BP_Prediction (Sarah's Version).ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"xwawsDvwZzZt"},"source":["# Import Statements"]},{"cell_type":"code","execution_count":1,"metadata":{"id":"2RY-A_Zvhtp_","executionInfo":{"status":"ok","timestamp":1708642927853,"user_tz":300,"elapsed":26588,"user":{"displayName":"Sarah Mia Shen Lee Liu","userId":"01042882719085922731"}}},"outputs":[],"source":["# Import all the necessary functions here\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","import pandas as pd\n","import dask.dataframe as dd\n","\n","import numpy as np\n","\n","import os\n","import tensorflow as tf\n","from tensorflow.keras.regularizers import L2\n","from tensorflow import keras\n","from tensorflow.keras import layers\n","\n","os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n","\n","from tensorflow.keras.callbacks import ModelCheckpoint\n","from tensorflow.keras import layers, regularizers\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.optimizers import Adam"]},{"cell_type":"markdown","metadata":{"id":"KD-uTRBlZ8RI"},"source":["# Importing and Prepping Data"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17708,"status":"ok","timestamp":1708642945559,"user":{"displayName":"Sarah Mia Shen Lee Liu","userId":"01042882719085922731"},"user_tz":300},"id":"2nxzUB8ch5IS","outputId":"3e6450c4-aec9-40a7-8423-88e73dc60955"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n"]}],"source":["# This gives google colab access to Google Drive\n","from google.colab import drive\n","drive.mount('/content/gdrive')"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"iYKdlV72iFEn","executionInfo":{"status":"ok","timestamp":1708642946584,"user_tz":300,"elapsed":1032,"user":{"displayName":"Sarah Mia Shen Lee Liu","userId":"01042882719085922731"}}},"outputs":[],"source":["# Pathing to file directory\n","pwd_lying = \"/content/gdrive/MyDrive/Body position prediction/lying data\"\n","pwd_sitting = \"/content/gdrive/MyDrive/Body position prediction/sitting data\"\n","pwd_standing = \"/content/gdrive/MyDrive/Body position prediction/standing data\"\n","#!cd \"$pwd\" && ls\n","\n","# Note: For you to connect to the shared folder, you first have to add a\n","# shortcut from the shared folder to your personal folder before you can path\n","# your way into it\n","data_files_lying = !ls gdrive/MyDrive/'Body position prediction'/'lying data'\n","data_files_sitting = !ls gdrive/MyDrive/'Body position prediction'/'sitting data'\n","data_files_standing = !ls gdrive/MyDrive/'Body position prediction'/'standing data'"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1708642946584,"user":{"displayName":"Sarah Mia Shen Lee Liu","userId":"01042882719085922731"},"user_tz":300},"id":"sDlzSLklgc83","outputId":"3d7c33a7-ed5c-4831-da07-997131e92e8a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['Ainsley_Standing_30_Minutes_2.csv',\n"," 'Bowen_Standing_30_Minutes.csv',\n"," 'Ainsley_Standing_30_Minutes.csv',\n"," 'Sarah_Standing_10_Minutes.csv',\n"," 'Bowen_Standing_10_Minutes2.csv',\n"," 'Sarah_Standing_11_Minutes.csv',\n"," 'Bowen_Standing_10_Minutes.csv',\n"," 'Sarah_Standing_20_Minutes_1.csv',\n"," 'Bowen_Standing_15_Minutes_Walking.csv',\n"," 'Sarah_Standing_20_Minutes_2.csv']"]},"metadata":{},"execution_count":4}],"source":["from numpy.lib.shape_base import split\n","new_data_files_lying = []\n","new_data_files_sitting = []\n","new_data_files_standing = []\n","\n","# I'll need to split the names up in the list since it's grouping 2 at a time\n","for i in data_files_lying:\n","  new_data_files_lying.extend(i.split())\n","for i in data_files_sitting:\n","  new_data_files_sitting.extend(i.split())\n","for i in data_files_standing:\n","  new_data_files_standing.extend(i.split())\n","\n","new_data_files_standing"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14966,"status":"ok","timestamp":1708642961546,"user":{"displayName":"Sarah Mia Shen Lee Liu","userId":"01042882719085922731"},"user_tz":300},"id":"cXnnYz7dWWpA","outputId":"8e63af04-fac5-41af-a563-7c2b5b4dcf26"},"outputs":[{"output_type":"stream","name":"stdout","text":["Ainsley_Lying_30_Minutes_2.csv\n","Bowen_Lying_20_Minutes.csv\n","Sarah_Lying_20_Minutes_3.csv\n","Ainsley_Lying_30_Minutes.csv\n","Bowen_Lying_21_Minutes.csv\n","Sarah_Lying_20_Minutes.csv\n","Bowen_Lying_20_Minutes_2.csv\n","Sarah_Lying_20_Minutes_2.csv\n","Ainsley_Sitting_30_Minutes_2.csv\n","Bowen_Sitting_11_Minutes_2.csv\n","Sarah_Sitting_20_Minutes_2.csv\n","Ainsley_Sitting_30_Minutes.csv\n","Bowen_Sitting_11_Minutes.csv\n","Sarah_Sitting_20_Minutes_3.csv\n","Bowen_Sitting_10_Minutes.csv\n","Bowen_Sitting_40_Minutes.csv\n","Sarah_Sitting_20_Minutes.csv\n","Ainsley_Standing_30_Minutes_2.csv\n","Bowen_Standing_30_Minutes.csv\n","Ainsley_Standing_30_Minutes.csv\n","Sarah_Standing_10_Minutes.csv\n","Bowen_Standing_10_Minutes2.csv\n","Sarah_Standing_11_Minutes.csv\n","Bowen_Standing_10_Minutes.csv\n","Sarah_Standing_20_Minutes_1.csv\n","Bowen_Standing_15_Minutes_Walking.csv\n","Sarah_Standing_20_Minutes_2.csv\n"]}],"source":["raw_lying_data = pd.DataFrame()\n","raw_sitting_data = pd.DataFrame()\n","raw_standing_data = pd.DataFrame()\n","\n","\n","for i in range(len(new_data_files_lying)):\n","  print(new_data_files_lying[i])\n","  df = pd.read_csv(pwd_lying + \"/\" + new_data_files_lying[i], header=1)\n","  raw_lying_data = pd.concat([raw_lying_data, df])\n","\n","for i in range(len(new_data_files_sitting)):\n","  print(new_data_files_sitting[i])\n","  df = pd.read_csv(pwd_sitting + \"/\" + new_data_files_sitting[i], header=1)\n","  raw_sitting_data = pd.concat([raw_sitting_data, df])\n","\n","for i in range(len(new_data_files_standing)):\n","  print(new_data_files_standing[i])\n","  df = pd.read_csv(pwd_standing + \"/\" + new_data_files_standing[i], header=1)\n","  raw_standing_data = pd.concat([raw_standing_data, df])\n"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"sFPC9ktTgA1s","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1708642961695,"user_tz":300,"elapsed":153,"user":{"displayName":"Sarah Mia Shen Lee Liu","userId":"01042882719085922731"}},"outputId":"2fc0ec48-c260-4a14-fb97-bb9582077d1a"},"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-6-31a3e4792fcd>:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  ly['body postion'] = 1\n","<ipython-input-6-31a3e4792fcd>:7: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  sit['body postion'] = 2\n","<ipython-input-6-31a3e4792fcd>:8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  stand['body postion'] = 3\n"]}],"source":["# Now, I'm filtering the table since I only want time and the accelerometer data\n","ly = raw_lying_data[[\"timestamp\", \"green\", \"green2\", \"ir\", \"red\", \"acceleration_x\", \"acceleration_y\", \"acceleration_z\"]]\n","sit = raw_sitting_data[[\"timestamp\", \"green\", \"green2\", \"ir\", \"red\", \"acceleration_x\", \"acceleration_y\", \"acceleration_z\"]]\n","stand = raw_standing_data[[\"timestamp\", \"green\", \"green2\", \"ir\", \"red\", \"acceleration_x\", \"acceleration_y\", \"acceleration_z\"]]\n","\n","ly['body postion'] = 1\n","sit['body postion'] = 2\n","stand['body postion'] = 3"]},{"cell_type":"code","source":["from sklearn import preprocessing\n","\n","min_max_scaler = preprocessing.MinMaxScaler()\n","\n","ppg_sitting_values = sit[[\"green\", \"green2\", \"ir\", \"red\"]].values\n","ppg_lying_values = ly[[\"green\", \"green2\", \"ir\", \"red\"]].values\n","ppg_standing_values = stand[[\"green\", \"green2\", \"ir\", \"red\"]].values #returns a numpy array\n","accel_sitting_values = sit[[\"acceleration_x\", \"acceleration_y\", \"acceleration_z\"]].values\n","accel_standing_values = stand[[\"acceleration_x\", \"acceleration_y\", \"acceleration_z\"]].values\n","accel_lying_values = ly[[\"acceleration_x\", \"acceleration_y\", \"acceleration_z\"]].values\n","\n","\n","sitting_values_norm = min_max_scaler.fit_transform(ppg_sitting_values)\n","lying_values_norm = min_max_scaler.fit_transform(ppg_lying_values)\n","standing_values_norm = min_max_scaler.fit_transform(ppg_standing_values)\n","\n","sitting_values = np.concatenate((sitting_values_norm, accel_sitting_values), axis=1)\n","standing_values = np.concatenate((standing_values_norm, accel_standing_values), axis=1)\n","lying_values = np.concatenate((lying_values_norm, accel_lying_values), axis=1)\n","\n","\n","sitting = pd.DataFrame(sitting_values)\n","lying = pd.DataFrame(lying_values)\n","standing = pd.DataFrame(standing_values)"],"metadata":{"id":"xDf6J-IK-iiA","executionInfo":{"status":"ok","timestamp":1708642962891,"user_tz":300,"elapsed":1059,"user":{"displayName":"Sarah Mia Shen Lee Liu","userId":"01042882719085922731"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["ppg_sitting_values"],"metadata":{"id":"RBO2obCiD-m3","executionInfo":{"status":"ok","timestamp":1708642962891,"user_tz":300,"elapsed":12,"user":{"displayName":"Sarah Mia Shen Lee Liu","userId":"01042882719085922731"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"3dc9189e-a1cc-4cf7-c84e-2b1f6c7d520b"},"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[    66,     65, 332306,   1038],\n","       [   223,    224, 429772,   3778],\n","       [   112,    111, 367795,   1366],\n","       ...,\n","       [ 58398,  58414, 392307, 378687],\n","       [ 58302,  58305, 392294, 378681],\n","       [ 58204,  58205, 392297, 378716]])"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["sitting"],"metadata":{"id":"i_zrc20I-2QT","colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"status":"ok","timestamp":1708642962892,"user_tz":300,"elapsed":8,"user":{"displayName":"Sarah Mia Shen Lee Liu","userId":"01042882719085922731"}},"outputId":"2b1b4ec8-9eba-4208-e2b6-576c99c09628"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["               0     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This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n","  saving_api.save_model(\n"]},{"output_type":"stream","name":"stdout","text":["400/400 [==============================] - ETA: 0s - loss: 0.5672 - accuracy: 0.8051\n","Epoch 2: val_accuracy improved from 0.81365 to 0.85092, saving model to best_model.h5\n","400/400 [==============================] - 9s 23ms/step - loss: 0.5672 - accuracy: 0.8051 - val_loss: 0.4298 - val_accuracy: 0.8509\n","Epoch 3/50\n","399/400 [============================>.] - ETA: 0s - loss: 0.4767 - accuracy: 0.8409\n","Epoch 3: val_accuracy improved from 0.85092 to 0.87159, saving model to best_model.h5\n","400/400 [==============================] - 9s 24ms/step - loss: 0.4766 - accuracy: 0.8409 - val_loss: 0.3555 - val_accuracy: 0.8716\n","Epoch 4/50\n","399/400 [============================>.] - ETA: 0s - loss: 0.4339 - accuracy: 0.8521\n","Epoch 4: val_accuracy improved from 0.87159 to 0.88162, saving model to best_model.h5\n","400/400 [==============================] - 8s 19ms/step - loss: 0.4339 - accuracy: 0.8521 - val_loss: 0.3544 - val_accuracy: 0.8816\n","Epoch 5/50\n","397/400 [============================>.] - ETA: 0s - loss: 0.4027 - accuracy: 0.8636\n","Epoch 5: val_accuracy improved from 0.88162 to 0.89978, saving model to best_model.h5\n","400/400 [==============================] - 10s 25ms/step - loss: 0.4023 - accuracy: 0.8637 - val_loss: 0.2939 - val_accuracy: 0.8998\n","Epoch 6/50\n","400/400 [==============================] - ETA: 0s - loss: 0.3632 - accuracy: 0.8791\n","Epoch 6: val_accuracy did not improve from 0.89978\n","400/400 [==============================] - 9s 23ms/step - loss: 0.3632 - accuracy: 0.8791 - val_loss: 0.3026 - val_accuracy: 0.8985\n","Epoch 7/50\n","397/400 [============================>.] - ETA: 0s - loss: 0.3576 - accuracy: 0.8818\n","Epoch 7: val_accuracy improved from 0.89978 to 0.90166, saving model to best_model.h5\n","400/400 [==============================] - 9s 22ms/step - loss: 0.3571 - accuracy: 0.8820 - val_loss: 0.2757 - val_accuracy: 0.9017\n","Epoch 8/50\n","398/400 [============================>.] - ETA: 0s - loss: 0.3341 - accuracy: 0.8875\n","Epoch 8: val_accuracy did not improve from 0.90166\n","400/400 [==============================] - 9s 23ms/step - loss: 0.3342 - accuracy: 0.8874 - val_loss: 0.3149 - val_accuracy: 0.8882\n","Epoch 9/50\n","397/400 [============================>.] - ETA: 0s - loss: 0.3255 - accuracy: 0.8881\n","Epoch 9: val_accuracy did not improve from 0.90166\n","400/400 [==============================] - 8s 21ms/step - loss: 0.3256 - accuracy: 0.8881 - val_loss: 0.2976 - val_accuracy: 0.8907\n","Epoch 10/50\n","397/400 [============================>.] - ETA: 0s - loss: 0.3121 - accuracy: 0.8978\n","Epoch 10: val_accuracy improved from 0.90166 to 0.91137, saving model to best_model.h5\n","400/400 [==============================] - 9s 22ms/step - loss: 0.3124 - accuracy: 0.8973 - val_loss: 0.2397 - val_accuracy: 0.9114\n","Epoch 11/50\n","399/400 [============================>.] - ETA: 0s - loss: 0.3052 - accuracy: 0.8942\n","Epoch 11: val_accuracy did not improve from 0.91137\n","400/400 [==============================] - 10s 25ms/step - loss: 0.3051 - accuracy: 0.8942 - val_loss: 0.2689 - val_accuracy: 0.9076\n","Epoch 12/50\n","397/400 [============================>.] - ETA: 0s - loss: 0.2759 - accuracy: 0.9033\n","Epoch 12: val_accuracy improved from 0.91137 to 0.91544, saving model to best_model.h5\n","400/400 [==============================] - 8s 20ms/step - loss: 0.2757 - accuracy: 0.9033 - val_loss: 0.2191 - val_accuracy: 0.9154\n","Epoch 13/50\n","397/400 [============================>.] - ETA: 0s - loss: 0.2548 - accuracy: 0.9111\n","Epoch 13: val_accuracy did not improve from 0.91544\n","400/400 [==============================] - 10s 25ms/step - loss: 0.2557 - accuracy: 0.9108 - val_loss: 0.2676 - val_accuracy: 0.8992\n","Epoch 14/50\n","400/400 [==============================] - ETA: 0s - loss: 0.2658 - accuracy: 0.9062\n","Epoch 14: val_accuracy improved from 0.91544 to 0.92233, saving model to best_model.h5\n","400/400 [==============================] - 10s 24ms/step - loss: 0.2658 - accuracy: 0.9062 - val_loss: 0.2050 - val_accuracy: 0.9223\n","Epoch 15/50\n","398/400 [============================>.] - ETA: 0s - loss: 0.2542 - accuracy: 0.9152\n","Epoch 15: val_accuracy did not improve from 0.92233\n","400/400 [==============================] - 8s 21ms/step - loss: 0.2538 - accuracy: 0.9153 - val_loss: 0.2383 - val_accuracy: 0.9104\n","Epoch 16/50\n","399/400 [============================>.] - ETA: 0s - loss: 0.2778 - accuracy: 0.9040\n","Epoch 16: val_accuracy did not improve from 0.92233\n","400/400 [==============================] - 10s 25ms/step - loss: 0.2777 - accuracy: 0.9040 - val_loss: 0.2793 - val_accuracy: 0.9013\n","Epoch 17/50\n","400/400 [==============================] - ETA: 0s - loss: 0.2215 - accuracy: 0.9285\n","Epoch 17: val_accuracy did not improve from 0.92233\n","400/400 [==============================] - 9s 22ms/step - loss: 0.2215 - accuracy: 0.9285 - val_loss: 0.2341 - val_accuracy: 0.9179\n","Epoch 18/50\n","397/400 [============================>.] - ETA: 0s - loss: 0.2130 - accuracy: 0.9261\n","Epoch 18: val_accuracy improved from 0.92233 to 0.92515, saving model to best_model.h5\n","400/400 [==============================] - 9s 21ms/step - loss: 0.2128 - accuracy: 0.9262 - val_loss: 0.1993 - val_accuracy: 0.9251\n","Epoch 19/50\n"," 69/400 [====>.........................] - ETA: 5s - loss: 0.2068 - accuracy: 0.9307"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-13-411c33abe9e9>\u001b[0m in \u001b[0;36m<cell line: 39>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;31m# Train the model with callbacks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mhistory\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[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\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[0;34m,\u001b[0m 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max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1805\u001b[0m                         ):\n\u001b[1;32m   1806\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-> 1807\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   1808\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   1809\u001b[0m                                 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\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":["def build_model():\n","    model = Sequential()\n","    model.add(layers.LSTM(32, activation='relu', return_sequences=True, input_shape=(7, 1000)))\n","    model.add(layers.LSTM(32, activation='relu'))\n","\n","    model.add(layers.Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n","    model.add(layers.Dropout(0.3))\n","    model.add(layers.BatchNormalization())\n","\n","    model.add(layers.Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n","    model.add(layers.Dropout(0.3))\n","    model.add(layers.BatchNormalization())\n","\n","    model.add(layers.Dense(3, activation='softmax'))\n","\n","    model.compile(\n","        optimizer=Adam(learning_rate=0.001),\n","        loss='sparse_categorical_crossentropy',\n","        metrics=['accuracy']\n","    )\n","    return model\n","\n","\n","# Define your training and validation data\n","x_train = train_tt_accels.numpy()\n","y_train = np.array(train_label)\n","x_val = test_tt_accels.numpy()\n","y_val = np.array(test_label)\n","\n","x_train.shape\n","\n","# Build the model\n","model = build_model()\n","\n","# Set up ModelCheckpoint callback to save the best model\n","checkpoint = ModelCheckpoint(\"best_model.h5\", monitor='val_accuracy', save_best_only=True, mode='max', verbose=1)\n","\n","# Train the model with callbacks\n","history = model.fit(x_train, y_train, epochs=50, validation_data=(x_val, y_val), callbacks=[checkpoint])\n","\n"]},{"cell_type":"code","source":["# Evaluate the best model\n","best_model = build_model()  # Rebuild the model\n","best_model.load_weights(\"best_model.h5\")  # Load the best weights\n","loss, accuracy = best_model.evaluate(x_val, y_val)\n","print(f\"Validation loss of the best model: {loss}, Validation accuracy of the best model: {accuracy}\")"],"metadata":{"id":"_RDDlVFMFO97","executionInfo":{"status":"ok","timestamp":1708643142345,"user_tz":300,"elapsed":2574,"user":{"displayName":"Sarah Mia Shen Lee Liu","userId":"01042882719085922731"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"c574f50f-4967-4b69-832f-160ddfe5ce77"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["100/100 [==============================] - 2s 8ms/step - loss: 0.1993 - accuracy: 0.9251\n","Validation loss of the best model: 0.199269101023674, Validation accuracy of the best model: 0.9251487851142883\n"]}]},{"cell_type":"code","source":["model.save('my_model.keras')"],"metadata":{"id":"pqZbLgtmHf5X","executionInfo":{"status":"ok","timestamp":1708643145140,"user_tz":300,"elapsed":250,"user":{"displayName":"Sarah Mia Shen Lee Liu","userId":"01042882719085922731"}}},"execution_count":15,"outputs":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}