03_0922_HAR(GRU).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "006e8491",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import mean, std, dstack\n",
    "import pandas as pd\n",
    "from pandas import read_csv\n",
    "from matplotlib import pyplot as plt\n",
    "import keras\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Activation\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Dense, Input, Dropout, Flatten, BatchNormalization, GRU, Bidirectional\n",
    "from tensorflow.keras.layers import concatenate, Dropout\n",
    "from tensorflow.keras import optimizers\n",
    "\n",
    "import zipfile\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f197229d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_file(filepath):\n",
    "    dataframe = read_csv(filepath, header=None, delim_whitespace=True)\n",
    "    return dataframe.values\n",
    " \n",
    "\n",
    "def load_group(filenames, prefix=''):\n",
    "    loaded = list()\n",
    "    for name in filenames:\n",
    "        data = load_file(prefix + name)\n",
    "        loaded.append(data)\n",
    "    loaded = dstack(loaded)\n",
    "    return loaded\n",
    " \n",
    "def load_dataset_group(group, prefix=''):\n",
    "    filepath = prefix + group + '/Inertial Signals/'\n",
    "    filenames = list()\n",
    "    filenames += ['total_acc_x_'+group+'.txt', 'total_acc_y_'+group+'.txt', 'total_acc_z_'+group+'.txt']\n",
    "    filenames += ['body_acc_x_'+group+'.txt', 'body_acc_y_'+group+'.txt', 'body_acc_z_'+group+'.txt']\n",
    "    filenames += ['body_gyro_x_'+group+'.txt', 'body_gyro_y_'+group+'.txt', 'body_gyro_z_'+group+'.txt']\n",
    "    X = load_group(filenames, filepath)\n",
    "    y = load_file(prefix + group + '/y_'+group+'.txt')\n",
    "    return X, y\n",
    "\n",
    "def load_dataset(prefix=''):\n",
    "    trainX, trainy = load_dataset_group('train', prefix + 'HAR/')\n",
    "    testX, testy = load_dataset_group('test', prefix + 'HAR/')\n",
    "    trainy = trainy - 1\n",
    "    testy = testy - 1\n",
    "    trainy = to_categorical(trainy)\n",
    "    testy = to_categorical(testy)\n",
    "    print(\"trainX shape: \",trainX.shape)\n",
    "    print(\"trainy shape: \", trainy.shape)\n",
    "    print(\"testX shape: \", testX.shape)\n",
    "    print(\"testy shape: \", testy.shape)\n",
    "    return trainX, trainy, testX, testy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "88582e36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainX shape:  (7352, 128, 9)\n",
      "trainy shape:  (7352, 6)\n",
      "testX shape:  (2947, 128, 9)\n",
      "testy shape:  (2947, 6)\n"
     ]
    }
   ],
   "source": [
    "trainX, trainy, testX, testy = load_dataset('data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "510e032f",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "validation_split = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "600725c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]\n",
    "model = Sequential()\n",
    "model.add(GRU(64, input_shape=(n_timesteps,n_features), return_sequences=True))\n",
    "model.add(BatchNormalization())\n",
    "model.add(GRU(32))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(16))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(n_outputs, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d53da06b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7aa95c45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "184/184 [==============================] - 28s 121ms/step - loss: 1.1049 - accuracy: 0.6002 - val_loss: 1.5318 - val_accuracy: 0.3753\n",
      "Epoch 2/50\n",
      "184/184 [==============================] - 21s 113ms/step - loss: 0.5897 - accuracy: 0.8045 - val_loss: 0.5647 - val_accuracy: 0.7539\n",
      "Epoch 3/50\n",
      "184/184 [==============================] - 20s 106ms/step - loss: 0.3351 - accuracy: 0.9068 - val_loss: 0.2760 - val_accuracy: 0.9082\n",
      "Epoch 4/50\n",
      "184/184 [==============================] - 20s 107ms/step - loss: 0.2028 - accuracy: 0.9417 - val_loss: 0.2666 - val_accuracy: 0.9103\n",
      "Epoch 5/50\n",
      "184/184 [==============================] - 20s 107ms/step - loss: 0.1602 - accuracy: 0.9466 - val_loss: 0.3215 - val_accuracy: 0.8450\n",
      "Epoch 6/50\n",
      "184/184 [==============================] - 20s 108ms/step - loss: 0.1584 - accuracy: 0.9459 - val_loss: 0.2815 - val_accuracy: 0.9266\n",
      "Epoch 7/50\n",
      "184/184 [==============================] - 19s 106ms/step - loss: 0.1396 - accuracy: 0.9497 - val_loss: 0.2231 - val_accuracy: 0.9232\n",
      "Epoch 8/50\n",
      "184/184 [==============================] - 19s 106ms/step - loss: 0.1412 - accuracy: 0.9464 - val_loss: 0.2376 - val_accuracy: 0.9191\n",
      "Epoch 9/50\n",
      "184/184 [==============================] - 19s 106ms/step - loss: 0.1277 - accuracy: 0.9519 - val_loss: 0.2223 - val_accuracy: 0.9191\n",
      "Epoch 10/50\n",
      "184/184 [==============================] - 19s 106ms/step - loss: 0.1195 - accuracy: 0.9543 - val_loss: 0.2149 - val_accuracy: 0.9171\n",
      "Epoch 11/50\n",
      "184/184 [==============================] - 21s 115ms/step - loss: 0.1152 - accuracy: 0.9531 - val_loss: 0.2835 - val_accuracy: 0.8674\n",
      "Epoch 12/50\n",
      "184/184 [==============================] - 20s 108ms/step - loss: 0.1299 - accuracy: 0.9486 - val_loss: 0.2317 - val_accuracy: 0.9177\n",
      "Epoch 13/50\n",
      "184/184 [==============================] - 19s 104ms/step - loss: 0.1157 - accuracy: 0.9520 - val_loss: 0.2931 - val_accuracy: 0.9245\n",
      "Epoch 14/50\n",
      "184/184 [==============================] - 19s 103ms/step - loss: 0.1160 - accuracy: 0.9485 - val_loss: 0.2264 - val_accuracy: 0.9143\n",
      "Epoch 15/50\n",
      "184/184 [==============================] - 22s 118ms/step - loss: 0.1120 - accuracy: 0.9544 - val_loss: 0.2971 - val_accuracy: 0.9143\n",
      "Epoch 16/50\n",
      "184/184 [==============================] - 20s 106ms/step - loss: 0.1072 - accuracy: 0.9537 - val_loss: 0.2205 - val_accuracy: 0.9150\n",
      "Epoch 17/50\n",
      "184/184 [==============================] - 19s 104ms/step - loss: 0.1038 - accuracy: 0.9573 - val_loss: 0.2455 - val_accuracy: 0.9103\n",
      "Epoch 18/50\n",
      "184/184 [==============================] - 19s 104ms/step - loss: 0.1111 - accuracy: 0.9539 - val_loss: 0.2319 - val_accuracy: 0.9218\n",
      "Epoch 19/50\n",
      "184/184 [==============================] - 20s 109ms/step - loss: 0.1078 - accuracy: 0.9549 - val_loss: 0.2730 - val_accuracy: 0.9028\n",
      "Epoch 20/50\n",
      "184/184 [==============================] - 25s 136ms/step - loss: 0.1113 - accuracy: 0.9520 - val_loss: 0.2480 - val_accuracy: 0.9130\n",
      "Epoch 21/50\n",
      "184/184 [==============================] - 24s 133ms/step - loss: 0.1046 - accuracy: 0.9531 - val_loss: 0.2440 - val_accuracy: 0.9116\n",
      "Epoch 22/50\n",
      "184/184 [==============================] - 23s 125ms/step - loss: 0.1011 - accuracy: 0.9568 - val_loss: 0.2266 - val_accuracy: 0.9225\n",
      "Epoch 23/50\n",
      "184/184 [==============================] - 22s 118ms/step - loss: 0.1009 - accuracy: 0.9551 - val_loss: 0.2494 - val_accuracy: 0.9143\n",
      "Epoch 24/50\n",
      "184/184 [==============================] - 20s 111ms/step - loss: 0.1062 - accuracy: 0.9568 - val_loss: 0.2647 - val_accuracy: 0.9082\n",
      "Epoch 25/50\n",
      "184/184 [==============================] - 23s 127ms/step - loss: 0.1065 - accuracy: 0.9551 - val_loss: 0.2300 - val_accuracy: 0.9177\n",
      "Epoch 26/50\n",
      "184/184 [==============================] - 22s 119ms/step - loss: 0.1042 - accuracy: 0.9536 - val_loss: 0.2152 - val_accuracy: 0.9164\n",
      "Epoch 27/50\n",
      "184/184 [==============================] - 21s 116ms/step - loss: 0.0959 - accuracy: 0.9580 - val_loss: 0.2559 - val_accuracy: 0.9041\n",
      "Epoch 28/50\n",
      "184/184 [==============================] - 23s 127ms/step - loss: 0.1039 - accuracy: 0.9539 - val_loss: 0.2274 - val_accuracy: 0.9225\n",
      "Epoch 29/50\n",
      "184/184 [==============================] - 23s 124ms/step - loss: 0.1028 - accuracy: 0.9541 - val_loss: 0.3068 - val_accuracy: 0.8987\n",
      "Epoch 30/50\n",
      "184/184 [==============================] - 23s 127ms/step - loss: 0.1011 - accuracy: 0.9534 - val_loss: 0.2677 - val_accuracy: 0.9055\n",
      "Epoch 31/50\n",
      "184/184 [==============================] - 23s 123ms/step - loss: 0.0980 - accuracy: 0.9575 - val_loss: 0.2858 - val_accuracy: 0.8736\n",
      "Epoch 32/50\n",
      "184/184 [==============================] - 23s 123ms/step - loss: 0.1000 - accuracy: 0.9537 - val_loss: 0.2337 - val_accuracy: 0.9062\n",
      "Epoch 33/50\n",
      "184/184 [==============================] - 22s 117ms/step - loss: 0.0961 - accuracy: 0.9607 - val_loss: 0.2219 - val_accuracy: 0.9239\n",
      "Epoch 34/50\n",
      "184/184 [==============================] - 21s 112ms/step - loss: 0.0949 - accuracy: 0.9568 - val_loss: 0.2809 - val_accuracy: 0.9103\n",
      "Epoch 35/50\n",
      "184/184 [==============================] - 23s 124ms/step - loss: 0.1088 - accuracy: 0.9536 - val_loss: 0.2333 - val_accuracy: 0.9245\n",
      "Epoch 36/50\n",
      "184/184 [==============================] - 22s 121ms/step - loss: 0.0939 - accuracy: 0.9563 - val_loss: 0.2728 - val_accuracy: 0.9123\n",
      "Epoch 37/50\n",
      "184/184 [==============================] - 21s 115ms/step - loss: 0.0915 - accuracy: 0.9599 - val_loss: 0.2714 - val_accuracy: 0.9103\n",
      "Epoch 38/50\n",
      "184/184 [==============================] - 21s 115ms/step - loss: 0.0888 - accuracy: 0.9580 - val_loss: 0.3369 - val_accuracy: 0.9218\n",
      "Epoch 39/50\n",
      "184/184 [==============================] - 21s 112ms/step - loss: 0.0894 - accuracy: 0.9595 - val_loss: 0.2588 - val_accuracy: 0.9218\n",
      "Epoch 40/50\n",
      "184/184 [==============================] - 19s 105ms/step - loss: 0.1200 - accuracy: 0.9505 - val_loss: 0.2618 - val_accuracy: 0.9103\n",
      "Epoch 41/50\n",
      "184/184 [==============================] - 20s 107ms/step - loss: 0.0969 - accuracy: 0.9565 - val_loss: 0.2433 - val_accuracy: 0.9198\n",
      "Epoch 42/50\n",
      "184/184 [==============================] - 20s 107ms/step - loss: 0.0904 - accuracy: 0.9577 - val_loss: 0.2515 - val_accuracy: 0.9232\n",
      "Epoch 43/50\n",
      "184/184 [==============================] - 23s 126ms/step - loss: 0.0940 - accuracy: 0.9565 - val_loss: 0.2355 - val_accuracy: 0.9266\n",
      "Epoch 44/50\n",
      "184/184 [==============================] - 23s 123ms/step - loss: 0.0917 - accuracy: 0.9573 - val_loss: 0.2969 - val_accuracy: 0.9062\n",
      "Epoch 45/50\n",
      "184/184 [==============================] - 23s 125ms/step - loss: 0.0862 - accuracy: 0.9599 - val_loss: 0.2547 - val_accuracy: 0.9157\n",
      "Epoch 46/50\n",
      "184/184 [==============================] - 21s 116ms/step - loss: 0.0899 - accuracy: 0.9589 - val_loss: 0.2475 - val_accuracy: 0.9191\n",
      "Epoch 47/50\n",
      "184/184 [==============================] - 22s 121ms/step - loss: 0.0893 - accuracy: 0.9592 - val_loss: 0.3254 - val_accuracy: 0.8946\n",
      "Epoch 48/50\n",
      "184/184 [==============================] - 23s 124ms/step - loss: 0.0866 - accuracy: 0.9607 - val_loss: 0.2844 - val_accuracy: 0.9116\n",
      "Epoch 49/50\n",
      "184/184 [==============================] - 21s 116ms/step - loss: 0.0872 - accuracy: 0.9619 - val_loss: 0.2440 - val_accuracy: 0.9211\n",
      "Epoch 50/50\n",
      "184/184 [==============================] - 22s 119ms/step - loss: 0.0892 - accuracy: 0.9587 - val_loss: 0.2361 - val_accuracy: 0.9218\n"
     ]
    }
   ],
   "source": [
    "train_epochs = 50\n",
    "history = model.fit(trainX, trainy, epochs=train_epochs, batch_size=batch_size, verbose=True,  validation_split=validation_split, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7e7b4894",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'Validation'], loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f70e9ddd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the GRU model 0.9402782320976257\n",
      "93/93 [==============================] - 4s 29ms/step\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_accuracy = model.evaluate(testX, testy, batch_size=batch_size, verbose=0)\n",
    "print(\"Accuracy of the GRU model\", test_accuracy)\n",
    "predictions=model.predict(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1ee31020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0635019e-06, 1.4994569e-07, 6.7912913e-07, 7.1018692e-03,\n",
       "        9.9289471e-01, 1.4890508e-06],\n",
       "       [1.5404735e-06, 9.9120257e-08, 8.5695785e-07, 4.3262090e-03,\n",
       "        9.9567032e-01, 8.9948981e-07],\n",
       "       [1.4215860e-06, 1.0515824e-07, 8.0529071e-07, 4.5040292e-03,\n",
       "        9.9549270e-01, 9.1995247e-07],\n",
       "       ...,\n",
       "       [3.7710001e-06, 9.9998033e-01, 5.2043574e-06, 6.3901302e-06,\n",
       "        2.7692190e-06, 1.4908716e-06],\n",
       "       [2.9859627e-06, 9.9999022e-01, 1.0153929e-06, 3.4868679e-06,\n",
       "        1.2328237e-06, 1.0288337e-06],\n",
       "       [2.7581600e-06, 9.9998891e-01, 7.1170723e-07, 4.3873802e-06,\n",
       "        1.6210705e-06, 1.5478693e-06]], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3184bb3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_results(testy,predictions,classNames):\n",
    "    y_test=np.argmax(testy, axis=1)\n",
    "    y_scores=np.argmax(predictions, axis=1)\n",
    "    classes = len(classNames)\n",
    "    cm = confusion_matrix(y_test, y_scores)\n",
    "    print(\"**** Confusion Matrix ****\")\n",
    "    print(cm)\n",
    "    print(\"**** Classification Report ****\")\n",
    "    print(classification_report(y_test, y_scores, target_names=classNames))\n",
    "    con = np.zeros((classes,classes))\n",
    "    for x in range(classes):\n",
    "        for y in range(classes):\n",
    "            con[x,y] = cm[x,y]\n",
    "\n",
    "    plt.figure(figsize=(10,8))\n",
    "    df = sns.heatmap(con, annot=True, fmt='g', cmap='Blues',xticklabels= classNames , yticklabels= classNames)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Actual')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "120c5f06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** Confusion Matrix ****\n",
      "[[486   1   9   0   0   0]\n",
      " [  2 445  23   1   0   0]\n",
      " [  1   0 419   0   0   0]\n",
      " [  0   0   0 410  74   7]\n",
      " [  0   0   0  58 474   0]\n",
      " [  0   0   0   0   0 537]]\n",
      "**** Classification Report ****\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Walking       0.99      0.98      0.99       496\n",
      "      Walkup       1.00      0.94      0.97       471\n",
      "    Walkdown       0.93      1.00      0.96       420\n",
      "     Sitting       0.87      0.84      0.85       491\n",
      "    Standing       0.86      0.89      0.88       532\n",
      "      Laying       0.99      1.00      0.99       537\n",
      "\n",
      "    accuracy                           0.94      2947\n",
      "   macro avg       0.94      0.94      0.94      2947\n",
      "weighted avg       0.94      0.94      0.94      2947\n",
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classNames=[\"Walking\",\"Walkup\",\"Walkdown\",\"Sitting\",\"Standing\",\"Laying\"]\n",
    "display_results(testy,predictions,classNames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f58bd3c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}