", "Epoch 71/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.0951 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.0951 - lr: 6.2500e-04\n", "Epoch 72/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.0429 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.0429 - lr: 6.2500e-04\n", "Epoch 73/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.2853 - lr: 6.2500e-04\n", "Epoch 74/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.0702 - lr: 6.2500e-04\n", "Epoch 75/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.2109 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.2109 - lr: 6.2500e-04\n", "Epoch 76/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.1927 - lr: 6.2500e-04\n", "Epoch 77/1024\n", @@ -296,9 +307,9 @@ "Epoch 83/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0273 - lr: 3.1250e-04\n", "Epoch 84/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.0988 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.0988 - lr: 3.1250e-04\n", "Epoch 85/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0657 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0657 - lr: 3.1250e-04\n", "Epoch 86/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.0328 - lr: 3.1250e-04\n", "Epoch 87/1024\n", @@ -306,7 +317,7 @@ "Epoch 88/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.0611 - lr: 3.1250e-04\n", "Epoch 89/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.0537 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.0537 - lr: 3.1250e-04\n", "Epoch 90/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.1233 - lr: 3.1250e-04\n", "Epoch 91/1024\n", @@ -318,39 +329,39 @@ "Epoch 94/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0546 - lr: 3.1250e-04\n", "Epoch 95/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0120 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0120 - lr: 3.1250e-04\n", "Epoch 96/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0978 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0978 - lr: 3.1250e-04\n", "Epoch 97/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.0384 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.0384 - lr: 3.1250e-04\n", "Epoch 98/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.0324 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.0324 - lr: 3.1250e-04\n", "Epoch 99/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.1121 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.1121 - lr: 3.1250e-04\n", "Epoch 100/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0442 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0442 - lr: 3.1250e-04\n", "Epoch 101/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.1185 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.1185 - lr: 3.1250e-04\n", "Epoch 102/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.0731 - lr: 3.1250e-04\n", "Epoch 103/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.1457 - lr: 3.1250e-04\n", "Epoch 104/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.2414 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.2414 - lr: 3.1250e-04\n", "Epoch 105/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.0337 - lr: 3.1250e-04\n", "Epoch 106/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.1618 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.1618 - lr: 3.1250e-04\n", "Epoch 107/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.0374 - lr: 3.1250e-04\n", "Epoch 108/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.0780 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.0780 - lr: 3.1250e-04\n", "Epoch 109/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0158 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0158 - lr: 3.1250e-04\n", "Epoch 110/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.2529 - lr: 3.1250e-04\n", "Epoch 111/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.2905 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.2905 - lr: 3.1250e-04\n", "Epoch 112/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.1713 - lr: 3.1250e-04\n", "Epoch 113/1024\n", @@ -360,41 +371,41 @@ "Epoch 115/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.1308 - lr: 3.1250e-04\n", "Epoch 116/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0473 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0473 - lr: 3.1250e-04\n", "Epoch 117/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.1393 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.1393 - lr: 3.1250e-04\n", "Epoch 118/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0336 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0336 - lr: 3.1250e-04\n", "Epoch 119/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0409 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0409 - lr: 1.5625e-04\n", "Epoch 120/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0932 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0932 - lr: 1.5625e-04\n", "Epoch 121/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0227 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0227 - lr: 1.5625e-04\n", "Epoch 122/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0491 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0491 - lr: 1.5625e-04\n", "Epoch 123/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0734 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0734 - lr: 1.5625e-04\n", "Epoch 124/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.1225 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.1225 - lr: 1.5625e-04\n", "Epoch 125/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0082 - lr: 1.5625e-04\n", "Epoch 126/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.1061 - lr: 1.5625e-04\n", "Epoch 127/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.0511 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.0511 - lr: 1.5625e-04\n", "Epoch 128/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0280 - lr: 1.5625e-04\n", "Epoch 129/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0899 - lr: 1.5625e-04\n", "Epoch 130/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0167 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0167 - lr: 1.5625e-04\n", "Epoch 131/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0133 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0133 - lr: 1.5625e-04\n", "Epoch 132/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0519 - lr: 1.5625e-04\n", "Epoch 133/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.1900 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.1900 - lr: 1.5625e-04\n", "Epoch 134/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.0392 - lr: 1.5625e-04\n", "Epoch 135/1024\n", @@ -402,55 +413,55 @@ "Epoch 136/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0120 - lr: 1.5625e-04\n", "Epoch 137/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0755 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0755 - lr: 1.5625e-04\n", "Epoch 138/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0820 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0820 - lr: 1.5625e-04\n", "Epoch 139/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0921 - lr: 1.5625e-04\n", "Epoch 140/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0145 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0145 - lr: 1.5625e-04\n", "Epoch 141/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0545 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0545 - lr: 1.5625e-04\n", "Epoch 142/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.1179 - lr: 1.5625e-04\n", "Epoch 143/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.1231 - lr: 1.5625e-04\n", "Epoch 144/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0159 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0159 - lr: 1.5625e-04\n", "Epoch 145/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0107 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0107 - lr: 1.5625e-04\n", "Epoch 146/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0575 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0575 - lr: 1.5625e-04\n", "Epoch 147/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.2076 - lr: 1.5625e-04\n", "Epoch 148/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0351 - lr: 1.5625e-04\n", "Epoch 149/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.1428 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.1428 - lr: 1.5625e-04\n", "Epoch 150/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.1004 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.1004 - lr: 1.5625e-04\n", "Epoch 151/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0082 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0082 - lr: 7.8125e-05\n", "Epoch 152/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0117 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0117 - lr: 7.8125e-05\n", "Epoch 153/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0295 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0295 - lr: 7.8125e-05\n", "Epoch 154/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0128 - lr: 7.8125e-05\n", "Epoch 155/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0414 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0414 - lr: 7.8125e-05\n", "Epoch 156/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0187 - lr: 7.8125e-05\n", "Epoch 157/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0387 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0387 - lr: 7.8125e-05\n", "Epoch 158/1024\n", - "90/90 [==============================] - 0s 3ms/step - loss: 0.0032 - val_loss: 0.0540 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0540 - lr: 7.8125e-05\n", "Epoch 159/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0196 - lr: 7.8125e-05\n", "Epoch 160/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0126 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0126 - lr: 7.8125e-05\n", "Epoch 161/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0174 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0174 - lr: 7.8125e-05\n", "Epoch 162/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0150 - lr: 7.8125e-05\n", "Epoch 163/1024\n", @@ -484,55 +495,55 @@ "Epoch 177/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0507 - lr: 7.8125e-05\n", "Epoch 178/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0291 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0291 - lr: 7.8125e-05\n", "Epoch 179/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0252 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0252 - lr: 7.8125e-05\n", "Epoch 180/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0259 - lr: 7.8125e-05\n", "Epoch 181/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0340 - lr: 7.8125e-05\n", "Epoch 182/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0094 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0094 - lr: 7.8125e-05\n", "Epoch 183/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0213 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0213 - lr: 7.8125e-05\n", "Epoch 184/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0055 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0055 - lr: 7.8125e-05\n", "Epoch 185/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0140 - lr: 7.8125e-05\n", "Epoch 186/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0448 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0448 - lr: 7.8125e-05\n", "Epoch 187/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0061 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0061 - lr: 7.8125e-05\n", "Epoch 188/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0218 - lr: 7.8125e-05\n", "Epoch 189/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0128 - lr: 7.8125e-05\n", "Epoch 190/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0624 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0624 - lr: 3.9062e-05\n", "Epoch 191/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0200 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0200 - lr: 3.9062e-05\n", "Epoch 192/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0434 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0434 - lr: 3.9062e-05\n", "Epoch 193/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0101 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0101 - lr: 3.9062e-05\n", "Epoch 194/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0087 - lr: 3.9062e-05\n", "Epoch 195/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0212 - lr: 3.9062e-05\n", "Epoch 196/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0185 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0185 - lr: 3.9062e-05\n", "Epoch 197/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0654 - lr: 3.9062e-05\n", "Epoch 198/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0122 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0122 - lr: 3.9062e-05\n", "Epoch 199/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0077 - lr: 3.9062e-05\n", "Epoch 200/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0086 - lr: 3.9062e-05\n", "Epoch 201/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0058 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0058 - lr: 3.9062e-05\n", "Epoch 202/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0134 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0134 - lr: 3.9062e-05\n", "Epoch 203/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0553 - lr: 3.9062e-05\n", "Epoch 204/1024\n", @@ -548,7 +559,7 @@ "Epoch 209/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0288 - lr: 3.9062e-05\n", "Epoch 210/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0236 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0236 - lr: 3.9062e-05\n", "Epoch 211/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0035 - lr: 3.9062e-05\n", "Epoch 212/1024\n", @@ -556,21 +567,21 @@ "Epoch 213/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0076 - lr: 3.9062e-05\n", "Epoch 214/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0043 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0043 - lr: 3.9062e-05\n", "Epoch 215/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0141 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0141 - lr: 3.9062e-05\n", "Epoch 216/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0274 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0274 - lr: 3.9062e-05\n", "Epoch 217/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0216 - lr: 3.9062e-05\n", "Epoch 218/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0063 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0063 - lr: 3.9062e-05\n", "Epoch 219/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0076 - lr: 3.9062e-05\n", "Epoch 220/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0061 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0061 - lr: 3.9062e-05\n", "Epoch 221/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0179 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0179 - lr: 3.9062e-05\n", "Epoch 222/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0463 - lr: 3.9062e-05\n", "Epoch 223/1024\n", @@ -582,19 +593,19 @@ "Epoch 226/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0254 - lr: 3.9062e-05\n", "Epoch 227/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0067 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0067 - lr: 3.9062e-05\n", "Epoch 228/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0080 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0080 - lr: 3.9062e-05\n", "Epoch 229/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0116 - lr: 3.9062e-05\n", "Epoch 230/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0381 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0381 - lr: 3.9062e-05\n", "Epoch 231/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0471 - lr: 3.9062e-05\n", "Epoch 232/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0042 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0042 - lr: 3.9062e-05\n", "Epoch 233/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0097 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0097 - lr: 3.9062e-05\n", "Epoch 234/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0033 - lr: 3.9062e-05\n", "Epoch 235/1024\n", @@ -602,15 +613,15 @@ "Epoch 236/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0184 - lr: 3.9062e-05\n", "Epoch 237/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0214 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0214 - lr: 3.9062e-05\n", "Epoch 238/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0520 - lr: 3.9062e-05\n", "Epoch 239/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0182 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0182 - lr: 3.9062e-05\n", "Epoch 240/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0103 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0103 - lr: 3.9062e-05\n", "Epoch 241/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0041 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0041 - lr: 3.9062e-05\n", "Epoch 242/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0509 - lr: 3.9062e-05\n", "Epoch 243/1024\n", @@ -620,41 +631,41 @@ "Epoch 245/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0174 - lr: 3.9062e-05\n", "Epoch 246/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0035 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0035 - lr: 3.9062e-05\n", "Epoch 247/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0113 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0113 - lr: 3.9062e-05\n", "Epoch 248/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0075 - lr: 3.9062e-05\n", "Epoch 249/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0139 - lr: 3.9062e-05\n", "Epoch 250/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.0072 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.0072 - lr: 3.9062e-05\n", "Epoch 251/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0197 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0197 - lr: 3.9062e-05\n", "Epoch 252/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0061 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0061 - lr: 3.9062e-05\n", "Epoch 253/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0181 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0181 - lr: 3.9062e-05\n", "Epoch 254/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0148 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0148 - lr: 3.9062e-05\n", "Epoch 255/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0328 - lr: 3.9062e-05\n", "Epoch 256/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0056 - lr: 3.9062e-05\n", "Epoch 257/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0374 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0374 - lr: 3.9062e-05\n", "Epoch 258/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0133 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0133 - lr: 3.9062e-05\n", "Epoch 259/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0173 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0173 - lr: 3.9062e-05\n", "Epoch 260/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0105 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0105 - lr: 3.9062e-05\n", "Epoch 261/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0116 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0116 - lr: 1.9531e-05\n", "Epoch 262/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0031 - lr: 1.9531e-05\n", "Epoch 263/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0093 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0093 - lr: 1.9531e-05\n", "Epoch 264/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0160 - lr: 1.9531e-05\n", "Epoch 265/1024\n", @@ -672,25 +683,25 @@ "Epoch 271/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0034 - lr: 1.9531e-05\n", "Epoch 272/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0053 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0053 - lr: 1.9531e-05\n", "Epoch 273/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0034 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0034 - lr: 1.9531e-05\n", "Epoch 274/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0035 - lr: 1.9531e-05\n", "Epoch 275/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0062 - lr: 1.9531e-05\n", "Epoch 276/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0040 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0040 - lr: 1.9531e-05\n", "Epoch 277/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0064 - lr: 1.9531e-05\n", "Epoch 278/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0050 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0050 - lr: 1.9531e-05\n", "Epoch 279/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0121 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0121 - lr: 1.9531e-05\n", "Epoch 280/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0031 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0031 - lr: 1.9531e-05\n", "Epoch 281/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0051 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0051 - lr: 1.9531e-05\n", "Epoch 282/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0028 - lr: 1.9531e-05\n", "Epoch 283/1024\n", @@ -700,21 +711,21 @@ "Epoch 285/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0041 - lr: 1.9531e-05\n", "Epoch 286/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0052 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0052 - lr: 1.9531e-05\n", "Epoch 287/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0064 - lr: 1.9531e-05\n", "Epoch 288/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0029 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0029 - lr: 1.9531e-05\n", "Epoch 289/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0057 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0057 - lr: 1.9531e-05\n", "Epoch 290/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0056 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0056 - lr: 1.9531e-05\n", "Epoch 291/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0096 - lr: 1.9531e-05\n", "Epoch 292/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0036 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0036 - lr: 1.9531e-05\n", "Epoch 293/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0075 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0075 - lr: 1.9531e-05\n", "Epoch 294/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0066 - lr: 1.9531e-05\n", "Epoch 295/1024\n", @@ -722,11 +733,11 @@ "Epoch 296/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0050 - lr: 1.9531e-05\n", "Epoch 297/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0056 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0056 - lr: 1.9531e-05\n", "Epoch 298/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0056 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0056 - lr: 1.9531e-05\n", "Epoch 299/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0140 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0140 - lr: 1.9531e-05\n", "Epoch 300/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0034 - lr: 1.9531e-05\n", "Epoch 301/1024\n", @@ -734,19 +745,19 @@ "Epoch 302/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0045 - lr: 1.9531e-05\n", "Epoch 303/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0078 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0078 - lr: 1.9531e-05\n", "Epoch 304/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0113 - lr: 1.9531e-05\n", "Epoch 305/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0028 - lr: 1.9531e-05\n", "Epoch 306/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0074 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0074 - lr: 1.9531e-05\n", "Epoch 307/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0069 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0069 - lr: 1.9531e-05\n", "Epoch 308/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0029 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0029 - lr: 1.9531e-05\n", "Epoch 309/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0054 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0054 - lr: 1.9531e-05\n", "Epoch 310/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0034 - lr: 1.9531e-05\n", "Epoch 311/1024\n", @@ -758,23 +769,23 @@ "Epoch 314/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0057 - lr: 1.9531e-05\n", "Epoch 315/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0036 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0036 - lr: 1.9531e-05\n", "Epoch 316/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0038 - lr: 1.9531e-05\n", "Epoch 317/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0172 - lr: 1.9531e-05\n", "Epoch 318/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0070 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0070 - lr: 1.9531e-05\n", "Epoch 319/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0079 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0079 - lr: 1.9531e-05\n", "Epoch 320/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0048 - lr: 1.9531e-05\n", "Epoch 321/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0073 - lr: 1.9531e-05\n", "Epoch 322/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0028 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0028 - lr: 1.9531e-05\n", "Epoch 323/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0166 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0166 - lr: 1.9531e-05\n", "Epoch 324/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0040 - lr: 1.9531e-05\n", "Epoch 325/1024\n", @@ -784,51 +795,51 @@ "Epoch 327/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0161 - lr: 1.9531e-05\n", "Epoch 328/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0036 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0036 - lr: 1.9531e-05\n", "Epoch 329/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0146 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0146 - lr: 1.9531e-05\n", "Epoch 330/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0042 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0042 - lr: 1.9531e-05\n", "Epoch 331/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0030 - lr: 9.7656e-06\n", "Epoch 332/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0058 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0058 - lr: 9.7656e-06\n", "Epoch 333/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0031 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0031 - lr: 9.7656e-06\n", "Epoch 334/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0055 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0055 - lr: 9.7656e-06\n", "Epoch 335/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0038 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0038 - lr: 9.7656e-06\n", "Epoch 336/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0030 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0030 - lr: 9.7656e-06\n", "Epoch 337/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0032 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0032 - lr: 9.7656e-06\n", "Epoch 338/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0037 - lr: 9.7656e-06\n", "Epoch 339/1024\n", - "90/90 [==============================] - 0s 3ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.7656e-06\n", "Epoch 340/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0031 - lr: 9.7656e-06\n", "Epoch 341/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0072 - lr: 9.7656e-06\n", "Epoch 342/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0038 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0038 - lr: 9.7656e-06\n", "Epoch 343/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0039 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0039 - lr: 9.7656e-06\n", "Epoch 344/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0051 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0051 - lr: 9.7656e-06\n", "Epoch 345/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0033 - lr: 9.7656e-06\n", "Epoch 346/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0034 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0034 - lr: 9.7656e-06\n", "Epoch 347/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0053 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0053 - lr: 9.7656e-06\n", "Epoch 348/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0062 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0062 - lr: 9.7656e-06\n", "Epoch 349/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0036 - lr: 9.7656e-06\n", "Epoch 350/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0029 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0029 - lr: 9.7656e-06\n", "Epoch 351/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0045 - lr: 9.7656e-06\n", "Epoch 352/1024\n", @@ -836,9 +847,9 @@ "Epoch 353/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0028 - lr: 9.7656e-06\n", "Epoch 354/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0031 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0031 - lr: 9.7656e-06\n", "Epoch 355/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0032 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0032 - lr: 9.7656e-06\n", "Epoch 356/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0034 - lr: 9.7656e-06\n", "Epoch 357/1024\n", @@ -848,11 +859,11 @@ "Epoch 359/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0029 - lr: 9.7656e-06\n", "Epoch 360/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0031 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0031 - lr: 9.7656e-06\n", "Epoch 361/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0037 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0037 - lr: 9.7656e-06\n", "Epoch 362/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0029 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0029 - lr: 9.7656e-06\n", "Epoch 363/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0033 - lr: 9.7656e-06\n", "Epoch 364/1024\n", @@ -862,25 +873,25 @@ "Epoch 366/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0033 - lr: 9.7656e-06\n", "Epoch 367/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0029 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0029 - lr: 9.7656e-06\n", "Epoch 368/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 9.7656e-06\n", "Epoch 369/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0031 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0031 - lr: 9.7656e-06\n", "Epoch 370/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0029 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0029 - lr: 9.7656e-06\n", "Epoch 371/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0037 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0037 - lr: 9.7656e-06\n", "Epoch 372/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0032 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0032 - lr: 9.7656e-06\n", "Epoch 373/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0036 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0036 - lr: 9.7656e-06\n", "Epoch 374/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0028 - lr: 9.7656e-06\n", "Epoch 375/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 9.7656e-06\n", "Epoch 376/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0032 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0032 - lr: 9.7656e-06\n", "Epoch 377/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0028 - lr: 9.7656e-06\n", "Epoch 378/1024\n", @@ -888,7 +899,7 @@ "Epoch 379/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0045 - lr: 9.7656e-06\n", "Epoch 380/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0058 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0058 - lr: 9.7656e-06\n", "Epoch 381/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0055 - lr: 9.7656e-06\n", "Epoch 382/1024\n", @@ -896,7 +907,7 @@ "Epoch 383/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0037 - lr: 9.7656e-06\n", "Epoch 384/1024\n", - "90/90 [==============================] - 0s 3ms/step - loss: 0.0031 - val_loss: 0.0030 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0030 - lr: 9.7656e-06\n", "Epoch 385/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0040 - lr: 9.7656e-06\n", "Epoch 386/1024\n", @@ -908,17 +919,17 @@ "Epoch 389/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0072 - lr: 9.7656e-06\n", "Epoch 390/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0037 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0037 - lr: 4.8828e-06\n", "Epoch 391/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 392/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0037 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0037 - lr: 4.8828e-06\n", "Epoch 393/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0029 - lr: 4.8828e-06\n", "Epoch 394/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0030 - lr: 4.8828e-06\n", "Epoch 395/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0034 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0034 - lr: 4.8828e-06\n", "Epoch 396/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0029 - lr: 4.8828e-06\n", "Epoch 397/1024\n", @@ -928,11 +939,11 @@ "Epoch 399/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 400/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0031 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0031 - lr: 4.8828e-06\n", "Epoch 401/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 402/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 403/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0029 - lr: 4.8828e-06\n", "Epoch 404/1024\n", @@ -942,7 +953,7 @@ "Epoch 406/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0030 - lr: 4.8828e-06\n", "Epoch 407/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 408/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0029 - lr: 4.8828e-06\n", "Epoch 409/1024\n", @@ -950,19 +961,19 @@ "Epoch 410/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0029 - lr: 4.8828e-06\n", "Epoch 411/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 412/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0028 - lr: 4.8828e-06\n", "Epoch 413/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 414/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0029 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0029 - lr: 4.8828e-06\n", "Epoch 415/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0032 - lr: 4.8828e-06\n", "Epoch 416/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0033 - lr: 4.8828e-06\n", "Epoch 417/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0028 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0028 - lr: 4.8828e-06\n", "Epoch 418/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0041 - lr: 4.8828e-06\n", "Epoch 419/1024\n", @@ -974,55 +985,55 @@ "Epoch 422/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0028 - lr: 4.8828e-06\n", "Epoch 423/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0028 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0028 - lr: 4.8828e-06\n", "Epoch 424/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0029 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0029 - lr: 4.8828e-06\n", "Epoch 425/1024\n", - "90/90 [==============================] - 0s 4ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 426/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0033 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0033 - lr: 4.8828e-06\n", "Epoch 427/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0029 - lr: 4.8828e-06\n", "Epoch 428/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0030 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0030 - lr: 4.8828e-06\n", "Epoch 429/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0029 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0029 - lr: 4.8828e-06\n", "Epoch 430/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0029 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0029 - lr: 4.8828e-06\n", "Epoch 431/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 432/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 433/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0027 - lr: 2.4414e-06\n", - "Epoch 434/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0027 - lr: 2.4414e-06\n", + "Epoch 434/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 435/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 436/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 437/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0028 - lr: 2.4414e-06\n", "Epoch 438/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0029 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0029 - lr: 2.4414e-06\n", "Epoch 439/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 440/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 441/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 442/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 443/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0030 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0030 - lr: 2.4414e-06\n", "Epoch 444/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 445/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 446/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 447/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0027 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 448/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 449/1024\n", @@ -1034,63 +1045,63 @@ "Epoch 452/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 453/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 454/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 455/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0027 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 456/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 1.2207e-06\n", "Epoch 457/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 1.2207e-06\n", "Epoch 458/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 1.2207e-06\n", "Epoch 459/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 460/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 461/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 462/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 1.2207e-06\n", "Epoch 463/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 464/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 465/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 466/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 1.2207e-06\n", "Epoch 467/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 1.2207e-06\n", "Epoch 468/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0027 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0027 - lr: 1.2207e-06\n", "Epoch 469/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 470/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 471/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 472/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0027 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0027 - lr: 1.2207e-06\n", "Epoch 473/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 1.2207e-06\n", "Epoch 474/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 475/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 476/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 477/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 478/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 479/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0027 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0027 - lr: 1.2207e-06\n", "Epoch 480/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 1.2207e-06\n", - "Epoch 481/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 1.2207e-06\n", + "Epoch 481/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 1.2207e-06\n", "Epoch 482/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 483/1024\n", @@ -1098,7 +1109,7 @@ "Epoch 484/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 485/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 486/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 487/1024\n", @@ -1106,45 +1117,45 @@ "Epoch 488/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 489/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 490/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 491/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 492/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 493/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 494/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 6.1035e-07\n", "Epoch 495/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 496/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 497/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 498/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 499/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", - "Epoch 500/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0026 - lr: 6.1035e-07\n", - "Epoch 501/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", + "Epoch 500/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0026 - lr: 6.1035e-07\n", + "Epoch 501/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 502/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 503/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 504/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 505/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0027 - lr: 6.1035e-07\n", "Epoch 506/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 507/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 508/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 509/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 510/1024\n", @@ -1152,31 +1163,31 @@ "Epoch 511/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 512/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 513/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 514/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 515/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 516/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", - "Epoch 517/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", - "Epoch 518/1024\n", + "Epoch 517/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", + "Epoch 518/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 519/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 520/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 521/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 522/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 523/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 524/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 525/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 526/1024\n", @@ -1184,115 +1195,115 @@ "Epoch 527/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 528/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 529/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 530/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 531/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 532/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 533/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 534/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 3.0518e-07\n", "Epoch 535/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 536/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 537/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 538/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 539/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 540/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 541/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 542/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 543/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 544/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 545/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 546/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 547/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 548/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 549/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 550/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 551/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 552/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", - "Epoch 553/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", - "Epoch 554/1024\n", + "Epoch 553/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", + "Epoch 554/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 555/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 556/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 557/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 558/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 559/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.5259e-07\n", "Epoch 560/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 561/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 562/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 563/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 564/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 565/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0026 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 566/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 567/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 568/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 569/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 570/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 571/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 572/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 573/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 574/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 575/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 576/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 577/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 578/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 579/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 580/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 581/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 582/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 583/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 7.6294e-08\n", "Epoch 584/1024\n", @@ -1300,41 +1311,41 @@ "Epoch 585/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 586/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 587/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 588/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 589/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 590/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 591/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 592/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 593/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 594/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 595/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 596/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 597/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 598/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 599/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 600/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 601/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 602/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 603/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 604/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 605/1024\n", @@ -1342,25 +1353,25 @@ "Epoch 606/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 607/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 608/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 609/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 3.8147e-08\n", "Epoch 610/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 611/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 612/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 613/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 614/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 615/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 616/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 617/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 618/1024\n", @@ -1370,25 +1381,25 @@ "Epoch 620/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 621/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 622/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 623/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", - "Epoch 624/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", + "Epoch 624/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 625/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 626/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 627/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 628/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 629/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", - "Epoch 630/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", + "Epoch 630/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 631/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 632/1024\n", @@ -1396,37 +1407,37 @@ "Epoch 633/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 634/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 1.9073e-08\n", "Epoch 635/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 636/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 9.5367e-09\n", + "90/90 [==============================] - 1005s 11s/step - loss: 0.0030 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 637/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 3ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 638/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 639/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 640/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 641/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 642/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 643/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 644/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 645/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 646/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 647/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 648/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", - "Epoch 649/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", + "Epoch 649/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 650/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 651/1024\n", @@ -1436,2065 +1447,2065 @@ "Epoch 653/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 654/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 655/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 656/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 657/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 658/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 659/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0026 - lr: 9.5367e-09\n", "Epoch 1/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0162 - val_loss: 0.0337 - lr: 0.0025\n", "Epoch 2/1024\n", - "90/90 [==============================] - 0s 889us/step - loss: 0.0086 - val_loss: 0.0453 - lr: 0.0025\n", + "90/90 [==============================] - 0s 905us/step - loss: 0.0086 - val_loss: 0.0453 - lr: 0.0025\n", "Epoch 3/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0070 - val_loss: 0.1119 - lr: 0.0025\n", + "90/90 [==============================] - 0s 895us/step - loss: 0.0070 - val_loss: 0.1119 - lr: 0.0025\n", "Epoch 4/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0061 - val_loss: 0.2422 - lr: 0.0025\n", + "90/90 [==============================] - 0s 839us/step - loss: 0.0061 - val_loss: 0.2422 - lr: 0.0025\n", "Epoch 5/1024\n", - "90/90 [==============================] - 0s 831us/step - loss: 0.0055 - val_loss: 0.1867 - lr: 0.0025\n", + "90/90 [==============================] - 0s 823us/step - loss: 0.0055 - val_loss: 0.1867 - lr: 0.0025\n", "Epoch 6/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0051 - val_loss: 0.0551 - lr: 0.0025\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0051 - val_loss: 0.0551 - lr: 0.0025\n", "Epoch 7/1024\n", - "90/90 [==============================] - 0s 840us/step - loss: 0.0046 - val_loss: 0.1507 - lr: 0.0025\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0046 - val_loss: 0.1507 - lr: 0.0025\n", "Epoch 8/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0043 - val_loss: 0.1627 - lr: 0.0025\n", + "90/90 [==============================] - 0s 901us/step - loss: 0.0043 - val_loss: 0.1627 - lr: 0.0025\n", "Epoch 9/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0045 - val_loss: 0.2282 - lr: 0.0025\n", + "90/90 [==============================] - 0s 804us/step - loss: 0.0045 - val_loss: 0.2282 - lr: 0.0025\n", "Epoch 10/1024\n", - "90/90 [==============================] - 0s 843us/step - loss: 0.0042 - val_loss: 0.0914 - lr: 0.0025\n", + "90/90 [==============================] - 0s 818us/step - loss: 0.0042 - val_loss: 0.0914 - lr: 0.0025\n", "Epoch 11/1024\n", - "90/90 [==============================] - 0s 887us/step - loss: 0.0043 - val_loss: 0.3210 - lr: 0.0025\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0043 - val_loss: 0.3210 - lr: 0.0025\n", "Epoch 12/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0040 - val_loss: 0.1090 - lr: 0.0025\n", + "90/90 [==============================] - 0s 803us/step - loss: 0.0040 - val_loss: 0.1090 - lr: 0.0025\n", "Epoch 13/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0040 - val_loss: 0.2283 - lr: 0.0025\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0040 - val_loss: 0.2283 - lr: 0.0025\n", "Epoch 14/1024\n", "90/90 [==============================] - 0s 853us/step - loss: 0.0041 - val_loss: 0.0339 - lr: 0.0025\n", "Epoch 15/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0042 - val_loss: 0.0163 - lr: 0.0025\n", + "90/90 [==============================] - 0s 998us/step - loss: 0.0042 - val_loss: 0.0163 - lr: 0.0025\n", "Epoch 16/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0041 - val_loss: 0.2283 - lr: 0.0025\n", + "90/90 [==============================] - 0s 844us/step - loss: 0.0041 - val_loss: 0.2283 - lr: 0.0025\n", "Epoch 17/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0039 - val_loss: 0.0743 - lr: 0.0025\n", + "90/90 [==============================] - 0s 809us/step - loss: 0.0039 - val_loss: 0.0743 - lr: 0.0025\n", "Epoch 18/1024\n", - "90/90 [==============================] - 0s 980us/step - loss: 0.0039 - val_loss: 0.2028 - lr: 0.0025\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0039 - val_loss: 0.2028 - lr: 0.0025\n", "Epoch 19/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0037 - val_loss: 0.3298 - lr: 0.0025\n", + "90/90 [==============================] - 0s 818us/step - loss: 0.0037 - val_loss: 0.3298 - lr: 0.0025\n", "Epoch 20/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0038 - val_loss: 0.2000 - lr: 0.0025\n", + "90/90 [==============================] - 0s 823us/step - loss: 0.0038 - val_loss: 0.2000 - lr: 0.0025\n", "Epoch 21/1024\n", - "90/90 [==============================] - 0s 837us/step - loss: 0.0036 - val_loss: 0.0345 - lr: 0.0025\n", + "90/90 [==============================] - 0s 836us/step - loss: 0.0036 - val_loss: 0.0345 - lr: 0.0025\n", "Epoch 22/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0037 - val_loss: 0.3248 - lr: 0.0025\n", + "90/90 [==============================] - 0s 822us/step - loss: 0.0037 - val_loss: 0.3248 - lr: 0.0025\n", "Epoch 23/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0037 - val_loss: 0.3253 - lr: 0.0025\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0037 - val_loss: 0.3253 - lr: 0.0025\n", "Epoch 24/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0035 - val_loss: 0.3070 - lr: 0.0025\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.3070 - lr: 0.0025\n", "Epoch 25/1024\n", - "90/90 [==============================] - 0s 833us/step - loss: 0.0036 - val_loss: 0.3283 - lr: 0.0025\n", + "90/90 [==============================] - 0s 837us/step - loss: 0.0036 - val_loss: 0.3283 - lr: 0.0025\n", "Epoch 26/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0036 - val_loss: 0.0853 - lr: 0.0025\n", + "90/90 [==============================] - 0s 840us/step - loss: 0.0036 - val_loss: 0.0853 - lr: 0.0025\n", "Epoch 27/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0036 - val_loss: 0.2262 - lr: 0.0025\n", + "90/90 [==============================] - 0s 812us/step - loss: 0.0036 - val_loss: 0.2262 - lr: 0.0025\n", "Epoch 28/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0035 - val_loss: 0.3298 - lr: 0.0025\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0035 - val_loss: 0.3298 - lr: 0.0025\n", "Epoch 29/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0038 - val_loss: 0.1514 - lr: 0.0025\n", + "90/90 [==============================] - 0s 832us/step - loss: 0.0038 - val_loss: 0.1514 - lr: 0.0025\n", "Epoch 30/1024\n", "90/90 [==============================] - 0s 859us/step - loss: 0.0035 - val_loss: 0.0532 - lr: 0.0025\n", "Epoch 31/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0039 - val_loss: 0.1852 - lr: 0.0025\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0039 - val_loss: 0.1852 - lr: 0.0025\n", "Epoch 32/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0035 - val_loss: 0.1469 - lr: 0.0025\n", + "90/90 [==============================] - 0s 844us/step - loss: 0.0035 - val_loss: 0.1469 - lr: 0.0025\n", "Epoch 33/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0036 - val_loss: 0.1926 - lr: 0.0025\n", + "90/90 [==============================] - 0s 882us/step - loss: 0.0036 - val_loss: 0.1926 - lr: 0.0025\n", "Epoch 34/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0034 - val_loss: 0.2283 - lr: 0.0025\n", + "90/90 [==============================] - 0s 803us/step - loss: 0.0034 - val_loss: 0.2283 - lr: 0.0025\n", "Epoch 35/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0035 - val_loss: 0.2885 - lr: 0.0025\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0035 - val_loss: 0.2885 - lr: 0.0025\n", "Epoch 36/1024\n", - "90/90 [==============================] - 0s 872us/step - loss: 0.0037 - val_loss: 0.2894 - lr: 0.0025\n", + "90/90 [==============================] - 0s 808us/step - loss: 0.0037 - val_loss: 0.2894 - lr: 0.0025\n", "Epoch 37/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0036 - val_loss: 0.2274 - lr: 0.0025\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0036 - val_loss: 0.2274 - lr: 0.0025\n", "Epoch 38/1024\n", - "90/90 [==============================] - 0s 846us/step - loss: 0.0035 - val_loss: 0.1992 - lr: 0.0025\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0035 - val_loss: 0.1992 - lr: 0.0025\n", "Epoch 39/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0035 - val_loss: 0.2283 - lr: 0.0025\n", + "90/90 [==============================] - 0s 828us/step - loss: 0.0035 - val_loss: 0.2283 - lr: 0.0025\n", "Epoch 40/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0034 - val_loss: 0.3245 - lr: 0.0025\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0034 - val_loss: 0.3245 - lr: 0.0025\n", "Epoch 41/1024\n", - "90/90 [==============================] - 0s 885us/step - loss: 0.0035 - val_loss: 0.3290 - lr: 0.0012\n", + "90/90 [==============================] - 0s 832us/step - loss: 0.0035 - val_loss: 0.3290 - lr: 0.0012\n", "Epoch 42/1024\n", - "90/90 [==============================] - 0s 879us/step - loss: 0.0034 - val_loss: 0.1089 - lr: 0.0012\n", + "90/90 [==============================] - 0s 808us/step - loss: 0.0034 - val_loss: 0.1089 - lr: 0.0012\n", "Epoch 43/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0033 - val_loss: 0.0756 - lr: 0.0012\n", + "90/90 [==============================] - 0s 815us/step - loss: 0.0033 - val_loss: 0.0756 - lr: 0.0012\n", "Epoch 44/1024\n", - "90/90 [==============================] - 0s 842us/step - loss: 0.0034 - val_loss: 0.0401 - lr: 0.0012\n", + "90/90 [==============================] - 0s 813us/step - loss: 0.0034 - val_loss: 0.0401 - lr: 0.0012\n", "Epoch 45/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0033 - val_loss: 0.3272 - lr: 0.0012\n", + "90/90 [==============================] - 0s 811us/step - loss: 0.0033 - val_loss: 0.3272 - lr: 0.0012\n", "Epoch 46/1024\n", - "90/90 [==============================] - 0s 841us/step - loss: 0.0033 - val_loss: 0.3168 - lr: 0.0012\n", + "90/90 [==============================] - 0s 827us/step - loss: 0.0033 - val_loss: 0.3168 - lr: 0.0012\n", "Epoch 47/1024\n", - "90/90 [==============================] - 0s 878us/step - loss: 0.0033 - val_loss: 0.3233 - lr: 0.0012\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0033 - val_loss: 0.3233 - lr: 0.0012\n", "Epoch 48/1024\n", - "90/90 [==============================] - 0s 868us/step - loss: 0.0031 - val_loss: 0.1908 - lr: 0.0012\n", + "90/90 [==============================] - 0s 830us/step - loss: 0.0031 - val_loss: 0.1908 - lr: 0.0012\n", "Epoch 49/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0034 - val_loss: 0.2271 - lr: 0.0012\n", + "90/90 [==============================] - 0s 825us/step - loss: 0.0034 - val_loss: 0.2271 - lr: 0.0012\n", "Epoch 50/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0032 - val_loss: 0.0582 - lr: 0.0012\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0032 - val_loss: 0.0582 - lr: 0.0012\n", "Epoch 51/1024\n", - "90/90 [==============================] - 0s 844us/step - loss: 0.0033 - val_loss: 0.2781 - lr: 0.0012\n", + "90/90 [==============================] - 0s 818us/step - loss: 0.0033 - val_loss: 0.2781 - lr: 0.0012\n", "Epoch 52/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0031 - val_loss: 0.1596 - lr: 0.0012\n", + "90/90 [==============================] - 0s 802us/step - loss: 0.0031 - val_loss: 0.1596 - lr: 0.0012\n", "Epoch 53/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0032 - val_loss: 0.0435 - lr: 0.0012\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0032 - val_loss: 0.0435 - lr: 0.0012\n", "Epoch 54/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0033 - val_loss: 0.0385 - lr: 0.0012\n", + "90/90 [==============================] - 0s 847us/step - loss: 0.0033 - val_loss: 0.0385 - lr: 0.0012\n", "Epoch 55/1024\n", - "90/90 [==============================] - 0s 881us/step - loss: 0.0032 - val_loss: 0.0780 - lr: 0.0012\n", + "90/90 [==============================] - 0s 823us/step - loss: 0.0032 - val_loss: 0.0780 - lr: 0.0012\n", "Epoch 56/1024\n", - "90/90 [==============================] - 0s 844us/step - loss: 0.0033 - val_loss: 0.3291 - lr: 0.0012\n", + "90/90 [==============================] - 0s 812us/step - loss: 0.0033 - val_loss: 0.3291 - lr: 0.0012\n", "Epoch 57/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0032 - val_loss: 0.1991 - lr: 0.0012\n", + "90/90 [==============================] - 0s 825us/step - loss: 0.0032 - val_loss: 0.1991 - lr: 0.0012\n", "Epoch 58/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0033 - val_loss: 0.2066 - lr: 0.0012\n", + "90/90 [==============================] - 0s 827us/step - loss: 0.0033 - val_loss: 0.2066 - lr: 0.0012\n", "Epoch 59/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0031 - val_loss: 0.2353 - lr: 0.0012\n", + "90/90 [==============================] - 0s 825us/step - loss: 0.0031 - val_loss: 0.2353 - lr: 0.0012\n", "Epoch 60/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0033 - val_loss: 0.1137 - lr: 0.0012\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0033 - val_loss: 0.1137 - lr: 0.0012\n", "Epoch 61/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0032 - val_loss: 0.3180 - lr: 0.0012\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0032 - val_loss: 0.3180 - lr: 0.0012\n", "Epoch 62/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0033 - val_loss: 0.2062 - lr: 0.0012\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0033 - val_loss: 0.2062 - lr: 0.0012\n", "Epoch 63/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0031 - val_loss: 0.2061 - lr: 0.0012\n", + "90/90 [==============================] - 0s 832us/step - loss: 0.0031 - val_loss: 0.2061 - lr: 0.0012\n", "Epoch 64/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.3123 - lr: 0.0012\n", + "90/90 [==============================] - 0s 823us/step - loss: 0.0032 - val_loss: 0.3123 - lr: 0.0012\n", "Epoch 65/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.3288 - lr: 0.0012\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0031 - val_loss: 0.3288 - lr: 0.0012\n", "Epoch 66/1024\n", - "90/90 [==============================] - 0s 955us/step - loss: 0.0032 - val_loss: 0.3214 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 834us/step - loss: 0.0032 - val_loss: 0.3214 - lr: 6.2500e-04\n", "Epoch 67/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0031 - val_loss: 0.1355 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 822us/step - loss: 0.0031 - val_loss: 0.1355 - lr: 6.2500e-04\n", "Epoch 68/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0030 - val_loss: 0.0484 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 843us/step - loss: 0.0030 - val_loss: 0.0484 - lr: 6.2500e-04\n", "Epoch 69/1024\n", - "90/90 [==============================] - 0s 838us/step - loss: 0.0031 - val_loss: 0.2270 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 830us/step - loss: 0.0031 - val_loss: 0.2270 - lr: 6.2500e-04\n", "Epoch 70/1024\n", - "90/90 [==============================] - 0s 841us/step - loss: 0.0030 - val_loss: 0.0456 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 827us/step - loss: 0.0030 - val_loss: 0.0456 - lr: 6.2500e-04\n", "Epoch 71/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0031 - val_loss: 0.1166 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 819us/step - loss: 0.0031 - val_loss: 0.1166 - lr: 6.2500e-04\n", "Epoch 72/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0031 - val_loss: 0.0350 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 827us/step - loss: 0.0031 - val_loss: 0.0350 - lr: 6.2500e-04\n", "Epoch 73/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0030 - val_loss: 0.2658 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0030 - val_loss: 0.2658 - lr: 6.2500e-04\n", "Epoch 74/1024\n", - "90/90 [==============================] - 0s 903us/step - loss: 0.0030 - val_loss: 0.0387 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 823us/step - loss: 0.0030 - val_loss: 0.0387 - lr: 6.2500e-04\n", "Epoch 75/1024\n", - "90/90 [==============================] - 0s 966us/step - loss: 0.0031 - val_loss: 0.2661 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0031 - val_loss: 0.2661 - lr: 6.2500e-04\n", "Epoch 76/1024\n", - "90/90 [==============================] - 0s 837us/step - loss: 0.0031 - val_loss: 0.0306 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0031 - val_loss: 0.0306 - lr: 6.2500e-04\n", "Epoch 77/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0031 - val_loss: 0.0508 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 820us/step - loss: 0.0031 - val_loss: 0.0508 - lr: 6.2500e-04\n", "Epoch 78/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0030 - val_loss: 0.0662 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 825us/step - loss: 0.0030 - val_loss: 0.0662 - lr: 6.2500e-04\n", "Epoch 79/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0032 - val_loss: 0.1038 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 841us/step - loss: 0.0032 - val_loss: 0.1038 - lr: 6.2500e-04\n", "Epoch 80/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0031 - val_loss: 0.0455 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 822us/step - loss: 0.0031 - val_loss: 0.0455 - lr: 6.2500e-04\n", "Epoch 81/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0032 - val_loss: 0.2104 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0032 - val_loss: 0.2104 - lr: 6.2500e-04\n", "Epoch 82/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0030 - val_loss: 0.0288 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 823us/step - loss: 0.0030 - val_loss: 0.0288 - lr: 6.2500e-04\n", "Epoch 83/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0030 - val_loss: 0.1063 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 850us/step - loss: 0.0030 - val_loss: 0.1063 - lr: 6.2500e-04\n", "Epoch 84/1024\n", - "90/90 [==============================] - 0s 835us/step - loss: 0.0031 - val_loss: 0.3056 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 852us/step - loss: 0.0031 - val_loss: 0.3056 - lr: 6.2500e-04\n", "Epoch 85/1024\n", - "90/90 [==============================] - 0s 838us/step - loss: 0.0030 - val_loss: 0.3213 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0030 - val_loss: 0.3213 - lr: 6.2500e-04\n", "Epoch 86/1024\n", - "90/90 [==============================] - 0s 911us/step - loss: 0.0032 - val_loss: 0.0759 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 834us/step - loss: 0.0032 - val_loss: 0.0759 - lr: 6.2500e-04\n", "Epoch 87/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0029 - val_loss: 0.2254 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 828us/step - loss: 0.0029 - val_loss: 0.2254 - lr: 6.2500e-04\n", "Epoch 88/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0031 - val_loss: 0.0319 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0031 - val_loss: 0.0319 - lr: 6.2500e-04\n", "Epoch 89/1024\n", - "90/90 [==============================] - 0s 843us/step - loss: 0.0031 - val_loss: 0.1777 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 801us/step - loss: 0.0031 - val_loss: 0.1777 - lr: 6.2500e-04\n", "Epoch 90/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0031 - val_loss: 0.1783 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0031 - val_loss: 0.1783 - lr: 6.2500e-04\n", "Epoch 91/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0056 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 988us/step - loss: 0.0029 - val_loss: 0.0056 - lr: 3.1250e-04\n", "Epoch 92/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0030 - val_loss: 0.0405 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 829us/step - loss: 0.0030 - val_loss: 0.0405 - lr: 3.1250e-04\n", "Epoch 93/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0030 - val_loss: 0.1284 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.1284 - lr: 3.1250e-04\n", "Epoch 94/1024\n", - "90/90 [==============================] - 0s 844us/step - loss: 0.0030 - val_loss: 0.0306 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 832us/step - loss: 0.0030 - val_loss: 0.0306 - lr: 3.1250e-04\n", "Epoch 95/1024\n", - "90/90 [==============================] - 0s 841us/step - loss: 0.0030 - val_loss: 0.0071 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0030 - val_loss: 0.0071 - lr: 3.1250e-04\n", "Epoch 96/1024\n", - "90/90 [==============================] - 0s 843us/step - loss: 0.0030 - val_loss: 0.1139 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 830us/step - loss: 0.0030 - val_loss: 0.1139 - lr: 3.1250e-04\n", "Epoch 97/1024\n", - "90/90 [==============================] - 0s 835us/step - loss: 0.0031 - val_loss: 0.0524 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 819us/step - loss: 0.0031 - val_loss: 0.0524 - lr: 3.1250e-04\n", "Epoch 98/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0031 - val_loss: 0.0218 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 831us/step - loss: 0.0031 - val_loss: 0.0218 - lr: 3.1250e-04\n", "Epoch 99/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0030 - val_loss: 0.2167 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0030 - val_loss: 0.2167 - lr: 3.1250e-04\n", "Epoch 100/1024\n", - "90/90 [==============================] - 0s 879us/step - loss: 0.0029 - val_loss: 0.0393 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 947us/step - loss: 0.0029 - val_loss: 0.0393 - lr: 3.1250e-04\n", "Epoch 101/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0030 - val_loss: 0.0430 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 810us/step - loss: 0.0030 - val_loss: 0.0430 - lr: 3.1250e-04\n", "Epoch 102/1024\n", - "90/90 [==============================] - 0s 844us/step - loss: 0.0031 - val_loss: 0.0761 - lr: 3.1250e-04\n", + "90/90 [==============================] - 272s 3s/step - loss: 0.0031 - val_loss: 0.0761 - lr: 3.1250e-04\n", "Epoch 103/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0030 - val_loss: 0.0112 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 914us/step - loss: 0.0030 - val_loss: 0.0112 - lr: 3.1250e-04\n", "Epoch 104/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0029 - val_loss: 0.1880 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 895us/step - loss: 0.0029 - val_loss: 0.1880 - lr: 3.1250e-04\n", "Epoch 105/1024\n", - "90/90 [==============================] - 0s 846us/step - loss: 0.0030 - val_loss: 0.0084 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 827us/step - loss: 0.0030 - val_loss: 0.0084 - lr: 3.1250e-04\n", "Epoch 106/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0030 - val_loss: 0.0204 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0030 - val_loss: 0.0204 - lr: 3.1250e-04\n", "Epoch 107/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0030 - val_loss: 0.0173 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 854us/step - loss: 0.0030 - val_loss: 0.0173 - lr: 3.1250e-04\n", "Epoch 108/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.2058 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 800us/step - loss: 0.0031 - val_loss: 0.2058 - lr: 3.1250e-04\n", "Epoch 109/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0875 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0029 - val_loss: 0.0875 - lr: 3.1250e-04\n", "Epoch 110/1024\n", - "90/90 [==============================] - 0s 964us/step - loss: 0.0029 - val_loss: 0.0100 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0100 - lr: 3.1250e-04\n", "Epoch 111/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0029 - val_loss: 0.1672 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 819us/step - loss: 0.0029 - val_loss: 0.1672 - lr: 3.1250e-04\n", "Epoch 112/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0030 - val_loss: 0.1869 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 843us/step - loss: 0.0030 - val_loss: 0.1869 - lr: 3.1250e-04\n", "Epoch 113/1024\n", - "90/90 [==============================] - 0s 837us/step - loss: 0.0029 - val_loss: 0.0400 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 928us/step - loss: 0.0029 - val_loss: 0.0400 - lr: 3.1250e-04\n", "Epoch 114/1024\n", - "90/90 [==============================] - 0s 835us/step - loss: 0.0029 - val_loss: 0.2014 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 832us/step - loss: 0.0029 - val_loss: 0.2014 - lr: 3.1250e-04\n", "Epoch 115/1024\n", - "90/90 [==============================] - 0s 841us/step - loss: 0.0030 - val_loss: 0.1869 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0030 - val_loss: 0.1869 - lr: 3.1250e-04\n", "Epoch 116/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0030 - val_loss: 0.0204 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0030 - val_loss: 0.0204 - lr: 3.1250e-04\n", "Epoch 117/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0048 - lr: 1.5625e-04\n", "Epoch 118/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0038 - lr: 1.5625e-04\n", "Epoch 119/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0029 - val_loss: 0.0644 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 957us/step - loss: 0.0029 - val_loss: 0.0644 - lr: 1.5625e-04\n", "Epoch 120/1024\n", - "90/90 [==============================] - 0s 886us/step - loss: 0.0028 - val_loss: 0.0170 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 850us/step - loss: 0.0028 - val_loss: 0.0170 - lr: 1.5625e-04\n", "Epoch 121/1024\n", - "90/90 [==============================] - 0s 890us/step - loss: 0.0029 - val_loss: 0.0230 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 818us/step - loss: 0.0029 - val_loss: 0.0230 - lr: 1.5625e-04\n", "Epoch 122/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0028 - val_loss: 0.0171 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 817us/step - loss: 0.0028 - val_loss: 0.0171 - lr: 1.5625e-04\n", "Epoch 123/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0029 - val_loss: 0.0114 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0029 - val_loss: 0.0114 - lr: 1.5625e-04\n", "Epoch 124/1024\n", - "90/90 [==============================] - 0s 886us/step - loss: 0.0030 - val_loss: 0.0643 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 820us/step - loss: 0.0030 - val_loss: 0.0643 - lr: 1.5625e-04\n", "Epoch 125/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0198 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 853us/step - loss: 0.0028 - val_loss: 0.0198 - lr: 1.5625e-04\n", "Epoch 126/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0527 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0527 - lr: 1.5625e-04\n", "Epoch 127/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0031 - val_loss: 0.1066 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0031 - val_loss: 0.1066 - lr: 1.5625e-04\n", "Epoch 128/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0029 - val_loss: 0.0126 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 829us/step - loss: 0.0029 - val_loss: 0.0126 - lr: 1.5625e-04\n", "Epoch 129/1024\n", - "90/90 [==============================] - 0s 880us/step - loss: 0.0029 - val_loss: 0.0094 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0094 - lr: 1.5625e-04\n", "Epoch 130/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0237 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 942us/step - loss: 0.0028 - val_loss: 0.0237 - lr: 1.5625e-04\n", "Epoch 131/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0030 - val_loss: 0.0102 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 955us/step - loss: 0.0030 - val_loss: 0.0102 - lr: 1.5625e-04\n", "Epoch 132/1024\n", - "90/90 [==============================] - 0s 844us/step - loss: 0.0029 - val_loss: 0.0119 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 988us/step - loss: 0.0029 - val_loss: 0.0119 - lr: 1.5625e-04\n", "Epoch 133/1024\n", - "90/90 [==============================] - 0s 835us/step - loss: 0.0029 - val_loss: 0.0440 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 967us/step - loss: 0.0029 - val_loss: 0.0440 - lr: 1.5625e-04\n", "Epoch 134/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0030 - val_loss: 0.1435 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 831us/step - loss: 0.0030 - val_loss: 0.1435 - lr: 1.5625e-04\n", "Epoch 135/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0029 - val_loss: 0.0172 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 834us/step - loss: 0.0029 - val_loss: 0.0172 - lr: 1.5625e-04\n", "Epoch 136/1024\n", - "90/90 [==============================] - 0s 879us/step - loss: 0.0030 - val_loss: 0.1439 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0030 - val_loss: 0.1439 - lr: 1.5625e-04\n", "Epoch 137/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0572 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 888us/step - loss: 0.0029 - val_loss: 0.0572 - lr: 1.5625e-04\n", "Epoch 138/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0029 - val_loss: 0.0409 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 809us/step - loss: 0.0029 - val_loss: 0.0409 - lr: 1.5625e-04\n", "Epoch 139/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0029 - val_loss: 0.0104 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 818us/step - loss: 0.0029 - val_loss: 0.0104 - lr: 1.5625e-04\n", "Epoch 140/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0067 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0029 - val_loss: 0.0067 - lr: 1.5625e-04\n", "Epoch 141/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0029 - val_loss: 0.0443 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0029 - val_loss: 0.0443 - lr: 1.5625e-04\n", "Epoch 142/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.1467 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0029 - val_loss: 0.1467 - lr: 1.5625e-04\n", "Epoch 143/1024\n", - "90/90 [==============================] - 0s 873us/step - loss: 0.0030 - val_loss: 0.2135 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0030 - val_loss: 0.2135 - lr: 1.5625e-04\n", "Epoch 144/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0078 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 805us/step - loss: 0.0028 - val_loss: 0.0078 - lr: 7.8125e-05\n", "Epoch 145/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0030 - val_loss: 0.0148 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 805us/step - loss: 0.0030 - val_loss: 0.0148 - lr: 7.8125e-05\n", "Epoch 146/1024\n", - "90/90 [==============================] - 0s 846us/step - loss: 0.0030 - val_loss: 0.0695 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 825us/step - loss: 0.0030 - val_loss: 0.0695 - lr: 7.8125e-05\n", "Epoch 147/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0029 - val_loss: 0.0059 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 811us/step - loss: 0.0029 - val_loss: 0.0059 - lr: 7.8125e-05\n", "Epoch 148/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0028 - val_loss: 0.0314 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0314 - lr: 7.8125e-05\n", "Epoch 149/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0029 - val_loss: 0.0089 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 893us/step - loss: 0.0029 - val_loss: 0.0089 - lr: 7.8125e-05\n", "Epoch 150/1024\n", - "90/90 [==============================] - 0s 992us/step - loss: 0.0029 - val_loss: 0.0090 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 814us/step - loss: 0.0029 - val_loss: 0.0090 - lr: 7.8125e-05\n", "Epoch 151/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0410 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 818us/step - loss: 0.0029 - val_loss: 0.0410 - lr: 7.8125e-05\n", "Epoch 152/1024\n", - "90/90 [==============================] - 0s 978us/step - loss: 0.0029 - val_loss: 0.0308 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0029 - val_loss: 0.0308 - lr: 7.8125e-05\n", "Epoch 153/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0029 - val_loss: 0.0352 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 811us/step - loss: 0.0029 - val_loss: 0.0352 - lr: 7.8125e-05\n", "Epoch 154/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0029 - val_loss: 0.0195 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 848us/step - loss: 0.0029 - val_loss: 0.0195 - lr: 7.8125e-05\n", "Epoch 155/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0083 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 985us/step - loss: 0.0029 - val_loss: 0.0083 - lr: 7.8125e-05\n", "Epoch 156/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0035 - lr: 7.8125e-05\n", "Epoch 157/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0030 - val_loss: 0.0118 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0118 - lr: 7.8125e-05\n", "Epoch 158/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0029 - val_loss: 0.0365 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0029 - val_loss: 0.0365 - lr: 7.8125e-05\n", "Epoch 159/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0038 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 835us/step - loss: 0.0029 - val_loss: 0.0038 - lr: 7.8125e-05\n", "Epoch 160/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0029 - val_loss: 0.0081 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 794us/step - loss: 0.0029 - val_loss: 0.0081 - lr: 7.8125e-05\n", "Epoch 161/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0292 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 803us/step - loss: 0.0029 - val_loss: 0.0292 - lr: 7.8125e-05\n", "Epoch 162/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0029 - val_loss: 0.0325 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0325 - lr: 7.8125e-05\n", "Epoch 163/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0029 - val_loss: 0.0128 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 825us/step - loss: 0.0029 - val_loss: 0.0128 - lr: 7.8125e-05\n", "Epoch 164/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0098 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 833us/step - loss: 0.0029 - val_loss: 0.0098 - lr: 7.8125e-05\n", "Epoch 165/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0029 - val_loss: 0.0058 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 778us/step - loss: 0.0029 - val_loss: 0.0058 - lr: 7.8125e-05\n", "Epoch 166/1024\n", - "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.0283 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 831us/step - loss: 0.0028 - val_loss: 0.0283 - lr: 7.8125e-05\n", "Epoch 167/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0029 - val_loss: 0.0385 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 789us/step - loss: 0.0029 - val_loss: 0.0385 - lr: 7.8125e-05\n", "Epoch 168/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0030 - val_loss: 0.0039 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 829us/step - loss: 0.0030 - val_loss: 0.0039 - lr: 7.8125e-05\n", "Epoch 169/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0028 - val_loss: 0.0042 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0042 - lr: 7.8125e-05\n", "Epoch 170/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0029 - val_loss: 0.0095 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0029 - val_loss: 0.0095 - lr: 7.8125e-05\n", "Epoch 171/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0029 - val_loss: 0.0312 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0029 - val_loss: 0.0312 - lr: 7.8125e-05\n", "Epoch 172/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0030 - val_loss: 0.0207 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0030 - val_loss: 0.0207 - lr: 7.8125e-05\n", "Epoch 173/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0028 - val_loss: 0.0326 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 835us/step - loss: 0.0028 - val_loss: 0.0326 - lr: 7.8125e-05\n", "Epoch 174/1024\n", - "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0063 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0028 - val_loss: 0.0063 - lr: 7.8125e-05\n", "Epoch 175/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0125 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0125 - lr: 7.8125e-05\n", "Epoch 176/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0029 - val_loss: 0.0036 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 905us/step - loss: 0.0029 - val_loss: 0.0036 - lr: 7.8125e-05\n", "Epoch 177/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0028 - val_loss: 0.0107 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 879us/step - loss: 0.0028 - val_loss: 0.0107 - lr: 7.8125e-05\n", "Epoch 178/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0029 - val_loss: 0.0442 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0029 - val_loss: 0.0442 - lr: 7.8125e-05\n", "Epoch 179/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0030 - val_loss: 0.0142 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0030 - val_loss: 0.0142 - lr: 7.8125e-05\n", "Epoch 180/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0029 - val_loss: 0.0171 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 843us/step - loss: 0.0029 - val_loss: 0.0171 - lr: 7.8125e-05\n", "Epoch 181/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0116 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 884us/step - loss: 0.0028 - val_loss: 0.0116 - lr: 7.8125e-05\n", "Epoch 182/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0029 - val_loss: 0.0040 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 788us/step - loss: 0.0029 - val_loss: 0.0040 - lr: 3.9062e-05\n", "Epoch 183/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0030 - val_loss: 0.0042 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 815us/step - loss: 0.0030 - val_loss: 0.0042 - lr: 3.9062e-05\n", "Epoch 184/1024\n", - "90/90 [==============================] - 0s 841us/step - loss: 0.0028 - val_loss: 0.0112 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 817us/step - loss: 0.0028 - val_loss: 0.0112 - lr: 3.9062e-05\n", "Epoch 185/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0047 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0047 - lr: 3.9062e-05\n", "Epoch 186/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0156 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 853us/step - loss: 0.0028 - val_loss: 0.0156 - lr: 3.9062e-05\n", "Epoch 187/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0030 - val_loss: 0.0150 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 835us/step - loss: 0.0030 - val_loss: 0.0150 - lr: 3.9062e-05\n", "Epoch 188/1024\n", - "90/90 [==============================] - 0s 846us/step - loss: 0.0028 - val_loss: 0.0223 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 817us/step - loss: 0.0028 - val_loss: 0.0223 - lr: 3.9062e-05\n", "Epoch 189/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0029 - val_loss: 0.0086 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 838us/step - loss: 0.0029 - val_loss: 0.0086 - lr: 3.9062e-05\n", "Epoch 190/1024\n", - "90/90 [==============================] - 0s 846us/step - loss: 0.0028 - val_loss: 0.0118 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 831us/step - loss: 0.0028 - val_loss: 0.0118 - lr: 3.9062e-05\n", "Epoch 191/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0030 - val_loss: 0.0062 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 851us/step - loss: 0.0030 - val_loss: 0.0062 - lr: 3.9062e-05\n", "Epoch 192/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0065 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 827us/step - loss: 0.0027 - val_loss: 0.0065 - lr: 3.9062e-05\n", "Epoch 193/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0084 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 830us/step - loss: 0.0029 - val_loss: 0.0084 - lr: 3.9062e-05\n", "Epoch 194/1024\n", - "90/90 [==============================] - 0s 987us/step - loss: 0.0030 - val_loss: 0.0120 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 814us/step - loss: 0.0030 - val_loss: 0.0120 - lr: 3.9062e-05\n", "Epoch 195/1024\n", - "90/90 [==============================] - 0s 832us/step - loss: 0.0030 - val_loss: 0.0056 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0030 - val_loss: 0.0056 - lr: 3.9062e-05\n", "Epoch 196/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0029 - val_loss: 0.0063 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 844us/step - loss: 0.0029 - val_loss: 0.0063 - lr: 3.9062e-05\n", "Epoch 197/1024\n", - "90/90 [==============================] - 0s 836us/step - loss: 0.0030 - val_loss: 0.0045 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 825us/step - loss: 0.0030 - val_loss: 0.0045 - lr: 3.9062e-05\n", "Epoch 198/1024\n", - "90/90 [==============================] - 0s 841us/step - loss: 0.0028 - val_loss: 0.0089 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 951us/step - loss: 0.0028 - val_loss: 0.0089 - lr: 3.9062e-05\n", "Epoch 199/1024\n", - "90/90 [==============================] - 0s 876us/step - loss: 0.0028 - val_loss: 0.0049 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 968us/step - loss: 0.0028 - val_loss: 0.0049 - lr: 3.9062e-05\n", "Epoch 200/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0059 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 822us/step - loss: 0.0028 - val_loss: 0.0059 - lr: 3.9062e-05\n", "Epoch 201/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0028 - val_loss: 0.0151 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0151 - lr: 3.9062e-05\n", "Epoch 202/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0030 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 989us/step - loss: 0.0029 - val_loss: 0.0030 - lr: 3.9062e-05\n", "Epoch 203/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0029 - val_loss: 0.0047 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 831us/step - loss: 0.0029 - val_loss: 0.0047 - lr: 3.9062e-05\n", "Epoch 204/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0030 - val_loss: 0.0040 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0030 - val_loss: 0.0040 - lr: 3.9062e-05\n", "Epoch 205/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.0070 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0070 - lr: 3.9062e-05\n", "Epoch 206/1024\n", - "90/90 [==============================] - 0s 840us/step - loss: 0.0029 - val_loss: 0.0060 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 848us/step - loss: 0.0029 - val_loss: 0.0060 - lr: 3.9062e-05\n", "Epoch 207/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0029 - val_loss: 0.0052 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 833us/step - loss: 0.0029 - val_loss: 0.0052 - lr: 3.9062e-05\n", "Epoch 208/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0029 - val_loss: 0.0135 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0029 - val_loss: 0.0135 - lr: 3.9062e-05\n", "Epoch 209/1024\n", - "90/90 [==============================] - 0s 840us/step - loss: 0.0029 - val_loss: 0.0046 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 838us/step - loss: 0.0029 - val_loss: 0.0046 - lr: 3.9062e-05\n", "Epoch 210/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0057 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0028 - val_loss: 0.0057 - lr: 3.9062e-05\n", "Epoch 211/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0028 - lr: 3.9062e-05\n", "Epoch 212/1024\n", - "90/90 [==============================] - 0s 836us/step - loss: 0.0029 - val_loss: 0.0089 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0029 - val_loss: 0.0089 - lr: 3.9062e-05\n", "Epoch 213/1024\n", - "90/90 [==============================] - 0s 883us/step - loss: 0.0028 - val_loss: 0.0048 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 811us/step - loss: 0.0028 - val_loss: 0.0048 - lr: 3.9062e-05\n", "Epoch 214/1024\n", - "90/90 [==============================] - 0s 868us/step - loss: 0.0029 - val_loss: 0.0050 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 883us/step - loss: 0.0029 - val_loss: 0.0050 - lr: 3.9062e-05\n", "Epoch 215/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0030 - val_loss: 0.0058 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 820us/step - loss: 0.0030 - val_loss: 0.0058 - lr: 3.9062e-05\n", "Epoch 216/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0037 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 823us/step - loss: 0.0029 - val_loss: 0.0037 - lr: 3.9062e-05\n", "Epoch 217/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0029 - val_loss: 0.0059 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 819us/step - loss: 0.0029 - val_loss: 0.0059 - lr: 3.9062e-05\n", "Epoch 218/1024\n", - "90/90 [==============================] - 0s 873us/step - loss: 0.0029 - val_loss: 0.0202 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 838us/step - loss: 0.0029 - val_loss: 0.0202 - lr: 3.9062e-05\n", "Epoch 219/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0029 - val_loss: 0.0313 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 830us/step - loss: 0.0029 - val_loss: 0.0313 - lr: 3.9062e-05\n", "Epoch 220/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0030 - val_loss: 0.0050 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 833us/step - loss: 0.0030 - val_loss: 0.0050 - lr: 3.9062e-05\n", "Epoch 221/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0029 - val_loss: 0.0115 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 829us/step - loss: 0.0029 - val_loss: 0.0115 - lr: 3.9062e-05\n", "Epoch 222/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0029 - val_loss: 0.0046 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 827us/step - loss: 0.0029 - val_loss: 0.0046 - lr: 3.9062e-05\n", "Epoch 223/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0065 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0029 - val_loss: 0.0065 - lr: 3.9062e-05\n", "Epoch 224/1024\n", - "90/90 [==============================] - 0s 898us/step - loss: 0.0029 - val_loss: 0.0044 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0029 - val_loss: 0.0044 - lr: 3.9062e-05\n", "Epoch 225/1024\n", - "90/90 [==============================] - 0s 980us/step - loss: 0.0029 - val_loss: 0.0489 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0029 - val_loss: 0.0489 - lr: 3.9062e-05\n", "Epoch 226/1024\n", - "90/90 [==============================] - 0s 841us/step - loss: 0.0027 - val_loss: 0.0035 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 835us/step - loss: 0.0027 - val_loss: 0.0035 - lr: 3.9062e-05\n", "Epoch 227/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0069 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 837us/step - loss: 0.0029 - val_loss: 0.0069 - lr: 3.9062e-05\n", "Epoch 228/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0030 - val_loss: 0.0081 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 840us/step - loss: 0.0030 - val_loss: 0.0081 - lr: 3.9062e-05\n", "Epoch 229/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0028 - lr: 3.9062e-05\n", "Epoch 230/1024\n", - "90/90 [==============================] - 0s 893us/step - loss: 0.0029 - val_loss: 0.0077 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 841us/step - loss: 0.0029 - val_loss: 0.0077 - lr: 3.9062e-05\n", "Epoch 231/1024\n", - "90/90 [==============================] - 0s 937us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 812us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 3.9062e-05\n", "Epoch 232/1024\n", - "90/90 [==============================] - 0s 922us/step - loss: 0.0028 - val_loss: 0.0095 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 851us/step - loss: 0.0028 - val_loss: 0.0095 - lr: 3.9062e-05\n", "Epoch 233/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0103 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0028 - val_loss: 0.0103 - lr: 3.9062e-05\n", "Epoch 234/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0034 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 979us/step - loss: 0.0028 - val_loss: 0.0034 - lr: 3.9062e-05\n", "Epoch 235/1024\n", - "90/90 [==============================] - 0s 939us/step - loss: 0.0029 - val_loss: 0.0053 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 818us/step - loss: 0.0029 - val_loss: 0.0053 - lr: 3.9062e-05\n", "Epoch 236/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0030 - val_loss: 0.0069 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 804us/step - loss: 0.0030 - val_loss: 0.0069 - lr: 3.9062e-05\n", "Epoch 237/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0029 - val_loss: 0.0173 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0173 - lr: 3.9062e-05\n", "Epoch 238/1024\n", - "90/90 [==============================] - 0s 841us/step - loss: 0.0028 - val_loss: 0.0057 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 803us/step - loss: 0.0028 - val_loss: 0.0057 - lr: 3.9062e-05\n", "Epoch 239/1024\n", - "90/90 [==============================] - 0s 846us/step - loss: 0.0029 - val_loss: 0.0139 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 813us/step - loss: 0.0029 - val_loss: 0.0139 - lr: 3.9062e-05\n", "Epoch 240/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0029 - val_loss: 0.0090 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 820us/step - loss: 0.0029 - val_loss: 0.0090 - lr: 3.9062e-05\n", "Epoch 241/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0029 - val_loss: 0.0045 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0045 - lr: 3.9062e-05\n", "Epoch 242/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0173 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 895us/step - loss: 0.0028 - val_loss: 0.0173 - lr: 3.9062e-05\n", "Epoch 243/1024\n", - "90/90 [==============================] - 0s 841us/step - loss: 0.0029 - val_loss: 0.0029 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0029 - val_loss: 0.0029 - lr: 3.9062e-05\n", "Epoch 244/1024\n", - "90/90 [==============================] - 0s 902us/step - loss: 0.0029 - val_loss: 0.0130 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0029 - val_loss: 0.0130 - lr: 3.9062e-05\n", "Epoch 245/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0029 - val_loss: 0.0190 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 801us/step - loss: 0.0029 - val_loss: 0.0190 - lr: 3.9062e-05\n", "Epoch 246/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0029 - val_loss: 0.0061 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 845us/step - loss: 0.0029 - val_loss: 0.0061 - lr: 3.9062e-05\n", "Epoch 247/1024\n", - "90/90 [==============================] - 0s 843us/step - loss: 0.0029 - val_loss: 0.0034 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0029 - val_loss: 0.0034 - lr: 3.9062e-05\n", "Epoch 248/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0028 - val_loss: 0.0163 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0163 - lr: 3.9062e-05\n", "Epoch 249/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0129 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0027 - val_loss: 0.0129 - lr: 3.9062e-05\n", "Epoch 250/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0031 - val_loss: 0.0044 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0031 - val_loss: 0.0044 - lr: 3.9062e-05\n", "Epoch 251/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0029 - val_loss: 0.0320 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 819us/step - loss: 0.0029 - val_loss: 0.0320 - lr: 3.9062e-05\n", "Epoch 252/1024\n", - "90/90 [==============================] - 0s 898us/step - loss: 0.0029 - val_loss: 0.0176 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0029 - val_loss: 0.0176 - lr: 3.9062e-05\n", "Epoch 253/1024\n", - "90/90 [==============================] - 0s 917us/step - loss: 0.0028 - val_loss: 0.0254 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 817us/step - loss: 0.0028 - val_loss: 0.0254 - lr: 3.9062e-05\n", "Epoch 254/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0027 - val_loss: 0.0031 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 827us/step - loss: 0.0027 - val_loss: 0.0031 - lr: 3.9062e-05\n", "Epoch 255/1024\n", - "90/90 [==============================] - 0s 874us/step - loss: 0.0029 - val_loss: 0.0085 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 845us/step - loss: 0.0029 - val_loss: 0.0085 - lr: 1.9531e-05\n", "Epoch 256/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0028 - val_loss: 0.0069 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.0069 - lr: 1.9531e-05\n", "Epoch 257/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0029 - val_loss: 0.0029 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0029 - val_loss: 0.0029 - lr: 1.9531e-05\n", "Epoch 258/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0030 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0028 - val_loss: 0.0030 - lr: 1.9531e-05\n", "Epoch 259/1024\n", - "90/90 [==============================] - 0s 879us/step - loss: 0.0029 - val_loss: 0.0029 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 822us/step - loss: 0.0029 - val_loss: 0.0029 - lr: 1.9531e-05\n", "Epoch 260/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0027 - lr: 1.9531e-05\n", "Epoch 261/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 1.9531e-05\n", "Epoch 262/1024\n", - "90/90 [==============================] - 0s 828us/step - loss: 0.0029 - val_loss: 0.0027 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 852us/step - loss: 0.0029 - val_loss: 0.0027 - lr: 1.9531e-05\n", "Epoch 263/1024\n", - "90/90 [==============================] - 0s 873us/step - loss: 0.0029 - val_loss: 0.0035 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0029 - val_loss: 0.0035 - lr: 1.9531e-05\n", "Epoch 264/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0029 - val_loss: 0.0036 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 892us/step - loss: 0.0029 - val_loss: 0.0036 - lr: 1.9531e-05\n", "Epoch 265/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0028 - val_loss: 0.0031 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0028 - val_loss: 0.0031 - lr: 1.9531e-05\n", "Epoch 266/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0033 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0033 - lr: 1.9531e-05\n", "Epoch 267/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0041 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 895us/step - loss: 0.0029 - val_loss: 0.0041 - lr: 1.9531e-05\n", "Epoch 268/1024\n", - "90/90 [==============================] - 0s 873us/step - loss: 0.0029 - val_loss: 0.0028 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0029 - val_loss: 0.0028 - lr: 1.9531e-05\n", "Epoch 269/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 839us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 1.9531e-05\n", "Epoch 270/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0027 - val_loss: 0.0050 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 886us/step - loss: 0.0027 - val_loss: 0.0050 - lr: 1.9531e-05\n", "Epoch 271/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0029 - val_loss: 0.0029 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0029 - val_loss: 0.0029 - lr: 1.9531e-05\n", "Epoch 272/1024\n", - "90/90 [==============================] - 0s 978us/step - loss: 0.0028 - val_loss: 0.0031 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 823us/step - loss: 0.0028 - val_loss: 0.0031 - lr: 1.9531e-05\n", "Epoch 273/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0035 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 828us/step - loss: 0.0028 - val_loss: 0.0035 - lr: 1.9531e-05\n", "Epoch 274/1024\n", - "90/90 [==============================] - 0s 978us/step - loss: 0.0029 - val_loss: 0.0039 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0029 - val_loss: 0.0039 - lr: 1.9531e-05\n", "Epoch 275/1024\n", - "90/90 [==============================] - 0s 838us/step - loss: 0.0029 - val_loss: 0.0039 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0029 - val_loss: 0.0039 - lr: 1.9531e-05\n", "Epoch 276/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0053 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 829us/step - loss: 0.0028 - val_loss: 0.0053 - lr: 1.9531e-05\n", "Epoch 277/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0029 - val_loss: 0.0033 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 832us/step - loss: 0.0029 - val_loss: 0.0033 - lr: 1.9531e-05\n", "Epoch 278/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0061 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 825us/step - loss: 0.0028 - val_loss: 0.0061 - lr: 1.9531e-05\n", "Epoch 279/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0029 - val_loss: 0.0072 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0029 - val_loss: 0.0072 - lr: 1.9531e-05\n", "Epoch 280/1024\n", - "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0055 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 840us/step - loss: 0.0028 - val_loss: 0.0055 - lr: 1.9531e-05\n", "Epoch 281/1024\n", - "90/90 [==============================] - 0s 868us/step - loss: 0.0029 - val_loss: 0.0031 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0029 - val_loss: 0.0031 - lr: 1.9531e-05\n", "Epoch 282/1024\n", - "90/90 [==============================] - 0s 909us/step - loss: 0.0029 - val_loss: 0.0040 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 818us/step - loss: 0.0029 - val_loss: 0.0040 - lr: 1.9531e-05\n", "Epoch 283/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 1.9531e-05\n", "Epoch 284/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0028 - val_loss: 0.0052 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0028 - val_loss: 0.0052 - lr: 1.9531e-05\n", "Epoch 285/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0030 - val_loss: 0.0031 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 829us/step - loss: 0.0030 - val_loss: 0.0031 - lr: 1.9531e-05\n", "Epoch 286/1024\n", - "90/90 [==============================] - 0s 875us/step - loss: 0.0029 - val_loss: 0.0037 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0037 - lr: 9.7656e-06\n", "Epoch 287/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.7656e-06\n", + "90/90 [==============================] - 12s 139ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.7656e-06\n", "Epoch 288/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0030 - val_loss: 0.0055 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 966us/step - loss: 0.0030 - val_loss: 0.0055 - lr: 9.7656e-06\n", "Epoch 289/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 894us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 9.7656e-06\n", "Epoch 290/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.7656e-06\n", "Epoch 291/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0032 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0032 - lr: 9.7656e-06\n", "Epoch 292/1024\n", - "90/90 [==============================] - 0s 892us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 839us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 9.7656e-06\n", "Epoch 293/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 966us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 9.7656e-06\n", "Epoch 294/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0038 - lr: 9.7656e-06\n", + "90/90 [==============================] - -0s -64us/step - loss: 0.0028 - val_loss: 0.0038 - lr: 9.7656e-06\n", "Epoch 295/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0031 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0031 - lr: 9.7656e-06\n", "Epoch 296/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 921us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 9.7656e-06\n", "Epoch 297/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0027 - val_loss: 0.0033 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 936us/step - loss: 0.0027 - val_loss: 0.0033 - lr: 9.7656e-06\n", "Epoch 298/1024\n", - "90/90 [==============================] - 0s 880us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 980us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 9.7656e-06\n", "Epoch 299/1024\n", - "90/90 [==============================] - 0s 844us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 819us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 9.7656e-06\n", "Epoch 300/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0029 - val_loss: 0.0035 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 902us/step - loss: 0.0029 - val_loss: 0.0035 - lr: 9.7656e-06\n", "Epoch 301/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.0030 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 889us/step - loss: 0.0028 - val_loss: 0.0030 - lr: 9.7656e-06\n", "Epoch 302/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 837us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 9.7656e-06\n", "Epoch 303/1024\n", - "90/90 [==============================] - 0s 876us/step - loss: 0.0029 - val_loss: 0.0031 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 843us/step - loss: 0.0029 - val_loss: 0.0031 - lr: 9.7656e-06\n", "Epoch 304/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 9.7656e-06\n", "Epoch 305/1024\n", - "90/90 [==============================] - 0s 887us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 9.7656e-06\n", "Epoch 306/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0052 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 847us/step - loss: 0.0028 - val_loss: 0.0052 - lr: 9.7656e-06\n", "Epoch 307/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0028 - lr: 9.7656e-06\n", "Epoch 308/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 9.7656e-06\n", "Epoch 309/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0032 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0032 - lr: 9.7656e-06\n", "Epoch 310/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0028 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0028 - lr: 9.7656e-06\n", "Epoch 311/1024\n", - "90/90 [==============================] - 0s 907us/step - loss: 0.0028 - val_loss: 0.0033 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0033 - lr: 9.7656e-06\n", "Epoch 312/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0029 - val_loss: 0.0027 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0027 - lr: 9.7656e-06\n", "Epoch 313/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 923us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 9.7656e-06\n", "Epoch 314/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0029 - val_loss: 0.0028 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0029 - val_loss: 0.0028 - lr: 9.7656e-06\n", "Epoch 315/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0029 - val_loss: 0.0028 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 836us/step - loss: 0.0029 - val_loss: 0.0028 - lr: 9.7656e-06\n", "Epoch 316/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 814us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.8828e-06\n", "Epoch 317/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 840us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", "Epoch 318/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 851us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 319/1024\n", - "90/90 [==============================] - 0s 890us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 811us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 320/1024\n", - "90/90 [==============================] - 0s 890us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 805us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", "Epoch 321/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 792us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", "Epoch 322/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 828us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 323/1024\n", - "90/90 [==============================] - 0s 880us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 800us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 324/1024\n", - "90/90 [==============================] - 0s 843us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.8828e-06\n", - "Epoch 325/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", - "Epoch 326/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0028 - val_loss: 0.0029 - lr: 4.8828e-06\n", - "Epoch 327/1024\n", - "90/90 [==============================] - 0s 889us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.8828e-06\n", - "Epoch 328/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", - "Epoch 329/1024\n", - "90/90 [==============================] - 0s 909us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 4.8828e-06\n", - "Epoch 330/1024\n", - "90/90 [==============================] - 0s 887us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 4.8828e-06\n", - "Epoch 331/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 4.8828e-06\n", - "Epoch 332/1024\n", - "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", - "Epoch 333/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 4.8828e-06\n", - "Epoch 334/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 4.8828e-06\n", - "Epoch 335/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0029 - val_loss: 0.0029 - lr: 4.8828e-06\n", - "Epoch 336/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", - "Epoch 337/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", - "Epoch 338/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", - "Epoch 339/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", - "Epoch 340/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", - "Epoch 341/1024\n", - "90/90 [==============================] - 0s 874us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 4.8828e-06\n", - "Epoch 342/1024\n", - "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", - "Epoch 343/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0030 - val_loss: 0.0032 - lr: 4.8828e-06\n", - "Epoch 344/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0029 - val_loss: 0.0029 - lr: 4.8828e-06\n", - "Epoch 345/1024\n", - "90/90 [==============================] - 0s 880us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", - "Epoch 346/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0029 - lr: 4.8828e-06\n", - "Epoch 347/1024\n", - "90/90 [==============================] - 0s 874us/step - loss: 0.0029 - val_loss: 0.0028 - lr: 4.8828e-06\n", - "Epoch 348/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0032 - lr: 4.8828e-06\n", - "Epoch 349/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "Epoch 325/1024\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "Epoch 326/1024\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0029 - lr: 4.8828e-06\n", + "Epoch 327/1024\n", + "90/90 [==============================] - 0s 833us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "Epoch 328/1024\n", + "90/90 [==============================] - 0s 825us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "Epoch 329/1024\n", + "90/90 [==============================] - 0s 911us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "Epoch 330/1024\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "Epoch 331/1024\n", + "90/90 [==============================] - 0s 923us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "Epoch 332/1024\n", + "90/90 [==============================] - 0s 815us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "Epoch 333/1024\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "Epoch 334/1024\n", + "90/90 [==============================] - 0s 893us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "Epoch 335/1024\n", + "90/90 [==============================] - 0s 805us/step - loss: 0.0029 - val_loss: 0.0029 - lr: 4.8828e-06\n", + "Epoch 336/1024\n", + "90/90 [==============================] - 0s 792us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", + "Epoch 337/1024\n", + "90/90 [==============================] - 0s 838us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "Epoch 338/1024\n", + "90/90 [==============================] - 0s 975us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "Epoch 339/1024\n", + "90/90 [==============================] - 0s 998us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "Epoch 340/1024\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "Epoch 341/1024\n", + "90/90 [==============================] - 0s 838us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "Epoch 342/1024\n", + "90/90 [==============================] - 0s 795us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "Epoch 343/1024\n", + "90/90 [==============================] - 0s 801us/step - loss: 0.0030 - val_loss: 0.0032 - lr: 4.8828e-06\n", + "Epoch 344/1024\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0029 - val_loss: 0.0029 - lr: 4.8828e-06\n", + "Epoch 345/1024\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "Epoch 346/1024\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0029 - lr: 4.8828e-06\n", + "Epoch 347/1024\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0029 - val_loss: 0.0028 - lr: 4.8828e-06\n", + "Epoch 348/1024\n", + "90/90 [==============================] - 0s 830us/step - loss: 0.0029 - val_loss: 0.0032 - lr: 4.8828e-06\n", + "Epoch 349/1024\n", + "90/90 [==============================] - 0s 790us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.8828e-06\n", "Epoch 350/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", - "Epoch 351/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.8828e-06\n", - "Epoch 352/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.8828e-06\n", - "Epoch 353/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", - "Epoch 354/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 4.8828e-06\n", - "Epoch 355/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.8828e-06\n", - "Epoch 356/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", - "Epoch 357/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", - "Epoch 358/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.8828e-06\n", - "Epoch 359/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", - "Epoch 360/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", - "Epoch 361/1024\n", - "90/90 [==============================] - 0s 828us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", - "Epoch 362/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.8828e-06\n", - "Epoch 363/1024\n", "90/90 [==============================] - 0s 848us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "Epoch 351/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "Epoch 352/1024\n", + "90/90 [==============================] - 0s 906us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "Epoch 353/1024\n", + "90/90 [==============================] - 0s 823us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "Epoch 354/1024\n", + "90/90 [==============================] - 0s 831us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "Epoch 355/1024\n", + "90/90 [==============================] - 0s 886us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "Epoch 356/1024\n", + "90/90 [==============================] - 0s 817us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "Epoch 357/1024\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "Epoch 358/1024\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "Epoch 359/1024\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "Epoch 360/1024\n", + "90/90 [==============================] - 0s 847us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "Epoch 361/1024\n", + "90/90 [==============================] - 0s 801us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "Epoch 362/1024\n", + "90/90 [==============================] - 0s 892us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "Epoch 363/1024\n", + "90/90 [==============================] - 0s 842us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", "Epoch 364/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 365/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 366/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 921us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 367/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 842us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 368/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 833us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 369/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 370/1024\n", - "90/90 [==============================] - 0s 873us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 371/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 823us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 372/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 827us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 373/1024\n", - "90/90 [==============================] - 0s 892us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 374/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 375/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 818us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 376/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 820us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 377/1024\n", - "90/90 [==============================] - 0s 841us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 811us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 378/1024\n", "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 379/1024\n", - "90/90 [==============================] - 0s 889us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 380/1024\n", - "90/90 [==============================] - 0s 827us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 820us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 381/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 838us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 382/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 823us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 383/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 819us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 384/1024\n", - "90/90 [==============================] - 0s 882us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 822us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 385/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0030 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 386/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 822us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 387/1024\n", - "90/90 [==============================] - 0s 882us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 825us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 388/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 829us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 389/1024\n", - "90/90 [==============================] - 0s 903us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 390/1024\n", - "90/90 [==============================] - 0s 840us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 833us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 391/1024\n", - "90/90 [==============================] - 0s 889us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 392/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 829us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 393/1024\n", - "90/90 [==============================] - 0s 899us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 394/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 814us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 395/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 829us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 396/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 397/1024\n", - "90/90 [==============================] - 0s 894us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 803us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 398/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 892us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 399/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 400/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 833us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 401/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 402/1024\n", - "90/90 [==============================] - 0s 886us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 403/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 829us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 404/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 831us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 405/1024\n", - "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 4ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 406/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 407/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 408/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 409/1024\n", - "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 410/1024\n", - "90/90 [==============================] - 0s 887us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 967us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 411/1024\n", - "90/90 [==============================] - 0s 888us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 412/1024\n", - "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 1s 12ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 413/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 3ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 414/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 415/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 416/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 926us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 417/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 418/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 419/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 420/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 845us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 421/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 834us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 422/1024\n", - "90/90 [==============================] - 0s 836us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 423/1024\n", - "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 995us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 424/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 888us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 425/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 426/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 838us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 427/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 884us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 428/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 928us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 429/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 904us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 430/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 840us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 431/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 432/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 433/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 844us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 434/1024\n", "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 435/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 986us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 436/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 882us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 437/1024\n", - "90/90 [==============================] - 0s 874us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 846us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 438/1024\n", - "90/90 [==============================] - 0s 887us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 439/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0031 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0031 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 440/1024\n", - "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 441/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 442/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 443/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 444/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 445/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 446/1024\n", - "90/90 [==============================] - 0s 893us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 447/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 448/1024\n", - "90/90 [==============================] - 0s 876us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 449/1024\n", - "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 450/1024\n", - "90/90 [==============================] - 0s 872us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 451/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 452/1024\n", - "90/90 [==============================] - 0s 843us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 453/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 454/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 455/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 456/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 442/1024\n", + "90/90 [==============================] - 0s 829us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 443/1024\n", + "90/90 [==============================] - 0s 945us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 444/1024\n", + "90/90 [==============================] - 0s 889us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 445/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 446/1024\n", + "90/90 [==============================] - 0s 907us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 447/1024\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 448/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 449/1024\n", + "90/90 [==============================] - 0s 882us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 450/1024\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 451/1024\n", + "90/90 [==============================] - 0s 853us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 452/1024\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 453/1024\n", + "90/90 [==============================] - 0s 811us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 454/1024\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 455/1024\n", + "90/90 [==============================] - 0s 828us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 456/1024\n", + "90/90 [==============================] - 0s 937us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 457/1024\n", - "90/90 [==============================] - 0s 952us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 458/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 459/1024\n", "90/90 [==============================] - 0s 834us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 460/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 5ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 461/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 937us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 462/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 463/1024\n", - "90/90 [==============================] - 0s 872us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 969us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 464/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 906us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 465/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 841us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 466/1024\n", - "90/90 [==============================] - 0s 837us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 894us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 467/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 961us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 468/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 818us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 469/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 470/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 471/1024\n", - "90/90 [==============================] - 0s 890us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 910us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 472/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 918us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 473/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 906us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 474/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 846us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 475/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 929us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 476/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 817us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 477/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 845us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 478/1024\n", - "90/90 [==============================] - 0s 842us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 479/1024\n", "90/90 [==============================] - 0s 848us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 480/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 481/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 903us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 482/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 483/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 842us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 484/1024\n", - "90/90 [==============================] - 0s 895us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 885us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 485/1024\n", - "90/90 [==============================] - 0s 844us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 815us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 486/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 487/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 488/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 837us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 489/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 812us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 490/1024\n", - "90/90 [==============================] - 0s 907us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 853us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 491/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 492/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 889us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 493/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 494/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 878us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 495/1024\n", - "90/90 [==============================] - 0s 844us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 496/1024\n", - "90/90 [==============================] - 0s 885us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 890us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 497/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 498/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 835us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 499/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 500/1024\n", - "90/90 [==============================] - 0s 894us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 845us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 501/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 502/1024\n", "90/90 [==============================] - 0s 891us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 503/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 504/1024\n", - "90/90 [==============================] - 0s 876us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 505/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 923us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 506/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 837us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 507/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 508/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 888us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 509/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 979us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 510/1024\n", - "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 511/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 512/1024\n", - "90/90 [==============================] - 0s 840us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 830us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 513/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 835us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 514/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 515/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 811us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 516/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 804us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 517/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", - "Epoch 518/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", - "Epoch 519/1024\n", - "90/90 [==============================] - 0s 872us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", - "Epoch 520/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0025 - lr: 7.6294e-08\n", - "Epoch 521/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", - "Epoch 522/1024\n", - "90/90 [==============================] - 0s 922us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", - "Epoch 523/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", - "Epoch 524/1024\n", "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "Epoch 518/1024\n", + "90/90 [==============================] - 0s 807us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "Epoch 519/1024\n", + "90/90 [==============================] - 0s 814us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "Epoch 520/1024\n", + "90/90 [==============================] - 0s 924us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "Epoch 521/1024\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "Epoch 522/1024\n", + "90/90 [==============================] - 0s 845us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "Epoch 523/1024\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "Epoch 524/1024\n", + "90/90 [==============================] - 0s 799us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 525/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 804us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 526/1024\n", - "90/90 [==============================] - 0s 838us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 527/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 825us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 528/1024\n", - "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 831us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 529/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 530/1024\n", - "90/90 [==============================] - 0s 888us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 817us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 531/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 790us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 532/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 853us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 533/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 799us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 534/1024\n", - "90/90 [==============================] - 0s 844us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 535/1024\n", - "90/90 [==============================] - 0s 889us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 536/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 814us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 537/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 799us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 538/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 829us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 539/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 796us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 540/1024\n", - "90/90 [==============================] - 0s 889us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 810us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 541/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 542/1024\n", - "90/90 [==============================] - 0s 846us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 543/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 544/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 545/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 546/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 547/1024\n", - "90/90 [==============================] - 0s 834us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 548/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 549/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 550/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 551/1024\n", - "90/90 [==============================] - 0s 884us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 552/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 553/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 554/1024\n", - "90/90 [==============================] - 0s 884us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 555/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 556/1024\n", - "90/90 [==============================] - 0s 844us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 557/1024\n", - "90/90 [==============================] - 0s 843us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 558/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 559/1024\n", - "90/90 [==============================] - 0s 829us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", - "Epoch 560/1024\n", "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 542/1024\n", + "90/90 [==============================] - 0s 802us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 543/1024\n", + "90/90 [==============================] - 0s 804us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 544/1024\n", + "90/90 [==============================] - 0s 828us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 545/1024\n", + "90/90 [==============================] - 0s 820us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 546/1024\n", + "90/90 [==============================] - 0s 818us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 547/1024\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 548/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 549/1024\n", + "90/90 [==============================] - 0s 840us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 550/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 551/1024\n", + "90/90 [==============================] - 0s 846us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 552/1024\n", + "90/90 [==============================] - 0s 836us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 553/1024\n", + "90/90 [==============================] - 0s 884us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 554/1024\n", + "90/90 [==============================] - 0s 834us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 555/1024\n", + "90/90 [==============================] - 0s 840us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 556/1024\n", + "90/90 [==============================] - 0s 889us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 557/1024\n", + "90/90 [==============================] - 0s 837us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 558/1024\n", + "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 559/1024\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "Epoch 560/1024\n", + "90/90 [==============================] - 0s 831us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 561/1024\n", "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 562/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 847us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 563/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 842us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 564/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 848us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 565/1024\n", - "90/90 [==============================] - 0s 880us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 566/1024\n", "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 567/1024\n", - "90/90 [==============================] - 0s 887us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 803us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 568/1024\n", - "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 569/1024\n", - "90/90 [==============================] - 0s 842us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 843us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 570/1024\n", - "90/90 [==============================] - 0s 835us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", - "Epoch 571/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.9073e-08\n", - "Epoch 572/1024\n", - "90/90 [==============================] - 0s 833us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", - "Epoch 573/1024\n", - "90/90 [==============================] - 0s 842us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", - "Epoch 574/1024\n", - "90/90 [==============================] - 0s 840us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", - "Epoch 575/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.9073e-08\n", - "Epoch 576/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", - "Epoch 577/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", - "Epoch 578/1024\n", - "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", - "Epoch 579/1024\n", - "90/90 [==============================] - 0s 846us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.9073e-08\n", - "Epoch 580/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.9073e-08\n", - "Epoch 581/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", - "Epoch 582/1024\n", "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "Epoch 571/1024\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "Epoch 572/1024\n", + "90/90 [==============================] - 0s 838us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "Epoch 573/1024\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "Epoch 574/1024\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "Epoch 575/1024\n", + "90/90 [==============================] - 0s 805us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "Epoch 576/1024\n", + "90/90 [==============================] - 0s 831us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "Epoch 577/1024\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "Epoch 578/1024\n", + "90/90 [==============================] - 0s 801us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "Epoch 579/1024\n", + "90/90 [==============================] - 0s 883us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "Epoch 580/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "Epoch 581/1024\n", + "90/90 [==============================] - 0s 814us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "Epoch 582/1024\n", + "90/90 [==============================] - 0s 813us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 583/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 831us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 584/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 814us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 585/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 810us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 586/1024\n", - "90/90 [==============================] - 0s 933us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 587/1024\n", - "90/90 [==============================] - 0s 844us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 817us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 588/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 820us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 589/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 893us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 590/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 956us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 591/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 813us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 592/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 840us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 593/1024\n", - "90/90 [==============================] - 0s 842us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 822us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 594/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 818us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 595/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 596/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 820us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 597/1024\n", - "90/90 [==============================] - 0s 896us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 598/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 848us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 599/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 825us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 600/1024\n", - "90/90 [==============================] - 0s 879us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 796us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 601/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 602/1024\n", - "90/90 [==============================] - 0s 897us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 788us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 603/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 810us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 604/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 833us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 605/1024\n", - "90/90 [==============================] - 0s 902us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 815us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 606/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 607/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 883us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 608/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 994us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 609/1024\n", - "90/90 [==============================] - 0s 885us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 817us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 610/1024\n", - "90/90 [==============================] - 0s 876us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 611/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 805us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 612/1024\n", - "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 809us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 613/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 614/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 791us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 615/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 616/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 974us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 617/1024\n", - "90/90 [==============================] - 0s 951us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 618/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 805us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 619/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 837us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 620/1024\n", - "90/90 [==============================] - 0s 878us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 830us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 621/1024\n", - "90/90 [==============================] - 0s 876us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 817us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 622/1024\n", - "90/90 [==============================] - 0s 886us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 623/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 819us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 624/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 818us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 625/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 626/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 820us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 627/1024\n", - "90/90 [==============================] - 0s 893us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 628/1024\n", - "90/90 [==============================] - 0s 846us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 629/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 818us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 630/1024\n", - "90/90 [==============================] - 0s 891us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 851us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 631/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 632/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 815us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 633/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 829us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 634/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 847us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 635/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 636/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 830us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 637/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", - "Epoch 638/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", - "Epoch 639/1024\n", "90/90 [==============================] - 0s 850us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "Epoch 638/1024\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "Epoch 639/1024\n", + "90/90 [==============================] - 0s 811us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 640/1024\n", - "90/90 [==============================] - 0s 874us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 641/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 817us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 642/1024\n", - "90/90 [==============================] - 0s 840us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 643/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 644/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 819us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 645/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 969us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 646/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 647/1024\n", - "90/90 [==============================] - 0s 946us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 822us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 648/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 811us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 649/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 840us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 650/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 651/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 804us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 652/1024\n", - "90/90 [==============================] - 0s 883us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 839us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 653/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 802us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 654/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 802us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 655/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 831us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 656/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 804us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 657/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 798us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 658/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 832us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 659/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 807us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 660/1024\n", - "90/90 [==============================] - 0s 887us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 803us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 661/1024\n", - "90/90 [==============================] - 0s 873us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 662/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 803us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 663/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 834us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 664/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 845us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 665/1024\n", - "90/90 [==============================] - 0s 888us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 808us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 666/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 667/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 805us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 668/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 971us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 669/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 843us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 670/1024\n", - "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 798us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 671/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 813us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 672/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", - "Epoch 673/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", - "Epoch 674/1024\n", - "90/90 [==============================] - 0s 982us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", - "Epoch 675/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "Epoch 673/1024\n", + "90/90 [==============================] - 0s 810us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "Epoch 674/1024\n", + "90/90 [==============================] - 0s 814us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "Epoch 675/1024\n", + "90/90 [==============================] - 0s 832us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 676/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 808us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 677/1024\n", - "90/90 [==============================] - 0s 840us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 805us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 678/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 833us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 679/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 807us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 680/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 799us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 681/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 832us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 682/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 804us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 683/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 803us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 684/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 841us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 685/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 796us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 686/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 914us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 687/1024\n", - "90/90 [==============================] - 0s 895us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", - "Epoch 688/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", - "Epoch 689/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", - "Epoch 690/1024\n", - "90/90 [==============================] - 0s 874us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", - "Epoch 691/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", - "Epoch 692/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", - "Epoch 693/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", - "Epoch 694/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", - "Epoch 695/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", - "Epoch 696/1024\n", - "90/90 [==============================] - 0s 882us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", - "Epoch 697/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", - "Epoch 698/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", - "Epoch 699/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", - "Epoch 700/1024\n", - "90/90 [==============================] - 0s 875us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", - "Epoch 701/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", - "Epoch 702/1024\n", "90/90 [==============================] - 0s 861us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "Epoch 688/1024\n", + "90/90 [==============================] - 0s 798us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "Epoch 689/1024\n", + "90/90 [==============================] - 0s 823us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "Epoch 690/1024\n", + "90/90 [==============================] - 0s 837us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "Epoch 691/1024\n", + "90/90 [==============================] - 0s 961us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "Epoch 692/1024\n", + "90/90 [==============================] - 0s 806us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "Epoch 693/1024\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "Epoch 694/1024\n", + "90/90 [==============================] - 0s 812us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "Epoch 695/1024\n", + "90/90 [==============================] - 0s 802us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "Epoch 696/1024\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "Epoch 697/1024\n", + "90/90 [==============================] - 0s 808us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "Epoch 698/1024\n", + "90/90 [==============================] - 0s 810us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "Epoch 699/1024\n", + "90/90 [==============================] - 0s 837us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "Epoch 700/1024\n", + "90/90 [==============================] - 0s 791us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "Epoch 701/1024\n", + "90/90 [==============================] - 0s 801us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "Epoch 702/1024\n", + "90/90 [==============================] - 0s 835us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 703/1024\n", - "90/90 [==============================] - 0s 834us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 809us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 704/1024\n", - "90/90 [==============================] - 0s 879us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 802us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 705/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 706/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 807us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 707/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 801us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 708/1024\n", - "90/90 [==============================] - 0s 872us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 836us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 709/1024\n", - "90/90 [==============================] - 0s 843us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 805us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 710/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 848us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 711/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 712/1024\n", - "90/90 [==============================] - 0s 873us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 808us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 713/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 804us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 714/1024\n", - "90/90 [==============================] - 0s 888us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 715/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 819us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 716/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 799us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 717/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 850us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 718/1024\n", - "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 969us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 719/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 795us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 720/1024\n", - "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 721/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 799us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 722/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 797us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 723/1024\n", - "90/90 [==============================] - 0s 872us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 724/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 830us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 725/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 802us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 726/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 727/1024\n", - "90/90 [==============================] - 0s 889us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 839us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 728/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 729/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 796us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 730/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 795us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 731/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 958us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 732/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 827us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 733/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 808us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 734/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 735/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 883us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 736/1024\n", - "90/90 [==============================] - 0s 846us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 801us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 737/1024\n", - "90/90 [==============================] - 0s 878us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 838us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 738/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 803us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 739/1024\n", - "90/90 [==============================] - 0s 891us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 843us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 740/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 800us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 741/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 742/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 743/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 775us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 744/1024\n", - "90/90 [==============================] - 0s 951us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 786us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 745/1024\n", - "90/90 [==============================] - 0s 937us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 833us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 746/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 776us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 747/1024\n", - "90/90 [==============================] - 0s 888us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 800us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 748/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 853us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 749/1024\n", - "90/90 [==============================] - 0s 876us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 817us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 750/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 803us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 751/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 854us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 752/1024\n", - "90/90 [==============================] - 0s 842us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 798us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 753/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 776us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 754/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 755/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 802us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 756/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 827us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 757/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 758/1024\n", - "90/90 [==============================] - 0s 887us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 804us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 759/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 760/1024\n", - "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 761/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 810us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 762/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 802us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 763/1024\n", - "90/90 [==============================] - 0s 957us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 853us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 764/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 818us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 765/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 766/1024\n", - "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 944us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 767/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 809us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 768/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.4506e-11\n", - "Epoch 769/1024\n", - "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", - "Epoch 770/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", - "Epoch 771/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", - "Epoch 772/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", - "Epoch 773/1024\n", "90/90 [==============================] - 0s 848us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "Epoch 769/1024\n", + "90/90 [==============================] - 0s 823us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "Epoch 770/1024\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "Epoch 771/1024\n", + "90/90 [==============================] - 0s 810us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "Epoch 772/1024\n", + "90/90 [==============================] - 0s 844us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "Epoch 773/1024\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 774/1024\n", - "90/90 [==============================] - 0s 903us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 796us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 775/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 798us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 776/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 777/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 809us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 778/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 846us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 779/1024\n", - "90/90 [==============================] - 0s 844us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 977us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 780/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 838us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 781/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 820us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 782/1024\n", - "90/90 [==============================] - 0s 923us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 800us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 783/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 966us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 784/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 785/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 925us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 786/1024\n", - "90/90 [==============================] - 0s 846us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 993us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 787/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 811us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 788/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 793us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 789/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 790/1024\n", - "90/90 [==============================] - 0s 915us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 812us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 791/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 792/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 822us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 793/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 810us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 794/1024\n", - "90/90 [==============================] - 0s 879us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 795/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 802us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 796/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 797/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 794us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 798/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 808us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 799/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 800/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 942us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 801/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 809us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 802/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 803/1024\n", - "90/90 [==============================] - 0s 885us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 806us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 804/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 804us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 805/1024\n", - "90/90 [==============================] - 0s 868us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 806/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 814us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 807/1024\n", - "90/90 [==============================] - 0s 890us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 801us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 808/1024\n", - "90/90 [==============================] - 0s 843us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 838us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 809/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 820us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 810/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 811/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "90/90 [==============================] - 0s 812us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", "Epoch 812/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "90/90 [==============================] - 0s 796us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", "Epoch 813/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 814/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 815/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 816/1024\n", - "90/90 [==============================] - 0s 996us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 817/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 818/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 819/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 820/1024\n", - "90/90 [==============================] - 0s 895us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 821/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 822/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 823/1024\n", - "90/90 [==============================] - 0s 841us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 824/1024\n", - "90/90 [==============================] - 0s 846us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 825/1024\n", + "Epoch 814/1024\n", + "90/90 [==============================] - 0s 832us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 815/1024\n", + "90/90 [==============================] - 0s 810us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 816/1024\n", + "90/90 [==============================] - 0s 841us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 817/1024\n", + "90/90 [==============================] - 0s 812us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 818/1024\n", "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 826/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 827/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 828/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 829/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 830/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 831/1024\n", - "90/90 [==============================] - 0s 888us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 832/1024\n", + "Epoch 819/1024\n", + "90/90 [==============================] - 0s 817us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 820/1024\n", + "90/90 [==============================] - 0s 806us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 821/1024\n", "90/90 [==============================] - 0s 853us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 833/1024\n", + "Epoch 822/1024\n", + "90/90 [==============================] - 0s 815us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 823/1024\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 824/1024\n", + "90/90 [==============================] - 0s 837us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 825/1024\n", + "90/90 [==============================] - 0s 808us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 826/1024\n", "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 827/1024\n", + "90/90 [==============================] - 0s 803us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 828/1024\n", + "90/90 [==============================] - 0s 814us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 829/1024\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 830/1024\n", + "90/90 [==============================] - 0s 823us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 831/1024\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 832/1024\n", + "90/90 [==============================] - 0s 809us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 833/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", "Epoch 834/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "90/90 [==============================] - 0s 817us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", "Epoch 835/1024\n", - "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", "Epoch 836/1024\n", - "90/90 [==============================] - 0s 880us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 837/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 838/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 813us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 839/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 853us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 840/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 820us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 841/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 791us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 842/1024\n", - "90/90 [==============================] - 0s 970us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 843/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 833us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 844/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 847us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 845/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 808us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 846/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 807us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 847/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 848/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 816us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 849/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 919us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 850/1024\n", - "90/90 [==============================] - 0s 911us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 851/1024\n", - "90/90 [==============================] - 0s 916us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 931us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 852/1024\n", - "90/90 [==============================] - 0s 842us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 822us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 853/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 937us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 854/1024\n", - "90/90 [==============================] - 0s 875us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 855/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 819us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 856/1024\n", - "90/90 [==============================] - 0s 903us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 822us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 857/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 858/1024\n", - "90/90 [==============================] - 0s 868us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 819us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 859/1024\n", - "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 850us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 860/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 823us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 861/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 815us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 862/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 846us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 863/1024\n", - "90/90 [==============================] - 0s 835us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 864/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 865/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 866/1024\n", - "90/90 [==============================] - 0s 913us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 867/1024\n", - "90/90 [==============================] - 0s 841us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 868/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 869/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 870/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 871/1024\n", - "90/90 [==============================] - 0s 921us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 872/1024\n", - "90/90 [==============================] - 0s 868us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 873/1024\n", - "90/90 [==============================] - 0s 894us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 874/1024\n", - "90/90 [==============================] - 0s 915us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 875/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 876/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 877/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 878/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 879/1024\n", "90/90 [==============================] - 0s 841us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 880/1024\n", - "90/90 [==============================] - 0s 887us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 881/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 882/1024\n", - "90/90 [==============================] - 0s 890us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 883/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 884/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 885/1024\n", + "Epoch 865/1024\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 866/1024\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 867/1024\n", + "90/90 [==============================] - 0s 884us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 868/1024\n", + "90/90 [==============================] - 0s 851us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 869/1024\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 870/1024\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 871/1024\n", + "90/90 [==============================] - 0s 797us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 872/1024\n", + "90/90 [==============================] - 0s 906us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 873/1024\n", + "90/90 [==============================] - 0s 909us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 874/1024\n", "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 875/1024\n", + "90/90 [==============================] - 0s 846us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 876/1024\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 877/1024\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 878/1024\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 879/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 880/1024\n", + "90/90 [==============================] - 0s 825us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 881/1024\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 882/1024\n", + "90/90 [==============================] - 0s 822us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 883/1024\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 884/1024\n", + "90/90 [==============================] - 0s 830us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 885/1024\n", + "90/90 [==============================] - 0s 818us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 886/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 887/1024\n", - "90/90 [==============================] - 0s 889us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 888/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 813us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 889/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 994us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 890/1024\n", - "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 822us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 891/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 892/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 835us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 893/1024\n", - "90/90 [==============================] - 0s 843us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", - "Epoch 894/1024\n", "90/90 [==============================] - 0s 833us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "Epoch 894/1024\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 895/1024\n", - "90/90 [==============================] - 0s 965us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 830us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 896/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 813us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 897/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 837us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 898/1024\n", - "90/90 [==============================] - 0s 844us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 899/1024\n", - "90/90 [==============================] - 0s 890us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 824us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 900/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 992us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 901/1024\n", - "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 926us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 902/1024\n", - "90/90 [==============================] - 0s 950us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 903/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 818us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 904/1024\n", - "90/90 [==============================] - 0s 880us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 891us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 905/1024\n", - "90/90 [==============================] - 0s 895us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 885us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 906/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 828us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 907/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 833us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 908/1024\n", - "90/90 [==============================] - 0s 898us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 909/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 819us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 910/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 911/1024\n", - "90/90 [==============================] - 0s 874us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 912/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 840us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 913/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 819us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 914/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 843us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 915/1024\n", - "90/90 [==============================] - 0s 841us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 916/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 917/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 918/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 919/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 920/1024\n", - "90/90 [==============================] - 0s 939us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 921/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 916/1024\n", + "90/90 [==============================] - 0s 817us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 917/1024\n", + "90/90 [==============================] - 0s 834us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 918/1024\n", + "90/90 [==============================] - 0s 827us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 919/1024\n", + "90/90 [==============================] - 0s 965us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 920/1024\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 921/1024\n", + "90/90 [==============================] - 0s 815us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 922/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 923/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 924/1024\n", - "90/90 [==============================] - 0s 832us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 852us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 925/1024\n", - "90/90 [==============================] - 0s 873us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 831us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 926/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 842us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 927/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 928/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 827us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 929/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 837us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 930/1024\n", - "90/90 [==============================] - 0s 899us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 820us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 931/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 932/1024\n", - "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 933/1024\n", - "90/90 [==============================] - 0s 876us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 815us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 934/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 935/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 829us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 936/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 825us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 937/1024\n", - "90/90 [==============================] - 0s 829us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 938/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 939/1024\n", - "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 940/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 819us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 941/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 820us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 942/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 943/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 944/1024\n", - "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 828us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 945/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 817us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 946/1024\n", - "90/90 [==============================] - 0s 962us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 825us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 947/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 948/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 949/1024\n", - "90/90 [==============================] - 0s 841us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 950/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 831us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 951/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 952/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 831us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 953/1024\n", - "90/90 [==============================] - 0s 848us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 946us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 954/1024\n", - "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 955/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 825us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 956/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 957/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 830us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 958/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 839us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 959/1024\n", - "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 836us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 960/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 817us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 961/1024\n", - "90/90 [==============================] - 0s 891us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 962/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 836us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 963/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 964/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 840us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 965/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 966/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 809us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 967/1024\n", - "90/90 [==============================] - 0s 855us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 968/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 883us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 969/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 970/1024\n", "90/90 [==============================] - 0s 835us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 971/1024\n", - "90/90 [==============================] - 0s 984us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 820us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 972/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 847us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 973/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 974/1024\n", - "90/90 [==============================] - 0s 832us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 792us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 975/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 976/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 833us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 977/1024\n", "90/90 [==============================] - 0s 843us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 978/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 831us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 979/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 829us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 980/1024\n", - "90/90 [==============================] - 0s 843us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 820us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 981/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 830us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 982/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 850us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 983/1024\n", - "90/90 [==============================] - 0s 897us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 984/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 833us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 985/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 827us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 986/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", "Epoch 987/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "90/90 [==============================] - 0s 989us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", "Epoch 988/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "90/90 [==============================] - 1s 9ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", "Epoch 989/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.4552e-13\n", "Epoch 990/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "90/90 [==============================] - 0s 921us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", "Epoch 991/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "90/90 [==============================] - 0s 928us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", "Epoch 992/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", "Epoch 993/1024\n", - "90/90 [==============================] - 0s 849us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "90/90 [==============================] - 0s 898us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", "Epoch 994/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "90/90 [==============================] - 0s 833us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", "Epoch 995/1024\n", - "90/90 [==============================] - 0s 988us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", "Epoch 996/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "90/90 [==============================] - 0s 930us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", "Epoch 997/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "90/90 [==============================] - 0s 889us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", "Epoch 998/1024\n", - "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", - "Epoch 999/1024\n", - "90/90 [==============================] - 0s 838us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", - "Epoch 1000/1024\n", - "90/90 [==============================] - 0s 885us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", - "Epoch 1001/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", - "Epoch 1002/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", - "Epoch 1003/1024\n", - "90/90 [==============================] - 0s 886us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", - "Epoch 1004/1024\n", - "90/90 [==============================] - 0s 869us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", - "Epoch 1005/1024\n", - "90/90 [==============================] - 0s 843us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", - "Epoch 1006/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.4552e-13\n", - "Epoch 1007/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", - "Epoch 1008/1024\n", - "90/90 [==============================] - 0s 883us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", - "Epoch 1009/1024\n", "90/90 [==============================] - 0s 841us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "Epoch 999/1024\n", + "90/90 [==============================] - 0s 837us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "Epoch 1000/1024\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "Epoch 1001/1024\n", + "90/90 [==============================] - 0s 957us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "Epoch 1002/1024\n", + "90/90 [==============================] - 0s 832us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "Epoch 1003/1024\n", + "90/90 [==============================] - 0s 826us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "Epoch 1004/1024\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "Epoch 1005/1024\n", + "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "Epoch 1006/1024\n", + "90/90 [==============================] - 0s 845us/step - loss: 0.0030 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "Epoch 1007/1024\n", + "90/90 [==============================] - 0s 838us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "Epoch 1008/1024\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "Epoch 1009/1024\n", + "90/90 [==============================] - 0s 828us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", "Epoch 1010/1024\n", - "90/90 [==============================] - 0s 841us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "90/90 [==============================] - 0s 838us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.4552e-13\n", "Epoch 1011/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.2760e-14\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.2760e-14\n", "Epoch 1012/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.2760e-14\n", + "90/90 [==============================] - 0s 832us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.2760e-14\n", "Epoch 1013/1024\n", - "90/90 [==============================] - 0s 881us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.2760e-14\n", + "90/90 [==============================] - 0s 984us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.2760e-14\n", "Epoch 1014/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.2760e-14\n", + "90/90 [==============================] - 0s 812us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.2760e-14\n", "Epoch 1015/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.2760e-14\n", + "90/90 [==============================] - 0s 935us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.2760e-14\n", "Epoch 1016/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.2760e-14\n", + "90/90 [==============================] - 0s 822us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.2760e-14\n", "Epoch 1017/1024\n", - "90/90 [==============================] - 0s 853us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.2760e-14\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.2760e-14\n", "Epoch 1018/1024\n", - "90/90 [==============================] - 0s 845us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.2760e-14\n", + "90/90 [==============================] - 0s 843us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.2760e-14\n", "Epoch 1019/1024\n", - "90/90 [==============================] - 0s 840us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.2760e-14\n", + "90/90 [==============================] - 0s 821us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.2760e-14\n", "Epoch 1020/1024\n", - "90/90 [==============================] - 0s 851us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.2760e-14\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.2760e-14\n", "Epoch 1021/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.2760e-14\n", + "90/90 [==============================] - 0s 845us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.2760e-14\n", "Epoch 1022/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.2760e-14\n", "Epoch 1023/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.2760e-14\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.2760e-14\n", "Epoch 1024/1024\n", - "90/90 [==============================] - 0s 847us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.2760e-14\n" + "90/90 [==============================] - 0s 837us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.2760e-14\n" ] } ], @@ -3517,7 +3528,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "pycharm": { "name": "#%%\n" @@ -3543,7 +3554,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "pycharm": { "name": "#%%\n" @@ -3558,7 +3569,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "pycharm": { "name": "#%%\n" @@ -3573,7 +3584,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 12, "metadata": { "pycharm": { "name": "#%%\n" @@ -3582,8 +3593,10 @@ "outputs": [ { "data": { - "text/plain": "
", - "image/png": "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\n" + "image/png": "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", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -3616,7 +3629,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "pycharm": { "name": "#%%\n" @@ -3629,7 +3642,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { "pycharm": { "name": "#%%\n" @@ -3642,7 +3655,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "pycharm": { "name": "#%%\n" @@ -3666,7 +3679,7 @@ "Epoch 6/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0059 - val_loss: 0.1138 - lr: 0.0025\n", "Epoch 7/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0048 - val_loss: 0.1172 - lr: 0.0025\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0048 - val_loss: 0.1172 - lr: 0.0025\n", "Epoch 8/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0047 - val_loss: 0.0186 - lr: 0.0025\n", "Epoch 9/1024\n", @@ -3684,11 +3697,11 @@ "Epoch 15/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0041 - val_loss: 0.0582 - lr: 0.0025\n", "Epoch 16/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0043 - val_loss: 0.2277 - lr: 0.0025\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0043 - val_loss: 0.2277 - lr: 0.0025\n", "Epoch 17/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0040 - val_loss: 0.2268 - lr: 0.0025\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0040 - val_loss: 0.2268 - lr: 0.0025\n", "Epoch 18/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0044 - val_loss: 0.2283 - lr: 0.0025\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0044 - val_loss: 0.2283 - lr: 0.0025\n", "Epoch 19/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0040 - val_loss: 0.3142 - lr: 0.0025\n", "Epoch 20/1024\n", @@ -3696,25 +3709,25 @@ "Epoch 21/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0038 - val_loss: 0.0617 - lr: 0.0025\n", "Epoch 22/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0040 - val_loss: 0.0920 - lr: 0.0025\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0040 - val_loss: 0.0920 - lr: 0.0025\n", "Epoch 23/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0040 - val_loss: 0.3005 - lr: 0.0025\n", "Epoch 24/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0038 - val_loss: 0.2490 - lr: 0.0025\n", "Epoch 25/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0038 - val_loss: 0.0489 - lr: 0.0025\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0038 - val_loss: 0.0489 - lr: 0.0025\n", "Epoch 26/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0039 - val_loss: 0.2725 - lr: 0.0025\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0039 - val_loss: 0.2725 - lr: 0.0025\n", "Epoch 27/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0038 - val_loss: 0.2280 - lr: 0.0025\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0038 - val_loss: 0.2280 - lr: 0.0025\n", "Epoch 28/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0038 - val_loss: 0.3246 - lr: 0.0025\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0038 - val_loss: 0.3246 - lr: 0.0025\n", "Epoch 29/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0040 - val_loss: 0.2171 - lr: 0.0025\n", "Epoch 30/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0037 - val_loss: 0.3298 - lr: 0.0025\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0037 - val_loss: 0.3298 - lr: 0.0025\n", "Epoch 31/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0042 - val_loss: 0.2163 - lr: 0.0025\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0042 - val_loss: 0.2163 - lr: 0.0025\n", "Epoch 32/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0038 - val_loss: 0.2283 - lr: 0.0025\n", "Epoch 33/1024\n", @@ -3750,7 +3763,7 @@ "Epoch 48/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.1367 - lr: 0.0012\n", "Epoch 49/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.2283 - lr: 0.0012\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.2283 - lr: 0.0012\n", "Epoch 50/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0999 - lr: 0.0012\n", "Epoch 51/1024\n", @@ -3758,7 +3771,7 @@ "Epoch 52/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0795 - lr: 0.0012\n", "Epoch 53/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.3165 - lr: 0.0012\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.3165 - lr: 0.0012\n", "Epoch 54/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.0367 - lr: 0.0012\n", "Epoch 55/1024\n", @@ -3766,7 +3779,7 @@ "Epoch 56/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.3071 - lr: 0.0012\n", "Epoch 57/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0255 - lr: 0.0012\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0255 - lr: 0.0012\n", "Epoch 58/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.2279 - lr: 0.0012\n", "Epoch 59/1024\n", @@ -3780,7 +3793,7 @@ "Epoch 63/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0691 - lr: 6.2500e-04\n", "Epoch 64/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.2389 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.2389 - lr: 6.2500e-04\n", "Epoch 65/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.2540 - lr: 6.2500e-04\n", "Epoch 66/1024\n", @@ -3820,13 +3833,13 @@ "Epoch 83/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.2199 - lr: 6.2500e-04\n", "Epoch 84/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.2611 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.2611 - lr: 3.1250e-04\n", "Epoch 85/1024\n", - "90/90 [==============================] - 0s 3ms/step - loss: 0.0029 - val_loss: 0.0573 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0573 - lr: 3.1250e-04\n", "Epoch 86/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.1257 - lr: 3.1250e-04\n", "Epoch 87/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.2239 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.2239 - lr: 3.1250e-04\n", "Epoch 88/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.1547 - lr: 3.1250e-04\n", "Epoch 89/1024\n", @@ -3840,9 +3853,9 @@ "Epoch 93/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0720 - lr: 3.1250e-04\n", "Epoch 94/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.1951 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.1951 - lr: 3.1250e-04\n", "Epoch 95/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.1836 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.1836 - lr: 3.1250e-04\n", "Epoch 96/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0294 - lr: 3.1250e-04\n", "Epoch 97/1024\n", @@ -3858,7 +3871,7 @@ "Epoch 102/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0453 - lr: 3.1250e-04\n", "Epoch 103/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.1782 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.1782 - lr: 3.1250e-04\n", "Epoch 104/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0238 - lr: 3.1250e-04\n", "Epoch 105/1024\n", @@ -3882,15 +3895,15 @@ "Epoch 114/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.2821 - lr: 3.1250e-04\n", "Epoch 115/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0383 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0383 - lr: 3.1250e-04\n", "Epoch 116/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.1079 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.1079 - lr: 3.1250e-04\n", "Epoch 117/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.1174 - lr: 3.1250e-04\n", "Epoch 118/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.2065 - lr: 3.1250e-04\n", "Epoch 119/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.2121 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.2121 - lr: 3.1250e-04\n", "Epoch 120/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.2459 - lr: 3.1250e-04\n", "Epoch 121/1024\n", @@ -3906,15 +3919,15 @@ "Epoch 126/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0519 - lr: 3.1250e-04\n", "Epoch 127/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0992 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0992 - lr: 3.1250e-04\n", "Epoch 128/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.1512 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.1512 - lr: 3.1250e-04\n", "Epoch 129/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0933 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0933 - lr: 3.1250e-04\n", "Epoch 130/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.2168 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.2168 - lr: 3.1250e-04\n", "Epoch 131/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.2016 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.2016 - lr: 3.1250e-04\n", "Epoch 132/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.1200 - lr: 3.1250e-04\n", "Epoch 133/1024\n", @@ -3922,7 +3935,7 @@ "Epoch 134/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0439 - lr: 3.1250e-04\n", "Epoch 135/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.2147 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.2147 - lr: 3.1250e-04\n", "Epoch 136/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.1953 - lr: 3.1250e-04\n", "Epoch 137/1024\n", @@ -3934,7 +3947,7 @@ "Epoch 140/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0063 - lr: 3.1250e-04\n", "Epoch 141/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.2162 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.2162 - lr: 3.1250e-04\n", "Epoch 142/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.1195 - lr: 3.1250e-04\n", "Epoch 143/1024\n", @@ -3942,7 +3955,7 @@ "Epoch 144/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.2873 - lr: 3.1250e-04\n", "Epoch 145/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.1387 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.1387 - lr: 3.1250e-04\n", "Epoch 146/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0527 - lr: 3.1250e-04\n", "Epoch 147/1024\n", @@ -3956,7 +3969,7 @@ "Epoch 151/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.2105 - lr: 3.1250e-04\n", "Epoch 152/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.1155 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.1155 - lr: 3.1250e-04\n", "Epoch 153/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.2155 - lr: 3.1250e-04\n", "Epoch 154/1024\n", @@ -3964,7 +3977,7 @@ "Epoch 155/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0111 - lr: 3.1250e-04\n", "Epoch 156/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.2359 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.2359 - lr: 3.1250e-04\n", "Epoch 157/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.2450 - lr: 3.1250e-04\n", "Epoch 158/1024\n", @@ -3976,7 +3989,7 @@ "Epoch 161/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.2382 - lr: 3.1250e-04\n", "Epoch 162/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.1973 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.1973 - lr: 3.1250e-04\n", "Epoch 163/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.2399 - lr: 3.1250e-04\n", "Epoch 164/1024\n", @@ -4000,9 +4013,9 @@ "Epoch 173/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.1020 - lr: 1.5625e-04\n", "Epoch 174/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0978 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0978 - lr: 1.5625e-04\n", "Epoch 175/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0829 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0829 - lr: 1.5625e-04\n", "Epoch 176/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0343 - lr: 1.5625e-04\n", "Epoch 177/1024\n", @@ -4044,11 +4057,11 @@ "Epoch 195/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0403 - lr: 7.8125e-05\n", "Epoch 196/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0055 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0055 - lr: 7.8125e-05\n", "Epoch 197/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0401 - lr: 7.8125e-05\n", "Epoch 198/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0092 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0092 - lr: 7.8125e-05\n", "Epoch 199/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0259 - lr: 7.8125e-05\n", "Epoch 200/1024\n", @@ -4068,21 +4081,21 @@ "Epoch 207/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0676 - lr: 7.8125e-05\n", "Epoch 208/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0220 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0220 - lr: 7.8125e-05\n", "Epoch 209/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0327 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0327 - lr: 7.8125e-05\n", "Epoch 210/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0070 - lr: 7.8125e-05\n", "Epoch 211/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0160 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0160 - lr: 7.8125e-05\n", "Epoch 212/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0820 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0820 - lr: 7.8125e-05\n", "Epoch 213/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0207 - lr: 7.8125e-05\n", "Epoch 214/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0066 - lr: 7.8125e-05\n", "Epoch 215/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0233 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0233 - lr: 7.8125e-05\n", "Epoch 216/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0210 - lr: 7.8125e-05\n", "Epoch 217/1024\n", @@ -4092,9 +4105,9 @@ "Epoch 219/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0057 - lr: 7.8125e-05\n", "Epoch 220/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0293 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0293 - lr: 3.9062e-05\n", "Epoch 221/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0143 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0143 - lr: 3.9062e-05\n", "Epoch 222/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0068 - lr: 3.9062e-05\n", "Epoch 223/1024\n", @@ -4102,9 +4115,9 @@ "Epoch 224/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0050 - lr: 3.9062e-05\n", "Epoch 225/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0089 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0089 - lr: 3.9062e-05\n", "Epoch 226/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0126 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0126 - lr: 3.9062e-05\n", "Epoch 227/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0092 - lr: 3.9062e-05\n", "Epoch 228/1024\n", @@ -4126,31 +4139,31 @@ "Epoch 236/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0066 - lr: 3.9062e-05\n", "Epoch 237/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0219 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0219 - lr: 3.9062e-05\n", "Epoch 238/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0559 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0559 - lr: 3.9062e-05\n", "Epoch 239/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0174 - lr: 3.9062e-05\n", "Epoch 240/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0114 - lr: 3.9062e-05\n", "Epoch 241/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0109 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0109 - lr: 3.9062e-05\n", "Epoch 242/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0045 - lr: 3.9062e-05\n", "Epoch 243/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0084 - lr: 3.9062e-05\n", "Epoch 244/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0225 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0225 - lr: 3.9062e-05\n", "Epoch 245/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0151 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0151 - lr: 3.9062e-05\n", "Epoch 246/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0138 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0138 - lr: 3.9062e-05\n", "Epoch 247/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0136 - lr: 3.9062e-05\n", "Epoch 248/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0480 - lr: 3.9062e-05\n", "Epoch 249/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0104 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0104 - lr: 3.9062e-05\n", "Epoch 250/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0088 - lr: 3.9062e-05\n", "Epoch 251/1024\n", @@ -4162,7 +4175,7 @@ "Epoch 254/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0184 - lr: 1.9531e-05\n", "Epoch 255/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0332 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0332 - lr: 1.9531e-05\n", "Epoch 256/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0105 - lr: 1.9531e-05\n", "Epoch 257/1024\n", @@ -4170,11 +4183,11 @@ "Epoch 258/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0062 - lr: 1.9531e-05\n", "Epoch 259/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0085 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0085 - lr: 1.9531e-05\n", "Epoch 260/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0028 - lr: 1.9531e-05\n", "Epoch 261/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0042 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0042 - lr: 1.9531e-05\n", "Epoch 262/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0057 - lr: 1.9531e-05\n", "Epoch 263/1024\n", @@ -4192,9 +4205,9 @@ "Epoch 269/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0076 - lr: 1.9531e-05\n", "Epoch 270/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0071 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0071 - lr: 1.9531e-05\n", "Epoch 271/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0062 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0062 - lr: 1.9531e-05\n", "Epoch 272/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0118 - lr: 1.9531e-05\n", "Epoch 273/1024\n", @@ -4206,15 +4219,15 @@ "Epoch 276/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0031 - lr: 1.9531e-05\n", "Epoch 277/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0033 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0033 - lr: 1.9531e-05\n", "Epoch 278/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0068 - lr: 1.9531e-05\n", "Epoch 279/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0040 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0040 - lr: 1.9531e-05\n", "Epoch 280/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0058 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0058 - lr: 1.9531e-05\n", "Epoch 281/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0038 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0038 - lr: 1.9531e-05\n", "Epoch 282/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0206 - lr: 1.9531e-05\n", "Epoch 283/1024\n", @@ -4254,7 +4267,7 @@ "Epoch 300/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0035 - lr: 9.7656e-06\n", "Epoch 301/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0029 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0029 - lr: 9.7656e-06\n", "Epoch 302/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0028 - lr: 9.7656e-06\n", "Epoch 303/1024\n", @@ -4288,33 +4301,33 @@ "Epoch 317/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 318/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 319/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 320/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 321/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 322/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", "Epoch 323/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 324/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0035 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0035 - lr: 4.8828e-06\n", "Epoch 325/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 326/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0030 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0030 - lr: 4.8828e-06\n", "Epoch 327/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 328/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.8828e-06\n", "Epoch 329/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 330/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0031 - lr: 4.8828e-06\n", "Epoch 331/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0029 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0029 - lr: 4.8828e-06\n", "Epoch 332/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 333/1024\n", @@ -4326,25 +4339,25 @@ "Epoch 336/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.8828e-06\n", "Epoch 337/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0031 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0031 - lr: 4.8828e-06\n", "Epoch 338/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", - "Epoch 339/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0031 - lr: 4.8828e-06\n", - "Epoch 340/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "Epoch 339/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0031 - lr: 4.8828e-06\n", + "Epoch 340/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 341/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.8828e-06\n", "Epoch 342/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 343/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0032 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0032 - lr: 4.8828e-06\n", "Epoch 344/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", "Epoch 345/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0029 - lr: 4.8828e-06\n", "Epoch 346/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.8828e-06\n", "Epoch 347/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.8828e-06\n", "Epoch 348/1024\n", @@ -4352,21 +4365,21 @@ "Epoch 349/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0040 - lr: 4.8828e-06\n", "Epoch 350/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 351/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 352/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 353/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 354/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 355/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", "Epoch 356/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0034 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0034 - lr: 4.8828e-06\n", "Epoch 357/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 358/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0032 - lr: 4.8828e-06\n", "Epoch 359/1024\n", @@ -4376,25 +4389,25 @@ "Epoch 361/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 362/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", "Epoch 363/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 364/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.8828e-06\n", "Epoch 365/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0028 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0028 - lr: 4.8828e-06\n", "Epoch 366/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0029 - lr: 4.8828e-06\n", "Epoch 367/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 368/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 369/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.8828e-06\n", "Epoch 370/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 371/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 372/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0029 - lr: 2.4414e-06\n", "Epoch 373/1024\n", @@ -4406,29 +4419,29 @@ "Epoch 376/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 377/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 378/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 379/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 380/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 381/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 382/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 383/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 384/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 385/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 386/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 387/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 388/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 389/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 390/1024\n", @@ -4438,35 +4451,35 @@ "Epoch 392/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 393/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 394/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 395/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 396/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 397/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 398/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 399/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 400/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 401/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 402/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0027 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 403/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 404/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 405/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 406/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 407/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 408/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 409/1024\n", @@ -4474,37 +4487,37 @@ "Epoch 410/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 411/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 412/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 413/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 414/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 415/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 416/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 417/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 418/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 419/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 420/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 421/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 422/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 423/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 424/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 425/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 426/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 427/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 428/1024\n", @@ -4524,23 +4537,23 @@ "Epoch 435/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 436/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 437/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 438/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 439/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 440/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 441/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 442/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 443/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 444/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 445/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 446/1024\n", @@ -4556,7 +4569,7 @@ "Epoch 451/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 452/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 453/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 454/1024\n", @@ -4566,21 +4579,21 @@ "Epoch 456/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 457/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 458/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 459/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 460/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 461/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 462/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 463/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 464/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 465/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 466/1024\n", @@ -4588,79 +4601,79 @@ "Epoch 467/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 468/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 469/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 470/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 471/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 472/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 473/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 474/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 475/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 476/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 477/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 478/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 479/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 480/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 481/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 482/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 483/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 484/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 485/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 486/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 487/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 488/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 489/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 490/1024\n", - "90/90 [==============================] - 0s 3ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 491/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 492/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 493/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 494/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 495/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 496/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 497/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 498/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 499/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 500/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 501/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 502/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 503/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 504/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 505/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 506/1024\n", @@ -4670,157 +4683,157 @@ "Epoch 508/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 509/1024\n", - "90/90 [==============================] - 0s 3ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 510/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 511/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 512/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 513/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 514/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 515/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 516/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 517/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 518/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 519/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 520/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 521/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 522/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 523/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 524/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 525/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 526/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 527/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 528/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 529/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 530/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 531/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 532/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 533/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 534/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 535/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 536/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 537/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 538/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 539/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 540/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 541/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 542/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 543/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 544/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 545/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 546/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 547/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 548/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 549/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 550/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 551/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 552/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 553/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 554/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 555/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 556/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 557/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 558/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 559/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 560/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 561/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 562/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 563/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 564/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 565/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 566/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 567/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 568/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 569/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 570/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 571/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 572/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 573/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 574/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 575/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 576/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 577/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 578/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 579/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 580/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 581/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 582/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 583/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 584/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 585/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 586/1024\n", @@ -4832,59 +4845,59 @@ "Epoch 589/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 590/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 591/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 592/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 593/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 594/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 595/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 596/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 597/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 598/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 599/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 600/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 601/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 602/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 603/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 604/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 605/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 606/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 607/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 608/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 609/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 610/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 611/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 612/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 613/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 614/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 615/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 616/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 617/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 618/1024\n", @@ -4896,71 +4909,71 @@ "Epoch 621/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 622/1024\n", - "90/90 [==============================] - 0s 3ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 623/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 624/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 625/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 626/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 627/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 628/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 629/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 630/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 631/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 632/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 633/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 634/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 635/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 636/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 637/1024\n", - "90/90 [==============================] - 0s 3ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 638/1024\n", - "90/90 [==============================] - 0s 3ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 639/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 640/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 641/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 642/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 643/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 644/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 645/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 646/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 647/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 648/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 649/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 650/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 651/1024\n", "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 652/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 653/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 1/1024\n", - "90/90 [==============================] - 1s 2ms/step - loss: 0.0198 - val_loss: 0.0598 - lr: 0.0025\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0198 - val_loss: 0.0598 - lr: 0.0025\n", "Epoch 2/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0112 - val_loss: 0.0525 - lr: 0.0025\n", "Epoch 3/1024\n", @@ -4972,2041 +4985,2041 @@ "Epoch 6/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0046 - val_loss: 0.0257 - lr: 0.0025\n", "Epoch 7/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0045 - val_loss: 0.0653 - lr: 0.0025\n", + "90/90 [==============================] - 0s 893us/step - loss: 0.0045 - val_loss: 0.0653 - lr: 0.0025\n", "Epoch 8/1024\n", - "90/90 [==============================] - 0s 903us/step - loss: 0.0044 - val_loss: 0.0471 - lr: 0.0025\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0044 - val_loss: 0.0471 - lr: 0.0025\n", "Epoch 9/1024\n", - "90/90 [==============================] - 0s 928us/step - loss: 0.0044 - val_loss: 0.2283 - lr: 0.0025\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0044 - val_loss: 0.2283 - lr: 0.0025\n", "Epoch 10/1024\n", "90/90 [==============================] - 0s 869us/step - loss: 0.0042 - val_loss: 0.2283 - lr: 0.0025\n", "Epoch 11/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0042 - val_loss: 0.1118 - lr: 0.0025\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0042 - val_loss: 0.1118 - lr: 0.0025\n", "Epoch 12/1024\n", - "90/90 [==============================] - 0s 876us/step - loss: 0.0042 - val_loss: 0.1824 - lr: 0.0025\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0042 - val_loss: 0.1824 - lr: 0.0025\n", "Epoch 13/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0042 - val_loss: 0.2283 - lr: 0.0025\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0042 - val_loss: 0.2283 - lr: 0.0025\n", "Epoch 14/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0042 - val_loss: 0.0377 - lr: 0.0025\n", + "90/90 [==============================] - 0s 886us/step - loss: 0.0042 - val_loss: 0.0377 - lr: 0.0025\n", "Epoch 15/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0040 - val_loss: 0.1167 - lr: 0.0025\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0040 - val_loss: 0.1167 - lr: 0.0025\n", "Epoch 16/1024\n", - "90/90 [==============================] - 0s 900us/step - loss: 0.0041 - val_loss: 0.2283 - lr: 0.0025\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0041 - val_loss: 0.2283 - lr: 0.0025\n", "Epoch 17/1024\n", - "90/90 [==============================] - 0s 886us/step - loss: 0.0040 - val_loss: 0.2175 - lr: 0.0025\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0040 - val_loss: 0.2175 - lr: 0.0025\n", "Epoch 18/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0041 - val_loss: 0.2283 - lr: 0.0025\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0041 - val_loss: 0.2283 - lr: 0.0025\n", "Epoch 19/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0039 - val_loss: 0.0520 - lr: 0.0025\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0039 - val_loss: 0.0520 - lr: 0.0025\n", "Epoch 20/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0039 - val_loss: 0.1469 - lr: 0.0025\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0039 - val_loss: 0.1469 - lr: 0.0025\n", "Epoch 21/1024\n", - "90/90 [==============================] - 0s 939us/step - loss: 0.0038 - val_loss: 0.0476 - lr: 0.0025\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0038 - val_loss: 0.0476 - lr: 0.0025\n", "Epoch 22/1024\n", - "90/90 [==============================] - 0s 970us/step - loss: 0.0039 - val_loss: 0.1398 - lr: 0.0025\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0039 - val_loss: 0.1398 - lr: 0.0025\n", "Epoch 23/1024\n", - "90/90 [==============================] - 0s 930us/step - loss: 0.0040 - val_loss: 0.0864 - lr: 0.0025\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0040 - val_loss: 0.0864 - lr: 0.0025\n", "Epoch 24/1024\n", - "90/90 [==============================] - 0s 917us/step - loss: 0.0037 - val_loss: 0.2282 - lr: 0.0025\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0037 - val_loss: 0.2282 - lr: 0.0025\n", "Epoch 25/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0038 - val_loss: 0.2278 - lr: 0.0025\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0038 - val_loss: 0.2278 - lr: 0.0025\n", "Epoch 26/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0039 - val_loss: 0.1589 - lr: 0.0025\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0039 - val_loss: 0.1589 - lr: 0.0025\n", "Epoch 27/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0037 - val_loss: 0.0401 - lr: 0.0025\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0037 - val_loss: 0.0401 - lr: 0.0025\n", "Epoch 28/1024\n", - "90/90 [==============================] - 0s 885us/step - loss: 0.0037 - val_loss: 0.0640 - lr: 0.0025\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0037 - val_loss: 0.0640 - lr: 0.0025\n", "Epoch 29/1024\n", - "90/90 [==============================] - 0s 894us/step - loss: 0.0038 - val_loss: 0.3123 - lr: 0.0025\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0038 - val_loss: 0.3123 - lr: 0.0025\n", "Epoch 30/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0037 - val_loss: 0.1451 - lr: 0.0025\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0037 - val_loss: 0.1451 - lr: 0.0025\n", "Epoch 31/1024\n", - "90/90 [==============================] - 0s 979us/step - loss: 0.0041 - val_loss: 0.2279 - lr: 0.0025\n", + "90/90 [==============================] - 0s 903us/step - loss: 0.0041 - val_loss: 0.2279 - lr: 0.0025\n", "Epoch 32/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0036 - val_loss: 0.0576 - lr: 0.0012\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0036 - val_loss: 0.0576 - lr: 0.0012\n", "Epoch 33/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0036 - val_loss: 0.0103 - lr: 0.0012\n", "Epoch 34/1024\n", - "90/90 [==============================] - 0s 898us/step - loss: 0.0034 - val_loss: 0.1927 - lr: 0.0012\n", + "90/90 [==============================] - 0s 899us/step - loss: 0.0034 - val_loss: 0.1927 - lr: 0.0012\n", "Epoch 35/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.1350 - lr: 0.0012\n", + "90/90 [==============================] - 0s 854us/step - loss: 0.0035 - val_loss: 0.1350 - lr: 0.0012\n", "Epoch 36/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0037 - val_loss: 0.0297 - lr: 0.0012\n", + "90/90 [==============================] - 0s 852us/step - loss: 0.0037 - val_loss: 0.0297 - lr: 0.0012\n", "Epoch 37/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.0853 - lr: 0.0012\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0035 - val_loss: 0.0853 - lr: 0.0012\n", "Epoch 38/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.2278 - lr: 0.0012\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0034 - val_loss: 0.2278 - lr: 0.0012\n", "Epoch 39/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.2013 - lr: 0.0012\n", "Epoch 40/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.2283 - lr: 0.0012\n", + "90/90 [==============================] - 0s 924us/step - loss: 0.0034 - val_loss: 0.2283 - lr: 0.0012\n", "Epoch 41/1024\n", - "90/90 [==============================] - 0s 965us/step - loss: 0.0036 - val_loss: 0.0482 - lr: 0.0012\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0036 - val_loss: 0.0482 - lr: 0.0012\n", "Epoch 42/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0035 - val_loss: 0.2170 - lr: 0.0012\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0035 - val_loss: 0.2170 - lr: 0.0012\n", "Epoch 43/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.2271 - lr: 0.0012\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0035 - val_loss: 0.2271 - lr: 0.0012\n", "Epoch 44/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.0587 - lr: 0.0012\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0034 - val_loss: 0.0587 - lr: 0.0012\n", "Epoch 45/1024\n", - "90/90 [==============================] - 0s 938us/step - loss: 0.0034 - val_loss: 0.1143 - lr: 0.0012\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0034 - val_loss: 0.1143 - lr: 0.0012\n", "Epoch 46/1024\n", - "90/90 [==============================] - 0s 909us/step - loss: 0.0034 - val_loss: 0.2379 - lr: 0.0012\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0034 - val_loss: 0.2379 - lr: 0.0012\n", "Epoch 47/1024\n", - "90/90 [==============================] - 0s 916us/step - loss: 0.0034 - val_loss: 0.1721 - lr: 0.0012\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0034 - val_loss: 0.1721 - lr: 0.0012\n", "Epoch 48/1024\n", - "90/90 [==============================] - 0s 949us/step - loss: 0.0033 - val_loss: 0.1126 - lr: 0.0012\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0033 - val_loss: 0.1126 - lr: 0.0012\n", "Epoch 49/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.0890 - lr: 0.0012\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0035 - val_loss: 0.0890 - lr: 0.0012\n", "Epoch 50/1024\n", - "90/90 [==============================] - 0s 925us/step - loss: 0.0033 - val_loss: 0.1434 - lr: 0.0012\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0033 - val_loss: 0.1434 - lr: 0.0012\n", "Epoch 51/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.2669 - lr: 0.0012\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0034 - val_loss: 0.2669 - lr: 0.0012\n", "Epoch 52/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0172 - lr: 0.0012\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0032 - val_loss: 0.0172 - lr: 0.0012\n", "Epoch 53/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.2013 - lr: 0.0012\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0033 - val_loss: 0.2013 - lr: 0.0012\n", "Epoch 54/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0034 - val_loss: 0.0816 - lr: 0.0012\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0034 - val_loss: 0.0816 - lr: 0.0012\n", "Epoch 55/1024\n", - "90/90 [==============================] - 0s 952us/step - loss: 0.0034 - val_loss: 0.2064 - lr: 0.0012\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0034 - val_loss: 0.2064 - lr: 0.0012\n", "Epoch 56/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.2262 - lr: 0.0012\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0034 - val_loss: 0.2262 - lr: 0.0012\n", "Epoch 57/1024\n", - "90/90 [==============================] - 0s 959us/step - loss: 0.0033 - val_loss: 0.0168 - lr: 0.0012\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0033 - val_loss: 0.0168 - lr: 0.0012\n", "Epoch 58/1024\n", - "90/90 [==============================] - 0s 897us/step - loss: 0.0034 - val_loss: 0.2105 - lr: 0.0012\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0034 - val_loss: 0.2105 - lr: 0.0012\n", "Epoch 59/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.1548 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0031 - val_loss: 0.1548 - lr: 6.2500e-04\n", "Epoch 60/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0658 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0033 - val_loss: 0.0658 - lr: 6.2500e-04\n", "Epoch 61/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0032 - val_loss: 0.0978 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0032 - val_loss: 0.0978 - lr: 6.2500e-04\n", "Epoch 62/1024\n", - "90/90 [==============================] - 0s 960us/step - loss: 0.0033 - val_loss: 0.0730 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0033 - val_loss: 0.0730 - lr: 6.2500e-04\n", "Epoch 63/1024\n", - "90/90 [==============================] - 0s 885us/step - loss: 0.0031 - val_loss: 0.0170 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0031 - val_loss: 0.0170 - lr: 6.2500e-04\n", "Epoch 64/1024\n", - "90/90 [==============================] - 0s 905us/step - loss: 0.0031 - val_loss: 0.2857 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0031 - val_loss: 0.2857 - lr: 6.2500e-04\n", "Epoch 65/1024\n", - "90/90 [==============================] - 0s 941us/step - loss: 0.0030 - val_loss: 0.2169 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0030 - val_loss: 0.2169 - lr: 6.2500e-04\n", "Epoch 66/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.2722 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0032 - val_loss: 0.2722 - lr: 6.2500e-04\n", "Epoch 67/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.2282 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0031 - val_loss: 0.2282 - lr: 6.2500e-04\n", "Epoch 68/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.2139 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0030 - val_loss: 0.2139 - lr: 6.2500e-04\n", "Epoch 69/1024\n", - "90/90 [==============================] - 0s 910us/step - loss: 0.0031 - val_loss: 0.0982 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0031 - val_loss: 0.0982 - lr: 6.2500e-04\n", "Epoch 70/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.2279 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0031 - val_loss: 0.2279 - lr: 6.2500e-04\n", "Epoch 71/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0761 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0031 - val_loss: 0.0761 - lr: 6.2500e-04\n", "Epoch 72/1024\n", - "90/90 [==============================] - 0s 978us/step - loss: 0.0031 - val_loss: 0.1284 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0031 - val_loss: 0.1284 - lr: 6.2500e-04\n", "Epoch 73/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.2601 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0031 - val_loss: 0.2601 - lr: 6.2500e-04\n", "Epoch 74/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0350 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0030 - val_loss: 0.0350 - lr: 6.2500e-04\n", "Epoch 75/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.1648 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 854us/step - loss: 0.0031 - val_loss: 0.1648 - lr: 6.2500e-04\n", "Epoch 76/1024\n", - "90/90 [==============================] - 0s 895us/step - loss: 0.0031 - val_loss: 0.2015 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0031 - val_loss: 0.2015 - lr: 6.2500e-04\n", "Epoch 77/1024\n", - "90/90 [==============================] - 0s 930us/step - loss: 0.0031 - val_loss: 0.0225 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0031 - val_loss: 0.0225 - lr: 6.2500e-04\n", "Epoch 78/1024\n", - "90/90 [==============================] - 0s 970us/step - loss: 0.0030 - val_loss: 0.2144 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0030 - val_loss: 0.2144 - lr: 6.2500e-04\n", "Epoch 79/1024\n", - "90/90 [==============================] - 0s 890us/step - loss: 0.0032 - val_loss: 0.0431 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0032 - val_loss: 0.0431 - lr: 6.2500e-04\n", "Epoch 80/1024\n", - "90/90 [==============================] - 0s 883us/step - loss: 0.0031 - val_loss: 0.0944 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0031 - val_loss: 0.0944 - lr: 6.2500e-04\n", "Epoch 81/1024\n", - "90/90 [==============================] - 0s 898us/step - loss: 0.0031 - val_loss: 0.1827 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0031 - val_loss: 0.1827 - lr: 6.2500e-04\n", "Epoch 82/1024\n", - "90/90 [==============================] - 0s 885us/step - loss: 0.0031 - val_loss: 0.1524 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.1524 - lr: 6.2500e-04\n", "Epoch 83/1024\n", - "90/90 [==============================] - 0s 905us/step - loss: 0.0030 - val_loss: 0.0833 - lr: 6.2500e-04\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0030 - val_loss: 0.0833 - lr: 6.2500e-04\n", "Epoch 84/1024\n", - "90/90 [==============================] - 0s 886us/step - loss: 0.0031 - val_loss: 0.0131 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0031 - val_loss: 0.0131 - lr: 3.1250e-04\n", "Epoch 85/1024\n", - "90/90 [==============================] - 0s 871us/step - loss: 0.0030 - val_loss: 0.0984 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0030 - val_loss: 0.0984 - lr: 3.1250e-04\n", "Epoch 86/1024\n", - "90/90 [==============================] - 0s 898us/step - loss: 0.0031 - val_loss: 0.0298 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0031 - val_loss: 0.0298 - lr: 3.1250e-04\n", "Epoch 87/1024\n", - "90/90 [==============================] - 0s 881us/step - loss: 0.0029 - val_loss: 0.2283 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0029 - val_loss: 0.2283 - lr: 3.1250e-04\n", "Epoch 88/1024\n", - "90/90 [==============================] - 0s 924us/step - loss: 0.0030 - val_loss: 0.2270 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0030 - val_loss: 0.2270 - lr: 3.1250e-04\n", "Epoch 89/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.1353 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0031 - val_loss: 0.1353 - lr: 3.1250e-04\n", "Epoch 90/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.2282 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0031 - val_loss: 0.2282 - lr: 3.1250e-04\n", "Epoch 91/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.1818 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0029 - val_loss: 0.1818 - lr: 3.1250e-04\n", "Epoch 92/1024\n", - "90/90 [==============================] - 0s 890us/step - loss: 0.0031 - val_loss: 0.0185 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0031 - val_loss: 0.0185 - lr: 3.1250e-04\n", "Epoch 93/1024\n", - "90/90 [==============================] - 0s 882us/step - loss: 0.0030 - val_loss: 0.0603 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0030 - val_loss: 0.0603 - lr: 3.1250e-04\n", "Epoch 94/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0085 - lr: 3.1250e-04\n", "Epoch 95/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0055 - lr: 3.1250e-04\n", "Epoch 96/1024\n", - "90/90 [==============================] - 0s 968us/step - loss: 0.0029 - val_loss: 0.1777 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 903us/step - loss: 0.0029 - val_loss: 0.1777 - lr: 3.1250e-04\n", "Epoch 97/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.2107 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0030 - val_loss: 0.2107 - lr: 3.1250e-04\n", "Epoch 98/1024\n", - "90/90 [==============================] - 0s 892us/step - loss: 0.0031 - val_loss: 0.2022 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0031 - val_loss: 0.2022 - lr: 3.1250e-04\n", "Epoch 99/1024\n", - "90/90 [==============================] - 0s 952us/step - loss: 0.0030 - val_loss: 0.0119 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0030 - val_loss: 0.0119 - lr: 3.1250e-04\n", "Epoch 100/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0247 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0029 - val_loss: 0.0247 - lr: 3.1250e-04\n", "Epoch 101/1024\n", - "90/90 [==============================] - 0s 962us/step - loss: 0.0030 - val_loss: 0.1989 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0030 - val_loss: 0.1989 - lr: 3.1250e-04\n", "Epoch 102/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0322 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 878us/step - loss: 0.0031 - val_loss: 0.0322 - lr: 3.1250e-04\n", "Epoch 103/1024\n", - "90/90 [==============================] - 0s 972us/step - loss: 0.0030 - val_loss: 0.2276 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0030 - val_loss: 0.2276 - lr: 3.1250e-04\n", "Epoch 104/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0326 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0029 - val_loss: 0.0326 - lr: 3.1250e-04\n", "Epoch 105/1024\n", - "90/90 [==============================] - 0s 1000us/step - loss: 0.0031 - val_loss: 0.2399 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0031 - val_loss: 0.2399 - lr: 3.1250e-04\n", "Epoch 106/1024\n", - "90/90 [==============================] - 0s 892us/step - loss: 0.0030 - val_loss: 0.0445 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0030 - val_loss: 0.0445 - lr: 3.1250e-04\n", "Epoch 107/1024\n", - "90/90 [==============================] - 0s 896us/step - loss: 0.0030 - val_loss: 0.2216 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0030 - val_loss: 0.2216 - lr: 3.1250e-04\n", "Epoch 108/1024\n", - "90/90 [==============================] - 0s 913us/step - loss: 0.0030 - val_loss: 0.0257 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0030 - val_loss: 0.0257 - lr: 3.1250e-04\n", "Epoch 109/1024\n", - "90/90 [==============================] - 0s 927us/step - loss: 0.0029 - val_loss: 0.1788 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0029 - val_loss: 0.1788 - lr: 3.1250e-04\n", "Epoch 110/1024\n", - "90/90 [==============================] - 0s 961us/step - loss: 0.0029 - val_loss: 0.0195 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0029 - val_loss: 0.0195 - lr: 3.1250e-04\n", "Epoch 111/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0308 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0029 - val_loss: 0.0308 - lr: 3.1250e-04\n", "Epoch 112/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0169 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 885us/step - loss: 0.0030 - val_loss: 0.0169 - lr: 3.1250e-04\n", "Epoch 113/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.2607 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0029 - val_loss: 0.2607 - lr: 3.1250e-04\n", "Epoch 114/1024\n", - "90/90 [==============================] - 0s 965us/step - loss: 0.0029 - val_loss: 0.2670 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0029 - val_loss: 0.2670 - lr: 3.1250e-04\n", "Epoch 115/1024\n", - "90/90 [==============================] - 0s 985us/step - loss: 0.0030 - val_loss: 0.0159 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0030 - val_loss: 0.0159 - lr: 3.1250e-04\n", "Epoch 116/1024\n", - "90/90 [==============================] - 0s 932us/step - loss: 0.0030 - val_loss: 0.1088 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0030 - val_loss: 0.1088 - lr: 3.1250e-04\n", "Epoch 117/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.2283 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0029 - val_loss: 0.2283 - lr: 3.1250e-04\n", "Epoch 118/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.1138 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 986us/step - loss: 0.0030 - val_loss: 0.1138 - lr: 3.1250e-04\n", "Epoch 119/1024\n", - "90/90 [==============================] - 0s 982us/step - loss: 0.0029 - val_loss: 0.0120 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0029 - val_loss: 0.0120 - lr: 3.1250e-04\n", "Epoch 120/1024\n", - "90/90 [==============================] - 0s 917us/step - loss: 0.0029 - val_loss: 0.1762 - lr: 3.1250e-04\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0029 - val_loss: 0.1762 - lr: 3.1250e-04\n", "Epoch 121/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0073 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0029 - val_loss: 0.0073 - lr: 1.5625e-04\n", "Epoch 122/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0924 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 902us/step - loss: 0.0028 - val_loss: 0.0924 - lr: 1.5625e-04\n", "Epoch 123/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0188 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0188 - lr: 1.5625e-04\n", "Epoch 124/1024\n", - "90/90 [==============================] - 0s 972us/step - loss: 0.0030 - val_loss: 0.0138 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0030 - val_loss: 0.0138 - lr: 1.5625e-04\n", "Epoch 125/1024\n", - "90/90 [==============================] - 0s 921us/step - loss: 0.0028 - val_loss: 0.0270 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 883us/step - loss: 0.0028 - val_loss: 0.0270 - lr: 1.5625e-04\n", "Epoch 126/1024\n", - "90/90 [==============================] - 0s 903us/step - loss: 0.0028 - val_loss: 0.0375 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0375 - lr: 1.5625e-04\n", "Epoch 127/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0750 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0031 - val_loss: 0.0750 - lr: 1.5625e-04\n", "Epoch 128/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0109 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0029 - val_loss: 0.0109 - lr: 1.5625e-04\n", "Epoch 129/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0467 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0028 - val_loss: 0.0467 - lr: 1.5625e-04\n", "Epoch 130/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.1844 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0029 - val_loss: 0.1844 - lr: 1.5625e-04\n", "Epoch 131/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0444 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0030 - val_loss: 0.0444 - lr: 1.5625e-04\n", "Epoch 132/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0101 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0029 - val_loss: 0.0101 - lr: 1.5625e-04\n", "Epoch 133/1024\n", - "90/90 [==============================] - 0s 963us/step - loss: 0.0029 - val_loss: 0.1559 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0029 - val_loss: 0.1559 - lr: 1.5625e-04\n", "Epoch 134/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.2269 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0030 - val_loss: 0.2269 - lr: 1.5625e-04\n", "Epoch 135/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.1741 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.1741 - lr: 1.5625e-04\n", "Epoch 136/1024\n", - "90/90 [==============================] - 0s 881us/step - loss: 0.0029 - val_loss: 0.0634 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0029 - val_loss: 0.0634 - lr: 1.5625e-04\n", "Epoch 137/1024\n", - "90/90 [==============================] - 0s 918us/step - loss: 0.0028 - val_loss: 0.1282 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.1282 - lr: 1.5625e-04\n", "Epoch 138/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.1617 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0029 - val_loss: 0.1617 - lr: 1.5625e-04\n", "Epoch 139/1024\n", - "90/90 [==============================] - 0s 955us/step - loss: 0.0028 - val_loss: 0.1587 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.1587 - lr: 1.5625e-04\n", "Epoch 140/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0652 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0652 - lr: 1.5625e-04\n", "Epoch 141/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.1194 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.1194 - lr: 1.5625e-04\n", "Epoch 142/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0069 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0029 - val_loss: 0.0069 - lr: 1.5625e-04\n", "Epoch 143/1024\n", - "90/90 [==============================] - 0s 908us/step - loss: 0.0030 - val_loss: 0.2232 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0030 - val_loss: 0.2232 - lr: 1.5625e-04\n", "Epoch 144/1024\n", - "90/90 [==============================] - 0s 964us/step - loss: 0.0028 - val_loss: 0.0199 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0199 - lr: 1.5625e-04\n", "Epoch 145/1024\n", - "90/90 [==============================] - 0s 964us/step - loss: 0.0029 - val_loss: 0.0634 - lr: 1.5625e-04\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0029 - val_loss: 0.0634 - lr: 1.5625e-04\n", "Epoch 146/1024\n", - "90/90 [==============================] - 0s 937us/step - loss: 0.0029 - val_loss: 0.0338 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0029 - val_loss: 0.0338 - lr: 7.8125e-05\n", "Epoch 147/1024\n", - "90/90 [==============================] - 0s 937us/step - loss: 0.0029 - val_loss: 0.0403 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0029 - val_loss: 0.0403 - lr: 7.8125e-05\n", "Epoch 148/1024\n", - "90/90 [==============================] - 0s 931us/step - loss: 0.0028 - val_loss: 0.0079 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0028 - val_loss: 0.0079 - lr: 7.8125e-05\n", "Epoch 149/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0402 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0029 - val_loss: 0.0402 - lr: 7.8125e-05\n", "Epoch 150/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0441 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.0441 - lr: 7.8125e-05\n", "Epoch 151/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0071 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0071 - lr: 7.8125e-05\n", "Epoch 152/1024\n", - "90/90 [==============================] - 0s 912us/step - loss: 0.0029 - val_loss: 0.0945 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0945 - lr: 7.8125e-05\n", "Epoch 153/1024\n", - "90/90 [==============================] - 0s 928us/step - loss: 0.0028 - val_loss: 0.0241 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0241 - lr: 7.8125e-05\n", "Epoch 154/1024\n", - "90/90 [==============================] - 0s 950us/step - loss: 0.0028 - val_loss: 0.0100 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.0100 - lr: 7.8125e-05\n", "Epoch 155/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0289 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 916us/step - loss: 0.0028 - val_loss: 0.0289 - lr: 7.8125e-05\n", "Epoch 156/1024\n", - "90/90 [==============================] - 0s 924us/step - loss: 0.0029 - val_loss: 0.0380 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0029 - val_loss: 0.0380 - lr: 7.8125e-05\n", "Epoch 157/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0557 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0557 - lr: 7.8125e-05\n", "Epoch 158/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0217 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0029 - val_loss: 0.0217 - lr: 7.8125e-05\n", "Epoch 159/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0129 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0129 - lr: 7.8125e-05\n", "Epoch 160/1024\n", - "90/90 [==============================] - 0s 936us/step - loss: 0.0028 - val_loss: 0.0220 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0220 - lr: 7.8125e-05\n", "Epoch 161/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0950 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0950 - lr: 7.8125e-05\n", "Epoch 162/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0053 - lr: 7.8125e-05\n", "Epoch 163/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0113 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 903us/step - loss: 0.0029 - val_loss: 0.0113 - lr: 7.8125e-05\n", "Epoch 164/1024\n", - "90/90 [==============================] - 0s 953us/step - loss: 0.0028 - val_loss: 0.0521 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0521 - lr: 7.8125e-05\n", "Epoch 165/1024\n", - "90/90 [==============================] - 0s 993us/step - loss: 0.0029 - val_loss: 0.0089 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0089 - lr: 7.8125e-05\n", "Epoch 166/1024\n", - "90/90 [==============================] - 0s 963us/step - loss: 0.0028 - val_loss: 0.0064 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0064 - lr: 7.8125e-05\n", "Epoch 167/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0489 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 890us/step - loss: 0.0028 - val_loss: 0.0489 - lr: 7.8125e-05\n", "Epoch 168/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0217 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0030 - val_loss: 0.0217 - lr: 7.8125e-05\n", "Epoch 169/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0157 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0157 - lr: 7.8125e-05\n", "Epoch 170/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0189 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0189 - lr: 7.8125e-05\n", "Epoch 171/1024\n", - "90/90 [==============================] - 0s 951us/step - loss: 0.0028 - val_loss: 0.0087 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0087 - lr: 7.8125e-05\n", "Epoch 172/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.1240 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0029 - val_loss: 0.1240 - lr: 7.8125e-05\n", "Epoch 173/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0183 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0027 - val_loss: 0.0183 - lr: 7.8125e-05\n", "Epoch 174/1024\n", - "90/90 [==============================] - 0s 976us/step - loss: 0.0028 - val_loss: 0.1380 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.1380 - lr: 7.8125e-05\n", "Epoch 175/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0104 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0104 - lr: 7.8125e-05\n", "Epoch 176/1024\n", - "90/90 [==============================] - 0s 984us/step - loss: 0.0028 - val_loss: 0.0084 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 879us/step - loss: 0.0028 - val_loss: 0.0084 - lr: 7.8125e-05\n", "Epoch 177/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0049 - lr: 7.8125e-05\n", "Epoch 178/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0705 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 901us/step - loss: 0.0028 - val_loss: 0.0705 - lr: 7.8125e-05\n", "Epoch 179/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0130 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0030 - val_loss: 0.0130 - lr: 7.8125e-05\n", "Epoch 180/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0066 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0029 - val_loss: 0.0066 - lr: 7.8125e-05\n", "Epoch 181/1024\n", - "90/90 [==============================] - 0s 902us/step - loss: 0.0028 - val_loss: 0.1058 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.1058 - lr: 7.8125e-05\n", "Epoch 182/1024\n", - "90/90 [==============================] - 0s 944us/step - loss: 0.0029 - val_loss: 0.0491 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0029 - val_loss: 0.0491 - lr: 7.8125e-05\n", "Epoch 183/1024\n", - "90/90 [==============================] - 0s 908us/step - loss: 0.0029 - val_loss: 0.0252 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0029 - val_loss: 0.0252 - lr: 7.8125e-05\n", "Epoch 184/1024\n", - "90/90 [==============================] - 0s 911us/step - loss: 0.0028 - val_loss: 0.0125 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0125 - lr: 7.8125e-05\n", "Epoch 185/1024\n", - "90/90 [==============================] - 0s 955us/step - loss: 0.0027 - val_loss: 0.0052 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0027 - val_loss: 0.0052 - lr: 7.8125e-05\n", "Epoch 186/1024\n", - "90/90 [==============================] - 0s 908us/step - loss: 0.0028 - val_loss: 0.0645 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0645 - lr: 7.8125e-05\n", "Epoch 187/1024\n", - "90/90 [==============================] - 0s 942us/step - loss: 0.0029 - val_loss: 0.0465 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0465 - lr: 7.8125e-05\n", "Epoch 188/1024\n", - "90/90 [==============================] - 0s 968us/step - loss: 0.0027 - val_loss: 0.0429 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0027 - val_loss: 0.0429 - lr: 7.8125e-05\n", "Epoch 189/1024\n", - "90/90 [==============================] - 0s 984us/step - loss: 0.0028 - val_loss: 0.0272 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0272 - lr: 7.8125e-05\n", "Epoch 190/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0577 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0577 - lr: 7.8125e-05\n", "Epoch 191/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0040 - lr: 7.8125e-05\n", "Epoch 192/1024\n", - "90/90 [==============================] - 0s 926us/step - loss: 0.0028 - val_loss: 0.0437 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 893us/step - loss: 0.0028 - val_loss: 0.0437 - lr: 7.8125e-05\n", "Epoch 193/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0036 - lr: 7.8125e-05\n", "Epoch 194/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0055 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 888us/step - loss: 0.0028 - val_loss: 0.0055 - lr: 7.8125e-05\n", "Epoch 195/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0131 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0029 - val_loss: 0.0131 - lr: 7.8125e-05\n", "Epoch 196/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0290 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0290 - lr: 7.8125e-05\n", "Epoch 197/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0913 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0913 - lr: 7.8125e-05\n", "Epoch 198/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0460 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0460 - lr: 7.8125e-05\n", "Epoch 199/1024\n", - "90/90 [==============================] - 0s 943us/step - loss: 0.0028 - val_loss: 0.1060 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.1060 - lr: 7.8125e-05\n", "Epoch 200/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0051 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 882us/step - loss: 0.0028 - val_loss: 0.0051 - lr: 7.8125e-05\n", "Epoch 201/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0334 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0334 - lr: 7.8125e-05\n", "Epoch 202/1024\n", - "90/90 [==============================] - 0s 990us/step - loss: 0.0028 - val_loss: 0.0753 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0028 - val_loss: 0.0753 - lr: 7.8125e-05\n", "Epoch 203/1024\n", - "90/90 [==============================] - 0s 964us/step - loss: 0.0029 - val_loss: 0.0238 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0238 - lr: 7.8125e-05\n", "Epoch 204/1024\n", - "90/90 [==============================] - 0s 953us/step - loss: 0.0029 - val_loss: 0.0136 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0029 - val_loss: 0.0136 - lr: 7.8125e-05\n", "Epoch 205/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.2139 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.2139 - lr: 7.8125e-05\n", "Epoch 206/1024\n", - "90/90 [==============================] - 0s 968us/step - loss: 0.0028 - val_loss: 0.0143 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0143 - lr: 7.8125e-05\n", "Epoch 207/1024\n", - "90/90 [==============================] - 0s 947us/step - loss: 0.0029 - val_loss: 0.1455 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0029 - val_loss: 0.1455 - lr: 7.8125e-05\n", "Epoch 208/1024\n", - "90/90 [==============================] - 0s 964us/step - loss: 0.0028 - val_loss: 0.0175 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0175 - lr: 7.8125e-05\n", "Epoch 209/1024\n", - "90/90 [==============================] - 0s 934us/step - loss: 0.0028 - val_loss: 0.0451 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0451 - lr: 7.8125e-05\n", "Epoch 210/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.1155 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.1155 - lr: 7.8125e-05\n", "Epoch 211/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0095 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0095 - lr: 7.8125e-05\n", "Epoch 212/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.1050 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.1050 - lr: 7.8125e-05\n", "Epoch 213/1024\n", - "90/90 [==============================] - 0s 973us/step - loss: 0.0028 - val_loss: 0.0601 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0601 - lr: 7.8125e-05\n", "Epoch 214/1024\n", - "90/90 [==============================] - 0s 957us/step - loss: 0.0028 - val_loss: 0.0070 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0070 - lr: 7.8125e-05\n", "Epoch 215/1024\n", - "90/90 [==============================] - 0s 925us/step - loss: 0.0028 - val_loss: 0.0444 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0444 - lr: 7.8125e-05\n", "Epoch 216/1024\n", - "90/90 [==============================] - 0s 923us/step - loss: 0.0029 - val_loss: 0.1996 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0029 - val_loss: 0.1996 - lr: 7.8125e-05\n", "Epoch 217/1024\n", - "90/90 [==============================] - 0s 929us/step - loss: 0.0028 - val_loss: 0.0211 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 906us/step - loss: 0.0028 - val_loss: 0.0211 - lr: 7.8125e-05\n", "Epoch 218/1024\n", - "90/90 [==============================] - 0s 953us/step - loss: 0.0029 - val_loss: 0.0245 - lr: 7.8125e-05\n", + "90/90 [==============================] - 0s 883us/step - loss: 0.0029 - val_loss: 0.0245 - lr: 7.8125e-05\n", "Epoch 219/1024\n", - "90/90 [==============================] - 0s 966us/step - loss: 0.0029 - val_loss: 0.0044 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0029 - val_loss: 0.0044 - lr: 3.9062e-05\n", "Epoch 220/1024\n", - "90/90 [==============================] - 0s 975us/step - loss: 0.0029 - val_loss: 0.0474 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0029 - val_loss: 0.0474 - lr: 3.9062e-05\n", "Epoch 221/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0457 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0457 - lr: 3.9062e-05\n", "Epoch 222/1024\n", - "90/90 [==============================] - 0s 978us/step - loss: 0.0029 - val_loss: 0.0045 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0029 - val_loss: 0.0045 - lr: 3.9062e-05\n", "Epoch 223/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0230 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0230 - lr: 3.9062e-05\n", "Epoch 224/1024\n", - "90/90 [==============================] - 0s 965us/step - loss: 0.0028 - val_loss: 0.0180 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0180 - lr: 3.9062e-05\n", "Epoch 225/1024\n", - "90/90 [==============================] - 0s 947us/step - loss: 0.0028 - val_loss: 0.0337 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0337 - lr: 3.9062e-05\n", "Epoch 226/1024\n", - "90/90 [==============================] - 0s 924us/step - loss: 0.0027 - val_loss: 0.0289 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0289 - lr: 3.9062e-05\n", "Epoch 227/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0036 - lr: 3.9062e-05\n", "Epoch 228/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0044 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 907us/step - loss: 0.0029 - val_loss: 0.0044 - lr: 3.9062e-05\n", "Epoch 229/1024\n", - "90/90 [==============================] - 0s 934us/step - loss: 0.0028 - val_loss: 0.0188 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0188 - lr: 3.9062e-05\n", "Epoch 230/1024\n", - "90/90 [==============================] - 0s 997us/step - loss: 0.0028 - val_loss: 0.0352 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0352 - lr: 3.9062e-05\n", "Epoch 231/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0164 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0164 - lr: 3.9062e-05\n", "Epoch 232/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0092 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0092 - lr: 3.9062e-05\n", "Epoch 233/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0099 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0027 - val_loss: 0.0099 - lr: 3.9062e-05\n", "Epoch 234/1024\n", - "90/90 [==============================] - 0s 876us/step - loss: 0.0028 - val_loss: 0.0159 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0159 - lr: 3.9062e-05\n", "Epoch 235/1024\n", - "90/90 [==============================] - 0s 928us/step - loss: 0.0028 - val_loss: 0.0126 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0126 - lr: 3.9062e-05\n", "Epoch 236/1024\n", - "90/90 [==============================] - 0s 888us/step - loss: 0.0029 - val_loss: 0.0117 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0029 - val_loss: 0.0117 - lr: 3.9062e-05\n", "Epoch 237/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0290 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 887us/step - loss: 0.0028 - val_loss: 0.0290 - lr: 3.9062e-05\n", "Epoch 238/1024\n", - "90/90 [==============================] - 0s 948us/step - loss: 0.0027 - val_loss: 0.0302 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0027 - val_loss: 0.0302 - lr: 3.9062e-05\n", "Epoch 239/1024\n", - "90/90 [==============================] - 0s 987us/step - loss: 0.0028 - val_loss: 0.0401 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0401 - lr: 3.9062e-05\n", "Epoch 240/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.1169 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0028 - val_loss: 0.1169 - lr: 3.9062e-05\n", "Epoch 241/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0076 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 932us/step - loss: 0.0029 - val_loss: 0.0076 - lr: 3.9062e-05\n", "Epoch 242/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0303 - lr: 3.9062e-05\n", "Epoch 243/1024\n", - "90/90 [==============================] - 0s 987us/step - loss: 0.0028 - val_loss: 0.0102 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 889us/step - loss: 0.0028 - val_loss: 0.0102 - lr: 3.9062e-05\n", "Epoch 244/1024\n", - "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0545 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0545 - lr: 3.9062e-05\n", "Epoch 245/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0034 - lr: 3.9062e-05\n", "Epoch 246/1024\n", - "90/90 [==============================] - 0s 976us/step - loss: 0.0029 - val_loss: 0.0135 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 903us/step - loss: 0.0029 - val_loss: 0.0135 - lr: 3.9062e-05\n", "Epoch 247/1024\n", - "90/90 [==============================] - 0s 971us/step - loss: 0.0028 - val_loss: 0.0045 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0045 - lr: 3.9062e-05\n", "Epoch 248/1024\n", - "90/90 [==============================] - 0s 945us/step - loss: 0.0028 - val_loss: 0.0153 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0153 - lr: 3.9062e-05\n", "Epoch 249/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0092 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0027 - val_loss: 0.0092 - lr: 3.9062e-05\n", "Epoch 250/1024\n", - "90/90 [==============================] - 0s 873us/step - loss: 0.0030 - val_loss: 0.0083 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 991us/step - loss: 0.0030 - val_loss: 0.0083 - lr: 3.9062e-05\n", "Epoch 251/1024\n", - "90/90 [==============================] - 0s 955us/step - loss: 0.0028 - val_loss: 0.0221 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0221 - lr: 3.9062e-05\n", "Epoch 252/1024\n", - "90/90 [==============================] - 0s 963us/step - loss: 0.0028 - val_loss: 0.0097 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0097 - lr: 3.9062e-05\n", "Epoch 253/1024\n", - "90/90 [==============================] - 0s 910us/step - loss: 0.0028 - val_loss: 0.0062 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0062 - lr: 3.9062e-05\n", "Epoch 254/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0032 - lr: 3.9062e-05\n", "Epoch 255/1024\n", - "90/90 [==============================] - 0s 903us/step - loss: 0.0028 - val_loss: 0.0411 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 905us/step - loss: 0.0028 - val_loss: 0.0411 - lr: 3.9062e-05\n", "Epoch 256/1024\n", - "90/90 [==============================] - 0s 934us/step - loss: 0.0028 - val_loss: 0.0529 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0529 - lr: 3.9062e-05\n", "Epoch 257/1024\n", - "90/90 [==============================] - 0s 930us/step - loss: 0.0028 - val_loss: 0.0063 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 892us/step - loss: 0.0028 - val_loss: 0.0063 - lr: 3.9062e-05\n", "Epoch 258/1024\n", - "90/90 [==============================] - 0s 973us/step - loss: 0.0028 - val_loss: 0.0036 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0036 - lr: 3.9062e-05\n", "Epoch 259/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0308 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0308 - lr: 3.9062e-05\n", "Epoch 260/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0085 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0027 - val_loss: 0.0085 - lr: 3.9062e-05\n", "Epoch 261/1024\n", - "90/90 [==============================] - 0s 979us/step - loss: 0.0028 - val_loss: 0.0068 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0068 - lr: 3.9062e-05\n", "Epoch 262/1024\n", - "90/90 [==============================] - 0s 982us/step - loss: 0.0028 - val_loss: 0.0074 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0074 - lr: 3.9062e-05\n", "Epoch 263/1024\n", - "90/90 [==============================] - 0s 910us/step - loss: 0.0028 - val_loss: 0.0079 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0079 - lr: 3.9062e-05\n", "Epoch 264/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0109 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.0109 - lr: 3.9062e-05\n", "Epoch 265/1024\n", - "90/90 [==============================] - 0s 963us/step - loss: 0.0027 - val_loss: 0.0214 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0214 - lr: 3.9062e-05\n", "Epoch 266/1024\n", - "90/90 [==============================] - 0s 928us/step - loss: 0.0027 - val_loss: 0.0143 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0143 - lr: 3.9062e-05\n", "Epoch 267/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0102 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0029 - val_loss: 0.0102 - lr: 3.9062e-05\n", "Epoch 268/1024\n", - "90/90 [==============================] - 0s 991us/step - loss: 0.0028 - val_loss: 0.0262 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0262 - lr: 3.9062e-05\n", "Epoch 269/1024\n", - "90/90 [==============================] - 0s 909us/step - loss: 0.0028 - val_loss: 0.0217 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.0217 - lr: 3.9062e-05\n", "Epoch 270/1024\n", - "90/90 [==============================] - 0s 912us/step - loss: 0.0027 - val_loss: 0.0221 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0221 - lr: 3.9062e-05\n", "Epoch 271/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0056 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0056 - lr: 3.9062e-05\n", "Epoch 272/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0146 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0146 - lr: 3.9062e-05\n", "Epoch 273/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0307 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0307 - lr: 3.9062e-05\n", "Epoch 274/1024\n", - "90/90 [==============================] - 0s 917us/step - loss: 0.0028 - val_loss: 0.0197 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0028 - val_loss: 0.0197 - lr: 3.9062e-05\n", "Epoch 275/1024\n", - "90/90 [==============================] - 0s 885us/step - loss: 0.0028 - val_loss: 0.0057 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0057 - lr: 3.9062e-05\n", "Epoch 276/1024\n", - "90/90 [==============================] - 0s 919us/step - loss: 0.0027 - val_loss: 0.0204 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0027 - val_loss: 0.0204 - lr: 3.9062e-05\n", "Epoch 277/1024\n", - "90/90 [==============================] - 0s 897us/step - loss: 0.0028 - val_loss: 0.0130 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.0130 - lr: 3.9062e-05\n", "Epoch 278/1024\n", - "90/90 [==============================] - 0s 941us/step - loss: 0.0027 - val_loss: 0.0049 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0027 - val_loss: 0.0049 - lr: 3.9062e-05\n", "Epoch 279/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0096 - lr: 3.9062e-05\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0096 - lr: 3.9062e-05\n", "Epoch 280/1024\n", - "90/90 [==============================] - 0s 894us/step - loss: 0.0027 - val_loss: 0.0226 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0226 - lr: 1.9531e-05\n", "Epoch 281/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0028 - lr: 1.9531e-05\n", "Epoch 282/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0031 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 904us/step - loss: 0.0028 - val_loss: 0.0031 - lr: 1.9531e-05\n", "Epoch 283/1024\n", - "90/90 [==============================] - 0s 910us/step - loss: 0.0027 - val_loss: 0.0043 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0043 - lr: 1.9531e-05\n", "Epoch 284/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0044 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0044 - lr: 1.9531e-05\n", "Epoch 285/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0151 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0029 - val_loss: 0.0151 - lr: 1.9531e-05\n", "Epoch 286/1024\n", - "90/90 [==============================] - 0s 900us/step - loss: 0.0028 - val_loss: 0.0076 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0076 - lr: 1.9531e-05\n", "Epoch 287/1024\n", - "90/90 [==============================] - 0s 905us/step - loss: 0.0028 - val_loss: 0.0042 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0042 - lr: 1.9531e-05\n", "Epoch 288/1024\n", - "90/90 [==============================] - 0s 888us/step - loss: 0.0029 - val_loss: 0.0032 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0029 - val_loss: 0.0032 - lr: 1.9531e-05\n", "Epoch 289/1024\n", - "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0045 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0045 - lr: 1.9531e-05\n", "Epoch 290/1024\n", - "90/90 [==============================] - 0s 937us/step - loss: 0.0027 - val_loss: 0.0042 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 882us/step - loss: 0.0027 - val_loss: 0.0042 - lr: 1.9531e-05\n", "Epoch 291/1024\n", - "90/90 [==============================] - 0s 924us/step - loss: 0.0027 - val_loss: 0.0037 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0027 - val_loss: 0.0037 - lr: 1.9531e-05\n", "Epoch 292/1024\n", - "90/90 [==============================] - 0s 946us/step - loss: 0.0028 - val_loss: 0.0048 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0048 - lr: 1.9531e-05\n", "Epoch 293/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0063 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0027 - val_loss: 0.0063 - lr: 1.9531e-05\n", "Epoch 294/1024\n", - "90/90 [==============================] - 0s 946us/step - loss: 0.0028 - val_loss: 0.0030 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0030 - lr: 1.9531e-05\n", "Epoch 295/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 1.9531e-05\n", "Epoch 296/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0126 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 898us/step - loss: 0.0028 - val_loss: 0.0126 - lr: 1.9531e-05\n", "Epoch 297/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0034 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 890us/step - loss: 0.0027 - val_loss: 0.0034 - lr: 1.9531e-05\n", "Epoch 298/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0035 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0027 - val_loss: 0.0035 - lr: 1.9531e-05\n", "Epoch 299/1024\n", - "90/90 [==============================] - 0s 910us/step - loss: 0.0027 - val_loss: 0.0056 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0027 - val_loss: 0.0056 - lr: 1.9531e-05\n", "Epoch 300/1024\n", - "90/90 [==============================] - 0s 921us/step - loss: 0.0028 - val_loss: 0.0107 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0107 - lr: 1.9531e-05\n", "Epoch 301/1024\n", - "90/90 [==============================] - 0s 912us/step - loss: 0.0028 - val_loss: 0.0060 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.0060 - lr: 1.9531e-05\n", "Epoch 302/1024\n", - "90/90 [==============================] - 0s 892us/step - loss: 0.0028 - val_loss: 0.0133 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0133 - lr: 1.9531e-05\n", "Epoch 303/1024\n", - "90/90 [==============================] - 0s 918us/step - loss: 0.0028 - val_loss: 0.0037 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0037 - lr: 1.9531e-05\n", "Epoch 304/1024\n", - "90/90 [==============================] - 0s 883us/step - loss: 0.0028 - val_loss: 0.0066 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0066 - lr: 1.9531e-05\n", "Epoch 305/1024\n", - "90/90 [==============================] - 0s 896us/step - loss: 0.0027 - val_loss: 0.0032 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0032 - lr: 1.9531e-05\n", "Epoch 306/1024\n", - "90/90 [==============================] - 0s 876us/step - loss: 0.0027 - val_loss: 0.0032 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0027 - val_loss: 0.0032 - lr: 1.9531e-05\n", "Epoch 307/1024\n", - "90/90 [==============================] - 0s 951us/step - loss: 0.0027 - val_loss: 0.0033 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0033 - lr: 1.9531e-05\n", "Epoch 308/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0039 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0027 - val_loss: 0.0039 - lr: 1.9531e-05\n", "Epoch 309/1024\n", - "90/90 [==============================] - 0s 916us/step - loss: 0.0028 - val_loss: 0.0067 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0067 - lr: 1.9531e-05\n", "Epoch 310/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0045 - lr: 1.9531e-05\n", "Epoch 311/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0039 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0039 - lr: 1.9531e-05\n", "Epoch 312/1024\n", - "90/90 [==============================] - 0s 978us/step - loss: 0.0028 - val_loss: 0.0043 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 900us/step - loss: 0.0028 - val_loss: 0.0043 - lr: 1.9531e-05\n", "Epoch 313/1024\n", - "90/90 [==============================] - 0s 895us/step - loss: 0.0028 - val_loss: 0.0040 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0040 - lr: 1.9531e-05\n", "Epoch 314/1024\n", - "90/90 [==============================] - 0s 906us/step - loss: 0.0028 - val_loss: 0.0108 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0108 - lr: 1.9531e-05\n", "Epoch 315/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 1.9531e-05\n", "Epoch 316/1024\n", - "90/90 [==============================] - 0s 894us/step - loss: 0.0028 - val_loss: 0.0041 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 902us/step - loss: 0.0028 - val_loss: 0.0041 - lr: 1.9531e-05\n", "Epoch 317/1024\n", - "90/90 [==============================] - 0s 902us/step - loss: 0.0028 - val_loss: 0.0030 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0030 - lr: 1.9531e-05\n", "Epoch 318/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0052 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0052 - lr: 1.9531e-05\n", "Epoch 319/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0112 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0027 - val_loss: 0.0112 - lr: 1.9531e-05\n", "Epoch 320/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0205 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0205 - lr: 1.9531e-05\n", "Epoch 321/1024\n", - "90/90 [==============================] - 0s 916us/step - loss: 0.0028 - val_loss: 0.0102 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0102 - lr: 1.9531e-05\n", "Epoch 322/1024\n", - "90/90 [==============================] - 0s 918us/step - loss: 0.0028 - val_loss: 0.0042 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0042 - lr: 1.9531e-05\n", "Epoch 323/1024\n", - "90/90 [==============================] - 0s 926us/step - loss: 0.0027 - val_loss: 0.0031 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0027 - val_loss: 0.0031 - lr: 1.9531e-05\n", "Epoch 324/1024\n", - "90/90 [==============================] - 0s 947us/step - loss: 0.0028 - val_loss: 0.0031 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0031 - lr: 1.9531e-05\n", "Epoch 325/1024\n", - "90/90 [==============================] - 0s 904us/step - loss: 0.0027 - val_loss: 0.0063 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 883us/step - loss: 0.0027 - val_loss: 0.0063 - lr: 1.9531e-05\n", "Epoch 326/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0166 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0166 - lr: 1.9531e-05\n", "Epoch 327/1024\n", "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0035 - lr: 1.9531e-05\n", "Epoch 328/1024\n", - "90/90 [==============================] - 0s 895us/step - loss: 0.0027 - val_loss: 0.0055 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0027 - val_loss: 0.0055 - lr: 1.9531e-05\n", "Epoch 329/1024\n", - "90/90 [==============================] - 0s 909us/step - loss: 0.0028 - val_loss: 0.0091 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 888us/step - loss: 0.0028 - val_loss: 0.0091 - lr: 1.9531e-05\n", "Epoch 330/1024\n", - "90/90 [==============================] - 0s 918us/step - loss: 0.0027 - val_loss: 0.0143 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0027 - val_loss: 0.0143 - lr: 1.9531e-05\n", "Epoch 331/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0056 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0056 - lr: 1.9531e-05\n", "Epoch 332/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0045 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0045 - lr: 1.9531e-05\n", "Epoch 333/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0048 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0048 - lr: 1.9531e-05\n", "Epoch 334/1024\n", - "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0057 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0027 - val_loss: 0.0057 - lr: 1.9531e-05\n", "Epoch 335/1024\n", - "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0142 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0028 - val_loss: 0.0142 - lr: 1.9531e-05\n", "Epoch 336/1024\n", - "90/90 [==============================] - 0s 922us/step - loss: 0.0028 - val_loss: 0.0052 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0052 - lr: 1.9531e-05\n", "Epoch 337/1024\n", - "90/90 [==============================] - 0s 883us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 1.9531e-05\n", "Epoch 338/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0027 - val_loss: 0.0054 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0054 - lr: 1.9531e-05\n", "Epoch 339/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0032 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0032 - lr: 1.9531e-05\n", "Epoch 340/1024\n", - "90/90 [==============================] - 0s 901us/step - loss: 0.0027 - val_loss: 0.0034 - lr: 1.9531e-05\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0034 - lr: 1.9531e-05\n", "Epoch 341/1024\n", - "90/90 [==============================] - 0s 850us/step - loss: 0.0028 - val_loss: 0.0037 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0037 - lr: 9.7656e-06\n", "Epoch 342/1024\n", - "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0034 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0034 - lr: 9.7656e-06\n", "Epoch 343/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0029 - val_loss: 0.0063 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0029 - val_loss: 0.0063 - lr: 9.7656e-06\n", "Epoch 344/1024\n", - "90/90 [==============================] - 0s 887us/step - loss: 0.0028 - val_loss: 0.0032 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0028 - val_loss: 0.0032 - lr: 9.7656e-06\n", "Epoch 345/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0037 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0037 - lr: 9.7656e-06\n", "Epoch 346/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0050 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0027 - val_loss: 0.0050 - lr: 9.7656e-06\n", "Epoch 347/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0042 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0042 - lr: 9.7656e-06\n", "Epoch 348/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0027 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 9.7656e-06\n", "Epoch 349/1024\n", - "90/90 [==============================] - 0s 879us/step - loss: 0.0028 - val_loss: 0.0034 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 886us/step - loss: 0.0028 - val_loss: 0.0034 - lr: 9.7656e-06\n", "Epoch 350/1024\n", - "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0032 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0032 - lr: 9.7656e-06\n", "Epoch 351/1024\n", - "90/90 [==============================] - 0s 883us/step - loss: 0.0028 - val_loss: 0.0032 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0032 - lr: 9.7656e-06\n", "Epoch 352/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0051 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0051 - lr: 9.7656e-06\n", "Epoch 353/1024\n", - "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0028 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0028 - lr: 9.7656e-06\n", "Epoch 354/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0040 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0040 - lr: 9.7656e-06\n", "Epoch 355/1024\n", - "90/90 [==============================] - 0s 904us/step - loss: 0.0028 - val_loss: 0.0032 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0032 - lr: 9.7656e-06\n", "Epoch 356/1024\n", - "90/90 [==============================] - 0s 924us/step - loss: 0.0027 - val_loss: 0.0033 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0027 - val_loss: 0.0033 - lr: 9.7656e-06\n", "Epoch 357/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0036 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0027 - val_loss: 0.0036 - lr: 9.7656e-06\n", "Epoch 358/1024\n", - "90/90 [==============================] - 0s 893us/step - loss: 0.0028 - val_loss: 0.0048 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0048 - lr: 9.7656e-06\n", "Epoch 359/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0027 - val_loss: 0.0062 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0062 - lr: 9.7656e-06\n", "Epoch 360/1024\n", - "90/90 [==============================] - 0s 880us/step - loss: 0.0027 - val_loss: 0.0030 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0027 - val_loss: 0.0030 - lr: 9.7656e-06\n", "Epoch 361/1024\n", - "90/90 [==============================] - 0s 992us/step - loss: 0.0028 - val_loss: 0.0054 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0054 - lr: 9.7656e-06\n", "Epoch 362/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0029 - val_loss: 0.0031 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 884us/step - loss: 0.0029 - val_loss: 0.0031 - lr: 9.7656e-06\n", "Epoch 363/1024\n", - "90/90 [==============================] - 0s 922us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 9.7656e-06\n", "Epoch 364/1024\n", - "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0034 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0034 - lr: 9.7656e-06\n", "Epoch 365/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0026 - val_loss: 0.0033 - lr: 9.7656e-06\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0026 - val_loss: 0.0033 - lr: 9.7656e-06\n", "Epoch 366/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0041 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 882us/step - loss: 0.0028 - val_loss: 0.0041 - lr: 4.8828e-06\n", "Epoch 367/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", "Epoch 368/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0035 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0035 - lr: 4.8828e-06\n", "Epoch 369/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 370/1024\n", - "90/90 [==============================] - 0s 924us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 904us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 371/1024\n", - "90/90 [==============================] - 0s 917us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 372/1024\n", - "90/90 [==============================] - 0s 912us/step - loss: 0.0028 - val_loss: 0.0029 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0029 - lr: 4.8828e-06\n", "Epoch 373/1024\n", - "90/90 [==============================] - 0s 923us/step - loss: 0.0027 - val_loss: 0.0032 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0032 - lr: 4.8828e-06\n", "Epoch 374/1024\n", - "90/90 [==============================] - 0s 982us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 375/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0032 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0032 - lr: 4.8828e-06\n", "Epoch 376/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", "Epoch 377/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 378/1024\n", - "90/90 [==============================] - 0s 897us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 904us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", "Epoch 379/1024\n", - "90/90 [==============================] - 0s 886us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 380/1024\n", - "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", "Epoch 381/1024\n", - "90/90 [==============================] - 0s 893us/step - loss: 0.0027 - val_loss: 0.0041 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0027 - val_loss: 0.0041 - lr: 4.8828e-06\n", "Epoch 382/1024\n", - "90/90 [==============================] - 0s 894us/step - loss: 0.0028 - val_loss: 0.0033 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0033 - lr: 4.8828e-06\n", "Epoch 383/1024\n", - "90/90 [==============================] - 0s 914us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 384/1024\n", - "90/90 [==============================] - 0s 880us/step - loss: 0.0027 - val_loss: 0.0031 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0031 - lr: 4.8828e-06\n", "Epoch 385/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", "Epoch 386/1024\n", - "90/90 [==============================] - 0s 905us/step - loss: 0.0029 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - ETA: 0s - loss: 0.002 - 0s 896us/step - loss: 0.0029 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 387/1024\n", - "90/90 [==============================] - 0s 924us/step - loss: 0.0027 - val_loss: 0.0029 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0027 - val_loss: 0.0029 - lr: 4.8828e-06\n", "Epoch 388/1024\n", - "90/90 [==============================] - 0s 936us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", "Epoch 389/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 390/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0031 - lr: 4.8828e-06\n", "Epoch 391/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0038 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0027 - val_loss: 0.0038 - lr: 4.8828e-06\n", "Epoch 392/1024\n", - "90/90 [==============================] - 0s 896us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 393/1024\n", - "90/90 [==============================] - 0s 907us/step - loss: 0.0027 - val_loss: 0.0028 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 879us/step - loss: 0.0027 - val_loss: 0.0028 - lr: 4.8828e-06\n", "Epoch 394/1024\n", - "90/90 [==============================] - 0s 900us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.8828e-06\n", "Epoch 395/1024\n", - "90/90 [==============================] - 0s 903us/step - loss: 0.0027 - val_loss: 0.0030 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0030 - lr: 4.8828e-06\n", "Epoch 396/1024\n", - "90/90 [==============================] - 0s 930us/step - loss: 0.0027 - val_loss: 0.0033 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0027 - val_loss: 0.0033 - lr: 4.8828e-06\n", "Epoch 397/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 398/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 399/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 400/1024\n", - "90/90 [==============================] - 0s 888us/step - loss: 0.0027 - val_loss: 0.0030 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0027 - val_loss: 0.0030 - lr: 4.8828e-06\n", "Epoch 401/1024\n", - "90/90 [==============================] - 0s 880us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 402/1024\n", - "90/90 [==============================] - 0s 914us/step - loss: 0.0029 - val_loss: 0.0027 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 882us/step - loss: 0.0029 - val_loss: 0.0027 - lr: 4.8828e-06\n", "Epoch 403/1024\n", - "90/90 [==============================] - 0s 895us/step - loss: 0.0029 - val_loss: 0.0030 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0029 - val_loss: 0.0030 - lr: 4.8828e-06\n", "Epoch 404/1024\n", - "90/90 [==============================] - 0s 873us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0029 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 405/1024\n", "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0028 - lr: 4.8828e-06\n", "Epoch 406/1024\n", - "90/90 [==============================] - 0s 879us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 888us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 407/1024\n", - "90/90 [==============================] - 0s 897us/step - loss: 0.0027 - val_loss: 0.0028 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 895us/step - loss: 0.0027 - val_loss: 0.0028 - lr: 4.8828e-06\n", "Epoch 408/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0033 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0027 - val_loss: 0.0033 - lr: 4.8828e-06\n", "Epoch 409/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0028 - lr: 4.8828e-06\n", "Epoch 410/1024\n", - "90/90 [==============================] - 0s 970us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 4.8828e-06\n", "Epoch 411/1024\n", - "90/90 [==============================] - 0s 861us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 412/1024\n", - "90/90 [==============================] - 0s 907us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 413/1024\n", - "90/90 [==============================] - 0s 899us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 414/1024\n", - "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 415/1024\n", - "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 416/1024\n", - "90/90 [==============================] - 0s 882us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 417/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 418/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 906us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 419/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 420/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0027 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 421/1024\n", - "90/90 [==============================] - 0s 965us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 422/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 423/1024\n", - "90/90 [==============================] - 0s 938us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 424/1024\n", - "90/90 [==============================] - 0s 896us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 425/1024\n", - "90/90 [==============================] - 0s 981us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 426/1024\n", - "90/90 [==============================] - 0s 990us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 427/1024\n", "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 428/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 429/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 430/1024\n", - "90/90 [==============================] - 0s 986us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 431/1024\n", - "90/90 [==============================] - 0s 882us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 432/1024\n", - "90/90 [==============================] - 0s 911us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 433/1024\n", - "90/90 [==============================] - 0s 982us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 434/1024\n", - "90/90 [==============================] - 0s 912us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.4414e-06\n", "Epoch 435/1024\n", - "90/90 [==============================] - 0s 919us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 436/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0027 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0027 - lr: 2.4414e-06\n", "Epoch 437/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 438/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0031 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0027 - val_loss: 0.0031 - lr: 2.4414e-06\n", "Epoch 439/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0030 - val_loss: 0.0028 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0030 - val_loss: 0.0028 - lr: 2.4414e-06\n", "Epoch 440/1024\n", - "90/90 [==============================] - 0s 896us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 441/1024\n", - "90/90 [==============================] - 0s 948us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 442/1024\n", - "90/90 [==============================] - 0s 918us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", + "90/90 [==============================] - 0s 879us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 2.4414e-06\n", "Epoch 443/1024\n", - "90/90 [==============================] - 0s 902us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 444/1024\n", - "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 445/1024\n", - "90/90 [==============================] - 0s 948us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 446/1024\n", - "90/90 [==============================] - 0s 922us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 447/1024\n", - "90/90 [==============================] - 0s 963us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 448/1024\n", - "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 449/1024\n", - "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 450/1024\n", - "90/90 [==============================] - 0s 911us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 885us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 451/1024\n", - "90/90 [==============================] - 0s 876us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 452/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 453/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 454/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 455/1024\n", - "90/90 [==============================] - 0s 997us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 456/1024\n", - "90/90 [==============================] - 0s 940us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 889us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 457/1024\n", - "90/90 [==============================] - 0s 894us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 458/1024\n", - "90/90 [==============================] - 0s 896us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 459/1024\n", - "90/90 [==============================] - 0s 958us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 460/1024\n", - "90/90 [==============================] - 0s 913us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 878us/step - loss: 0.0027 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 461/1024\n", - "90/90 [==============================] - 0s 902us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 462/1024\n", - "90/90 [==============================] - 0s 900us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 463/1024\n", - "90/90 [==============================] - 0s 912us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 464/1024\n", - "90/90 [==============================] - 0s 879us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 465/1024\n", - "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 1.2207e-06\n", "Epoch 466/1024\n", - "90/90 [==============================] - 0s 911us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 467/1024\n", - "90/90 [==============================] - 0s 878us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.2207e-06\n", "Epoch 468/1024\n", - "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 469/1024\n", - "90/90 [==============================] - 0s 881us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 470/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 471/1024\n", - "90/90 [==============================] - 0s 892us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 891us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 472/1024\n", - "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 473/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 474/1024\n", - "90/90 [==============================] - 0s 898us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 475/1024\n", - "90/90 [==============================] - 0s 888us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 476/1024\n", - "90/90 [==============================] - 0s 884us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 477/1024\n", - "90/90 [==============================] - 0s 883us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 478/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 479/1024\n", - "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", - "Epoch 480/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 475/1024\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 476/1024\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 477/1024\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 478/1024\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 479/1024\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "Epoch 480/1024\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 481/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 482/1024\n", - "90/90 [==============================] - 0s 959us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 483/1024\n", - "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 484/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 879us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 485/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 486/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 487/1024\n", - "90/90 [==============================] - 0s 912us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 488/1024\n", - "90/90 [==============================] - 0s 903us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 489/1024\n", - "90/90 [==============================] - 0s 986us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 490/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0026 - lr: 6.1035e-07\n", "Epoch 491/1024\n", - "90/90 [==============================] - 0s 945us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 884us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 492/1024\n", - "90/90 [==============================] - 0s 921us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 493/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 929us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 494/1024\n", - "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 976us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 495/1024\n", - "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 6.1035e-07\n", "Epoch 496/1024\n", - "90/90 [==============================] - 0s 909us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 497/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 498/1024\n", - "90/90 [==============================] - 0s 962us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 879us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 499/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 500/1024\n", - "90/90 [==============================] - 0s 905us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 938us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 501/1024\n", - "90/90 [==============================] - 0s 885us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 502/1024\n", - "90/90 [==============================] - 0s 984us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 503/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 504/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 898us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 505/1024\n", - "90/90 [==============================] - 0s 906us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 506/1024\n", - "90/90 [==============================] - 0s 935us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 507/1024\n", - "90/90 [==============================] - 0s 928us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 508/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 509/1024\n", - "90/90 [==============================] - 0s 920us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 510/1024\n", - "90/90 [==============================] - 0s 913us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", - "Epoch 511/1024\n", - "90/90 [==============================] - 0s 894us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", - "Epoch 512/1024\n", - "90/90 [==============================] - 0s 933us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", - "Epoch 513/1024\n", - "90/90 [==============================] - 0s 910us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", - "Epoch 514/1024\n", - "90/90 [==============================] - 0s 913us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", - "Epoch 515/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", - "Epoch 516/1024\n", - "90/90 [==============================] - 0s 962us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", - "Epoch 517/1024\n", - "90/90 [==============================] - 0s 929us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", - "Epoch 518/1024\n", - "90/90 [==============================] - 0s 962us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", - "Epoch 519/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "Epoch 511/1024\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "Epoch 512/1024\n", + "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "Epoch 513/1024\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "Epoch 514/1024\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "Epoch 515/1024\n", + "90/90 [==============================] - 0s 986us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "Epoch 516/1024\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "Epoch 517/1024\n", + "90/90 [==============================] - 0s 879us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "Epoch 518/1024\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "Epoch 519/1024\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 520/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", + "90/90 [==============================] - 0s 883us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.0518e-07\n", "Epoch 521/1024\n", - "90/90 [==============================] - 0s 948us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 522/1024\n", - "90/90 [==============================] - 0s 914us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 523/1024\n", - "90/90 [==============================] - 0s 918us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 524/1024\n", - "90/90 [==============================] - 0s 900us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 525/1024\n", - "90/90 [==============================] - 0s 930us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 526/1024\n", - "90/90 [==============================] - 0s 997us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 886us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 527/1024\n", - "90/90 [==============================] - 0s 931us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 528/1024\n", - "90/90 [==============================] - 0s 992us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 529/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 530/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 531/1024\n", - "90/90 [==============================] - 0s 979us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 532/1024\n", - "90/90 [==============================] - 0s 908us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 533/1024\n", - "90/90 [==============================] - 0s 918us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 932us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 534/1024\n", - "90/90 [==============================] - 0s 946us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 535/1024\n", - "90/90 [==============================] - 0s 899us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 892us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 536/1024\n", - "90/90 [==============================] - 0s 947us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 537/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 538/1024\n", - "90/90 [==============================] - 0s 879us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 539/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 540/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 541/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 542/1024\n", - "90/90 [==============================] - 0s 936us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 543/1024\n", - "90/90 [==============================] - 0s 987us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 544/1024\n", - "90/90 [==============================] - 0s 879us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 545/1024\n", - "90/90 [==============================] - 0s 881us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.5259e-07\n", "Epoch 546/1024\n", - "90/90 [==============================] - 0s 890us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 885us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 547/1024\n", - "90/90 [==============================] - 0s 862us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 886us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 548/1024\n", - "90/90 [==============================] - 0s 883us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 549/1024\n", - "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 550/1024\n", - "90/90 [==============================] - 0s 902us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 551/1024\n", - "90/90 [==============================] - 0s 974us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 552/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 553/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 554/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 555/1024\n", - "90/90 [==============================] - 0s 968us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 556/1024\n", - "90/90 [==============================] - 0s 933us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 557/1024\n", - "90/90 [==============================] - 0s 928us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 878us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 558/1024\n", - "90/90 [==============================] - 0s 893us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 559/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 560/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 561/1024\n", - "90/90 [==============================] - 0s 991us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 562/1024\n", - "90/90 [==============================] - 0s 931us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 563/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 564/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 565/1024\n", - "90/90 [==============================] - 0s 887us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 566/1024\n", - "90/90 [==============================] - 0s 923us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 878us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 567/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 568/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 885us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 569/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 570/1024\n", - "90/90 [==============================] - 0s 889us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.6294e-08\n", "Epoch 571/1024\n", - "90/90 [==============================] - 0s 921us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 572/1024\n", - "90/90 [==============================] - 0s 962us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 573/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 574/1024\n", - "90/90 [==============================] - 0s 952us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 886us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 575/1024\n", - "90/90 [==============================] - 0s 984us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 576/1024\n", - "90/90 [==============================] - 0s 975us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 577/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 578/1024\n", - "90/90 [==============================] - 0s 921us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 579/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 580/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 581/1024\n", - "90/90 [==============================] - 0s 926us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 582/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 583/1024\n", - "90/90 [==============================] - 0s 981us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 882us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 584/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 585/1024\n", "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 586/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 887us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 587/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 588/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 589/1024\n", - "90/90 [==============================] - 0s 924us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 590/1024\n", - "90/90 [==============================] - 0s 912us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 591/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 592/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 593/1024\n", - "90/90 [==============================] - 0s 923us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 594/1024\n", - "90/90 [==============================] - 0s 960us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 888us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 595/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.8147e-08\n", "Epoch 596/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 597/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 892us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 598/1024\n", - "90/90 [==============================] - 0s 955us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 599/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 600/1024\n", - "90/90 [==============================] - 0s 879us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 848us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 601/1024\n", - "90/90 [==============================] - 0s 916us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 602/1024\n", - "90/90 [==============================] - 0s 951us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 841us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 603/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 604/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 605/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 850us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 606/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 607/1024\n", - "90/90 [==============================] - 0s 917us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 608/1024\n", - "90/90 [==============================] - 0s 994us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 974us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 609/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 843us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 610/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 611/1024\n", - "90/90 [==============================] - 0s 992us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 852us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 612/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 891us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 613/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", - "Epoch 614/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "Epoch 614/1024\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 615/1024\n", - "90/90 [==============================] - 0s 966us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 616/1024\n", - "90/90 [==============================] - 0s 942us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 904us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 617/1024\n", - "90/90 [==============================] - 0s 966us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 618/1024\n", - "90/90 [==============================] - 0s 911us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 619/1024\n", - "90/90 [==============================] - 0s 895us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 620/1024\n", - "90/90 [==============================] - 0s 922us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.9073e-08\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.9073e-08\n", "Epoch 621/1024\n", - "90/90 [==============================] - 0s 953us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 906us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 622/1024\n", - "90/90 [==============================] - 0s 960us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 882us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 623/1024\n", - "90/90 [==============================] - 0s 989us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 850us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 624/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 845us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 625/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 626/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 627/1024\n", - "90/90 [==============================] - 0s 919us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 628/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 854us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 629/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 851us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 630/1024\n", - "90/90 [==============================] - 0s 993us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 913us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 631/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 632/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 633/1024\n", - "90/90 [==============================] - 0s 966us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 845us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 634/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 888us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 635/1024\n", - "90/90 [==============================] - 0s 966us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 636/1024\n", - "90/90 [==============================] - 0s 918us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 637/1024\n", - "90/90 [==============================] - 0s 954us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 950us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 638/1024\n", - "90/90 [==============================] - 0s 957us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 850us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 639/1024\n", - "90/90 [==============================] - 0s 934us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 835us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 640/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 641/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 844us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 642/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 914us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 643/1024\n", - "90/90 [==============================] - 0s 904us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 842us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 644/1024\n", - "90/90 [==============================] - 0s 943us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 645/1024\n", - "90/90 [==============================] - 0s 944us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", + "90/90 [==============================] - 0s 848us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.5367e-09\n", "Epoch 646/1024\n", - "90/90 [==============================] - 0s 922us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 647/1024\n", - "90/90 [==============================] - 0s 955us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 648/1024\n", - "90/90 [==============================] - 0s 996us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 853us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 649/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 846us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 650/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 651/1024\n", - "90/90 [==============================] - 0s 987us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 895us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 652/1024\n", - "90/90 [==============================] - 0s 991us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 883us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 653/1024\n", - "90/90 [==============================] - 0s 912us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 654/1024\n", - "90/90 [==============================] - 0s 927us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 655/1024\n", - "90/90 [==============================] - 0s 921us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 656/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 884us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 657/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 658/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 659/1024\n", - "90/90 [==============================] - 0s 874us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 660/1024\n", - "90/90 [==============================] - 0s 929us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 661/1024\n", - "90/90 [==============================] - 0s 960us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 662/1024\n", - "90/90 [==============================] - 0s 937us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 879us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 663/1024\n", - "90/90 [==============================] - 0s 927us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 851us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 664/1024\n", - "90/90 [==============================] - 0s 922us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 665/1024\n", - "90/90 [==============================] - 0s 917us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 666/1024\n", - "90/90 [==============================] - 0s 995us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 667/1024\n", - "90/90 [==============================] - 0s 942us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 668/1024\n", - "90/90 [==============================] - 0s 944us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 669/1024\n", - "90/90 [==============================] - 0s 919us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 837us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 670/1024\n", - "90/90 [==============================] - 0s 941us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.7684e-09\n", "Epoch 671/1024\n", - "90/90 [==============================] - 0s 918us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 672/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 850us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 673/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 841us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 674/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 909us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 675/1024\n", - "90/90 [==============================] - 0s 919us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 838us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 676/1024\n", - "90/90 [==============================] - 0s 945us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 853us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 677/1024\n", - "90/90 [==============================] - 0s 940us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 678/1024\n", - "90/90 [==============================] - 0s 926us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 679/1024\n", - "90/90 [==============================] - 0s 926us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 831us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 680/1024\n", - "90/90 [==============================] - 0s 951us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 681/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 682/1024\n", - "90/90 [==============================] - 0s 918us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 683/1024\n", - "90/90 [==============================] - 0s 966us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 882us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 684/1024\n", - "90/90 [==============================] - 0s 943us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 685/1024\n", - "90/90 [==============================] - 0s 930us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 686/1024\n", - "90/90 [==============================] - 0s 938us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", - "Epoch 687/1024\n", - "90/90 [==============================] - 0s 955us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", - "Epoch 688/1024\n", - "90/90 [==============================] - 0s 932us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", - "Epoch 689/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "Epoch 687/1024\n", + "90/90 [==============================] - 0s 945us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "Epoch 688/1024\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "Epoch 689/1024\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 690/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 691/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 692/1024\n", - "90/90 [==============================] - 0s 967us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 693/1024\n", - "90/90 [==============================] - 0s 948us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 885us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 694/1024\n", - "90/90 [==============================] - 0s 1000us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 843us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 695/1024\n", - "90/90 [==============================] - 0s 975us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", + "90/90 [==============================] - 0s 892us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3842e-09\n", "Epoch 696/1024\n", - "90/90 [==============================] - 0s 947us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 697/1024\n", - "90/90 [==============================] - 0s 951us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 698/1024\n", - "90/90 [==============================] - 0s 976us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 699/1024\n", - "90/90 [==============================] - 0s 961us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 887us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 700/1024\n", - "90/90 [==============================] - 0s 952us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 701/1024\n", - "90/90 [==============================] - 0s 939us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 840us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 702/1024\n", - "90/90 [==============================] - 0s 944us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 703/1024\n", - "90/90 [==============================] - 0s 945us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 704/1024\n", - "90/90 [==============================] - 0s 905us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 705/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 848us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 706/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 707/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 708/1024\n", - "90/90 [==============================] - 0s 945us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 709/1024\n", - "90/90 [==============================] - 0s 928us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 710/1024\n", - "90/90 [==============================] - 0s 958us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 711/1024\n", - "90/90 [==============================] - 0s 912us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 830us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 712/1024\n", - "90/90 [==============================] - 0s 919us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 713/1024\n", - "90/90 [==============================] - 0s 985us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 838us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 714/1024\n", - "90/90 [==============================] - 0s 959us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 715/1024\n", - "90/90 [==============================] - 0s 912us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 716/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 717/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 834us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 718/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 719/1024\n", - "90/90 [==============================] - 0s 911us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 720/1024\n", - "90/90 [==============================] - 0s 949us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", + "90/90 [==============================] - 0s 836us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1921e-09\n", "Epoch 721/1024\n", - "90/90 [==============================] - 0s 937us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 843us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 722/1024\n", - "90/90 [==============================] - 0s 951us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 723/1024\n", - "90/90 [==============================] - 0s 929us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 724/1024\n", - "90/90 [==============================] - 0s 910us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 973us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 725/1024\n", - "90/90 [==============================] - 0s 972us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 991us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 726/1024\n", - "90/90 [==============================] - 0s 916us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 883us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 727/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 728/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 729/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 884us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 730/1024\n", - "90/90 [==============================] - 0s 887us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 731/1024\n", - "90/90 [==============================] - 0s 949us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 732/1024\n", - "90/90 [==============================] - 0s 957us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 879us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 733/1024\n", - "90/90 [==============================] - 0s 923us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 841us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 734/1024\n", - "90/90 [==============================] - 0s 926us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 735/1024\n", - "90/90 [==============================] - 0s 941us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 736/1024\n", - "90/90 [==============================] - 0s 920us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 737/1024\n", - "90/90 [==============================] - 0s 949us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 738/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 879us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 739/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 740/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 741/1024\n", - "90/90 [==============================] - 0s 915us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 852us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 742/1024\n", - "90/90 [==============================] - 0s 926us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 844us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 743/1024\n", - "90/90 [==============================] - 0s 952us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 882us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 744/1024\n", - "90/90 [==============================] - 0s 940us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 999us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 745/1024\n", - "90/90 [==============================] - 0s 949us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.9605e-10\n", "Epoch 746/1024\n", - "90/90 [==============================] - 0s 900us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 747/1024\n", - "90/90 [==============================] - 0s 953us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 748/1024\n", - "90/90 [==============================] - 0s 940us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 749/1024\n", - "90/90 [==============================] - 0s 921us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 750/1024\n", - "90/90 [==============================] - 0s 973us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 751/1024\n", - "90/90 [==============================] - 0s 917us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 752/1024\n", - "90/90 [==============================] - 0s 924us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 846us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 753/1024\n", - "90/90 [==============================] - 0s 917us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 754/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 888us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 755/1024\n", - "90/90 [==============================] - 0s 4ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 756/1024\n", - "90/90 [==============================] - 0s 4ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 848us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 757/1024\n", - "90/90 [==============================] - 0s 3ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 852us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 758/1024\n", - "90/90 [==============================] - 0s 4ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 759/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 760/1024\n", - "90/90 [==============================] - 0s 929us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 761/1024\n", - "90/90 [==============================] - 0s 945us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 762/1024\n", - "90/90 [==============================] - 0s 941us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 763/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 764/1024\n", - "90/90 [==============================] - 0s 923us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 765/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 766/1024\n", - "90/90 [==============================] - 0s 924us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 767/1024\n", - "90/90 [==============================] - 0s 948us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 768/1024\n", - "90/90 [==============================] - 0s 944us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 769/1024\n", - "90/90 [==============================] - 0s 948us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 770/1024\n", - "90/90 [==============================] - 0s 925us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9802e-10\n", "Epoch 771/1024\n", - "90/90 [==============================] - 0s 986us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 772/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 846us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 773/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 774/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 884us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 775/1024\n", - "90/90 [==============================] - 0s 936us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 776/1024\n", - "90/90 [==============================] - 0s 941us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 777/1024\n", - "90/90 [==============================] - 0s 895us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "90/90 [==============================] - 0s 909us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 778/1024\n", - "90/90 [==============================] - 0s 920us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", - "Epoch 779/1024\n", - "90/90 [==============================] - 0s 956us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", - "Epoch 780/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", - "Epoch 781/1024\n", - "90/90 [==============================] - 0s 892us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", - "Epoch 782/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", - "Epoch 783/1024\n", - "90/90 [==============================] - 0s 945us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", - "Epoch 784/1024\n", - "90/90 [==============================] - 0s 991us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", - "Epoch 785/1024\n", - "90/90 [==============================] - 0s 903us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", - "Epoch 786/1024\n", - "90/90 [==============================] - 0s 928us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", - "Epoch 787/1024\n", - "90/90 [==============================] - 0s 925us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", - "Epoch 788/1024\n", - "90/90 [==============================] - 0s 942us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", - "Epoch 789/1024\n", - "90/90 [==============================] - 0s 916us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", - "Epoch 790/1024\n", - "90/90 [==============================] - 0s 917us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", - "Epoch 791/1024\n", - "90/90 [==============================] - 0s 952us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", - "Epoch 792/1024\n", - "90/90 [==============================] - 0s 943us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", - "Epoch 793/1024\n", - "90/90 [==============================] - 0s 934us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", - "Epoch 794/1024\n", - "90/90 [==============================] - 0s 937us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", - "Epoch 795/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "Epoch 779/1024\n", + "90/90 [==============================] - 0s 885us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "Epoch 780/1024\n", + "90/90 [==============================] - 0s 842us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "Epoch 781/1024\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "Epoch 782/1024\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "Epoch 783/1024\n", + "90/90 [==============================] - 0s 892us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "Epoch 784/1024\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "Epoch 785/1024\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "Epoch 786/1024\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "Epoch 787/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "Epoch 788/1024\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "Epoch 789/1024\n", + "90/90 [==============================] - 0s 848us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "Epoch 790/1024\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "Epoch 791/1024\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "Epoch 792/1024\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "Epoch 793/1024\n", + "90/90 [==============================] - 0s 889us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "Epoch 794/1024\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4901e-10\n", + "Epoch 795/1024\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4901e-10\n", "Epoch 796/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 848us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 797/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 912us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 798/1024\n", - "90/90 [==============================] - 0s 903us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 841us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 799/1024\n", - "90/90 [==============================] - 0s 933us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 800/1024\n", - "90/90 [==============================] - 0s 911us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 879us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 801/1024\n", - "90/90 [==============================] - 0s 954us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 841us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 802/1024\n", - "90/90 [==============================] - 0s 941us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 803/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 804/1024\n", - "90/90 [==============================] - 0s 927us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 805/1024\n", - "90/90 [==============================] - 0s 908us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 806/1024\n", - "90/90 [==============================] - 0s 942us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 807/1024\n", - "90/90 [==============================] - 0s 922us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 808/1024\n", - "90/90 [==============================] - 0s 943us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", - "Epoch 809/1024\n", - "90/90 [==============================] - 0s 903us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", - "Epoch 810/1024\n", "90/90 [==============================] - 0s 977us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "Epoch 809/1024\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "Epoch 810/1024\n", + "90/90 [==============================] - 0s 844us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 811/1024\n", - "90/90 [==============================] - 0s 956us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 812/1024\n", - "90/90 [==============================] - 0s 932us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 847us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 813/1024\n", - "90/90 [==============================] - 0s 925us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 814/1024\n", - "90/90 [==============================] - 0s 925us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 815/1024\n", - "90/90 [==============================] - 0s 956us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 844us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 816/1024\n", - "90/90 [==============================] - 0s 908us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 838us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 817/1024\n", - "90/90 [==============================] - 0s 928us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 844us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 818/1024\n", - "90/90 [==============================] - 0s 917us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 819/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 820/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 7.4506e-11\n", "Epoch 821/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 901us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 822/1024\n", - "90/90 [==============================] - 0s 909us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 823/1024\n", - "90/90 [==============================] - 0s 914us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 848us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 824/1024\n", - "90/90 [==============================] - 0s 973us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 825/1024\n", - "90/90 [==============================] - 0s 939us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 826/1024\n", - "90/90 [==============================] - 0s 947us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 827/1024\n", - "90/90 [==============================] - 0s 938us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 885us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 828/1024\n", - "90/90 [==============================] - 0s 971us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 829/1024\n", - "90/90 [==============================] - 0s 934us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 830/1024\n", - "90/90 [==============================] - 0s 931us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 843us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 831/1024\n", - "90/90 [==============================] - 0s 924us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 832/1024\n", - "90/90 [==============================] - 0s 967us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 833/1024\n", - "90/90 [==============================] - 0s 911us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 834/1024\n", - "90/90 [==============================] - 0s 936us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 835/1024\n", - "90/90 [==============================] - 0s 924us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 850us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 836/1024\n", - "90/90 [==============================] - 0s 950us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 846us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 837/1024\n", - "90/90 [==============================] - 0s 928us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 838/1024\n", - "90/90 [==============================] - 0s 918us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 840us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 839/1024\n", - "90/90 [==============================] - 0s 920us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 840/1024\n", - "90/90 [==============================] - 0s 926us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 852us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 841/1024\n", - "90/90 [==============================] - 0s 928us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 843us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 842/1024\n", - "90/90 [==============================] - 0s 912us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 879us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 843/1024\n", - "90/90 [==============================] - 0s 923us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 845us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 844/1024\n", - "90/90 [==============================] - 0s 920us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 845/1024\n", - "90/90 [==============================] - 0s 925us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", + "90/90 [==============================] - 0s 913us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 3.7253e-11\n", "Epoch 846/1024\n", - "90/90 [==============================] - 0s 949us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 847/1024\n", - "90/90 [==============================] - 0s 938us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 848/1024\n", - "90/90 [==============================] - 0s 919us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 849/1024\n", - "90/90 [==============================] - 0s 918us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 850/1024\n", - "90/90 [==============================] - 0s 949us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 851/1024\n", - "90/90 [==============================] - 0s 924us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 852/1024\n", - "90/90 [==============================] - 0s 925us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 853/1024\n", - "90/90 [==============================] - 0s 916us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 854/1024\n", - "90/90 [==============================] - 0s 922us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 855/1024\n", - "90/90 [==============================] - 0s 938us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 856/1024\n", - "90/90 [==============================] - 0s 929us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 857/1024\n", - "90/90 [==============================] - 0s 922us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 858/1024\n", - "90/90 [==============================] - 0s 945us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 859/1024\n", - "90/90 [==============================] - 0s 938us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 860/1024\n", - "90/90 [==============================] - 0s 922us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 861/1024\n", - "90/90 [==============================] - 0s 939us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", - "Epoch 862/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 847/1024\n", + "90/90 [==============================] - 0s 886us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 848/1024\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 849/1024\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 850/1024\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 851/1024\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 852/1024\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 853/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 854/1024\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 855/1024\n", + "90/90 [==============================] - 0s 842us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 856/1024\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 857/1024\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 858/1024\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 859/1024\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 860/1024\n", + "90/90 [==============================] - 0s 884us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 861/1024\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "Epoch 862/1024\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", "Epoch 863/1024\n", - "90/90 [==============================] - 0s 969us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "90/90 [==============================] - 0s 854us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", "Epoch 864/1024\n", - "90/90 [==============================] - 0s 944us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", "Epoch 865/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "90/90 [==============================] - 0s 846us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", "Epoch 866/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.8626e-11\n", "Epoch 867/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", "Epoch 868/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", "Epoch 869/1024\n", - "90/90 [==============================] - 0s 908us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "90/90 [==============================] - 0s 897us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.8626e-11\n", "Epoch 870/1024\n", - "90/90 [==============================] - 0s 953us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.8626e-11\n", "Epoch 871/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 872/1024\n", - "90/90 [==============================] - 0s 916us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 873/1024\n", - "90/90 [==============================] - 0s 916us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 847us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 874/1024\n", - "90/90 [==============================] - 0s 930us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 926us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 875/1024\n", - "90/90 [==============================] - 0s 915us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 876/1024\n", - "90/90 [==============================] - 0s 943us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 877/1024\n", - "90/90 [==============================] - 0s 923us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 878/1024\n", - "90/90 [==============================] - 0s 957us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 879/1024\n", - "90/90 [==============================] - 0s 912us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 880/1024\n", - "90/90 [==============================] - 0s 907us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 891us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 881/1024\n", - "90/90 [==============================] - 0s 935us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 854us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 882/1024\n", - "90/90 [==============================] - 0s 941us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 850us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 883/1024\n", - "90/90 [==============================] - 0s 931us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 884/1024\n", - "90/90 [==============================] - 0s 930us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 885/1024\n", - "90/90 [==============================] - 0s 904us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 888us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 886/1024\n", - "90/90 [==============================] - 0s 921us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 887/1024\n", - "90/90 [==============================] - 0s 919us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 883us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 888/1024\n", - "90/90 [==============================] - 0s 968us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 889/1024\n", - "90/90 [==============================] - 0s 931us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 890/1024\n", - "90/90 [==============================] - 0s 940us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 891/1024\n", - "90/90 [==============================] - 0s 967us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 846us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 892/1024\n", - "90/90 [==============================] - 0s 949us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 893/1024\n", - "90/90 [==============================] - 0s 934us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 894/1024\n", - "90/90 [==============================] - 0s 930us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 843us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 895/1024\n", - "90/90 [==============================] - 0s 924us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 9.3132e-12\n", "Epoch 896/1024\n", - "90/90 [==============================] - 0s 915us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 897/1024\n", - "90/90 [==============================] - 0s 932us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 898/1024\n", - "90/90 [==============================] - 0s 955us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 899/1024\n", - "90/90 [==============================] - 0s 937us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 900/1024\n", - "90/90 [==============================] - 0s 954us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 901/1024\n", - "90/90 [==============================] - 0s 937us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 902/1024\n", - "90/90 [==============================] - 0s 3ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 903/1024\n", - "90/90 [==============================] - 0s 3ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 904/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 905/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0026 - val_loss: 0.0025 - lr: 4.6566e-12\n", - "Epoch 906/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 903/1024\n", + "90/90 [==============================] - 0s 847us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 904/1024\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 905/1024\n", + "90/90 [==============================] - 0s 849us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "Epoch 906/1024\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 907/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 908/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 853us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 909/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 841us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 910/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 911/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 912/1024\n", - "90/90 [==============================] - 0s 932us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 913/1024\n", - "90/90 [==============================] - 0s 919us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 914/1024\n", - "90/90 [==============================] - 0s 950us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 900us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 915/1024\n", - "90/90 [==============================] - 0s 936us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 887us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 916/1024\n", - "90/90 [==============================] - 0s 990us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 868us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 917/1024\n", - "90/90 [==============================] - 0s 921us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 918/1024\n", - "90/90 [==============================] - 0s 930us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 888us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 919/1024\n", - "90/90 [==============================] - 0s 967us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 920/1024\n", - "90/90 [==============================] - 0s 925us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", + "90/90 [==============================] - 0s 889us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 4.6566e-12\n", "Epoch 921/1024\n", - "90/90 [==============================] - 0s 937us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 922/1024\n", - "90/90 [==============================] - 0s 939us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 895us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 923/1024\n", - "90/90 [==============================] - 0s 923us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 879us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 924/1024\n", - "90/90 [==============================] - 0s 952us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 925/1024\n", - "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 926/1024\n", - "90/90 [==============================] - 0s 906us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 927/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 928/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 854us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 929/1024\n", - "90/90 [==============================] - 0s 989us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 930/1024\n", - "90/90 [==============================] - 0s 900us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 931/1024\n", - "90/90 [==============================] - 0s 902us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 845us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 932/1024\n", - "90/90 [==============================] - 0s 932us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 863us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 933/1024\n", - "90/90 [==============================] - 0s 885us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 850us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 934/1024\n", - "90/90 [==============================] - 0s 990us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 835us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 935/1024\n", - "90/90 [==============================] - 0s 930us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 936/1024\n", - "90/90 [==============================] - 0s 987us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", - "Epoch 937/1024\n", - "90/90 [==============================] - 0s 911us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3283e-12\n", - "Epoch 938/1024\n", - "90/90 [==============================] - 0s 889us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", - "Epoch 939/1024\n", - "90/90 [==============================] - 0s 896us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3283e-12\n", - "Epoch 940/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", - "Epoch 941/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", - "Epoch 942/1024\n", "90/90 [==============================] - 0s 852us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "Epoch 937/1024\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "Epoch 938/1024\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "Epoch 939/1024\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "Epoch 940/1024\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "Epoch 941/1024\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "Epoch 942/1024\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 943/1024\n", - "90/90 [==============================] - 0s 899us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 889us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 944/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 945/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", + "90/90 [==============================] - 0s 882us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.3283e-12\n", "Epoch 946/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 947/1024\n", - "90/90 [==============================] - 0s 916us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 876us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 948/1024\n", - "90/90 [==============================] - 0s 880us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 853us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 949/1024\n", - "90/90 [==============================] - 0s 884us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 950/1024\n", - "90/90 [==============================] - 0s 915us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 951/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 952/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 953/1024\n", - "90/90 [==============================] - 0s 982us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 954/1024\n", - "90/90 [==============================] - 0s 941us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 955/1024\n", - "90/90 [==============================] - 0s 994us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 956/1024\n", "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 957/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 958/1024\n", - "90/90 [==============================] - 0s 973us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 959/1024\n", - "90/90 [==============================] - 0s 943us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 960/1024\n", - "90/90 [==============================] - 0s 942us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 961/1024\n", - "90/90 [==============================] - 0s 965us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 962/1024\n", - "90/90 [==============================] - 0s 957us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 963/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 964/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 965/1024\n", - "90/90 [==============================] - 0s 973us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 966/1024\n", - "90/90 [==============================] - 0s 917us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", - "Epoch 967/1024\n", + "Epoch 950/1024\n", + "90/90 [==============================] - 0s 930us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 951/1024\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 952/1024\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 953/1024\n", + "90/90 [==============================] - 0s 860us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 954/1024\n", + "90/90 [==============================] - 0s 994us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 955/1024\n", "90/90 [==============================] - 0s 884us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 956/1024\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 957/1024\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 958/1024\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 959/1024\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 960/1024\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 961/1024\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 962/1024\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 963/1024\n", + "90/90 [==============================] - 0s 871us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 964/1024\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 965/1024\n", + "90/90 [==============================] - 0s 842us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 966/1024\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "Epoch 967/1024\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 968/1024\n", - "90/90 [==============================] - 0s 881us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 841us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 969/1024\n", - "90/90 [==============================] - 0s 887us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 845us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 970/1024\n", - "90/90 [==============================] - 0s 918us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", + "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.1642e-12\n", "Epoch 971/1024\n", - "90/90 [==============================] - 0s 858us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 972/1024\n", - "90/90 [==============================] - 0s 939us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 850us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 973/1024\n", - "90/90 [==============================] - 0s 884us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 974/1024\n", - "90/90 [==============================] - 0s 879us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 975/1024\n", - "90/90 [==============================] - 0s 852us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 976/1024\n", - "90/90 [==============================] - 0s 976us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 977/1024\n", - "90/90 [==============================] - 0s 868us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 978/1024\n", - "90/90 [==============================] - 0s 890us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 916us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 979/1024\n", - "90/90 [==============================] - 0s 860us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 857us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 980/1024\n", - "90/90 [==============================] - 0s 895us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 906us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 981/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 982/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 983/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 856us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 984/1024\n", - "90/90 [==============================] - 0s 919us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", - "Epoch 985/1024\n", - "90/90 [==============================] - 0s 909us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 5.8208e-13\n", - "Epoch 986/1024\n", "90/90 [==============================] - 0s 882us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "Epoch 985/1024\n", + "90/90 [==============================] - 0s 873us/step - loss: 0.0026 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "Epoch 986/1024\n", + "90/90 [==============================] - 0s 883us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 987/1024\n", - "90/90 [==============================] - 0s 873us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 988/1024\n", - "90/90 [==============================] - 0s 923us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 989/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 990/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 886us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 991/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 887us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 992/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 867us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 993/1024\n", - "90/90 [==============================] - 0s 952us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 994/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 995/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", + "90/90 [==============================] - 0s 874us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 5.8208e-13\n", "Epoch 996/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 926us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 997/1024\n", - "90/90 [==============================] - 0s 899us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 998/1024\n", - "90/90 [==============================] - 0s 930us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 937us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 999/1024\n", - "90/90 [==============================] - 0s 998us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1000/1024\n", - "90/90 [==============================] - 0s 956us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 957us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1001/1024\n", - "90/90 [==============================] - 0s 916us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 909us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1002/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 861us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1003/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 851us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1004/1024\n", - "90/90 [==============================] - 0s 914us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 859us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1005/1024\n", - "90/90 [==============================] - 0s 934us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 864us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1006/1024\n", - "90/90 [==============================] - 0s 932us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 842us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1007/1024\n", - "90/90 [==============================] - 0s 992us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1008/1024\n", - "90/90 [==============================] - 0s 942us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 843us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1009/1024\n", - "90/90 [==============================] - 0s 963us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1010/1024\n", - "90/90 [==============================] - 0s 916us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 862us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1011/1024\n", - "90/90 [==============================] - 0s 966us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 870us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1012/1024\n", - "90/90 [==============================] - 0s 908us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 869us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1013/1024\n", - "90/90 [==============================] - 0s 865us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 877us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1014/1024\n", - "90/90 [==============================] - 0s 876us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 872us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1015/1024\n", - "90/90 [==============================] - 0s 895us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 858us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1016/1024\n", - "90/90 [==============================] - 0s 885us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 882us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1017/1024\n", - "90/90 [==============================] - 0s 866us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 880us/step - loss: 0.0029 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1018/1024\n", - "90/90 [==============================] - 0s 937us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 855us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1019/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 878us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1020/1024\n", - "90/90 [==============================] - 0s 2ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 2.9104e-13\n", "Epoch 1021/1024\n", - "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "90/90 [==============================] - 0s 881us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4552e-13\n", "Epoch 1022/1024\n", - "90/90 [==============================] - 0s 931us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "90/90 [==============================] - 0s 884us/step - loss: 0.0028 - val_loss: 0.0025 - lr: 1.4552e-13\n", "Epoch 1023/1024\n", - "90/90 [==============================] - 0s 901us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4552e-13\n", + "90/90 [==============================] - 0s 875us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4552e-13\n", "Epoch 1024/1024\n", - "90/90 [==============================] - 0s 877us/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4552e-13\n" + "90/90 [==============================] - 0s 1ms/step - loss: 0.0027 - val_loss: 0.0025 - lr: 1.4552e-13\n" ] } ], @@ -7018,7 +7031,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, "metadata": { "pycharm": { "name": "#%%\n" @@ -7027,8 +7040,10 @@ "outputs": [ { "data": { - "text/plain": "
", - "image/png": "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\n" + "image/png": "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", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -7060,7 +7075,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "pycharm": { "name": "#%%\n" @@ -7073,7 +7088,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": { "pycharm": { "name": "#%%\n" @@ -7087,7 +7102,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -7121,4 +7136,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} \ No newline at end of file +} diff --git a/model_evaluating.ipynb b/03_model_evaluating.ipynb similarity index 67% rename from model_evaluating.ipynb rename to 03_model_evaluating.ipynb index 2c5fd23..a9b35c4 100644 --- a/model_evaluating.ipynb +++ b/03_model_evaluating.ipynb @@ -14,7 +14,13 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 16, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -25,39 +31,39 @@ "from sklearn.metrics import mean_squared_error\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" - ], + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "In this experiment, we load model weights from the experiment1 and evaluate them on test dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "markdown", - "source": [ - "In this experiment, we load model weights from the experiment1 and evaluate them on test dataset." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 30, + }, "outputs": [ { "name": "stdout", @@ -71,7 +77,10 @@ } ], "source": [ - "data = loadmat('./preprocess/dataset/mango/mango_dm_split.mat')\n", + "data = loadmat('./dataset/mango/mango_dm_split.mat')\n", + "\n", + "min_value, max_value = data['min_y'][-1][-1], data['max_y'][-1][-1]\n", + "retransform = lambda x: x * (max_value - min_value)\n", "x_train, y_train, x_test, y_test = data['x_train'], data['y_train'], data['x_test'], data['y_test']\n", "x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.3, random_state=12, shuffle=True)\n", "x_train, x_val, x_test = x_train[:, np.newaxis, :], x_val[:, np.newaxis, :], x_test[:, np.newaxis, :]\n", @@ -79,77 +88,87 @@ " f\"x_train: {x_train.shape}, y_train: {y_train.shape},\\n\"\n", " f\"x_val: {x_val.shape}, y_val: {y_val.shape}\\n\"\n", " f\"x_test: {x_test.shape}, y_test: {y_test.shape}\")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 18, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "plain 5 mse : 0.05133910188824081\n", + "plain 5 Dry matter content error 0.7758644362065223\n", + "plain 5 r^2 : 0.902928516828363\n", + "plain 11 mse : 0.05200769624271875\n", + "plain 11 Dry matter content error 0.7859685978067217\n", + "plain 11 r^2 : 0.9003837097594369\n", + "shortcut 5 mse : 0.051382735052895194\n", + "shortcut 5 Dry matter content error 0.7765238443272209\n", + "shortcut 5 r^2 : 0.9027634443691182\n", + "shortcut11 mse : 0.05078784364469306\n", + "shortcut11 Dry matter content error 0.7675335217455442\n", + "shortcut11 r^2 : 0.9050019525259844\n" + ] + } + ], "source": [ + "from sklearn.metrics import r2_score\n", + "\n", "## Build model and load weights\n", "plain_5, plain_11 = load_model('./checkpoints/plain5.hdf5'), load_model('./checkpoints/plain11.hdf5')\n", "shortcut5, shortcut11 = load_model('./checkpoints/shortcut5.hdf5'), load_model('./checkpoints/shortcut11.hdf5')\n", "models = {'plain 5': plain_5, 'plain 11': plain_11, 'shortcut 5': shortcut5, 'shortcut11': shortcut11}\n", "results = {model_name: model.predict(x_test).reshape((-1, )) for model_name, model in models.items()}\n", "for model_name, model_result in results.items():\n", - " print(model_name, \" : \", mean_squared_error(y_test, model_result)*100, \"%\")" - ], + " rmse = np.sqrt(mean_squared_error(y_test, model_result))\n", + " print(model_name, \"mse : \", rmse)\n", + " print(model_name, \"Dry matter content error\", retransform(rmse))\n", + " print(model_name, \"r^2 :\", r2_score(y_test, model_result))" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, - "execution_count": 31, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "plain 5 : 0.2707851525589865 %\n", - "plain 11 : 0.26240810192725905 %\n", - "shortcut 5 : 0.28330442301217196 %\n", - "shortcut11 : 0.25743312483685266 %\n" - ] - } - ] - }, - { - "cell_type": "code", - "execution_count": 31, "outputs": [], - "source": [], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + "source": [] } ], "metadata": { + "interpreter": { + "hash": "7f619fc91ee8bdab81d49e7c14228037474662e3f2d607687ae505108922fa06" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/04_model_comparision.ipynb b/04_model_comparision.ipynb new file mode 100644 index 0000000..083b34b --- /dev/null +++ b/04_model_comparision.ipynb @@ -0,0 +1,152 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Model comparison" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## PLS" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from sklearn.neural_network import MLPRegressor\n", + "from sklearn.svm import SVR\n", + "import numpy as np\n", + "from scipy.io import loadmat\n", + "from sklearn.cross_decomposition import PLSRegression\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "\n", + "data = loadmat('./dataset/mango/mango_dm_split.mat')\n", + "min_value, max_value = data['min_y'][-1][-1], data['max_y'][-1][-1]\n", + "retransform = lambda x: x * (max_value - min_value)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of data:\n", + "x_train: (8183, 102), y_train: (8183, 1),\n", + "x_test: (3508, 102), y_test: (3508, 1)\n" + ] + } + ], + "source": [ + "x_train, y_train, x_test, y_test = data['x_train'], data['y_train'], data['x_test'], data['y_test']\n", + "print(f\"shape of data:\\n\"\n", + " f\"x_train: {x_train.shape}, y_train: {y_train.shape},\\n\"\n", + " f\"x_test: {x_test.shape}, y_test: {y_test.shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PLS RMSE: 0.05722520296881164\n", + "PLS Dry matter content error 0.8648183977750965\n", + "PLS R^2: 0.8793937498230511\n", + "SVR RMSE: 0.1139650997574326\n", + "SVR Dry matter content error 1.7223025845485895\n", + "SVR R^2: 0.5216575965112935\n", + "MLP RMSE: 0.15508626630172465\n", + "MLP Dry matter content error 2.343748023280531\n", + "MLP R^2: 0.11418748397100065\n" + ] + } + ], + "source": [ + "pls = PLSRegression(n_components=90)\n", + "svr = SVR(kernel=\"rbf\", degree=30, gamma=\"scale\")\n", + "mlp = MLPRegressor(hidden_layer_sizes=(60, 50, ))\n", + "pls = pls.fit(x_train, y_train.ravel())\n", + "svr = svr.fit(x_train, y_train.ravel())\n", + "mlp = mlp.fit(x_train, y_train.ravel())\n", + "\n", + "models = {'PLS': pls, \"SVR\": svr, \"MLP\": mlp}\n", + "results = {model_name: model.predict(x_test).reshape((-1, )) for model_name, model in models.items()}\n", + "for model_name, model_result in results.items():\n", + " rmse = np.sqrt(mean_squared_error(y_test, model_result))\n", + " print(model_name, \"RMSE: \", rmse)\n", + " print(model_name, \"Dry matter content error\", retransform(rmse))\n", + " print(model_name, \"R^2: \", r2_score(y_test, model_result))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "7f619fc91ee8bdab81d49e7c14228037474662e3f2d607687ae505108922fa06" + }, + "kernelspec": { + "display_name": "Python 3.9.7 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/05_network_parameter_optimization.ipynb b/05_network_parameter_optimization.ipynb new file mode 100644 index 0000000..94d0857 --- /dev/null +++ b/05_network_parameter_optimization.ipynb @@ -0,0 +1,446 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Network Parameter Optimization" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of data:\n", + "x_train: (5728, 1, 102), y_train: (5728, 1),\n", + "x_val: (2455, 1, 102), y_val: (2455, 1)\n", + "x_test: (3508, 1, 102), y_test: (3508, 1)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from keras.models import load_model\n", + "from sklearn.metrics import r2_score, mean_squared_error\n", + "from sklearn.model_selection import train_test_split\n", + "from scipy.io import loadmat\n", + "from models import ShortCut11\n", + "from numpy.random import seed\n", + "import tensorflow\n", + "import time\n", + "seed(4750)\n", + "tensorflow.random.set_seed(4750)\n", + "time1 = time.time()\n", + "data = loadmat('./dataset/mango/mango_dm_split.mat')\n", + "x_train, y_train, x_test, y_test = data['x_train'], data['y_train'], data['x_test'], data['y_test']\n", + "x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.3, random_state=12, shuffle=True)\n", + "x_train, x_val, x_test = x_train[:, np.newaxis, :], x_val[:, np.newaxis, :], x_test[:, np.newaxis, :]\n", + "print(f\"shape of data:\\n\"\n", + " f\"x_train: {x_train.shape}, y_train: {y_train.shape},\\n\"\n", + " f\"x_val: {x_val.shape}, y_val: {y_val.shape}\\n\"\n", + " f\"x_test: {x_test.shape}, y_test: {y_test.shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-05-28 22:54:57.730239: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1024\n", + "90/90 [==============================] - 1s 5ms/step - loss: 0.0262 - val_loss: 0.0274 - lr: 0.0025\n", + "Epoch 2/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0220 - val_loss: 0.0284 - lr: 0.0025\n", + "Epoch 3/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0166 - val_loss: 0.0279 - lr: 0.0025\n", + "Epoch 4/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0120 - val_loss: 0.0358 - lr: 0.0025\n", + "Epoch 5/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0103 - val_loss: 0.0847 - lr: 0.0025\n", + "Epoch 6/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0092 - val_loss: 0.1446 - lr: 0.0025\n", + "Epoch 7/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0085 - val_loss: 0.0410 - lr: 0.0025\n", + "Epoch 8/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0082 - val_loss: 0.2241 - lr: 0.0025\n", + "Epoch 9/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0076 - val_loss: 0.0755 - lr: 0.0025\n", + "Epoch 10/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0071 - val_loss: 0.2266 - lr: 0.0025\n", + "Epoch 11/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0066 - val_loss: 0.1989 - lr: 0.0025\n", + "Epoch 12/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0057 - val_loss: 0.0612 - lr: 0.0025\n", + "Epoch 13/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0053 - val_loss: 0.2283 - lr: 0.0025\n", + "Epoch 14/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0051 - val_loss: 0.0494 - lr: 0.0025\n", + "Epoch 15/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0045 - val_loss: 0.0220 - lr: 0.0025\n", + "Epoch 16/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0048 - val_loss: 0.2283 - lr: 0.0025\n", + "Epoch 17/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0045 - val_loss: 0.2282 - lr: 0.0025\n", + "Epoch 18/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0047 - val_loss: 0.2219 - lr: 0.0025\n", + "Epoch 19/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0044 - val_loss: 0.2074 - lr: 0.0025\n", + "Epoch 20/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0044 - val_loss: 0.1128 - lr: 0.0025\n", + "Epoch 21/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0043 - val_loss: 0.1590 - lr: 0.0025\n", + "Epoch 22/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0045 - val_loss: 0.2283 - lr: 0.0025\n", + "Epoch 23/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0043 - val_loss: 0.1145 - lr: 0.0025\n", + "Epoch 24/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0041 - val_loss: 0.0923 - lr: 0.0025\n", + "Epoch 25/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0041 - val_loss: 0.2192 - lr: 0.0025\n", + "Epoch 26/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0042 - val_loss: 0.1295 - lr: 0.0025\n", + "Epoch 27/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0040 - val_loss: 0.0876 - lr: 0.0025\n", + "Epoch 28/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0040 - val_loss: 0.1489 - lr: 0.0025\n", + "Epoch 29/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0042 - val_loss: 0.1198 - lr: 0.0025\n", + "Epoch 30/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0040 - val_loss: 0.2951 - lr: 0.0025\n", + "Epoch 31/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0043 - val_loss: 0.1440 - lr: 0.0025\n", + "Epoch 32/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0041 - val_loss: 0.2407 - lr: 0.0025\n", + "Epoch 33/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0042 - val_loss: 0.2239 - lr: 0.0025\n", + "Epoch 34/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0039 - val_loss: 0.2283 - lr: 0.0025\n", + "Epoch 35/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0040 - val_loss: 0.1126 - lr: 0.0025\n", + "Epoch 36/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0042 - val_loss: 0.1264 - lr: 0.0025\n", + "Epoch 37/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0039 - val_loss: 0.1036 - lr: 0.0025\n", + "Epoch 38/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0039 - val_loss: 0.2206 - lr: 0.0025\n", + "Epoch 39/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0040 - val_loss: 0.1827 - lr: 0.0025\n", + "Epoch 40/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0039 - val_loss: 0.0397 - lr: 0.0025\n", + "Epoch 41/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0040 - val_loss: 0.1369 - lr: 0.0012\n", + "Epoch 42/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0038 - val_loss: 0.1498 - lr: 0.0012\n", + "Epoch 43/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0038 - val_loss: 0.0496 - lr: 0.0012\n", + "Epoch 44/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0038 - val_loss: 0.0279 - lr: 0.0012\n", + "Epoch 45/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0037 - val_loss: 0.2063 - lr: 0.0012\n", + "Epoch 46/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0038 - val_loss: 0.2871 - lr: 0.0012\n", + "Epoch 47/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0037 - val_loss: 0.1589 - lr: 0.0012\n", + "Epoch 48/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0036 - val_loss: 0.0689 - lr: 0.0012\n", + "Epoch 49/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0038 - val_loss: 0.2208 - lr: 0.0012\n", + "Epoch 50/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0036 - val_loss: 0.0737 - lr: 0.0012\n", + "Epoch 51/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0037 - val_loss: 0.1130 - lr: 0.0012\n", + "Epoch 52/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0036 - val_loss: 0.1367 - lr: 0.0012\n", + "Epoch 53/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0037 - val_loss: 0.2286 - lr: 0.0012\n", + "Epoch 54/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0037 - val_loss: 0.1944 - lr: 0.0012\n", + "Epoch 55/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0036 - val_loss: 0.0737 - lr: 0.0012\n", + "Epoch 56/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0037 - val_loss: 0.2995 - lr: 0.0012\n", + "Epoch 57/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0036 - val_loss: 0.1348 - lr: 0.0012\n", + "Epoch 58/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0037 - val_loss: 0.0215 - lr: 0.0012\n", + "Epoch 59/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.2241 - lr: 0.0012\n", + "Epoch 60/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0037 - val_loss: 0.1410 - lr: 0.0012\n", + "Epoch 61/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0036 - val_loss: 0.3292 - lr: 0.0012\n", + "Epoch 62/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0037 - val_loss: 0.1807 - lr: 0.0012\n", + "Epoch 63/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0036 - val_loss: 0.1881 - lr: 0.0012\n", + "Epoch 64/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.2342 - lr: 0.0012\n", + "Epoch 65/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.3063 - lr: 0.0012\n", + "Epoch 66/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0036 - val_loss: 0.1621 - lr: 0.0012\n", + "Epoch 67/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.3250 - lr: 0.0012\n", + "Epoch 68/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.1367 - lr: 0.0012\n", + "Epoch 69/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.0997 - lr: 0.0012\n", + "Epoch 70/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.1286 - lr: 0.0012\n", + "Epoch 71/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.0901 - lr: 0.0012\n", + "Epoch 72/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.2270 - lr: 0.0012\n", + "Epoch 73/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.0607 - lr: 0.0012\n", + "Epoch 74/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.2278 - lr: 0.0012\n", + "Epoch 75/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.3097 - lr: 0.0012\n", + "Epoch 76/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.3258 - lr: 0.0012\n", + "Epoch 77/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0036 - val_loss: 0.0706 - lr: 0.0012\n", + "Epoch 78/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.2239 - lr: 0.0012\n", + "Epoch 79/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.0187 - lr: 0.0012\n", + "Epoch 80/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.0448 - lr: 0.0012\n", + "Epoch 81/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0036 - val_loss: 0.2271 - lr: 0.0012\n", + "Epoch 82/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.0075 - lr: 0.0012\n", + "Epoch 83/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0744 - lr: 0.0012\n", + "Epoch 84/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.0631 - lr: 0.0012\n", + "Epoch 85/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.3098 - lr: 0.0012\n", + "Epoch 86/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0036 - val_loss: 0.3298 - lr: 0.0012\n", + "Epoch 87/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.1986 - lr: 0.0012\n", + "Epoch 88/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.2268 - lr: 0.0012\n", + "Epoch 89/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.2252 - lr: 0.0012\n", + "Epoch 90/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0035 - val_loss: 0.2258 - lr: 0.0012\n", + "Epoch 91/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.3283 - lr: 0.0012\n", + "Epoch 92/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.0965 - lr: 0.0012\n", + "Epoch 93/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.3204 - lr: 0.0012\n", + "Epoch 94/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.2080 - lr: 0.0012\n", + "Epoch 95/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.3149 - lr: 0.0012\n", + "Epoch 96/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0237 - lr: 0.0012\n", + "Epoch 97/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0034 - val_loss: 0.2182 - lr: 0.0012\n", + "Epoch 98/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.1821 - lr: 0.0012\n", + "Epoch 99/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.2133 - lr: 0.0012\n", + "Epoch 100/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.0865 - lr: 0.0012\n", + "Epoch 101/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.2045 - lr: 0.0012\n", + "Epoch 102/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.1170 - lr: 0.0012\n", + "Epoch 103/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.2241 - lr: 0.0012\n", + "Epoch 104/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.3278 - lr: 0.0012\n", + "Epoch 105/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.2543 - lr: 0.0012\n", + "Epoch 106/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0035 - val_loss: 0.1133 - lr: 0.0012\n", + "Epoch 107/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0034 - val_loss: 0.2277 - lr: 0.0012\n", + "Epoch 108/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.1973 - lr: 6.2500e-04\n", + "Epoch 109/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0904 - lr: 6.2500e-04\n", + "Epoch 110/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.3171 - lr: 6.2500e-04\n", + "Epoch 111/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0444 - lr: 6.2500e-04\n", + "Epoch 112/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0033 - val_loss: 0.1970 - lr: 6.2500e-04\n", + "Epoch 113/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.1268 - lr: 6.2500e-04\n", + "Epoch 114/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.1479 - lr: 6.2500e-04\n", + "Epoch 115/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0683 - lr: 6.2500e-04\n", + "Epoch 116/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0963 - lr: 6.2500e-04\n", + "Epoch 117/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0153 - lr: 6.2500e-04\n", + "Epoch 118/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.1128 - lr: 6.2500e-04\n", + "Epoch 119/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.3028 - lr: 6.2500e-04\n", + "Epoch 120/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0946 - lr: 6.2500e-04\n", + "Epoch 121/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.3050 - lr: 6.2500e-04\n", + "Epoch 122/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.2079 - lr: 6.2500e-04\n", + "Epoch 123/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.2074 - lr: 6.2500e-04\n", + "Epoch 124/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0567 - lr: 6.2500e-04\n", + "Epoch 125/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.1866 - lr: 6.2500e-04\n", + "Epoch 126/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.1740 - lr: 6.2500e-04\n", + "Epoch 127/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0033 - val_loss: 0.0270 - lr: 6.2500e-04\n", + "Epoch 128/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0321 - lr: 6.2500e-04\n", + "Epoch 129/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.1747 - lr: 6.2500e-04\n", + "Epoch 130/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0297 - lr: 6.2500e-04\n", + "Epoch 131/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0032 - val_loss: 0.0329 - lr: 6.2500e-04\n", + "Epoch 132/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0031 - val_loss: 0.0515 - lr: 6.2500e-04\n", + "Epoch 133/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.0786 - lr: 3.1250e-04\n", + "Epoch 134/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0983 - lr: 3.1250e-04\n", + "Epoch 135/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0030 - val_loss: 0.1133 - lr: 3.1250e-04\n", + "Epoch 136/1024\n", + "90/90 [==============================] - 0s 2ms/step - loss: 0.0031 - val_loss: 0.0323 - lr: 3.1250e-04\n", + "Epoch 137/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0029 - val_loss: 0.0484 - lr: 3.1250e-04\n", + "Epoch 138/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0828 - lr: 3.1250e-04\n", + "Epoch 139/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0304 - lr: 3.1250e-04\n", + "Epoch 140/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.0792 - lr: 3.1250e-04\n", + "Epoch 141/1024\n", + "90/90 [==============================] - 0s 1ms/step - loss: 0.0030 - val_loss: 0.2074 - lr: 3.1250e-04\n", + "Epoch 142/1024\n", + "83/90 [==========================>...] - ETA: 0s - loss: 0.0030" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)", + "\u001B[0;32m/var/folders/wh/kr5c3dr12834pfk3j7yqnrq40000gn/T/ipykernel_68464/326725923.py\u001B[0m in \u001B[0;36m\u001B[0;34m\u001B[0m\n\u001B[1;32m 4\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mi\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mrange\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;36m2\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m1000\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 5\u001B[0m \u001B[0mmodel\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mShortCut11\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mnetwork_parameter\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mi\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0minput_shape\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m102\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 6\u001B[0;31m \u001B[0mhistory_shortcut_11\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmodel\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx_train\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my_train\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mx_val\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0my_val\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mepoch\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mepoch\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mbatch_size\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mbatch_size\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0msave\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m\"/tmp/temp.hdf5\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 7\u001B[0m \u001B[0mmodel\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mload_model\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\"/tmp/temp.hdf5\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 8\u001B[0m \u001B[0my_pred\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmodel\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mpredict\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx_test\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mreshape\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m-\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;32m~/PycharmProjects/sccnn/models.py\u001B[0m in \u001B[0;36mfit\u001B[0;34m(self, x, y, x_val, y_val, epoch, batch_size, save)\u001B[0m\n\u001B[1;32m 197\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 198\u001B[0m history = self.model.fit(x, y, validation_data=(x_val, y_val), epochs=epoch, verbose=1,\n\u001B[0;32m--> 199\u001B[0;31m callbacks=callbacks, batch_size=batch_size)\n\u001B[0m\u001B[1;32m 200\u001B[0m \u001B[0;32mreturn\u001B[0m \u001B[0mhistory\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 201\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;32m~/miniforge3/lib/python3.9/site-packages/keras/utils/traceback_utils.py\u001B[0m in \u001B[0;36merror_handler\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 62\u001B[0m \u001B[0mfiltered_tb\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 63\u001B[0m \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 64\u001B[0;31m \u001B[0;32mreturn\u001B[0m \u001B[0mfn\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 65\u001B[0m \u001B[0;32mexcept\u001B[0m \u001B[0mException\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0me\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0;31m# pylint: disable=broad-except\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 66\u001B[0m \u001B[0mfiltered_tb\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0m_process_traceback_frames\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0me\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m__traceback__\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;32m~/miniforge3/lib/python3.9/site-packages/keras/engine/training.py\u001B[0m in \u001B[0;36mfit\u001B[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001B[0m\n\u001B[1;32m 1214\u001B[0m _r=1):\n\u001B[1;32m 1215\u001B[0m \u001B[0mcallbacks\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mon_train_batch_begin\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mstep\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1216\u001B[0;31m \u001B[0mtmp_logs\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtrain_function\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0miterator\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 1217\u001B[0m \u001B[0;32mif\u001B[0m \u001B[0mdata_handler\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mshould_sync\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 1218\u001B[0m \u001B[0mcontext\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0masync_wait\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;32m~/miniforge3/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py\u001B[0m in \u001B[0;36merror_handler\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 148\u001B[0m \u001B[0mfiltered_tb\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 149\u001B[0m \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 150\u001B[0;31m \u001B[0;32mreturn\u001B[0m \u001B[0mfn\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 151\u001B[0m \u001B[0;32mexcept\u001B[0m \u001B[0mException\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0me\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 152\u001B[0m \u001B[0mfiltered_tb\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0m_process_traceback_frames\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0me\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m__traceback__\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;32m~/miniforge3/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self, *args, **kwds)\u001B[0m\n\u001B[1;32m 908\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 909\u001B[0m \u001B[0;32mwith\u001B[0m \u001B[0mOptionalXlaContext\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_jit_compile\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 910\u001B[0;31m \u001B[0mresult\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_call\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwds\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 911\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 912\u001B[0m \u001B[0mnew_tracing_count\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mexperimental_get_tracing_count\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;32m~/miniforge3/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py\u001B[0m in \u001B[0;36m_call\u001B[0;34m(self, *args, **kwds)\u001B[0m\n\u001B[1;32m 940\u001B[0m \u001B[0;31m# In this case we have created variables on the first call, so we run the\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 941\u001B[0m \u001B[0;31m# defunned version which is guaranteed to never create variables.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 942\u001B[0;31m \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_stateless_fn\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwds\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;31m# pylint: disable=not-callable\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 943\u001B[0m \u001B[0;32melif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_stateful_fn\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 944\u001B[0m \u001B[0;31m# Release the lock early so that multiple threads can perform the call\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;32m~/miniforge3/lib/python3.9/site-packages/tensorflow/python/eager/function.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 3128\u001B[0m (graph_function,\n\u001B[1;32m 3129\u001B[0m filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001B[0;32m-> 3130\u001B[0;31m return graph_function._call_flat(\n\u001B[0m\u001B[1;32m 3131\u001B[0m filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access\n\u001B[1;32m 3132\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;32m~/miniforge3/lib/python3.9/site-packages/tensorflow/python/eager/function.py\u001B[0m in \u001B[0;36m_call_flat\u001B[0;34m(self, args, captured_inputs, cancellation_manager)\u001B[0m\n\u001B[1;32m 1957\u001B[0m and executing_eagerly):\n\u001B[1;32m 1958\u001B[0m \u001B[0;31m# No tape is watching; skip to running the function.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1959\u001B[0;31m return self._build_call_outputs(self._inference_function.call(\n\u001B[0m\u001B[1;32m 1960\u001B[0m ctx, args, cancellation_manager=cancellation_manager))\n\u001B[1;32m 1961\u001B[0m forward_backward = self._select_forward_and_backward_functions(\n", + "\u001B[0;32m~/miniforge3/lib/python3.9/site-packages/tensorflow/python/eager/function.py\u001B[0m in \u001B[0;36mcall\u001B[0;34m(self, ctx, args, cancellation_manager)\u001B[0m\n\u001B[1;32m 596\u001B[0m \u001B[0;32mwith\u001B[0m \u001B[0m_InterpolateFunctionError\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 597\u001B[0m \u001B[0;32mif\u001B[0m \u001B[0mcancellation_manager\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 598\u001B[0;31m outputs = execute.execute(\n\u001B[0m\u001B[1;32m 599\u001B[0m \u001B[0mstr\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msignature\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mname\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 600\u001B[0m \u001B[0mnum_outputs\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_num_outputs\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;32m~/miniforge3/lib/python3.9/site-packages/tensorflow/python/eager/execute.py\u001B[0m in \u001B[0;36mquick_execute\u001B[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001B[0m\n\u001B[1;32m 56\u001B[0m \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 57\u001B[0m \u001B[0mctx\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mensure_initialized\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 58\u001B[0;31m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001B[0m\u001B[1;32m 59\u001B[0m inputs, attrs, num_outputs)\n\u001B[1;32m 60\u001B[0m \u001B[0;32mexcept\u001B[0m \u001B[0mcore\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_NotOkStatusException\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0me\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", + "\u001B[0;31mKeyboardInterrupt\u001B[0m: " + ] + } + ], + "source": [ + "model_parameter_optimization = {\"neuron num\":[], \"r2\":[], \"rmse\":[]}\n", + "epoch, batch_size = 1024, 64\n", + "\n", + "for i in range(2, 500):\n", + " model = ShortCut11(network_parameter=i, input_shape=(1, 102))\n", + " history_shortcut_11 = model.fit(x_train, y_train, x_val, y_val, epoch=epoch, batch_size=batch_size, save=\"/tmp/temp.hdf5\")\n", + " model = load_model(\"/tmp/temp.hdf5\")\n", + " y_pred = model.predict(x_test).reshape((-1, ))\n", + " model_parameter_optimization['neuron num'].append(i)\n", + " model_parameter_optimization['r2'].append(r2_score(y_test, y_pred))\n", + " model_parameter_optimization['rmse'].append(mean_squared_error(y_test, y_pred))\n", + "pd.DataFrame(model_parameter_optimization).to_csv(\"./dataset/test_result.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Deepo", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/README.md b/README.md index 4153a42..b7bdbec 100644 --- a/README.md +++ b/README.md @@ -1,24 +1,25 @@ # SCNet: A deep learning network framework for analyzing near-infrared spectroscopy using short-cut + ## Pre-processing Since the method we proposed is a regression model, the classification dataset weat kernel is not used in this work. -The other three dataset (corn, marzipan, soil) were preprocessed manually with Matlab and saved in the sub dictionary of `./preprocess` dir. The original dataset of these three dataset were stored in the `./preprocess/dataset/`. +The other three dataset (corn, marzipan, soil) were preprocessed manually with Matlab and saved in the sub dictionary of `./dataset` dir. The original dataset of these three dataset were stored in the `./dataset/`. And the data are shared with google drive with this [link](https://drive.google.com/drive/folders/1RFREskNcI2sDv6p7lvLhxFRLUgVTwho6?usp=sharing) -The mango dataset is not in Matlab .m file format, so we save them with the `process.py`. -Meanwhile, we drop the useless part and only save the data between  684 and 900 nm. +The mango dataset is not in Matlab .m file format, so we save them with the `process.py`. +Meanwhile, we drop the useless part and only save the data between 684 and 900 nm. -All these datasets are available at this [link](https://drive.google.com/drive/folders/1RFREskNcI2sDv6p7lvLhxFRLUgVTwho6?usp=sharing) +> The data set used in this study comprises a total of 11,691 NIR spectra (684–990 nm in 3 nm sampling with a total 103 variables) and DM measurements performed on 4675 mango fruit across 4 harvest seasons 2015, 2016, 2017 and 2018 [24]. - -> The data set used in this study comprises a total of 11,691 NIR spectra (684–990 nm in 3 nm sampling with a total 103 variables) and DM measurements performed on 4675 mango fruit across 4 harvest seasons 2015, 2016, 2017 and 2018 [24]. - -The detailed preprocessing progress can be found in [./preprocess.ipynb](./preprocess.ipynb) +The detailed preprocessing progress can be found in [./preprocess.ipynb](./01_preprocess.ipynb) ## Network Training -In order to show our network can prevent degration problem, we hold the experiment which contains the training loss curve of four models. The detailed information can be found in [model_training.ipynb](./model_training.ipynb). +In order to show our network can prevent degration problem, we hold the experiment which contains the training loss curve of four models. The detailed information can be found in [model_training.ipynb](./02_model_training.ipynb). + +The training results were saved on the google drive, here is the [link](https://drive.google.com/drive/folders/1-p1SPg-6lt7i6NkgzUOf5GDhh0cDePsr?usp=sharing]) ## Network evaluation -After training our model on training set, we evaluate the models on testing dataset that spared before. The evaluation is done with [model_evaluation.ipynb](model_evaluating.ipynb). + +After training our model on training set, we evaluate the models on testing dataset that spared before. The evaluation is done with [model_evaluation.ipynb](03_model_evaluating.ipynb). diff --git a/models.py b/models.py index 2dae6dd..9bef846 100644 --- a/models.py +++ b/models.py @@ -1,3 +1,4 @@ +from tkinter import N import keras.callbacks import keras.layers as KL from keras import Model @@ -9,7 +10,6 @@ class Plain5(object): self.model = None self.input_shape = input_shape if model_path is not None: - # TODO: loading from the file pass else: self.model = self.build_model() @@ -135,9 +135,10 @@ class ShortCut5(object): class ShortCut11(object): - def __init__(self, model_path=None, input_shape=None): + def __init__(self, model_path=None, input_shape=None, network_parameter=200): self.model = None self.input_shape = input_shape + self.network_parameter = network_parameter if model_path is not None: # TODO: loading from the file pass @@ -177,21 +178,25 @@ class ShortCut11(object): fx3 = KL.Activation('relu')(x) x = KL.Concatenate(axis=2)([x_raw, fx1, fx2, fx3]) - x = KL.Dense(200, activation='relu', name='dense1')(x) + x = KL.Dense(self.network_parameter, activation='relu', name='dense1')(x) x = KL.Dense(1, activation='sigmoid', name='output')(x) model = Model(input_layer, x) return model - def fit(self, x, y, x_val, y_val, epoch, batch_size): + def fit(self, x, y, x_val, y_val, epoch, batch_size, save='checkpoints/shortcut11.hdf5', is_show=True): self.model.compile(loss='mse', optimizer=adam_v2.Adam(learning_rate=0.01 * (batch_size / 256))) - checkpoint = keras.callbacks.ModelCheckpoint(filepath='checkpoints/shortcut11.hdf5', monitor='val_loss', - mode="min", save_best_only=True) + callbacks = [] + checkpoint = keras.callbacks.ModelCheckpoint(filepath=save, monitor='val_loss', + mode="min", save_best_only=True) + callbacks.append(checkpoint) early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=1e-6, patience=200, verbose=0, mode='auto') lr_decay = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=25, min_delta=1e-6) - callbacks = [checkpoint, early_stop, lr_decay] - history = self.model.fit(x, y, validation_data=(x_val, y_val), epochs=epoch, verbose=1, + callbacks.append(early_stop) + callbacks.append(lr_decay) + verbose_num = 1 if is_show else 0 + history = self.model.fit(x, y, validation_data=(x_val, y_val), epochs=epoch, verbose=verbose_num, callbacks=callbacks, batch_size=batch_size) return history @@ -257,8 +262,56 @@ class Plain11(object): return history +class SimpleCNN(object): + def __init__(self, model_path=None, input_shape=None): + self.model = None + self.input_shape = input_shape + if model_path is not None: + pass + else: + self.model = self.build_model() + + def build_model(self): + input_layer = KL.Input(self.input_shape, name='input') + x = KL.Conv1D(8, 7, padding='same', strides=3, name='Conv1')(input_layer) + x = KL.BatchNormalization()(x) + x = KL.Activation('relu')(x) + + x = KL.Conv1D(8, 3, padding='same', strides=3, name='Conv2')(x) + x = KL.BatchNormalization()(x) + x = KL.Activation('relu')(x) + + x = KL.Conv1D(8, 3, padding='same', strides=1, name='Conv3')(x) + x = KL.BatchNormalization()(x) + x = KL.Activation('relu')(x) + + x = KL.Conv1D(8, 9, padding='same', strides=3, name='Conv4')(x) + x = KL.BatchNormalization()(x) + x = KL.Activation('relu')(x) + + x = KL.Dense(20, activation='relu', name='dense')(x) + x = KL.Dense(1, activation='sigmoid', name='output')(x) + model = Model(input_layer, x) + return model + + def fit(self, x, y, x_val, y_val, epoch, batch_size): + self.model.compile(loss='mse', optimizer=adam_v2.Adam(learning_rate=0.01 * (batch_size / 256))) + checkpoint = keras.callbacks.ModelCheckpoint(filepath='checkpoints/plain5.hdf5', monitor='val_loss', + mode="min", save_best_only=True) + early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, + patience=1000, verbose=0, mode='auto') + lr_decay = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=25, min_delta=1e-6) + callbacks = [checkpoint, early_stop, lr_decay] + history = self.model.fit(x, y, validation_data=(x_val, y_val), epochs=epoch, verbose=1, + callbacks=callbacks, batch_size=batch_size) + return history + + + if __name__ == '__main__': # plain5 = Plain5(model_path=None, input_shape=(1, 102)) # plain11 = Plain11(model_path=None, input_shape=(1, 102)) residual5 = Residual5(model_path=None, input_shape=(1, 102)) short5 = ShortCut5(model_path=None, input_shape=(1, 102)) + sample = SimpleCNN(model_path=None, input_shape=(1, 102)) +