From 19d8a7c5882b182a22e81f56be036732d725d2b0 Mon Sep 17 00:00:00 2001
From: karllzy <li.zhenye@qq.com>
Date: Tue, 28 Mar 2023 10:49:37 +0800
Subject: [PATCH] Add visualization

add visualization for our network
---
 03_model_evaluating.ipynb               | 176 +++++++++++++++---------
 05_network_parameter_optimization.ipynb |  89 ++++++------
 05_network_parameter_optimization.py    |  54 ++++++++
 assets/shortcut5.png                    | Bin 0 -> 129709 bytes
 models.py                               |   6 +-
 preprocess/draw_pics_origin.m           |  88 ++++++------
 preprocess/draw_pics_preprocessed.m     |  94 ++++++-------
 preprocess/preprocess.m                 |  16 +--
 preprocess/preprocess_mango.m           |  30 ++--
 preprocess/train_test_split.m           |  30 ++--
 utils.py                                |   0
 11 files changed, 347 insertions(+), 236 deletions(-)
 create mode 100644 05_network_parameter_optimization.py
 create mode 100644 assets/shortcut5.png
 mode change 100755 => 100644 preprocess/draw_pics_origin.m
 mode change 100755 => 100644 preprocess/draw_pics_preprocessed.m
 mode change 100755 => 100644 preprocess/preprocess.m
 mode change 100755 => 100644 preprocess/preprocess_mango.m
 mode change 100755 => 100644 preprocess/train_test_split.m
 mode change 100755 => 100644 utils.py

diff --git a/03_model_evaluating.ipynb b/03_model_evaluating.ipynb
index a3e2920..1b39688 100644
--- a/03_model_evaluating.ipynb
+++ b/03_model_evaluating.ipynb
@@ -3,7 +3,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -14,7 +13,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -25,39 +33,40 @@
     "from sklearn.metrics import mean_squared_error\n",
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "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"
     }
-   }
+   },
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -79,84 +88,123 @@
     "      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}\")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-03-27 10:23:44.563867: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+      "2023-03-27 10:23:44.592365: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory\n",
+      "2023-03-27 10:23:44.592382: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
+      "Skipping registering GPU devices...\n",
+      "2023-03-27 10:23:44.592795: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "110/110 [==============================] - 0s 650us/step\n",
+      "110/110 [==============================] - 0s 803us/step\n",
+      "110/110 [==============================] - 0s 660us/step\n",
+      "110/110 [==============================] - 0s 782us/step\n",
+      "plain 5  :  99.41745237394272 %\n",
+      "plain 5 : 0.9029283888753278\n",
+      "plain 11  :  99.40218375195113 %\n",
+      "plain 11 : 0.9003841338306197\n",
+      "shortcut 5  :  99.41646209430799 %\n",
+      "shortcut 5 : 0.9027633756896097\n",
+      "shortcut11  :  99.42990078655805 %\n",
+      "shortcut11 : 0.9050027042007411\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.metrics import r2_score\n",
     "\n",
-    "## Build model and load weights\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, \" : \", (1 - mean_squared_error(y_test, model_result)/np.mean(y_test))*100, \"%\")\n",
-    "      print(model_name, \":\", r2_score(y_test, model_result))"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "execution_count": 13,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "plain 5  :  99.41745314181642 %\n",
-      "plain 5 : 0.902928516828363\n",
-      "plain 11  :  99.4021812070087 %\n",
-      "plain 11 : 0.9003837097594369\n",
-      "shortcut 5  :  99.41646250646849 %\n",
-      "shortcut 5 : 0.9027634443691182\n",
-      "shortcut11  :  99.42989627559609 %\n",
-      "shortcut11 : 0.9050019525259844\n"
-     ]
-    }
+    "    print(model_name, \" : \", (1 - mean_squared_error(y_test, model_result)/np.mean(y_test))*100, \"%\")\n",
+    "    print(model_name, \":\", r2_score(y_test, model_result))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "outputs": [],
-   "source": [],
+   "execution_count": 5,
    "metadata": {
     "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
     "pycharm": {
      "name": "#%%\n"
     }
-   }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from keras.utils import plot_model\n",
+    "\n",
+    "plot_model(shortcut5, to_file='assets/shortcut5.png', show_shapes=True, show_layer_names=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Shades",
    "language": "python",
-   "name": "python3"
+   "name": "shades"
   },
   "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": "3.9.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
+ "nbformat_minor": 4
+}
diff --git a/05_network_parameter_optimization.ipynb b/05_network_parameter_optimization.ipynb
index b562106..b6cd5d5 100644
--- a/05_network_parameter_optimization.ipynb
+++ b/05_network_parameter_optimization.ipynb
@@ -3,7 +3,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -15,6 +14,15 @@
   {
    "cell_type": "code",
    "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -49,17 +57,20 @@
     "      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}\")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -363,20 +374,20 @@
      "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<module>\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: "
+      "\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<module>\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: "
      ]
     }
    ],
@@ -386,53 +397,51 @@
     "\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",
+    "      history_shortcut_11 = model.fit(x_train, y_train, x_val, y_val, epoch=epoch, batch_size=batch_size, save=\"/tmp/temp.hdf5\", is_show=False)\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",
+    "      print(f\"model with parameter {i}: r2: {model_parameter_optimization['r2'][-1]}, rmse: {model_parameter_optimization['rmse'][-1]}\")\n",
     "pd.DataFrame(model_parameter_optimization).to_csv(\"./dataset/test_result.csv\")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "outputs": [],
-   "source": [],
    "metadata": {
     "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
     "pycharm": {
      "name": "#%%\n"
     }
-   }
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Shades",
    "language": "python",
-   "name": "python3"
+   "name": "shades"
   },
   "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": "3.9.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
+ "nbformat_minor": 4
+}
diff --git a/05_network_parameter_optimization.py b/05_network_parameter_optimization.py
new file mode 100644
index 0000000..be52773
--- /dev/null
+++ b/05_network_parameter_optimization.py
@@ -0,0 +1,54 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Network Parameter Optimization
+
+# In[2]:
+
+
+import numpy as np
+import pandas as pd
+from keras.models import load_model
+from sklearn.metrics import r2_score, mean_squared_error
+from sklearn.model_selection import train_test_split
+from scipy.io import loadmat
+from models import ShortCut11
+from numpy.random import seed
+import tensorflow
+import time
+seed(4750)
+tensorflow.random.set_seed(4750)
+time1 = time.time()
+data = loadmat('./dataset/mango/mango_dm_split.mat')
+x_train, y_train, x_test, y_test = data['x_train'], data['y_train'], data['x_test'], data['y_test']
+x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.3, random_state=12, shuffle=True)
+x_train, x_val, x_test = x_train[:, np.newaxis, :], x_val[:, np.newaxis, :], x_test[:, np.newaxis, :]
+print(f"shape of data:\n"
+      f"x_train: {x_train.shape}, y_train: {y_train.shape},\n"
+      f"x_val: {x_val.shape}, y_val: {y_val.shape}\n"
+      f"x_test: {x_test.shape}, y_test: {y_test.shape}")
+
+
+# In[4]:
+
+
+model_parameter_optimization = {"neuron num":[], "r2":[], "rmse":[]}
+epoch, batch_size = 1024, 64
+
+for i in range(2, 500):
+      model = ShortCut11(network_parameter=i, input_shape=(1, 102))
+      history_shortcut_11 = model.fit(x_train, y_train, x_val, y_val, epoch=epoch, batch_size=batch_size, save="/tmp/temp.hdf5", is_show=False)
+      model = load_model("/tmp/temp.hdf5")
+      y_pred = model.predict(x_test).reshape((-1, ))
+      model_parameter_optimization['neuron num'].append(i)
+      model_parameter_optimization['r2'].append(r2_score(y_test, y_pred))
+      model_parameter_optimization['rmse'].append(mean_squared_error(y_test, y_pred))
+      print(f"model with parameter {i}: r2: {model_parameter_optimization['r2'][-1]}, rmse: {model_parameter_optimization['rmse'][-1]}")
+pd.DataFrame(model_parameter_optimization).to_csv("./dataset/test_result.csv")
+
+
+# In[ ]:
+
+
+
+
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literal 0
HcmV?d00001

diff --git a/models.py b/models.py
index 1e860d7..2d23118 100644
--- a/models.py
+++ b/models.py
@@ -182,7 +182,7 @@ class ShortCut11(object):
         model = Model(input_layer, x)
         return model
 
-    def fit(self, x, y, x_val, y_val, epoch, batch_size, save='checkpoints/shortcut11.hdf5'):
+    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)))
         callbacks = []
         checkpoint = keras.callbacks.ModelCheckpoint(filepath=save, monitor='val_loss',
@@ -194,8 +194,8 @@ class ShortCut11(object):
                                                      patience=25, min_delta=1e-6)
         callbacks.append(early_stop)
         callbacks.append(lr_decay)
-
-        history = self.model.fit(x, y, validation_data=(x_val, y_val), epochs=epoch, verbose=1,
+        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
 
diff --git a/preprocess/draw_pics_origin.m b/preprocess/draw_pics_origin.m
old mode 100755
new mode 100644
index 11e63cb..0e65e53
--- a/preprocess/draw_pics_origin.m
+++ b/preprocess/draw_pics_origin.m
@@ -1,45 +1,45 @@
-set(gca,'LooseInset',get(gca,'TightInset'))
-f = figure;
-f.Position(3:4) = [1331 331];
-%%% draw the pic of corn spectra
-load('dataset/corn.mat');
-x = m5spec.data;
-wave_length = m5spec.axisscale{2, 1};
-subplot(1, 4, 1)
-plot(wave_length, x');
-xlim([wave_length(1) wave_length(end)]);
-xlabel('Wavelength(nm)');
-ylabel('Absorbance');
-clear
-
-%%% draw the pic of Marzipan spectra
-load('dataset/marzipan.mat');
-x = NIRS1;
-wave_length = NIRS1_axis;
-subplot(1, 4, 2)
-plot(wave_length, x');
-xlim([wave_length(1) wave_length(end)]);
-xlabel('Wavelength(nm)');
-ylabel('Absorbance');
-clear
-
-%%% draw the pic of Marzipan spectra
-load('dataset/soil.mat');
-x = soil.data;
-wave_length = soil.axisscale{2, 1};
-subplot(1, 4, 3)
-plot(wave_length, x');
-xlim([wave_length(1) wave_length(end)]);
-xlabel('Wavelength(nm)');
-ylabel('Absorbance');
-clear
-
-% draw the pic of Mango spectra
-load('dataset/mango/mango_origin.mat');
-wave_length = 684: 3: 990;
-subplot(1, 4, 4)
-plot(wave_length, x');
-xlim([wave_length(1) wave_length(end)]);
-xlabel('Wavelength(nm)');
-ylabel('Signal intensity');
+set(gca,'LooseInset',get(gca,'TightInset'))
+f = figure;
+f.Position(3:4) = [1331 331];
+%%% draw the pic of corn spectra
+load('dataset/corn.mat');
+x = m5spec.data;
+wave_length = m5spec.axisscale{2, 1};
+subplot(1, 4, 1)
+plot(wave_length, x');
+xlim([wave_length(1) wave_length(end)]);
+xlabel('Wavelength(nm)');
+ylabel('Absorbance');
+clear
+
+%%% draw the pic of Marzipan spectra
+load('dataset/marzipan.mat');
+x = NIRS1;
+wave_length = NIRS1_axis;
+subplot(1, 4, 2)
+plot(wave_length, x');
+xlim([wave_length(1) wave_length(end)]);
+xlabel('Wavelength(nm)');
+ylabel('Absorbance');
+clear
+
+%%% draw the pic of Marzipan spectra
+load('dataset/soil.mat');
+x = soil.data;
+wave_length = soil.axisscale{2, 1};
+subplot(1, 4, 3)
+plot(wave_length, x');
+xlim([wave_length(1) wave_length(end)]);
+xlabel('Wavelength(nm)');
+ylabel('Absorbance');
+clear
+
+% draw the pic of Mango spectra
+load('dataset/mango/mango_origin.mat');
+wave_length = 684: 3: 990;
+subplot(1, 4, 4)
+plot(wave_length, x');
+xlim([wave_length(1) wave_length(end)]);
+xlabel('Wavelength(nm)');
+ylabel('Signal intensity');
 clear
\ No newline at end of file
diff --git a/preprocess/draw_pics_preprocessed.m b/preprocess/draw_pics_preprocessed.m
old mode 100755
new mode 100644
index 7d01bf3..271b9fb
--- a/preprocess/draw_pics_preprocessed.m
+++ b/preprocess/draw_pics_preprocessed.m
@@ -1,48 +1,48 @@
-set(gca,'LooseInset',get(gca,'TightInset'))
-f = figure;
-f.Position(3:4) = [1331 331];
-%%% draw the pic of corn spectra
-load('dataset/corn.mat');
-x = m5spec.data;
-wave_length = m5spec.axisscale{2, 1};
-preprocess;
-subplot(1, 4, 1)
-plot(wave_length(1, 1:end-1), x');
-xlim([wave_length(1) wave_length(end)]);
-xlabel('Wavelength(nm)');
-ylabel('Absorbance');
-clear
-
-%%% draw the pic of Marzipan spectra
-load('dataset/marzipan.mat');
-x = NIRS1;
-wave_length = NIRS1_axis;
-preprocess;
-subplot(1, 4, 2)
-plot(wave_length(1, 1:end-1), x');
-xlim([wave_length(1) wave_length(end)]);
-xlabel('Wavelength(nm)');
-ylabel('Absorbance');
-clear
-
-%%% draw the pic of Marzipan spectra
-load('dataset/soil.mat');
-x = soil.data;
-wave_length = soil.axisscale{2, 1};
-preprocess;
-subplot(1, 4, 3)
-plot(wave_length(1, 1:end-1), x');
-xlim([wave_length(1) wave_length(end)]);
-xlabel('Wavelength(nm)');
-ylabel('Absorbance');
-clear
-
-% draw the pic of Mango spectra
-load('dataset/mango/mango_preprocessed.mat');
-wave_length = 687: 3: 990;
-subplot(1, 4, 4)
-plot(wave_length, x');
-xlim([wave_length(1) wave_length(end)]);
-xlabel('Wavelength(nm)');
-ylabel('Signal intensity');
+set(gca,'LooseInset',get(gca,'TightInset'))
+f = figure;
+f.Position(3:4) = [1331 331];
+%%% draw the pic of corn spectra
+load('dataset/corn.mat');
+x = m5spec.data;
+wave_length = m5spec.axisscale{2, 1};
+preprocess;
+subplot(1, 4, 1)
+plot(wave_length(1, 1:end-1), x');
+xlim([wave_length(1) wave_length(end)]);
+xlabel('Wavelength(nm)');
+ylabel('Absorbance');
+clear
+
+%%% draw the pic of Marzipan spectra
+load('dataset/marzipan.mat');
+x = NIRS1;
+wave_length = NIRS1_axis;
+preprocess;
+subplot(1, 4, 2)
+plot(wave_length(1, 1:end-1), x');
+xlim([wave_length(1) wave_length(end)]);
+xlabel('Wavelength(nm)');
+ylabel('Absorbance');
+clear
+
+%%% draw the pic of Marzipan spectra
+load('dataset/soil.mat');
+x = soil.data;
+wave_length = soil.axisscale{2, 1};
+preprocess;
+subplot(1, 4, 3)
+plot(wave_length(1, 1:end-1), x');
+xlim([wave_length(1) wave_length(end)]);
+xlabel('Wavelength(nm)');
+ylabel('Absorbance');
+clear
+
+% draw the pic of Mango spectra
+load('dataset/mango/mango_preprocessed.mat');
+wave_length = 687: 3: 990;
+subplot(1, 4, 4)
+plot(wave_length, x');
+xlim([wave_length(1) wave_length(end)]);
+xlabel('Wavelength(nm)');
+ylabel('Signal intensity');
 clear
\ No newline at end of file
diff --git a/preprocess/preprocess.m b/preprocess/preprocess.m
old mode 100755
new mode 100644
index f774a57..359ca95
--- a/preprocess/preprocess.m
+++ b/preprocess/preprocess.m
@@ -1,8 +1,8 @@
-%% x preprocessing
-x = x';
-x = sgolayfilt(x,2,17);
-x =diff(x);
-max_x=max(max(x));
-min_x=min(min(x));
-x=(x-min_x)/(max_x-min_x);
-x = x';
+%% x preprocessing
+x = x';
+x = sgolayfilt(x,2,17);
+x =diff(x);
+max_x=max(max(x));
+min_x=min(min(x));
+x=(x-min_x)/(max_x-min_x);
+x = x';
diff --git a/preprocess/preprocess_mango.m b/preprocess/preprocess_mango.m
old mode 100755
new mode 100644
index 99cf502..6c903c5
--- a/preprocess/preprocess_mango.m
+++ b/preprocess/preprocess_mango.m
@@ -1,15 +1,15 @@
-%% x preprocessing
-clear;
-load('dataset/mango/mango_origin.mat')
-x = x';
-x = sgolayfilt(x,2,17);
-x =diff(x);
-max_x=max(max(x));
-min_x=min(min(x));
-x=(x-min_x)/(max_x-min_x);
-x = x';
-y = y';
-min_y = min(min(y));
-max_y = max(max(y));
-y = (y-min_y)/(max_y-min_y);
-save('dataset/mango/mango_preprocessed.mat')
+%% x preprocessing
+clear;
+load('dataset/mango/mango_origin.mat')
+x = x';
+x = sgolayfilt(x,2,17);
+x =diff(x);
+max_x=max(max(x));
+min_x=min(min(x));
+x=(x-min_x)/(max_x-min_x);
+x = x';
+y = y';
+min_y = min(min(y));
+max_y = max(max(y));
+y = (y-min_y)/(max_y-min_y);
+save('dataset/mango/mango_preprocessed.mat')
diff --git a/preprocess/train_test_split.m b/preprocess/train_test_split.m
old mode 100755
new mode 100644
index 88f6a92..8c07bbc
--- a/preprocess/train_test_split.m
+++ b/preprocess/train_test_split.m
@@ -1,15 +1,15 @@
-data=[x,y];
-test_rate = 0.3;
-data_num = size(x, 1);
-train_num = round((1-test_rate) * data_num);
-idx=randperm(data_num);
-train_idx=idx(1:train_num);
-test_idx=idx(train_num+1:data_num);
-data_train=data(train_idx,:);
-x_train=data_train(:,1:size(x, 2));
-y_train=data_train(:,size(x, 2)+1);
-test_data=data(test_idx,:);
-x_test=test_data(:,1:size(x, 2));
-y_test=test_data(:,size(x, 2)+1);
-clear data_num train_num idx train_idx test_idx test_data train_data x y;
-clear data data_train test_rate;
+data=[x,y];
+test_rate = 0.3;
+data_num = size(x, 1);
+train_num = round((1-test_rate) * data_num);
+idx=randperm(data_num);
+train_idx=idx(1:train_num);
+test_idx=idx(train_num+1:data_num);
+data_train=data(train_idx,:);
+x_train=data_train(:,1:size(x, 2));
+y_train=data_train(:,size(x, 2)+1);
+test_data=data(test_idx,:);
+x_test=test_data(:,1:size(x, 2));
+y_test=test_data(:,size(x, 2)+1);
+clear data_num train_num idx train_idx test_idx test_data train_data x y;
+clear data data_train test_rate;
diff --git a/utils.py b/utils.py
old mode 100755
new mode 100644