IT416_Fusion_Model_with_only_one_ConvLSTM2D_layer.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Df58j5DDSOJJ"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "-Ct301PLSg7m"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
        "\n",
        "import cv2\n",
        "import random\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.utils import shuffle\n",
        "from IPython.display import display\n",
        "from keras.utils.vis_utils import plot_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VfIMZ4RwSnsu"
      },
      "outputs": [],
      "source": [
        "if not os.path.isfile('processed_dataset.zip'):\n",
        "  os.system(' '.join(['cp', 'drive/MyDrive/IT416/processed_dataset.zip', 'processed_dataset.zip']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "86yQ1B5DS2WE"
      },
      "outputs": [],
      "source": [
        "if not os.path.isdir('processed_dataset'):\n",
        "  os.system(' '.join(['unzip', 'processed_dataset.zip']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "zI1Kv-SoS50G"
      },
      "outputs": [],
      "source": [
        "def get_dataset(num_people):\n",
        "  x1 = []\n",
        "  x2 = []\n",
        "  y  = []\n",
        "  for i in range(num_people):\n",
        "      for j in range(10):\n",
        "          path = 'processed_dataset' + '/' + 'person' + str(i+1) + '/' + 'face' + '/' + str(j+1) + '.pgm'\n",
        "          image = cv2.imread(path, -1)\n",
        "          image = cv2.resize(image, (12, 12))\n",
        "          image = image.reshape(12, 12, 1)\n",
        "          x1.append(image)\n",
        "      gait_folder = 'processed_dataset' + '/' + 'person' + str(i+1) + '/' + 'gait' + '/'\n",
        "      styles = os.listdir(gait_folder)\n",
        "      styles.sort()\n",
        "      for j, style in enumerate(styles):\n",
        "          style_folder = gait_folder + style + '/'\n",
        "          angles = os.listdir(style_folder)\n",
        "          angles.sort()\n",
        "          x = []\n",
        "          for angle in angles:\n",
        "              angle_folder = style_folder + angle + '/'\n",
        "              images = os.listdir(angle_folder)\n",
        "              images.sort()\n",
        "              random.shuffle(images)\n",
        "              images = images[: 16]\n",
        "              images.sort()\n",
        "              xx = []\n",
        "              for image in images:\n",
        "                  path = angle_folder + image\n",
        "                  img = cv2.imread(path, -1)\n",
        "                  img = cv2.resize(img, (32, 32))\n",
        "                  img = np.array(img, dtype=np.float16)\n",
        "                  xx.append(img)\n",
        "              xx = np.array(xx, dtype=np.float16)\n",
        "              x.append(xx)\n",
        "          x = np.stack(x, axis=3)\n",
        "          x2.append(x)\n",
        "      for j in range(10):\n",
        "          y.append(i)\n",
        "  x1 = np.array(x1, dtype = np.float32)\n",
        "  x2 = np.array(x2, dtype = np.float32)\n",
        "  y = tf.keras.utils.to_categorical(y)\n",
        "  x1, x2, y = shuffle(x1, x2, y)\n",
        "  return x1, x2, y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y8BXapYfTNxT",
        "outputId": "8ece04e4-db96-4c8e-b25f-7205731a9c4f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_face: (400, 12, 12, 1)\n",
            "x_gait: (400, 16, 32, 32, 11)\n",
            "y     : (400, 40)\n"
          ]
        }
      ],
      "source": [
        "num_people = 40\n",
        "x_face, x_gait, y = get_dataset(num_people)\n",
        "\n",
        "print('x_face:', x_face.shape)\n",
        "print('x_gait:', x_gait.shape)\n",
        "print('y     :', y.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P6I7HJaJTbM-",
        "outputId": "8e183529-a534-4994-b2a6-3e1096183875"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " face_input (InputLayer)        [(None, 12, 12, 1)]  0           []                               \n",
            "                                                                                                  \n",
            " gait_input (InputLayer)        [(None, 16, 32, 32,  0           []                               \n",
            "                                 11)]                                                             \n",
            "                                                                                                  \n",
            " face_conv_1 (Conv2D)           (None, 10, 10, 32)   320         ['face_input[0][0]']             \n",
            "                                                                                                  \n",
            " gait_convlstm_1 (ConvLSTM2D)   (None, 30, 30, 16)   15616       ['gait_input[0][0]']             \n",
            "                                                                                                  \n",
            " face_dropout_1 (Dropout)       (None, 10, 10, 32)   0           ['face_conv_1[0][0]']            \n",
            "                                                                                                  \n",
            " gait_dropout_1 (Dropout)       (None, 30, 30, 16)   0           ['gait_convlstm_1[0][0]']        \n",
            "                                                                                                  \n",
            " face_flatten_1 (Flatten)       (None, 3200)         0           ['face_dropout_1[0][0]']         \n",
            "                                                                                                  \n",
            " gait_flatten_1 (Flatten)       (None, 14400)        0           ['gait_dropout_1[0][0]']         \n",
            "                                                                                                  \n",
            " face_dense_1 (Dense)           (None, 64)           204864      ['face_flatten_1[0][0]']         \n",
            "                                                                                                  \n",
            " gait_dense_1 (Dense)           (None, 64)           921664      ['gait_flatten_1[0][0]']         \n",
            "                                                                                                  \n",
            " face_dropout_2 (Dropout)       (None, 64)           0           ['face_dense_1[0][0]']           \n",
            "                                                                                                  \n",
            " gait_dropout_4 (Dropout)       (None, 64)           0           ['gait_dense_1[0][0]']           \n",
            "                                                                                                  \n",
            " batch_normalization (BatchNorm  (None, 64)          256         ['face_dropout_2[0][0]']         \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " batch_normalization_1 (BatchNo  (None, 64)          256         ['gait_dropout_4[0][0]']         \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " face_gait_weighted_average (La  (None, 64)          0           ['batch_normalization[0][0]',    \n",
            " mbda)                                                            'batch_normalization_1[0][0]']  \n",
            "                                                                                                  \n",
            " output (Dense)                 (None, 40)           2600        ['face_gait_weighted_average[0][0\n",
            "                                                                 ]']                              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 1,145,576\n",
            "Trainable params: 1,145,320\n",
            "Non-trainable params: 256\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "face_input = tf.keras.layers.Input(shape = (12, 12, 1), name = 'face_input')\n",
        "\n",
        "face_encoder = tf.keras.layers.Conv2D(32, kernel_size = 3, activation = 'relu', name = 'face_conv_1')(face_input)\n",
        "face_encoder = tf.keras.layers.Dropout(0.2, name = 'face_dropout_1')(face_encoder)\n",
        "face_encoder = tf.keras.layers.Flatten(name = 'face_flatten_1')(face_encoder)\n",
        "face_encoder = tf.keras.layers.Dense(64, activation = 'relu', name = 'face_dense_1')(face_encoder)\n",
        "face_encoder = tf.keras.layers.Dropout(0.2, name = 'face_dropout_2')(face_encoder)\n",
        "face_encoder = tf.keras.layers.BatchNormalization()(face_encoder)\n",
        "\n",
        "gait_input = tf.keras.layers.Input(shape = (16, 32, 32, 11), name = 'gait_input')\n",
        "\n",
        "gait_encoder = tf.keras.layers.ConvLSTM2D(16, kernel_size = 3, name = 'gait_convlstm_1')(gait_input)\n",
        "gait_encoder = tf.keras.layers.Dropout(0.4, name = 'gait_dropout_1')(gait_encoder)\n",
        "gait_encoder = tf.keras.layers.Flatten(name = 'gait_flatten_1')(gait_encoder)\n",
        "gait_encoder = tf.keras.layers.Dense(64, activation = 'relu', name = 'gait_dense_1')(gait_encoder)\n",
        "gait_encoder = tf.keras.layers.Dropout(0.4, name = 'gait_dropout_4')(gait_encoder)\n",
        "gait_encoder = tf.keras.layers.BatchNormalization()(gait_encoder)\n",
        "\n",
        "def weighted_average(tensors):\n",
        "  alpha = 0.5\n",
        "  face = tensors[0]\n",
        "  gait = tensors[1]\n",
        "  weighted_average = alpha * face + (1 - alpha) * gait\n",
        "  return weighted_average\n",
        "\n",
        "decoder = tf.keras.layers.Lambda(weighted_average, name = 'face_gait_weighted_average')([face_encoder, gait_encoder])\n",
        "\n",
        "decoder = tf.keras.layers.Dense(40, activation = 'softmax', name = 'output')(decoder)\n",
        "\n",
        "model = tf.keras.models.Model(\n",
        "      inputs = [face_input, gait_input], \n",
        "      outputs = decoder)\n",
        "\n",
        "model.compile(optimizer = tf.keras.optimizers.Adam(learning_rate = 0.0003), \n",
        "              loss = 'categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 976
        },
        "id": "rY6ced4-Vv-_",
        "outputId": "6b788c7d-56a4-4a18-b52a-6e22e1ab1f7f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot = plot_model(model, show_shapes = True, to_file = 'model.png', show_layer_names = True)\n",
        "display(plot)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "mB-791cqUL_h"
      },
      "outputs": [],
      "source": [
        "EPOCHS = 60\n",
        "BATCH_SIZE = 4\n",
        "VALID_SPLIT = 0.3\n",
        "\n",
        "acc_train = []\n",
        "acc_val = []"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit([x_face, x_gait], y, batch_size = BATCH_SIZE, epochs = EPOCHS, validation_split = VALID_SPLIT)\n",
        "acc_train += history.history['accuracy']\n",
        "acc_val += history.history['val_accuracy']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9w8Z_owEEIvy",
        "outputId": "c7f1aba4-86e7-4c11-e350-964df3765849"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/60\n",
            "70/70 [==============================] - 20s 111ms/step - loss: 3.7461 - accuracy: 0.0321 - val_loss: 3.8650 - val_accuracy: 0.0250\n",
            "Epoch 2/60\n",
            "70/70 [==============================] - 8s 111ms/step - loss: 3.4524 - accuracy: 0.0857 - val_loss: 3.5586 - val_accuracy: 0.0750\n",
            "Epoch 3/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 3.3140 - accuracy: 0.1643 - val_loss: 3.4573 - val_accuracy: 0.1583\n",
            "Epoch 4/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 3.1688 - accuracy: 0.2429 - val_loss: 3.4288 - val_accuracy: 0.1583\n",
            "Epoch 5/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 3.0433 - accuracy: 0.2821 - val_loss: 3.2290 - val_accuracy: 0.2250\n",
            "Epoch 6/60\n",
            "70/70 [==============================] - 8s 109ms/step - loss: 2.9015 - accuracy: 0.3357 - val_loss: 3.2410 - val_accuracy: 0.1083\n",
            "Epoch 7/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 2.8357 - accuracy: 0.3821 - val_loss: 3.2481 - val_accuracy: 0.2167\n",
            "Epoch 8/60\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 2.7697 - accuracy: 0.4643 - val_loss: 3.1569 - val_accuracy: 0.3000\n",
            "Epoch 9/60\n",
            "70/70 [==============================] - 8s 111ms/step - loss: 2.6650 - accuracy: 0.5321 - val_loss: 3.2499 - val_accuracy: 0.1750\n",
            "Epoch 10/60\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 2.6030 - accuracy: 0.5643 - val_loss: 3.1519 - val_accuracy: 0.2417\n",
            "Epoch 11/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 2.6243 - accuracy: 0.5071 - val_loss: 3.1257 - val_accuracy: 0.2667\n",
            "Epoch 12/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 2.4956 - accuracy: 0.5714 - val_loss: 3.0609 - val_accuracy: 0.3167\n",
            "Epoch 13/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 2.3866 - accuracy: 0.6536 - val_loss: 3.2087 - val_accuracy: 0.1917\n",
            "Epoch 14/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 2.3394 - accuracy: 0.6571 - val_loss: 3.0787 - val_accuracy: 0.2083\n",
            "Epoch 15/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 2.3216 - accuracy: 0.6750 - val_loss: 3.1995 - val_accuracy: 0.1500\n",
            "Epoch 16/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 2.2439 - accuracy: 0.7000 - val_loss: 3.0059 - val_accuracy: 0.4083\n",
            "Epoch 17/60\n",
            "70/70 [==============================] - 7s 102ms/step - loss: 2.2708 - accuracy: 0.6750 - val_loss: 2.9701 - val_accuracy: 0.3750\n",
            "Epoch 18/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 2.1504 - accuracy: 0.7286 - val_loss: 2.9042 - val_accuracy: 0.4333\n",
            "Epoch 19/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 2.1186 - accuracy: 0.7107 - val_loss: 2.9399 - val_accuracy: 0.4333\n",
            "Epoch 20/60\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 1.9901 - accuracy: 0.8214 - val_loss: 3.0436 - val_accuracy: 0.3000\n",
            "Epoch 21/60\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 1.9838 - accuracy: 0.7429 - val_loss: 2.8887 - val_accuracy: 0.2750\n",
            "Epoch 22/60\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 1.9763 - accuracy: 0.7500 - val_loss: 3.1468 - val_accuracy: 0.2417\n",
            "Epoch 23/60\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 1.8411 - accuracy: 0.7679 - val_loss: 2.9632 - val_accuracy: 0.2833\n",
            "Epoch 24/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 1.8443 - accuracy: 0.7929 - val_loss: 2.7522 - val_accuracy: 0.4833\n",
            "Epoch 25/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 1.7585 - accuracy: 0.8179 - val_loss: 2.7708 - val_accuracy: 0.3500\n",
            "Epoch 26/60\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 1.5770 - accuracy: 0.8286 - val_loss: 2.8418 - val_accuracy: 0.3417\n",
            "Epoch 27/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 1.6254 - accuracy: 0.8679 - val_loss: 2.6889 - val_accuracy: 0.4667\n",
            "Epoch 28/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 1.5812 - accuracy: 0.8643 - val_loss: 2.6732 - val_accuracy: 0.4667\n",
            "Epoch 29/60\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 1.5781 - accuracy: 0.8536 - val_loss: 2.5006 - val_accuracy: 0.5833\n",
            "Epoch 30/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 1.4743 - accuracy: 0.8643 - val_loss: 2.4852 - val_accuracy: 0.5250\n",
            "Epoch 31/60\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 1.4362 - accuracy: 0.8286 - val_loss: 2.5673 - val_accuracy: 0.3917\n",
            "Epoch 32/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 1.3750 - accuracy: 0.8679 - val_loss: 2.3836 - val_accuracy: 0.5667\n",
            "Epoch 33/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 1.3592 - accuracy: 0.8500 - val_loss: 2.4944 - val_accuracy: 0.4083\n",
            "Epoch 34/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 1.2278 - accuracy: 0.9036 - val_loss: 2.4500 - val_accuracy: 0.5000\n",
            "Epoch 35/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 1.2482 - accuracy: 0.9071 - val_loss: 2.3622 - val_accuracy: 0.5500\n",
            "Epoch 36/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 1.1566 - accuracy: 0.8929 - val_loss: 2.3180 - val_accuracy: 0.5333\n",
            "Epoch 37/60\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 1.1222 - accuracy: 0.9071 - val_loss: 2.4538 - val_accuracy: 0.4000\n",
            "Epoch 38/60\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 1.1274 - accuracy: 0.8857 - val_loss: 2.3187 - val_accuracy: 0.5333\n",
            "Epoch 39/60\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 1.0406 - accuracy: 0.9250 - val_loss: 2.1971 - val_accuracy: 0.6000\n",
            "Epoch 40/60\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 1.0566 - accuracy: 0.8929 - val_loss: 2.2619 - val_accuracy: 0.5500\n",
            "Epoch 41/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 1.0086 - accuracy: 0.9179 - val_loss: 2.1157 - val_accuracy: 0.6083\n",
            "Epoch 42/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 0.9115 - accuracy: 0.9250 - val_loss: 2.2549 - val_accuracy: 0.4833\n",
            "Epoch 43/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 0.8277 - accuracy: 0.9500 - val_loss: 2.0827 - val_accuracy: 0.7000\n",
            "Epoch 44/60\n",
            "70/70 [==============================] - 8s 111ms/step - loss: 0.9056 - accuracy: 0.9214 - val_loss: 2.0662 - val_accuracy: 0.6333\n",
            "Epoch 45/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 0.8074 - accuracy: 0.9321 - val_loss: 2.0827 - val_accuracy: 0.6250\n",
            "Epoch 46/60\n",
            "70/70 [==============================] - 7s 102ms/step - loss: 0.7281 - accuracy: 0.9500 - val_loss: 1.9520 - val_accuracy: 0.6583\n",
            "Epoch 47/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 0.8165 - accuracy: 0.9214 - val_loss: 2.0835 - val_accuracy: 0.6500\n",
            "Epoch 48/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 0.7223 - accuracy: 0.9571 - val_loss: 2.0062 - val_accuracy: 0.6083\n",
            "Epoch 49/60\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 0.7498 - accuracy: 0.9464 - val_loss: 1.9550 - val_accuracy: 0.7000\n",
            "Epoch 50/60\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 0.6875 - accuracy: 0.9500 - val_loss: 2.2106 - val_accuracy: 0.4750\n",
            "Epoch 51/60\n",
            "70/70 [==============================] - 8s 111ms/step - loss: 0.6821 - accuracy: 0.9571 - val_loss: 2.1471 - val_accuracy: 0.5167\n",
            "Epoch 52/60\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 0.6340 - accuracy: 0.9643 - val_loss: 2.1085 - val_accuracy: 0.5250\n",
            "Epoch 53/60\n",
            "70/70 [==============================] - 7s 102ms/step - loss: 0.6118 - accuracy: 0.9607 - val_loss: 1.9311 - val_accuracy: 0.5667\n",
            "Epoch 54/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 0.6265 - accuracy: 0.9679 - val_loss: 1.9018 - val_accuracy: 0.6333\n",
            "Epoch 55/60\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 0.6132 - accuracy: 0.9607 - val_loss: 1.8960 - val_accuracy: 0.6583\n",
            "Epoch 56/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 0.6170 - accuracy: 0.9571 - val_loss: 2.1735 - val_accuracy: 0.4500\n",
            "Epoch 57/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 0.5412 - accuracy: 0.9571 - val_loss: 2.0940 - val_accuracy: 0.5500\n",
            "Epoch 58/60\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 0.5022 - accuracy: 0.9786 - val_loss: 2.0813 - val_accuracy: 0.5000\n",
            "Epoch 59/60\n",
            "70/70 [==============================] - 7s 102ms/step - loss: 0.5057 - accuracy: 0.9714 - val_loss: 1.8236 - val_accuracy: 0.6667\n",
            "Epoch 60/60\n",
            "70/70 [==============================] - 8s 111ms/step - loss: 0.4632 - accuracy: 0.9714 - val_loss: 1.7517 - val_accuracy: 0.6750\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit([x_face, x_gait], y, batch_size = BATCH_SIZE, epochs = 10, validation_split = VALID_SPLIT)\n",
        "acc_train += history.history['accuracy']\n",
        "acc_val += history.history['val_accuracy']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c-YIcOuqc_TD",
        "outputId": "f2702402-d6ad-4173-a77d-f39f9c3c6b9e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "70/70 [==============================] - 7s 103ms/step - loss: 0.5001 - accuracy: 0.9679 - val_loss: 1.7578 - val_accuracy: 0.6583\n",
            "Epoch 2/10\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 0.5070 - accuracy: 0.9571 - val_loss: 1.6130 - val_accuracy: 0.6833\n",
            "Epoch 3/10\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 0.4844 - accuracy: 0.9464 - val_loss: 1.6543 - val_accuracy: 0.6583\n",
            "Epoch 4/10\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 0.3811 - accuracy: 0.9893 - val_loss: 1.6775 - val_accuracy: 0.6250\n",
            "Epoch 5/10\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 0.3837 - accuracy: 0.9786 - val_loss: 1.6320 - val_accuracy: 0.6667\n",
            "Epoch 6/10\n",
            "70/70 [==============================] - 7s 102ms/step - loss: 0.3775 - accuracy: 0.9857 - val_loss: 1.5574 - val_accuracy: 0.7000\n",
            "Epoch 7/10\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 0.3650 - accuracy: 0.9786 - val_loss: 1.6739 - val_accuracy: 0.6333\n",
            "Epoch 8/10\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 0.4222 - accuracy: 0.9679 - val_loss: 1.7719 - val_accuracy: 0.6000\n",
            "Epoch 9/10\n",
            "70/70 [==============================] - 8s 110ms/step - loss: 0.4812 - accuracy: 0.9679 - val_loss: 1.7643 - val_accuracy: 0.6167\n",
            "Epoch 10/10\n",
            "70/70 [==============================] - 7s 101ms/step - loss: 0.4759 - accuracy: 0.9429 - val_loss: 1.7544 - val_accuracy: 0.6167\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams['figure.figsize'] = (12, 8)\n",
        "plt.rcParams['font.size'] = 18\n",
        "\n",
        "plt.plot(acc_train, linewidth=2)\n",
        "plt.plot(acc_val, linewidth=2)\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Test'], loc='upper left')\n",
        "plt.grid()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "id": "KE5Eq9M_d4H7",
        "outputId": "de666481-ded7-4389-94eb-82c838eb5e5a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "6Mqc8-s9ep_j"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "IT416_Fusion_Model_with_only_one_ConvLSTM2D_layer.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.9.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}