train_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c0ae9eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d4065af5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1584, 15), (1584, 1))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.load('./extracted_features/X.npy')\n",
    "X = np.mean(X, axis=2)\n",
    "\n",
    "y = np.load('./extracted_features/y.npy')\n",
    "y = y.reshape(-1, 1)\n",
    "\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a2226903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1267, 15), (317, 15), (1267, 1), (317, 1))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, y, random_state=42, stratify=y, test_size=0.2)\n",
    "\n",
    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "65efce65",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "x_train = scaler.fit_transform(x_train)\n",
    "\n",
    "with open('scaler.pkl', 'wb') as f:\n",
    "    pickle.dump(scaler, f)\n",
    "    \n",
    "with open('scaler.pkl', 'rb') as f:\n",
    "    scaler = pickle.load(f)\n",
    "    \n",
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9db822db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_2 (LSTM)               (None, 32)                4352      \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 3)                 99        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 5,507\n",
      "Trainable params: 5,507\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.LSTM(32, input_shape=(15, 1)))\n",
    "model.add(tf.keras.layers.Dense(32, activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(3, activation='softmax'))\n",
    "\n",
    "model.compile(loss='sparse_categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1d79810a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-26 20:18:39.508784: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-11-26 20:18:39.611954: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-11-26 20:18:39.677751: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 2s 30ms/step - loss: 1.0731 - accuracy: 0.4736 - val_loss: 1.0389 - val_accuracy: 0.5268\n",
      "Epoch 2/20\n",
      " 1/20 [>.............................] - ETA: 0s - loss: 1.0403 - accuracy: 0.5469"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-26 20:18:40.175609: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-11-26 20:18:40.224375: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 0s 15ms/step - loss: 0.9992 - accuracy: 0.5478 - val_loss: 0.9363 - val_accuracy: 0.5552\n",
      "Epoch 3/20\n",
      "20/20 [==============================] - 0s 14ms/step - loss: 0.8484 - accuracy: 0.6054 - val_loss: 0.7277 - val_accuracy: 0.6341\n",
      "Epoch 4/20\n",
      "20/20 [==============================] - 0s 13ms/step - loss: 0.6458 - accuracy: 0.6780 - val_loss: 0.6167 - val_accuracy: 0.6877\n",
      "Epoch 5/20\n",
      "20/20 [==============================] - 0s 13ms/step - loss: 0.5580 - accuracy: 0.7214 - val_loss: 0.5482 - val_accuracy: 0.7697\n",
      "Epoch 6/20\n",
      "20/20 [==============================] - 0s 13ms/step - loss: 0.4958 - accuracy: 0.7782 - val_loss: 0.4808 - val_accuracy: 0.8139\n",
      "Epoch 7/20\n",
      "20/20 [==============================] - 0s 13ms/step - loss: 0.4298 - accuracy: 0.8208 - val_loss: 0.4170 - val_accuracy: 0.8423\n",
      "Epoch 8/20\n",
      "20/20 [==============================] - 0s 13ms/step - loss: 0.3481 - accuracy: 0.8619 - val_loss: 0.3411 - val_accuracy: 0.8738\n",
      "Epoch 9/20\n",
      "20/20 [==============================] - 0s 12ms/step - loss: 0.2779 - accuracy: 0.8998 - val_loss: 0.2602 - val_accuracy: 0.9085\n",
      "Epoch 10/20\n",
      "20/20 [==============================] - 0s 13ms/step - loss: 0.2101 - accuracy: 0.9211 - val_loss: 0.1852 - val_accuracy: 0.9306\n",
      "Epoch 11/20\n",
      "20/20 [==============================] - 0s 13ms/step - loss: 0.1513 - accuracy: 0.9479 - val_loss: 0.1254 - val_accuracy: 0.9590\n",
      "Epoch 12/20\n",
      "20/20 [==============================] - 0s 13ms/step - loss: 0.1036 - accuracy: 0.9629 - val_loss: 0.0844 - val_accuracy: 0.9653\n",
      "Epoch 13/20\n",
      "20/20 [==============================] - 0s 13ms/step - loss: 0.0799 - accuracy: 0.9708 - val_loss: 0.0697 - val_accuracy: 0.9779\n",
      "Epoch 14/20\n",
      "20/20 [==============================] - 0s 13ms/step - loss: 0.0587 - accuracy: 0.9787 - val_loss: 0.0600 - val_accuracy: 0.9842\n",
      "Epoch 15/20\n",
      "20/20 [==============================] - 0s 13ms/step - loss: 0.0492 - accuracy: 0.9818 - val_loss: 0.0613 - val_accuracy: 0.9842\n",
      "Epoch 16/20\n",
      "20/20 [==============================] - 0s 13ms/step - loss: 0.0491 - accuracy: 0.9811 - val_loss: 0.0559 - val_accuracy: 0.9811\n",
      "Epoch 17/20\n",
      "20/20 [==============================] - 0s 13ms/step - loss: 0.0522 - accuracy: 0.9803 - val_loss: 0.0374 - val_accuracy: 0.9905\n",
      "Epoch 18/20\n",
      "20/20 [==============================] - 0s 13ms/step - loss: 0.0430 - accuracy: 0.9858 - val_loss: 0.0381 - val_accuracy: 0.9874\n",
      "Epoch 19/20\n",
      "20/20 [==============================] - 0s 13ms/step - loss: 0.0372 - accuracy: 0.9874 - val_loss: 0.0334 - val_accuracy: 0.9905\n",
      "Epoch 20/20\n",
      "20/20 [==============================] - 0s 13ms/step - loss: 0.0330 - accuracy: 0.9874 - val_loss: 0.0334 - val_accuracy: 0.9937\n"
     ]
    }
   ],
   "source": [
    "es = tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', restore_best_weights=True, verbose=1, patience=10)\n",
    "\n",
    "history = model.fit(x_train, y_train, batch_size=64, epochs=20, validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3778d121",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(acc, label='Training Accuracy')\n",
    "plt.plot(val_acc, label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(loss, label='Training Loss')\n",
    "plt.plot(val_loss, label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.ylabel('Cross Entropy')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "334a64b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('best.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c3c8f7e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.0267\n",
      "Training Accuracy: 99.13%\n",
      "\n",
      "Testing loss: 0.0334\n",
      "Testing Accuracy: 99.37%\n"
     ]
    }
   ],
   "source": [
    "loss, acc = model.evaluate(x_train, y_train, verbose=0)\n",
    "print(f\"Training loss: {loss:.4f}\")\n",
    "print(f\"Training Accuracy: {(acc * 100):.2f}%\\n\")\n",
    "\n",
    "loss, acc = model.evaluate(x_test, y_test, verbose=0)\n",
    "print(f\"Testing loss: {loss:.4f}\")\n",
    "print(f\"Testing Accuracy: {(acc * 100):.2f}%\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}