{ "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", 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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 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[==============================] - 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 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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 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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 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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 }