IT416_Fusion_Model_without_batch_normalisation.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": 3,
      "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": 7,
      "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": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y8BXapYfTNxT",
        "outputId": "488ad9be-0139-4873-c8ad-5d842fab3dda"
      },
      "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": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P6I7HJaJTbM-",
        "outputId": "5daa1e5d-3945-4e7d-830b-4135ecd9e5c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " gait_input (InputLayer)        [(None, 16, 32, 32,  0           []                               \n",
            "                                 11)]                                                             \n",
            "                                                                                                  \n",
            " gait_convlstm_1 (ConvLSTM2D)   (None, 16, 30, 30,   15616       ['gait_input[0][0]']             \n",
            "                                16)                                                               \n",
            "                                                                                                  \n",
            " gait_dropout_1 (Dropout)       (None, 16, 30, 30,   0           ['gait_convlstm_1[0][0]']        \n",
            "                                16)                                                               \n",
            "                                                                                                  \n",
            " gait_convlstm_2 (ConvLSTM2D)   (None, 16, 28, 28,   6944        ['gait_dropout_1[0][0]']         \n",
            "                                8)                                                                \n",
            "                                                                                                  \n",
            " face_input (InputLayer)        [(None, 12, 12, 1)]  0           []                               \n",
            "                                                                                                  \n",
            " gait_dropout_2 (Dropout)       (None, 16, 28, 28,   0           ['gait_convlstm_2[0][0]']        \n",
            "                                8)                                                                \n",
            "                                                                                                  \n",
            " face_conv_1 (Conv2D)           (None, 10, 10, 32)   320         ['face_input[0][0]']             \n",
            "                                                                                                  \n",
            " gait_convlstm_3 (ConvLSTM2D)   (None, 26, 26, 1)    328         ['gait_dropout_2[0][0]']         \n",
            "                                                                                                  \n",
            " face_dropout_1 (Dropout)       (None, 10, 10, 32)   0           ['face_conv_1[0][0]']            \n",
            "                                                                                                  \n",
            " gait_dropout_3 (Dropout)       (None, 26, 26, 1)    0           ['gait_convlstm_3[0][0]']        \n",
            "                                                                                                  \n",
            " face_flatten_1 (Flatten)       (None, 3200)         0           ['face_dropout_1[0][0]']         \n",
            "                                                                                                  \n",
            " gait_flatten_1 (Flatten)       (None, 676)          0           ['gait_dropout_3[0][0]']         \n",
            "                                                                                                  \n",
            " face_dense_1 (Dense)           (None, 64)           204864      ['face_flatten_1[0][0]']         \n",
            "                                                                                                  \n",
            " gait_dense_1 (Dense)           (None, 64)           43328       ['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",
            " face_gait_weighted_average (La  (None, 64)          0           ['face_dropout_2[0][0]',         \n",
            " mbda)                                                            'gait_dropout_4[0][0]']         \n",
            "                                                                                                  \n",
            " output (Dense)                 (None, 40)           2600        ['face_gait_weighted_average[0][0\n",
            "                                                                 ]']                              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 274,000\n",
            "Trainable params: 274,000\n",
            "Non-trainable params: 0\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",
        "\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, return_sequences = True, 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.ConvLSTM2D(8, kernel_size = 3, return_sequences = True, name = 'gait_convlstm_2')(gait_encoder)\n",
        "gait_encoder = tf.keras.layers.Dropout(0.4, name = 'gait_dropout_2')(gait_encoder)\n",
        "gait_encoder = tf.keras.layers.ConvLSTM2D(1, kernel_size = 3, name = 'gait_convlstm_3')(gait_encoder)\n",
        "gait_encoder = tf.keras.layers.Dropout(0.4, name = 'gait_dropout_3')(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",
        "\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": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rY6ced4-Vv-_",
        "outputId": "8b86d780-bb93-41ec-fdbd-e233135648c9"
      },
      "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": 15,
      "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": "9e7ffe10-1399-4c84-8a84-2a55d97a1c25"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/60\n",
            "70/70 [==============================] - 15s 155ms/step - loss: 6.4792 - accuracy: 0.0250 - val_loss: 3.6889 - val_accuracy: 0.0250\n",
            "Epoch 2/60\n",
            "70/70 [==============================] - 10s 146ms/step - loss: 3.6881 - accuracy: 0.0214 - val_loss: 3.6887 - val_accuracy: 0.0250\n",
            "Epoch 3/60\n",
            "70/70 [==============================] - 10s 146ms/step - loss: 3.6808 - accuracy: 0.0464 - val_loss: 3.6827 - val_accuracy: 0.0417\n",
            "Epoch 4/60\n",
            "70/70 [==============================] - 10s 146ms/step - loss: 3.6485 - accuracy: 0.0607 - val_loss: 3.6685 - val_accuracy: 0.0500\n",
            "Epoch 5/60\n",
            "70/70 [==============================] - 11s 155ms/step - loss: 3.6018 - accuracy: 0.1143 - val_loss: 3.6283 - val_accuracy: 0.0833\n",
            "Epoch 6/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 3.4732 - accuracy: 0.1107 - val_loss: 3.5390 - val_accuracy: 0.1167\n",
            "Epoch 7/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 3.2320 - accuracy: 0.1464 - val_loss: 3.3889 - val_accuracy: 0.1000\n",
            "Epoch 8/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 3.0545 - accuracy: 0.1536 - val_loss: 3.2149 - val_accuracy: 0.1333\n",
            "Epoch 9/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 2.8060 - accuracy: 0.2464 - val_loss: 3.0786 - val_accuracy: 0.1667\n",
            "Epoch 10/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 2.6468 - accuracy: 0.2786 - val_loss: 2.9319 - val_accuracy: 0.1500\n",
            "Epoch 11/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 2.4877 - accuracy: 0.3107 - val_loss: 2.8767 - val_accuracy: 0.2000\n",
            "Epoch 12/60\n",
            "70/70 [==============================] - 10s 150ms/step - loss: 2.3983 - accuracy: 0.3500 - val_loss: 2.7559 - val_accuracy: 0.2167\n",
            "Epoch 13/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 2.1043 - accuracy: 0.4179 - val_loss: 2.6600 - val_accuracy: 0.2500\n",
            "Epoch 14/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 1.9551 - accuracy: 0.4893 - val_loss: 2.5906 - val_accuracy: 0.2917\n",
            "Epoch 15/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 1.9365 - accuracy: 0.4643 - val_loss: 2.5126 - val_accuracy: 0.3083\n",
            "Epoch 16/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 1.7956 - accuracy: 0.5036 - val_loss: 2.4542 - val_accuracy: 0.2667\n",
            "Epoch 17/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 1.5993 - accuracy: 0.5714 - val_loss: 2.3500 - val_accuracy: 0.3333\n",
            "Epoch 18/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 1.5629 - accuracy: 0.5750 - val_loss: 2.3181 - val_accuracy: 0.3417\n",
            "Epoch 19/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 1.4905 - accuracy: 0.5714 - val_loss: 2.2983 - val_accuracy: 0.3500\n",
            "Epoch 20/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 1.3689 - accuracy: 0.6321 - val_loss: 2.2078 - val_accuracy: 0.3833\n",
            "Epoch 21/60\n",
            "70/70 [==============================] - 10s 147ms/step - loss: 1.2208 - accuracy: 0.6786 - val_loss: 2.2853 - val_accuracy: 0.3500\n",
            "Epoch 22/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 1.2436 - accuracy: 0.6607 - val_loss: 2.1699 - val_accuracy: 0.3417\n",
            "Epoch 23/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 1.1031 - accuracy: 0.7143 - val_loss: 2.1649 - val_accuracy: 0.3167\n",
            "Epoch 24/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 1.0747 - accuracy: 0.6893 - val_loss: 2.0760 - val_accuracy: 0.3417\n",
            "Epoch 25/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 1.0516 - accuracy: 0.7000 - val_loss: 2.0188 - val_accuracy: 0.3833\n",
            "Epoch 26/60\n",
            "70/70 [==============================] - 10s 143ms/step - loss: 0.9937 - accuracy: 0.7321 - val_loss: 2.0480 - val_accuracy: 0.3750\n",
            "Epoch 27/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.9619 - accuracy: 0.7429 - val_loss: 2.0114 - val_accuracy: 0.4583\n",
            "Epoch 28/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 0.8855 - accuracy: 0.7821 - val_loss: 2.0149 - val_accuracy: 0.4167\n",
            "Epoch 29/60\n",
            "70/70 [==============================] - 10s 143ms/step - loss: 0.9194 - accuracy: 0.7643 - val_loss: 2.0016 - val_accuracy: 0.4083\n",
            "Epoch 30/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.7833 - accuracy: 0.7857 - val_loss: 1.9981 - val_accuracy: 0.3833\n",
            "Epoch 31/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.7823 - accuracy: 0.8036 - val_loss: 1.9142 - val_accuracy: 0.4333\n",
            "Epoch 32/60\n",
            "70/70 [==============================] - 10s 143ms/step - loss: 0.7021 - accuracy: 0.8393 - val_loss: 1.9146 - val_accuracy: 0.4333\n",
            "Epoch 33/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.7943 - accuracy: 0.7964 - val_loss: 1.9440 - val_accuracy: 0.4250\n",
            "Epoch 34/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.6805 - accuracy: 0.8286 - val_loss: 1.8986 - val_accuracy: 0.4000\n",
            "Epoch 35/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.7173 - accuracy: 0.7893 - val_loss: 1.8477 - val_accuracy: 0.3917\n",
            "Epoch 36/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.6794 - accuracy: 0.8071 - val_loss: 1.8042 - val_accuracy: 0.4333\n",
            "Epoch 37/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.5498 - accuracy: 0.8786 - val_loss: 1.7825 - val_accuracy: 0.4333\n",
            "Epoch 38/60\n",
            "70/70 [==============================] - 10s 143ms/step - loss: 0.5698 - accuracy: 0.8464 - val_loss: 1.8221 - val_accuracy: 0.4583\n",
            "Epoch 39/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.5630 - accuracy: 0.8536 - val_loss: 1.7366 - val_accuracy: 0.4750\n",
            "Epoch 40/60\n",
            "70/70 [==============================] - 10s 147ms/step - loss: 0.5697 - accuracy: 0.8643 - val_loss: 1.7487 - val_accuracy: 0.4500\n",
            "Epoch 41/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.5902 - accuracy: 0.8500 - val_loss: 1.8219 - val_accuracy: 0.4083\n",
            "Epoch 42/60\n",
            "70/70 [==============================] - 10s 143ms/step - loss: 0.5608 - accuracy: 0.8571 - val_loss: 1.8079 - val_accuracy: 0.4250\n",
            "Epoch 43/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 0.5097 - accuracy: 0.8643 - val_loss: 1.8458 - val_accuracy: 0.4500\n",
            "Epoch 44/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.4832 - accuracy: 0.8893 - val_loss: 1.8608 - val_accuracy: 0.4583\n",
            "Epoch 45/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 0.5081 - accuracy: 0.8857 - val_loss: 1.7716 - val_accuracy: 0.4250\n",
            "Epoch 46/60\n",
            "70/70 [==============================] - 10s 146ms/step - loss: 0.5317 - accuracy: 0.8607 - val_loss: 1.8006 - val_accuracy: 0.4083\n",
            "Epoch 47/60\n",
            "70/70 [==============================] - 10s 142ms/step - loss: 0.4407 - accuracy: 0.9143 - val_loss: 1.7715 - val_accuracy: 0.4417\n",
            "Epoch 48/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.3764 - accuracy: 0.9107 - val_loss: 1.8290 - val_accuracy: 0.4333\n",
            "Epoch 49/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 0.4496 - accuracy: 0.8821 - val_loss: 1.7718 - val_accuracy: 0.4083\n",
            "Epoch 50/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 0.4351 - accuracy: 0.9000 - val_loss: 1.8313 - val_accuracy: 0.4250\n",
            "Epoch 51/60\n",
            "70/70 [==============================] - 10s 143ms/step - loss: 0.4388 - accuracy: 0.8929 - val_loss: 1.7193 - val_accuracy: 0.4333\n",
            "Epoch 52/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 0.3289 - accuracy: 0.9393 - val_loss: 1.7469 - val_accuracy: 0.4167\n",
            "Epoch 53/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.3374 - accuracy: 0.9250 - val_loss: 1.7334 - val_accuracy: 0.4250\n",
            "Epoch 54/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 0.3660 - accuracy: 0.9179 - val_loss: 1.7511 - val_accuracy: 0.4417\n",
            "Epoch 55/60\n",
            "70/70 [==============================] - 10s 146ms/step - loss: 0.3770 - accuracy: 0.9000 - val_loss: 1.7218 - val_accuracy: 0.4583\n",
            "Epoch 56/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 0.2971 - accuracy: 0.9357 - val_loss: 1.7080 - val_accuracy: 0.4417\n",
            "Epoch 57/60\n",
            "70/70 [==============================] - 10s 143ms/step - loss: 0.3525 - accuracy: 0.9071 - val_loss: 1.6666 - val_accuracy: 0.4500\n",
            "Epoch 58/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 0.3145 - accuracy: 0.9214 - val_loss: 1.7603 - val_accuracy: 0.4167\n",
            "Epoch 59/60\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.3393 - accuracy: 0.9107 - val_loss: 1.7398 - val_accuracy: 0.4250\n",
            "Epoch 60/60\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 0.3532 - accuracy: 0.9143 - val_loss: 1.7649 - val_accuracy: 0.4417\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": "d5a93479-ac21-4991-b2c8-82cc991dd481"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "70/70 [==============================] - 11s 150ms/step - loss: 0.3629 - accuracy: 0.9107 - val_loss: 1.7555 - val_accuracy: 0.4417\n",
            "Epoch 2/10\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 0.2853 - accuracy: 0.9250 - val_loss: 1.6945 - val_accuracy: 0.4917\n",
            "Epoch 3/10\n",
            "70/70 [==============================] - 10s 146ms/step - loss: 0.2503 - accuracy: 0.9357 - val_loss: 1.7322 - val_accuracy: 0.4500\n",
            "Epoch 4/10\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.2960 - accuracy: 0.9250 - val_loss: 1.7441 - val_accuracy: 0.4500\n",
            "Epoch 5/10\n",
            "70/70 [==============================] - 10s 143ms/step - loss: 0.2685 - accuracy: 0.9357 - val_loss: 1.7890 - val_accuracy: 0.4750\n",
            "Epoch 6/10\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 0.2806 - accuracy: 0.9321 - val_loss: 1.7506 - val_accuracy: 0.4000\n",
            "Epoch 7/10\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.2900 - accuracy: 0.9286 - val_loss: 1.7256 - val_accuracy: 0.4500\n",
            "Epoch 8/10\n",
            "70/70 [==============================] - 10s 143ms/step - loss: 0.2539 - accuracy: 0.9500 - val_loss: 1.8466 - val_accuracy: 0.4333\n",
            "Epoch 9/10\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.2798 - accuracy: 0.9357 - val_loss: 1.8106 - val_accuracy: 0.4250\n",
            "Epoch 10/10\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.2749 - accuracy: 0.9143 - val_loss: 1.7966 - val_accuracy: 0.4500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "wdrujuYPmxKG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25a469bb-c983-496c-91c0-fcbb9c80ba7d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "70/70 [==============================] - 10s 147ms/step - loss: 0.2023 - accuracy: 0.9643 - val_loss: 1.7374 - val_accuracy: 0.4750\n",
            "Epoch 2/10\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 0.2302 - accuracy: 0.9464 - val_loss: 1.7557 - val_accuracy: 0.4667\n",
            "Epoch 3/10\n",
            "70/70 [==============================] - 10s 146ms/step - loss: 0.1805 - accuracy: 0.9750 - val_loss: 1.7995 - val_accuracy: 0.4417\n",
            "Epoch 4/10\n",
            "70/70 [==============================] - 10s 147ms/step - loss: 0.2220 - accuracy: 0.9393 - val_loss: 1.8350 - val_accuracy: 0.4583\n",
            "Epoch 5/10\n",
            "70/70 [==============================] - 10s 146ms/step - loss: 0.2108 - accuracy: 0.9536 - val_loss: 1.8313 - val_accuracy: 0.4417\n",
            "Epoch 6/10\n",
            "70/70 [==============================] - 10s 146ms/step - loss: 0.2306 - accuracy: 0.9393 - val_loss: 1.7637 - val_accuracy: 0.4667\n",
            "Epoch 7/10\n",
            "70/70 [==============================] - 10s 146ms/step - loss: 0.2183 - accuracy: 0.9571 - val_loss: 1.7769 - val_accuracy: 0.4833\n",
            "Epoch 8/10\n",
            "70/70 [==============================] - 10s 148ms/step - loss: 0.1720 - accuracy: 0.9679 - val_loss: 1.8046 - val_accuracy: 0.4333\n",
            "Epoch 9/10\n",
            "70/70 [==============================] - 10s 148ms/step - loss: 0.2064 - accuracy: 0.9500 - val_loss: 1.7654 - val_accuracy: 0.4500\n",
            "Epoch 10/10\n",
            "70/70 [==============================] - 10s 146ms/step - loss: 0.2603 - accuracy: 0.9250 - val_loss: 1.7971 - val_accuracy: 0.4833\n"
          ]
        }
      ],
      "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']"
      ]
    },
    {
      "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": "h74enlnVfeBG",
        "outputId": "57f870c7-d246-4c97-c08b-e65f7b159dd1"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "70/70 [==============================] - 10s 146ms/step - loss: 0.2297 - accuracy: 0.9571 - val_loss: 1.8141 - val_accuracy: 0.4583\n",
            "Epoch 2/10\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.2014 - accuracy: 0.9500 - val_loss: 1.7695 - val_accuracy: 0.4583\n",
            "Epoch 3/10\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 0.2042 - accuracy: 0.9464 - val_loss: 1.8092 - val_accuracy: 0.5250\n",
            "Epoch 4/10\n",
            "70/70 [==============================] - 10s 146ms/step - loss: 0.1846 - accuracy: 0.9679 - val_loss: 1.7721 - val_accuracy: 0.4750\n",
            "Epoch 5/10\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.2135 - accuracy: 0.9429 - val_loss: 2.5031 - val_accuracy: 0.4333\n",
            "Epoch 6/10\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.1820 - accuracy: 0.9571 - val_loss: 1.7473 - val_accuracy: 0.5000\n",
            "Epoch 7/10\n",
            "70/70 [==============================] - 10s 146ms/step - loss: 0.1734 - accuracy: 0.9679 - val_loss: 1.8375 - val_accuracy: 0.4750\n",
            "Epoch 8/10\n",
            "70/70 [==============================] - 10s 148ms/step - loss: 0.1912 - accuracy: 0.9536 - val_loss: 1.7212 - val_accuracy: 0.4250\n",
            "Epoch 9/10\n",
            "70/70 [==============================] - 10s 143ms/step - loss: 0.2241 - accuracy: 0.9464 - val_loss: 1.6519 - val_accuracy: 0.4583\n",
            "Epoch 10/10\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 0.1987 - accuracy: 0.9607 - val_loss: 1.7436 - val_accuracy: 0.4750\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": "9mfEIZpugKyp",
        "outputId": "56c36d39-52f7-42e0-8d21-b3cd0167a919"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 0.1520 - accuracy: 0.9643 - val_loss: 1.6724 - val_accuracy: 0.5167\n",
            "Epoch 2/10\n",
            "70/70 [==============================] - 10s 145ms/step - loss: 0.2163 - accuracy: 0.9429 - val_loss: 1.6804 - val_accuracy: 0.5250\n",
            "Epoch 3/10\n",
            "70/70 [==============================] - 10s 143ms/step - loss: 0.1650 - accuracy: 0.9714 - val_loss: 1.7752 - val_accuracy: 0.5250\n",
            "Epoch 4/10\n",
            "70/70 [==============================] - 10s 143ms/step - loss: 0.2108 - accuracy: 0.9536 - val_loss: 1.8171 - val_accuracy: 0.4333\n",
            "Epoch 5/10\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.1816 - accuracy: 0.9500 - val_loss: 1.7648 - val_accuracy: 0.5000\n",
            "Epoch 6/10\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.1653 - accuracy: 0.9679 - val_loss: 1.7131 - val_accuracy: 0.5000\n",
            "Epoch 7/10\n",
            "70/70 [==============================] - 10s 144ms/step - loss: 0.1581 - accuracy: 0.9607 - val_loss: 1.8075 - val_accuracy: 0.4667\n",
            "Epoch 8/10\n",
            "70/70 [==============================] - 10s 143ms/step - loss: 0.1777 - accuracy: 0.9500 - val_loss: 1.8070 - val_accuracy: 0.4917\n",
            "Epoch 9/10\n",
            "70/70 [==============================] - 10s 142ms/step - loss: 0.1494 - accuracy: 0.9464 - val_loss: 1.7066 - val_accuracy: 0.4583\n",
            "Epoch 10/10\n",
            "70/70 [==============================] - 10s 143ms/step - loss: 0.1836 - accuracy: 0.9643 - val_loss: 1.7522 - val_accuracy: 0.4833\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": "511970b0-b951-4354-da0b-d66c6c1e7db3"
      },
      "execution_count": 21,
      "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_without_batch_normalisation.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
}