CNN.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtQyWcjBAv45"
      },
      "source": [
        "## Connect to Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KBENs6v7y5D",
        "outputId": "bb593c73-f483-4e31-e9a6-5d4bfafa2d19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ukK6ei6Atcr"
      },
      "source": [
        "## Library"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "drnJKFcg76WC"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "import os\n",
        "import numpy as np\n",
        "import glob\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D, BatchNormalization, Reshape, GlobalAveragePooling2D\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tqdm import tqdm\n",
        "import random\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "import keras\n",
        "from tensorflow.keras.applications import MobileNet\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MbXiwi13AqVB"
      },
      "source": [
        "## Getting Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BULOVveT8llD"
      },
      "outputs": [],
      "source": [
        "# path to the dataset directory\n",
        "dataset_path = \"/content/drive/MyDrive/IJS/fall_dataset\"\n",
        "\n",
        "\n",
        "# load the training images\n",
        "train_image_files = glob.glob(os.path.join(dataset_path, \"images\", \"train\") + '/*')\n",
        "train_image_files.sort()\n",
        "\n",
        "# extract the bounding box coordinates for each person in the images\n",
        "train_label_files = glob.glob(os.path.join(dataset_path, \"labels\", \"train\") + '/*.txt')\n",
        "train_label_files.sort()\n",
        "\n",
        "# load the val images\n",
        "val_image_files = glob.glob(os.path.join(dataset_path, \"images\", \"val\") + '/*')\n",
        "val_image_files.sort()\n",
        "\n",
        "# extract the bounding box coordinates for each person in the images\n",
        "val_label_files = glob.glob(os.path.join(dataset_path, \"labels\", \"val\") + '/*.txt')\n",
        "val_label_files.sort()\n",
        "\n",
        "IMG_SIZE = (224,224) #set the image size\n",
        "batch_size = 32 #set the batch number\n",
        "class_name = ['Falling', 'Standing', 'Sitting']\n",
        "num_class = len(class_name)\n",
        "epoch = 25"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4cG21_UBEGp"
      },
      "source": [
        "# This part help in load and pair the image and label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f7_CJd0f9d0P"
      },
      "outputs": [],
      "source": [
        "training_data = [] #for training data listing\n",
        "\n",
        "#load and pair training image and label\n",
        "for i in range(len(train_image_files)): \n",
        "    image_path = train_image_files[i] #get all image\n",
        "    label_path = train_label_files[i] #get all label\n",
        "\n",
        "    with open(label_path, \"r\") as f:\n",
        "        for line in f.readlines():\n",
        "          line = line.strip().split(\" \")\n",
        "          class_label = int(line[0])\n",
        "          x_center, y_center, width, height = map(float, line[1:])\n",
        "\n",
        "                \n",
        "          # read the image\n",
        "          img_array = cv2.imread(image_path)\n",
        "          img_array = cv2.cvtColor(img_array, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "          # extract the bounding box coordinates\n",
        "          x_min = int((x_center - width/2) * img_array.shape[1])\n",
        "          y_min = int((y_center - height/2) * img_array.shape[0])\n",
        "          x_max = int((x_center + width/2) * img_array.shape[1])\n",
        "          y_max = int((y_center + height/2) * img_array.shape[0])\n",
        "\n",
        "          # crop the bounding box around the person\n",
        "          person_image = img_array[y_min:y_max, x_min:x_max]\n",
        "\n",
        "          # resize the cropped image to a fixed size and place the bounding box in the center\n",
        "          person_image_resized = cv2.resize(person_image, IMG_SIZE[:2])\n",
        "          person_height, person_width, _ = person_image_resized.shape\n",
        "          center_x, center_y = int(person_width/2), int(person_height/2)\n",
        "          x_min = center_x - int(IMG_SIZE[0]/2)\n",
        "          y_min = center_y - int(IMG_SIZE[0]/2)\n",
        "          x_max = center_x + int(IMG_SIZE[0]/2)\n",
        "          y_max = center_y + int(IMG_SIZE[0]/2)\n",
        "          cropped_image = person_image_resized[y_min:y_max, x_min:x_max]\n",
        "          # add this to training_data\n",
        "          training_data.append([cropped_image, class_label])\n",
        "\n",
        "random.shuffle(training_data) #suffle training data\n",
        "\n",
        "\n",
        "val_data=[] #for validation data listing\n",
        "\n",
        "#pair validation image and label\n",
        "for i in range(len(val_image_files)):\n",
        "    image_path = val_image_files[i] #get all image\n",
        "    label_path = val_label_files[i] #get all label\n",
        "\n",
        "    with open(label_path, \"r\") as f:\n",
        "        for line in f.readlines():\n",
        "          line = line.strip().split(\" \")\n",
        "          class_label = int(line[0])\n",
        "          x_center, y_center, width, height = map(float, line[1:])\n",
        "\n",
        "                \n",
        "          # read the image\n",
        "          img_array = cv2.imread(image_path, cv2.COLOR_BGR2RGB)\n",
        "          img_array = cv2.cvtColor(img_array, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "          # extract the bounding box coordinates\n",
        "          x_min = int((x_center - width/2) * img_array.shape[1])\n",
        "          y_min = int((y_center - height/2) * img_array.shape[0])\n",
        "          x_max = int((x_center + width/2) * img_array.shape[1])\n",
        "          y_max = int((y_center + height/2) * img_array.shape[0])\n",
        "\n",
        "          # crop the bounding box around the person\n",
        "          person_image = img_array[y_min:y_max, x_min:x_max]\n",
        "\n",
        "          # resize the cropped image to a fixed size and place the bounding box in the center\n",
        "          person_image_resized = cv2.resize(person_image, IMG_SIZE[:2])\n",
        "          person_height, person_width, _ = person_image_resized.shape\n",
        "          center_x, center_y = int(person_width/2), int(person_height/2)\n",
        "          x_min = center_x - int(IMG_SIZE[0]/2)\n",
        "          y_min = center_y - int(IMG_SIZE[0]/2)\n",
        "          x_max = center_x + int(IMG_SIZE[0]/2)\n",
        "          y_max = center_y + int(IMG_SIZE[0]/2)\n",
        "          cropped_image = person_image_resized[y_min:y_max, x_min:x_max]\n",
        "\n",
        "          # add this to validate_data\n",
        "          val_data.append([cropped_image, class_label])\n",
        "\n",
        "random.shuffle(val_data) #suffle validation data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JCap9hRDCMCQ"
      },
      "source": [
        "Split validation data into validation and test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fNF2VGUuBU8H"
      },
      "outputs": [],
      "source": [
        "num_data = int(len(val_data) * 0.5)\n",
        "validate_dataset = val_data[:num_data]\n",
        "test_dataset = val_data[num_data:]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2KTHo4iDCYMF"
      },
      "source": [
        "Separate data into image and label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3cVZNwkzTU9u"
      },
      "outputs": [],
      "source": [
        "x_train = []\n",
        "y_train = []\n",
        "x_val = []\n",
        "y_val = []\n",
        "x_test = []\n",
        "y_test = []\n",
        "\n",
        "for i in range(len(training_data)):\n",
        "    x_train.append(training_data[i][0])\n",
        "    y_train.append(training_data[i][1])\n",
        "\n",
        "for i in range(len(validate_dataset)):\n",
        "    x_val.append(validate_dataset[i][0])\n",
        "    y_val.append(validate_dataset[i][1])\n",
        "\n",
        "for i in range(len(test_dataset)):\n",
        "    x_test.append(test_dataset[i][0])\n",
        "    y_test.append(test_dataset[i][1])\n",
        "\n",
        "x_training = np.array(x_train)\n",
        "y_training = y_train\n",
        "x_validate = np.array(x_val)\n",
        "y_validate = y_val\n",
        "x_test = np.array(x_test)\n",
        "y_test = y_test\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v8HsMqlUC8Fn"
      },
      "source": [
        "Pre-process the data as before by making sure it's float32 and normalized between 0 and 1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "upmFcrXOSmes"
      },
      "outputs": [],
      "source": [
        "# normalize data\n",
        "x_train = x_training.astype('float32') / 255.\n",
        "x_val = x_validate.astype('float32') / 255.\n",
        "x_test = x_test.astype('float32') / 255.\n",
        "\n",
        "# convert labels to one-hot vectors\n",
        "y_train = keras.utils.to_categorical(y_training, num_class)\n",
        "y_val = keras.utils.to_categorical(y_validate, num_class)\n",
        "y_test = keras.utils.to_categorical(y_test, num_class)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gpxWTtgdHOCR"
      },
      "source": [
        "Summary for current process"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7QHaQ-zBZ_9K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08181610-f742-4d9e-c605-9c4bd41b49ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train / validation / test split: 453, 57, 57\n",
            "training data shape:  (453, 224, 224, 3)\n",
            "training labels shape:  (453, 3)\n"
          ]
        }
      ],
      "source": [
        "# summary\n",
        "print(\"train / validation / test split: %d, %d, %d\"%(len(x_train), len(x_val), len(x_test)))\n",
        "print(\"training data shape: \", x_train.shape)\n",
        "print(\"training labels shape: \", y_train.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0Aiof0xHUwi"
      },
      "source": [
        "# Building CNN for scratch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gmaypc1fawjv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ebf4978-43db-428c-d04e-85c9a15bcf9b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input dimensions:  (224, 224, 3)\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 222, 222, 32)      896       \n",
            "                                                                 \n",
            " activation (Activation)     (None, 222, 222, 32)      0         \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 111, 111, 32)     0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 109, 109, 32)      9248      \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 109, 109, 32)      0         \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 54, 54, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 54, 54, 32)        0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 52, 52, 32)        9248      \n",
            "                                                                 \n",
            " activation_2 (Activation)   (None, 52, 52, 32)        0         \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 26, 26, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 24, 24, 32)        9248      \n",
            "                                                                 \n",
            " activation_3 (Activation)   (None, 24, 24, 32)        0         \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 12, 12, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 12, 12, 32)        0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 4608)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               1179904   \n",
            "                                                                 \n",
            " activation_4 (Activation)   (None, 256)               0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 3)                 771       \n",
            "                                                                 \n",
            " activation_5 (Activation)   (None, 3)                 0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,209,315\n",
            "Trainable params: 1,209,315\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# build the network\n",
        "model = Sequential()\n",
        "print(\"Input dimensions: \",x_train.shape[1:])\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), input_shape=x_train.shape[1:]))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256))\n",
        "model.add(Activation('relu'))\n",
        "\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(num_class))\n",
        "model.add(Activation('softmax'))\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kr2mDpltHiJ_"
      },
      "source": [
        "Train Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yj-tKVwZbDjT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e340172-fb9a-4e48-8876-89281b637071"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "15/15 [==============================] - 15s 131ms/step - loss: 1.1110 - accuracy: 0.4150 - val_loss: 0.9939 - val_accuracy: 0.6316\n",
            "Epoch 2/25\n",
            "15/15 [==============================] - 1s 72ms/step - loss: 1.0753 - accuracy: 0.4724 - val_loss: 1.0013 - val_accuracy: 0.6316\n",
            "Epoch 3/25\n",
            "15/15 [==============================] - 1s 71ms/step - loss: 1.0642 - accuracy: 0.4746 - val_loss: 1.0586 - val_accuracy: 0.6140\n",
            "Epoch 4/25\n",
            "15/15 [==============================] - 1s 70ms/step - loss: 1.0433 - accuracy: 0.4989 - val_loss: 0.9930 - val_accuracy: 0.6316\n",
            "Epoch 5/25\n",
            "15/15 [==============================] - 1s 70ms/step - loss: 1.0457 - accuracy: 0.5121 - val_loss: 1.0445 - val_accuracy: 0.5614\n",
            "Epoch 6/25\n",
            "15/15 [==============================] - 1s 71ms/step - loss: 1.0069 - accuracy: 0.5188 - val_loss: 0.9482 - val_accuracy: 0.6316\n",
            "Epoch 7/25\n",
            "15/15 [==============================] - 1s 79ms/step - loss: 0.9594 - accuracy: 0.5408 - val_loss: 0.9555 - val_accuracy: 0.6491\n",
            "Epoch 8/25\n",
            "15/15 [==============================] - 1s 77ms/step - loss: 0.9181 - accuracy: 0.5850 - val_loss: 0.9845 - val_accuracy: 0.5088\n",
            "Epoch 9/25\n",
            "15/15 [==============================] - 1s 80ms/step - loss: 0.9413 - accuracy: 0.5916 - val_loss: 0.8480 - val_accuracy: 0.6491\n",
            "Epoch 10/25\n",
            "15/15 [==============================] - 1s 74ms/step - loss: 0.8912 - accuracy: 0.5717 - val_loss: 0.8355 - val_accuracy: 0.7018\n",
            "Epoch 11/25\n",
            "15/15 [==============================] - 1s 72ms/step - loss: 0.7975 - accuracy: 0.6667 - val_loss: 0.8522 - val_accuracy: 0.6667\n",
            "Epoch 12/25\n",
            "15/15 [==============================] - 1s 71ms/step - loss: 0.7033 - accuracy: 0.6932 - val_loss: 0.7638 - val_accuracy: 0.6667\n",
            "Epoch 13/25\n",
            "15/15 [==============================] - 1s 72ms/step - loss: 0.6914 - accuracy: 0.7064 - val_loss: 0.7722 - val_accuracy: 0.6316\n",
            "Epoch 14/25\n",
            "15/15 [==============================] - 1s 73ms/step - loss: 0.6163 - accuracy: 0.7594 - val_loss: 0.8519 - val_accuracy: 0.6316\n",
            "Epoch 15/25\n",
            "15/15 [==============================] - 1s 72ms/step - loss: 0.4912 - accuracy: 0.7815 - val_loss: 0.8644 - val_accuracy: 0.6667\n",
            "Epoch 16/25\n",
            "15/15 [==============================] - 1s 74ms/step - loss: 0.5039 - accuracy: 0.7991 - val_loss: 0.9050 - val_accuracy: 0.5965\n",
            "Epoch 17/25\n",
            "15/15 [==============================] - 1s 72ms/step - loss: 0.4241 - accuracy: 0.8389 - val_loss: 1.0430 - val_accuracy: 0.5789\n",
            "Epoch 18/25\n",
            "15/15 [==============================] - 1s 73ms/step - loss: 0.3718 - accuracy: 0.8653 - val_loss: 1.1281 - val_accuracy: 0.6491\n",
            "Epoch 19/25\n",
            "15/15 [==============================] - 1s 76ms/step - loss: 0.2703 - accuracy: 0.9007 - val_loss: 1.0843 - val_accuracy: 0.6667\n",
            "Epoch 20/25\n",
            "15/15 [==============================] - 1s 81ms/step - loss: 0.2688 - accuracy: 0.8918 - val_loss: 1.2557 - val_accuracy: 0.6491\n",
            "Epoch 21/25\n",
            "15/15 [==============================] - 1s 80ms/step - loss: 0.2450 - accuracy: 0.9139 - val_loss: 1.4771 - val_accuracy: 0.6140\n",
            "Epoch 22/25\n",
            "15/15 [==============================] - 1s 77ms/step - loss: 0.1700 - accuracy: 0.9316 - val_loss: 1.4900 - val_accuracy: 0.7193\n",
            "Epoch 23/25\n",
            "15/15 [==============================] - 1s 74ms/step - loss: 0.2310 - accuracy: 0.9183 - val_loss: 1.1283 - val_accuracy: 0.6491\n",
            "Epoch 24/25\n",
            "15/15 [==============================] - 1s 73ms/step - loss: 0.1777 - accuracy: 0.9382 - val_loss: 1.2983 - val_accuracy: 0.6316\n",
            "Epoch 25/25\n",
            "15/15 [==============================] - 1s 73ms/step - loss: 0.1810 - accuracy: 0.9492 - val_loss: 1.2004 - val_accuracy: 0.6140\n"
          ]
        }
      ],
      "source": [
        "# compile the model to use categorical cross-entropy loss function and adadelta optimizer\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(x_train, y_train,\n",
        "                    batch_size=batch_size,\n",
        "                    epochs=epoch,\n",
        "                    validation_data=(x_val, y_val))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mz_isHE0g132"
      },
      "outputs": [],
      "source": [
        "# Save the model\n",
        "model.save('/content/drive/MyDrive/IJS/cnn.h5')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zZwmASNILIt8"
      },
      "source": [
        "Plot the validation loss and accuracy over epochs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YliAnFsOHRZ7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "bdf09d6d-d129-4360-d8dc-08ce8a45e836"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(16,4))\n",
        "\n",
        "# plot training and validation loss\n",
        "ax = fig.add_subplot(121)\n",
        "ax.plot(history.history[\"loss\"], label=\"training\")\n",
        "ax.plot(history.history[\"val_loss\"], label=\"validation\")\n",
        "ax.set_title(\"Model Loss\")\n",
        "ax.set_xlabel(\"epochs\")\n",
        "ax.legend()\n",
        "\n",
        "# plot training and validation accuracy\n",
        "ax2 = fig.add_subplot(122)\n",
        "ax2.plot(history.history[\"accuracy\"], label=\"training\")\n",
        "ax2.plot(history.history[\"val_accuracy\"], label=\"validation\")\n",
        "ax2.set_title(\"Model Accuracy\")\n",
        "ax2.set_xlabel(\"epochs\")\n",
        "ax2.set_ylim(0, 1)\n",
        "ax2.legend()\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EqHgEWK8bosZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "733a927c-2b03-42a0-f08c-b5106c2be792"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 1.580958604812622\n",
            "Test accuracy: 0.5964912176132202\n"
          ]
        }
      ],
      "source": [
        "loss, accuracy = model.evaluate(x_test, y_test, verbose=0)\n",
        "print('Test loss:', loss)\n",
        "print('Test accuracy:', accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JkYtFpZ_Txym",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "outputId": "56b60368-103e-48e1-f5af-8d26a2dbe006"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 14ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "# predict the classes for the test set using the trained model\n",
        "y_pred = model.predict(x_test)\n",
        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "\n",
        "# create the confusion matrix\n",
        "cm = confusion_matrix(np.argmax(y_test, axis=1), y_pred_classes)\n",
        "\n",
        "# plot the confusion matrix as a heatmap\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='g', xticklabels=class_name, yticklabels=class_name)\n",
        "plt.xlabel('Predicted label')\n",
        "plt.ylabel('True label')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qcHKbtUiLYf5"
      },
      "source": [
        "## Transfer Learning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uqNFQiHcbpkr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85a56271-430c-4061-d35e-fb8b2f45924c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " mobilenet_1.00_224 (Functio  (None, 7, 7, 1024)       3228864   \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " global_average_pooling2d_3   (None, 1024)             0         \n",
            " (GlobalAveragePooling2D)                                        \n",
            "                                                                 \n",
            " dense_14 (Dense)            (None, 512)               524800    \n",
            "                                                                 \n",
            " dense_15 (Dense)            (None, 256)               131328    \n",
            "                                                                 \n",
            " dropout_6 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " reshape_3 (Reshape)         (None, 8, 8, 4)           0         \n",
            "                                                                 \n",
            " max_pooling2d_7 (MaxPooling  (None, 4, 4, 4)          0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_4 (Flatten)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_16 (Dense)            (None, 128)               8320      \n",
            "                                                                 \n",
            " dense_17 (Dense)            (None, 3)                 387       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,893,699\n",
            "Trainable params: 3,871,811\n",
            "Non-trainable params: 21,888\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Load the MobileNet model pretrained on ImageNet\n",
        "base_model = MobileNet(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "\n",
        "# Create a new Sequential model to combine the MobileNet base and additional layers\n",
        "model = Sequential()\n",
        "model.add(base_model)\n",
        "model.add(GlobalAveragePooling2D())\n",
        "model.add(Dense(512, activation='relu'))\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Reshape((8, 8, 4)))\n",
        "model.add(MaxPooling2D(2, 2))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(num_class, activation='softmax'))\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Freeze the model layres"
      ],
      "metadata": {
        "id": "SE3rcaZG5m-9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z59PIwrnDYoA"
      },
      "outputs": [],
      "source": [
        "# Freeze the layers in the base model\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hVQhPES4ta8x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f983e1d0-a139-435d-ab80-1591be254518"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "15/15 [==============================] - 6s 131ms/step - loss: 0.9194 - accuracy: 0.5740 - val_loss: 0.5955 - val_accuracy: 0.7544\n",
            "Epoch 2/25\n",
            "15/15 [==============================] - 1s 46ms/step - loss: 0.5106 - accuracy: 0.7815 - val_loss: 0.3811 - val_accuracy: 0.7719\n",
            "Epoch 3/25\n",
            "15/15 [==============================] - 1s 45ms/step - loss: 0.3436 - accuracy: 0.8764 - val_loss: 0.2926 - val_accuracy: 0.9298\n",
            "Epoch 4/25\n",
            "15/15 [==============================] - 1s 46ms/step - loss: 0.2652 - accuracy: 0.8852 - val_loss: 0.2684 - val_accuracy: 0.9123\n",
            "Epoch 5/25\n",
            "15/15 [==============================] - 1s 47ms/step - loss: 0.2217 - accuracy: 0.9139 - val_loss: 0.3291 - val_accuracy: 0.8421\n",
            "Epoch 6/25\n",
            "15/15 [==============================] - 1s 45ms/step - loss: 0.1352 - accuracy: 0.9448 - val_loss: 0.3200 - val_accuracy: 0.9298\n",
            "Epoch 7/25\n",
            "15/15 [==============================] - 1s 46ms/step - loss: 0.1462 - accuracy: 0.9272 - val_loss: 0.2894 - val_accuracy: 0.9298\n",
            "Epoch 8/25\n",
            "15/15 [==============================] - 1s 51ms/step - loss: 0.1607 - accuracy: 0.9558 - val_loss: 0.2036 - val_accuracy: 0.9474\n",
            "Epoch 9/25\n",
            "15/15 [==============================] - 1s 46ms/step - loss: 0.0812 - accuracy: 0.9647 - val_loss: 0.1953 - val_accuracy: 0.9474\n",
            "Epoch 10/25\n",
            "15/15 [==============================] - 1s 45ms/step - loss: 0.0308 - accuracy: 0.9934 - val_loss: 0.2568 - val_accuracy: 0.9474\n",
            "Epoch 11/25\n",
            "15/15 [==============================] - 1s 46ms/step - loss: 0.0389 - accuracy: 0.9823 - val_loss: 0.2234 - val_accuracy: 0.9474\n",
            "Epoch 12/25\n",
            "15/15 [==============================] - 1s 46ms/step - loss: 0.0175 - accuracy: 0.9956 - val_loss: 0.2213 - val_accuracy: 0.9474\n",
            "Epoch 13/25\n",
            "15/15 [==============================] - 1s 51ms/step - loss: 0.0179 - accuracy: 0.9956 - val_loss: 0.4673 - val_accuracy: 0.8596\n",
            "Epoch 14/25\n",
            "15/15 [==============================] - 1s 52ms/step - loss: 0.0149 - accuracy: 1.0000 - val_loss: 0.2517 - val_accuracy: 0.9474\n",
            "Epoch 15/25\n",
            "15/15 [==============================] - 1s 56ms/step - loss: 0.0222 - accuracy: 0.9912 - val_loss: 0.2238 - val_accuracy: 0.9474\n",
            "Epoch 16/25\n",
            "15/15 [==============================] - 1s 54ms/step - loss: 0.0112 - accuracy: 0.9978 - val_loss: 0.3741 - val_accuracy: 0.9298\n",
            "Epoch 17/25\n",
            "15/15 [==============================] - 1s 58ms/step - loss: 0.0067 - accuracy: 1.0000 - val_loss: 0.2463 - val_accuracy: 0.9474\n",
            "Epoch 18/25\n",
            "15/15 [==============================] - 1s 54ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.3322 - val_accuracy: 0.9298\n",
            "Epoch 19/25\n",
            "15/15 [==============================] - 1s 52ms/step - loss: 0.0035 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9474\n",
            "Epoch 20/25\n",
            "15/15 [==============================] - 1s 45ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.2967 - val_accuracy: 0.9474\n",
            "Epoch 21/25\n",
            "15/15 [==============================] - 1s 46ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.3288 - val_accuracy: 0.9474\n",
            "Epoch 22/25\n",
            "15/15 [==============================] - 1s 52ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.2800 - val_accuracy: 0.9474\n",
            "Epoch 23/25\n",
            "15/15 [==============================] - 1s 51ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.3026 - val_accuracy: 0.9474\n",
            "Epoch 24/25\n",
            "15/15 [==============================] - 1s 50ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.2997 - val_accuracy: 0.9474\n",
            "Epoch 25/25\n",
            "15/15 [==============================] - 1s 45ms/step - loss: 6.5423e-04 - accuracy: 1.0000 - val_loss: 0.3010 - val_accuracy: 0.9474\n"
          ]
        }
      ],
      "source": [
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(\n",
        "    x=x_train,\n",
        "    y=y_train,\n",
        "    batch_size=batch_size,\n",
        "    epochs=epoch,\n",
        "    validation_data=(x_val, y_val)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "koX39WW-kJzB"
      },
      "outputs": [],
      "source": [
        "# Save the model\n",
        "model.save('/content/drive/MyDrive/IJS/transfer.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6vAs8IoLcUyu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee0ddf69-ddee-4aa7-f1e1-a84b1294bf5d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.623613178730011\n",
            "Test accuracy: 0.8771929740905762\n"
          ]
        }
      ],
      "source": [
        "# Evaluate the model on the test set\n",
        "loss, accuracy = model.evaluate(x_test, y_test, verbose=0)\n",
        "\n",
        "print('Test loss:', loss)\n",
        "print('Test accuracy:', accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "byp9Bd_kcTBe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "50dd9460-107c-487e-9ea7-bf2fe2239312"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f659c268760>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(16,4))\n",
        "# Plot the training and validation loss over epochs\n",
        "ax = fig.add_subplot(121)\n",
        "ax.plot(history.history['loss'], label='Training Loss')\n",
        "ax.plot(history.history['val_loss'], label='Validation Loss')\n",
        "ax.set_title(\"Model Loss\")\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Loss')\n",
        "ax.legend()\n",
        "\n",
        "# Plot the training and validation accuracy over epochs\n",
        "ax2 = fig.add_subplot(122)\n",
        "ax2.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "ax2.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "ax2.set_title(\"Model Accuracy\")\n",
        "ax2.set_xlabel('Epochs')\n",
        "ax2.set_ylabel('Accuracy')\n",
        "ax2.legend()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5C5SBEfZVx8_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "outputId": "41075597-6da0-4ddb-8989-ffd3321d0ed5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 1s 43ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# predict the classes for the test set using the trained model\n",
        "y_pred = model.predict(x_test)\n",
        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "y_true_classes = np.argmax(y_test, axis=1)\n",
        "\n",
        "# create the confusion matrix\n",
        "cm = confusion_matrix(np.argmax(y_test, axis=1), y_pred_classes)\n",
        "\n",
        "# plot the confusion matrix as a heatmap with class names\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='g', xticklabels=class_name, yticklabels=class_name)\n",
        "plt.xlabel('Predicted label')\n",
        "plt.ylabel('True label')\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Unfreeze model layer"
      ],
      "metadata": {
        "id": "hTtL7DWm5sA1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jauibd9BHS-4"
      },
      "outputs": [],
      "source": [
        "# Unfreeze the layers in the base model\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable = True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JSEhnN3wHYDU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd4fb9c4-6ea1-4052-f461-92b4f214fba2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "15/15 [==============================] - 23s 226ms/step - loss: 1.2183 - accuracy: 0.7616 - val_loss: 3.9645 - val_accuracy: 0.2456\n",
            "Epoch 2/25\n",
            "15/15 [==============================] - 2s 117ms/step - loss: 0.3221 - accuracy: 0.8786 - val_loss: 4.7951 - val_accuracy: 0.1754\n",
            "Epoch 3/25\n",
            "15/15 [==============================] - 2s 114ms/step - loss: 0.1331 - accuracy: 0.9536 - val_loss: 5.3252 - val_accuracy: 0.1754\n",
            "Epoch 4/25\n",
            "15/15 [==============================] - 2s 112ms/step - loss: 0.0804 - accuracy: 0.9801 - val_loss: 5.4644 - val_accuracy: 0.1754\n",
            "Epoch 5/25\n",
            "15/15 [==============================] - 2s 118ms/step - loss: 0.1476 - accuracy: 0.9470 - val_loss: 2.6987 - val_accuracy: 0.4211\n",
            "Epoch 6/25\n",
            "15/15 [==============================] - 2s 119ms/step - loss: 0.0893 - accuracy: 0.9713 - val_loss: 0.7953 - val_accuracy: 0.8596\n",
            "Epoch 7/25\n",
            "15/15 [==============================] - 2s 113ms/step - loss: 0.3910 - accuracy: 0.9294 - val_loss: 0.2711 - val_accuracy: 0.8596\n",
            "Epoch 8/25\n",
            "15/15 [==============================] - 2s 121ms/step - loss: 0.3547 - accuracy: 0.9117 - val_loss: 1.2580 - val_accuracy: 0.6316\n",
            "Epoch 9/25\n",
            "15/15 [==============================] - 2s 126ms/step - loss: 0.1444 - accuracy: 0.9360 - val_loss: 0.4078 - val_accuracy: 0.8421\n",
            "Epoch 10/25\n",
            "15/15 [==============================] - 2s 122ms/step - loss: 0.0595 - accuracy: 0.9845 - val_loss: 0.4827 - val_accuracy: 0.8947\n",
            "Epoch 11/25\n",
            "15/15 [==============================] - 2s 113ms/step - loss: 0.0159 - accuracy: 0.9934 - val_loss: 2.2392 - val_accuracy: 0.7719\n",
            "Epoch 12/25\n",
            "15/15 [==============================] - 2s 114ms/step - loss: 0.0062 - accuracy: 0.9978 - val_loss: 2.0113 - val_accuracy: 0.7895\n",
            "Epoch 13/25\n",
            "15/15 [==============================] - 2s 120ms/step - loss: 0.0441 - accuracy: 0.9890 - val_loss: 0.2196 - val_accuracy: 0.9474\n",
            "Epoch 14/25\n",
            "15/15 [==============================] - 2s 115ms/step - loss: 0.1567 - accuracy: 0.9558 - val_loss: 0.5533 - val_accuracy: 0.7895\n",
            "Epoch 15/25\n",
            "15/15 [==============================] - 2s 115ms/step - loss: 0.1566 - accuracy: 0.9426 - val_loss: 0.7848 - val_accuracy: 0.8070\n",
            "Epoch 16/25\n",
            "15/15 [==============================] - 2s 122ms/step - loss: 0.1199 - accuracy: 0.9603 - val_loss: 0.4714 - val_accuracy: 0.9123\n",
            "Epoch 17/25\n",
            "15/15 [==============================] - 2s 121ms/step - loss: 0.1416 - accuracy: 0.9514 - val_loss: 0.4779 - val_accuracy: 0.9123\n",
            "Epoch 18/25\n",
            "15/15 [==============================] - 2s 127ms/step - loss: 0.0536 - accuracy: 0.9890 - val_loss: 0.4655 - val_accuracy: 0.8596\n",
            "Epoch 19/25\n",
            "15/15 [==============================] - 2s 115ms/step - loss: 0.0523 - accuracy: 0.9845 - val_loss: 0.4086 - val_accuracy: 0.8772\n",
            "Epoch 20/25\n",
            "15/15 [==============================] - 2s 115ms/step - loss: 0.0770 - accuracy: 0.9868 - val_loss: 0.3124 - val_accuracy: 0.8772\n",
            "Epoch 21/25\n",
            "15/15 [==============================] - 2s 115ms/step - loss: 0.2503 - accuracy: 0.9161 - val_loss: 1.4364 - val_accuracy: 0.7018\n",
            "Epoch 22/25\n",
            "15/15 [==============================] - 2s 115ms/step - loss: 0.0874 - accuracy: 0.9581 - val_loss: 0.5984 - val_accuracy: 0.8772\n",
            "Epoch 23/25\n",
            "15/15 [==============================] - 2s 115ms/step - loss: 0.0920 - accuracy: 0.9801 - val_loss: 1.0351 - val_accuracy: 0.8421\n",
            "Epoch 24/25\n",
            "15/15 [==============================] - 2s 125ms/step - loss: 0.0829 - accuracy: 0.9735 - val_loss: 0.9909 - val_accuracy: 0.7368\n",
            "Epoch 25/25\n",
            "15/15 [==============================] - 2s 127ms/step - loss: 0.0649 - accuracy: 0.9845 - val_loss: 0.8307 - val_accuracy: 0.7895\n"
          ]
        }
      ],
      "source": [
        "# Compile the model\n",
        "model.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(\n",
        "    x=x_train,\n",
        "    y=y_train,\n",
        "    batch_size=batch_size,\n",
        "    epochs=epoch,\n",
        "    validation_data=(x_val, y_val)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yRpkZWwbMFe_"
      },
      "outputs": [],
      "source": [
        "# Save the model\n",
        "model.save('/content/drive/MyDrive/IJS/transfer(x).h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mb1YnspFMViu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd3c866c-5d52-45b8-e652-41d7d9607e58"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 1.0880365371704102\n",
            "Test accuracy: 0.7894737124443054\n"
          ]
        }
      ],
      "source": [
        "# Evaluate the model on the test set\n",
        "loss, accuracy = model.evaluate(x_test, y_test, verbose=0)\n",
        "\n",
        "print('Test loss:', loss)\n",
        "print('Test accuracy:', accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rKUMcxUTMSrk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "f1a86d19-b18f-40cf-9717-8a3170bfa04f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f661c077460>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot the training and validation loss over epochs\n",
        "fig = plt.figure(figsize=(16,4))\n",
        "\n",
        "ax = fig.add_subplot(121)\n",
        "ax.plot(history.history['loss'], label='Training Loss')\n",
        "ax.plot(history.history['val_loss'], label='Validation Loss')\n",
        "ax.set_title(\"Model Loss\")\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Loss')\n",
        "ax.legend()\n",
        "\n",
        "# Plot the training and validation accuracy over epochs\n",
        "ax2 = fig.add_subplot(122)\n",
        "ax2.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "ax2.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "ax2.set_title(\"Model Accuracy\")\n",
        "ax2.set_xlabel('Epochs')\n",
        "ax2.set_ylabel('Accuracy')\n",
        "ax2.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DsRs0uwvMWT0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "outputId": "e71dd9d7-db73-4ce2-f77e-c2396d09d47c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 1s 43ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# predict the classes for the test set using the trained model\n",
        "y_pred = model.predict(x_test)\n",
        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "y_true_classes = np.argmax(y_test, axis=1)\n",
        "\n",
        "# create the confusion matrix\n",
        "cm = confusion_matrix(np.argmax(y_test, axis=1), y_pred_classes)\n",
        "\n",
        "# plot the confusion matrix as a heatmap with class names\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='g', xticklabels=class_name, yticklabels=class_name)\n",
        "plt.xlabel('Predicted label')\n",
        "plt.ylabel('True label')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}