Celebrity_Face_Recognition .ipynb

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      "name": "Celebrity Face Recognition.ipynb",
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      "metadata": {
        "id": "h3GHTpi2tNwA"
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      "source": [
        "\n",
        "> # **Pins Face Recognition**\n",
        "\n",
        "\n",
        "> **Facial Recognition Dataset collected from Pinterest**\n",
        "\n",
        ">[https://www.kaggle.com/hereisburak/pins-face-recognition](https://)\n",
        "\n",
        "**Dataset Content:**\n",
        "\n",
        ">This images has been collected from Pinterest and cropped. There are 105 celebrities and 17534 faces.\n",
        "\n",
        "**Description:**\n",
        ">The below Python code will unzip the above downloaded dataset by the user and split it into the training set and and validation set based on the given ratio.**Transfer Learning** has been implemented  with the **MobileNetV2** model and **imagenet** weights. The pre-trained model consists of 155 layers and the whole layers of the pretrained model is retrained to get the better accuracy.Image Augmentation has been implemented to avoid the overfitting of the model.\n",
        "The model has been trained for the 50 epochs and the **Validation Accuracy of 85%** has been yielded.Note:The model performs well on the cropped images.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mllk2Jj-iAYy"
      },
      "source": [
        "# Move the downloaded the zipfile of dataset into the current working directory or modify the path in the code to the location of the downloaded dataset.\n",
        "import os\n",
        "from zipfile import ZipFile\n",
        "work_dir = os.getcwd()                                                  #Saves the current working directory.\n",
        "with ZipFile(os.path.join(work_dir ,'pins-face-recognition.zip'),'r') as zipobject:\n",
        "  zipobject.extractall()                                                #Unzip the downloaded dataset.s"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8LFM_S4Zf4X4"
      },
      "source": [
        "train_dir = os.path.join(work_dir,'train')       #Path of train directory. \n",
        "test_dir = os.path.join(work_dir,'test')         #Path of test directory.\n",
        "os.makedirs(train_dir, exist_ok=True)  #Creating a directory for training examples. \n",
        "os.makedirs(test_dir, exist_ok=True)   #Creating a directory for testing examples. "
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "id": "GYEGc9Zff4X7",
        "outputId": "e4e7a57c-959a-4544-e580-ba2e5150b4f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_ratio=0.8\n",
        "test_ratio=0.2\n",
        "dir_list=os.listdir(os.path.join(work_dir ,'105_classes_pins_dataset'))\n",
        "print('The Number of Classes in the Dataset is:{}'.format(len(dir_list)))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Number of Classes in the Dataset is:105\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-output": false,
        "id": "uFZbByBhf4X-"
      },
      "source": [
        "#This Code will split up the unzipped dataset into training and testing set with respect to the values of train_ratio and test_ratio.\n",
        "from shutil import copyfile\n",
        "import numpy as np\n",
        "\n",
        "source_dir = os.path.join(work_dir ,'105_classes_pins_dataset')        #Unzipped dataset directory \n",
        "\n",
        "dir_list = os.listdir(source_dir)\n",
        "\n",
        "for folder in dir_list:\n",
        "    data_dir = os.listdir(os.path.join(source_dir,folder))\n",
        "    np.random.shuffle(data_dir)                            #Shuffling the examples of the classes inside the dataset.\n",
        "    os.makedirs(os.path.join(train_dir , folder), exist_ok=True)\n",
        "    os.makedirs(os.path.join(test_dir , folder), exist_ok=True)\n",
        "    train_data = data_dir[:int(len(data_dir)*train_ratio+1)] #Splitting the training dataset with respect to the train_ratio.\n",
        "    test_data = data_dir[-int(len(data_dir)*test_ratio):]\n",
        "  \n",
        "    for image in train_data:\n",
        "        copyfile(os.path.join(source_dir,folder,image) , os.path.join(train_dir,folder,image)) #Copying the Training files from dataset to training directory.\n",
        "    \n",
        "    for image in test_data:\n",
        "       copyfile(os.path.join(source_dir,folder,image) , os.path.join(test_dir,folder,image))  #Copying the Training files from dataset to training directory.\n",
        "    "
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "msQNITN4f4YB",
        "outputId": "dfff3c13-da55-4691-f0c5-f780988b1095",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "\n",
        "print(\"The Number of Classes in the Training Set:{}\".format(len(os.listdir(train_dir))))\n",
        "print(\"The Number Of Classes in the Testing Set:{}\".format(len(os.listdir(test_dir))))\n",
        "print('For Verification of the Split.....')\n",
        "print(\"The Number of Examples in the Class='pins_Katherine Langford' in the Dataset Directory:{}\".format(len(os.listdir(os.path.join(source_dir,'pins_Katherine Langford')))))\n",
        "print(\"The Number of Examples in the Class='pins_Katherine Langford' in the Train Directory:{}\".format(len(os.listdir(os.path.join(train_dir,'pins_Katherine Langford')))))\n",
        "print(\"The Number of Examples in the Class='pins_Katherine Langford' in the Test Directory:{}\".format(len(os.listdir(os.path.join(test_dir,'pins_Katherine Langford')))))\n"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Number of Classes in the Training Set:105\n",
            "The Number Of Classes in the Testing Set:105\n",
            "For Verification of the Split.....\n",
            "The Number of Examples in the Class='pins_Katherine Langford' in the Dataset Directory:226\n",
            "The Number of Examples in the Class='pins_Katherine Langford' in the Train Directory:181\n",
            "The Number of Examples in the Class='pins_Katherine Langford' in the Test Directory:45\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqe5t2ezf4YE"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications import MobileNetV2\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "rows=160  #Number of Pixels in the Rows for Input. \n",
        "cols=160  #Number of Pixels in Columns for Output"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6tbvZdY-f4YG",
        "outputId": "c24a7761-a12c-4ada-bd23-540df1b1e893",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "trained_model=MobileNetV2(input_shape=(rows,cols,3),           \n",
        "                    include_top=False,\n",
        "                    weights='imagenet')   \n",
        "\n",
        "\n",
        "trained_model.trainable=True  #Un-Freeze all the pretrained layers of 'MobileNetV2 for Training.\n",
        "    \n",
        "trained_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"mobilenetv2_1.00_160\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            [(None, 160, 160, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_pad (ZeroPadding2D)       (None, 161, 161, 3)  0           input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "Conv1 (Conv2D)                  (None, 80, 80, 32)   864         Conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "bn_Conv1 (BatchNormalization)   (None, 80, 80, 32)   128         Conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_relu (ReLU)               (None, 80, 80, 32)   0           bn_Conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise (Depthw (None, 80, 80, 32)   288         Conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_BN (Bat (None, 80, 80, 32)   128         expanded_conv_depthwise[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_relu (R (None, 80, 80, 32)   0           expanded_conv_depthwise_BN[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project (Conv2D)  (None, 80, 80, 16)   512         expanded_conv_depthwise_relu[0][0\n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project_BN (Batch (None, 80, 80, 16)   64          expanded_conv_project[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand (Conv2D)         (None, 80, 80, 96)   1536        expanded_conv_project_BN[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_BN (BatchNormali (None, 80, 80, 96)   384         block_1_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_relu (ReLU)      (None, 80, 80, 96)   0           block_1_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_pad (ZeroPadding2D)     (None, 81, 81, 96)   0           block_1_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise (DepthwiseCon (None, 40, 40, 96)   864         block_1_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_BN (BatchNorm (None, 40, 40, 96)   384         block_1_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_relu (ReLU)   (None, 40, 40, 96)   0           block_1_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project (Conv2D)        (None, 40, 40, 24)   2304        block_1_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project_BN (BatchNormal (None, 40, 40, 24)   96          block_1_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand (Conv2D)         (None, 40, 40, 144)  3456        block_1_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_BN (BatchNormali (None, 40, 40, 144)  576         block_2_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_relu (ReLU)      (None, 40, 40, 144)  0           block_2_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise (DepthwiseCon (None, 40, 40, 144)  1296        block_2_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_BN (BatchNorm (None, 40, 40, 144)  576         block_2_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_relu (ReLU)   (None, 40, 40, 144)  0           block_2_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project (Conv2D)        (None, 40, 40, 24)   3456        block_2_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project_BN (BatchNormal (None, 40, 40, 24)   96          block_2_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_add (Add)               (None, 40, 40, 24)   0           block_1_project_BN[0][0]         \n",
            "                                                                 block_2_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand (Conv2D)         (None, 40, 40, 144)  3456        block_2_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_BN (BatchNormali (None, 40, 40, 144)  576         block_3_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_relu (ReLU)      (None, 40, 40, 144)  0           block_3_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_pad (ZeroPadding2D)     (None, 41, 41, 144)  0           block_3_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise (DepthwiseCon (None, 20, 20, 144)  1296        block_3_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_BN (BatchNorm (None, 20, 20, 144)  576         block_3_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_relu (ReLU)   (None, 20, 20, 144)  0           block_3_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project (Conv2D)        (None, 20, 20, 32)   4608        block_3_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project_BN (BatchNormal (None, 20, 20, 32)   128         block_3_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand (Conv2D)         (None, 20, 20, 192)  6144        block_3_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_4_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_4_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise (DepthwiseCon (None, 20, 20, 192)  1728        block_4_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_BN (BatchNorm (None, 20, 20, 192)  768         block_4_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_relu (ReLU)   (None, 20, 20, 192)  0           block_4_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project (Conv2D)        (None, 20, 20, 32)   6144        block_4_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project_BN (BatchNormal (None, 20, 20, 32)   128         block_4_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_add (Add)               (None, 20, 20, 32)   0           block_3_project_BN[0][0]         \n",
            "                                                                 block_4_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand (Conv2D)         (None, 20, 20, 192)  6144        block_4_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_5_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_5_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise (DepthwiseCon (None, 20, 20, 192)  1728        block_5_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_BN (BatchNorm (None, 20, 20, 192)  768         block_5_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_relu (ReLU)   (None, 20, 20, 192)  0           block_5_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project (Conv2D)        (None, 20, 20, 32)   6144        block_5_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project_BN (BatchNormal (None, 20, 20, 32)   128         block_5_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_5_add (Add)               (None, 20, 20, 32)   0           block_4_add[0][0]                \n",
            "                                                                 block_5_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand (Conv2D)         (None, 20, 20, 192)  6144        block_5_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_6_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_6_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_pad (ZeroPadding2D)     (None, 21, 21, 192)  0           block_6_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise (DepthwiseCon (None, 10, 10, 192)  1728        block_6_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_BN (BatchNorm (None, 10, 10, 192)  768         block_6_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_relu (ReLU)   (None, 10, 10, 192)  0           block_6_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project (Conv2D)        (None, 10, 10, 64)   12288       block_6_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project_BN (BatchNormal (None, 10, 10, 64)   256         block_6_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand (Conv2D)         (None, 10, 10, 384)  24576       block_6_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_7_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_7_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_7_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_7_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_7_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project (Conv2D)        (None, 10, 10, 64)   24576       block_7_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project_BN (BatchNormal (None, 10, 10, 64)   256         block_7_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_add (Add)               (None, 10, 10, 64)   0           block_6_project_BN[0][0]         \n",
            "                                                                 block_7_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand (Conv2D)         (None, 10, 10, 384)  24576       block_7_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_8_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_8_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_8_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_8_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_8_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project (Conv2D)        (None, 10, 10, 64)   24576       block_8_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project_BN (BatchNormal (None, 10, 10, 64)   256         block_8_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_8_add (Add)               (None, 10, 10, 64)   0           block_7_add[0][0]                \n",
            "                                                                 block_8_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand (Conv2D)         (None, 10, 10, 384)  24576       block_8_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_9_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_9_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_9_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_9_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_9_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project (Conv2D)        (None, 10, 10, 64)   24576       block_9_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project_BN (BatchNormal (None, 10, 10, 64)   256         block_9_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_9_add (Add)               (None, 10, 10, 64)   0           block_8_add[0][0]                \n",
            "                                                                 block_9_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand (Conv2D)        (None, 10, 10, 384)  24576       block_9_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_BN (BatchNormal (None, 10, 10, 384)  1536        block_10_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_relu (ReLU)     (None, 10, 10, 384)  0           block_10_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise (DepthwiseCo (None, 10, 10, 384)  3456        block_10_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_BN (BatchNor (None, 10, 10, 384)  1536        block_10_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_relu (ReLU)  (None, 10, 10, 384)  0           block_10_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project (Conv2D)       (None, 10, 10, 96)   36864       block_10_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project_BN (BatchNorma (None, 10, 10, 96)   384         block_10_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand (Conv2D)        (None, 10, 10, 576)  55296       block_10_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_11_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_11_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise (DepthwiseCo (None, 10, 10, 576)  5184        block_11_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_BN (BatchNor (None, 10, 10, 576)  2304        block_11_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_relu (ReLU)  (None, 10, 10, 576)  0           block_11_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project (Conv2D)       (None, 10, 10, 96)   55296       block_11_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project_BN (BatchNorma (None, 10, 10, 96)   384         block_11_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_add (Add)              (None, 10, 10, 96)   0           block_10_project_BN[0][0]        \n",
            "                                                                 block_11_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand (Conv2D)        (None, 10, 10, 576)  55296       block_11_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_12_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_12_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise (DepthwiseCo (None, 10, 10, 576)  5184        block_12_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_BN (BatchNor (None, 10, 10, 576)  2304        block_12_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_relu (ReLU)  (None, 10, 10, 576)  0           block_12_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project (Conv2D)       (None, 10, 10, 96)   55296       block_12_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project_BN (BatchNorma (None, 10, 10, 96)   384         block_12_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_12_add (Add)              (None, 10, 10, 96)   0           block_11_add[0][0]               \n",
            "                                                                 block_12_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand (Conv2D)        (None, 10, 10, 576)  55296       block_12_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_13_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_13_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_pad (ZeroPadding2D)    (None, 11, 11, 576)  0           block_13_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise (DepthwiseCo (None, 5, 5, 576)    5184        block_13_pad[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_BN (BatchNor (None, 5, 5, 576)    2304        block_13_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_relu (ReLU)  (None, 5, 5, 576)    0           block_13_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project (Conv2D)       (None, 5, 5, 160)    92160       block_13_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project_BN (BatchNorma (None, 5, 5, 160)    640         block_13_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand (Conv2D)        (None, 5, 5, 960)    153600      block_13_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_14_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_14_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_14_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_14_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_14_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project (Conv2D)       (None, 5, 5, 160)    153600      block_14_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project_BN (BatchNorma (None, 5, 5, 160)    640         block_14_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_add (Add)              (None, 5, 5, 160)    0           block_13_project_BN[0][0]        \n",
            "                                                                 block_14_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand (Conv2D)        (None, 5, 5, 960)    153600      block_14_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_15_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_15_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_15_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_15_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_15_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project (Conv2D)       (None, 5, 5, 160)    153600      block_15_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project_BN (BatchNorma (None, 5, 5, 160)    640         block_15_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_15_add (Add)              (None, 5, 5, 160)    0           block_14_add[0][0]               \n",
            "                                                                 block_15_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand (Conv2D)        (None, 5, 5, 960)    153600      block_15_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_16_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_16_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_16_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_16_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_16_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project (Conv2D)       (None, 5, 5, 320)    307200      block_16_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project_BN (BatchNorma (None, 5, 5, 320)    1280        block_16_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1 (Conv2D)                 (None, 5, 5, 1280)   409600      block_16_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1_bn (BatchNormalization)  (None, 5, 5, 1280)   5120        Conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "out_relu (ReLU)                 (None, 5, 5, 1280)   0           Conv_1_bn[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 2,257,984\n",
            "Trainable params: 2,223,872\n",
            "Non-trainable params: 34,112\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EH49t2IQf4YK"
      },
      "source": [
        "last_layer=trained_model.get_layer('out_relu')   \n",
        "last_layer_output=last_layer.output  #Saves the output of the last layer of the MobileNetV2."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gCxxAGmHf4YM"
      },
      "source": [
        "#Callback Function which stops training when accuracy reaches 98%.\n",
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('accuracy') > 0.98):\n",
        "      print(\"\\nReached 98% accuracy so cancelling training!\")\n",
        "      self.model.stop_training = True\n",
        "callbacks=myCallback()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QHDCzpfUf4YP",
        "outputId": "b4c8f0b4-dbf0-4fbf-ee70-f657ca421caa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "x = tf.keras.layers.GlobalAveragePooling2D()(last_layer_output)\n",
        "#Add a Dropout layer.\n",
        "x = tf.keras.layers.Dropout(0.8)(x)                      \n",
        "# Add a final softmax layer for classification.\n",
        "x = tf.keras.layers.Dense(105,activation='softmax')(x)\n",
        "\n",
        "model = tf.keras.Model(trained_model.input,x) \n",
        "\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer=tf.keras.optimizers.Adam(1e-4),\n",
        "              metrics=['accuracy'])\n",
        "#Summary of the model.\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_3\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            [(None, 160, 160, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_pad (ZeroPadding2D)       (None, 161, 161, 3)  0           input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "Conv1 (Conv2D)                  (None, 80, 80, 32)   864         Conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "bn_Conv1 (BatchNormalization)   (None, 80, 80, 32)   128         Conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_relu (ReLU)               (None, 80, 80, 32)   0           bn_Conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise (Depthw (None, 80, 80, 32)   288         Conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_BN (Bat (None, 80, 80, 32)   128         expanded_conv_depthwise[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_relu (R (None, 80, 80, 32)   0           expanded_conv_depthwise_BN[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project (Conv2D)  (None, 80, 80, 16)   512         expanded_conv_depthwise_relu[0][0\n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project_BN (Batch (None, 80, 80, 16)   64          expanded_conv_project[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand (Conv2D)         (None, 80, 80, 96)   1536        expanded_conv_project_BN[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_BN (BatchNormali (None, 80, 80, 96)   384         block_1_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_relu (ReLU)      (None, 80, 80, 96)   0           block_1_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_pad (ZeroPadding2D)     (None, 81, 81, 96)   0           block_1_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise (DepthwiseCon (None, 40, 40, 96)   864         block_1_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_BN (BatchNorm (None, 40, 40, 96)   384         block_1_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_relu (ReLU)   (None, 40, 40, 96)   0           block_1_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project (Conv2D)        (None, 40, 40, 24)   2304        block_1_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project_BN (BatchNormal (None, 40, 40, 24)   96          block_1_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand (Conv2D)         (None, 40, 40, 144)  3456        block_1_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_BN (BatchNormali (None, 40, 40, 144)  576         block_2_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_relu (ReLU)      (None, 40, 40, 144)  0           block_2_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise (DepthwiseCon (None, 40, 40, 144)  1296        block_2_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_BN (BatchNorm (None, 40, 40, 144)  576         block_2_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_relu (ReLU)   (None, 40, 40, 144)  0           block_2_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project (Conv2D)        (None, 40, 40, 24)   3456        block_2_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project_BN (BatchNormal (None, 40, 40, 24)   96          block_2_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_add (Add)               (None, 40, 40, 24)   0           block_1_project_BN[0][0]         \n",
            "                                                                 block_2_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand (Conv2D)         (None, 40, 40, 144)  3456        block_2_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_BN (BatchNormali (None, 40, 40, 144)  576         block_3_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_relu (ReLU)      (None, 40, 40, 144)  0           block_3_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_pad (ZeroPadding2D)     (None, 41, 41, 144)  0           block_3_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise (DepthwiseCon (None, 20, 20, 144)  1296        block_3_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_BN (BatchNorm (None, 20, 20, 144)  576         block_3_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_relu (ReLU)   (None, 20, 20, 144)  0           block_3_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project (Conv2D)        (None, 20, 20, 32)   4608        block_3_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project_BN (BatchNormal (None, 20, 20, 32)   128         block_3_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand (Conv2D)         (None, 20, 20, 192)  6144        block_3_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_4_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_4_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise (DepthwiseCon (None, 20, 20, 192)  1728        block_4_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_BN (BatchNorm (None, 20, 20, 192)  768         block_4_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_relu (ReLU)   (None, 20, 20, 192)  0           block_4_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project (Conv2D)        (None, 20, 20, 32)   6144        block_4_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project_BN (BatchNormal (None, 20, 20, 32)   128         block_4_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_add (Add)               (None, 20, 20, 32)   0           block_3_project_BN[0][0]         \n",
            "                                                                 block_4_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand (Conv2D)         (None, 20, 20, 192)  6144        block_4_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_5_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_5_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise (DepthwiseCon (None, 20, 20, 192)  1728        block_5_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_BN (BatchNorm (None, 20, 20, 192)  768         block_5_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_relu (ReLU)   (None, 20, 20, 192)  0           block_5_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project (Conv2D)        (None, 20, 20, 32)   6144        block_5_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project_BN (BatchNormal (None, 20, 20, 32)   128         block_5_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_5_add (Add)               (None, 20, 20, 32)   0           block_4_add[0][0]                \n",
            "                                                                 block_5_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand (Conv2D)         (None, 20, 20, 192)  6144        block_5_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_6_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_6_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_pad (ZeroPadding2D)     (None, 21, 21, 192)  0           block_6_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise (DepthwiseCon (None, 10, 10, 192)  1728        block_6_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_BN (BatchNorm (None, 10, 10, 192)  768         block_6_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_relu (ReLU)   (None, 10, 10, 192)  0           block_6_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project (Conv2D)        (None, 10, 10, 64)   12288       block_6_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project_BN (BatchNormal (None, 10, 10, 64)   256         block_6_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand (Conv2D)         (None, 10, 10, 384)  24576       block_6_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_7_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_7_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_7_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_7_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_7_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project (Conv2D)        (None, 10, 10, 64)   24576       block_7_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project_BN (BatchNormal (None, 10, 10, 64)   256         block_7_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_add (Add)               (None, 10, 10, 64)   0           block_6_project_BN[0][0]         \n",
            "                                                                 block_7_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand (Conv2D)         (None, 10, 10, 384)  24576       block_7_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_8_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_8_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_8_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_8_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_8_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project (Conv2D)        (None, 10, 10, 64)   24576       block_8_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project_BN (BatchNormal (None, 10, 10, 64)   256         block_8_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_8_add (Add)               (None, 10, 10, 64)   0           block_7_add[0][0]                \n",
            "                                                                 block_8_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand (Conv2D)         (None, 10, 10, 384)  24576       block_8_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_9_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_9_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_9_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_9_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_9_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project (Conv2D)        (None, 10, 10, 64)   24576       block_9_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project_BN (BatchNormal (None, 10, 10, 64)   256         block_9_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_9_add (Add)               (None, 10, 10, 64)   0           block_8_add[0][0]                \n",
            "                                                                 block_9_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand (Conv2D)        (None, 10, 10, 384)  24576       block_9_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_BN (BatchNormal (None, 10, 10, 384)  1536        block_10_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_relu (ReLU)     (None, 10, 10, 384)  0           block_10_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise (DepthwiseCo (None, 10, 10, 384)  3456        block_10_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_BN (BatchNor (None, 10, 10, 384)  1536        block_10_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_relu (ReLU)  (None, 10, 10, 384)  0           block_10_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project (Conv2D)       (None, 10, 10, 96)   36864       block_10_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project_BN (BatchNorma (None, 10, 10, 96)   384         block_10_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand (Conv2D)        (None, 10, 10, 576)  55296       block_10_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_11_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_11_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise (DepthwiseCo (None, 10, 10, 576)  5184        block_11_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_BN (BatchNor (None, 10, 10, 576)  2304        block_11_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_relu (ReLU)  (None, 10, 10, 576)  0           block_11_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project (Conv2D)       (None, 10, 10, 96)   55296       block_11_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project_BN (BatchNorma (None, 10, 10, 96)   384         block_11_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_add (Add)              (None, 10, 10, 96)   0           block_10_project_BN[0][0]        \n",
            "                                                                 block_11_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand (Conv2D)        (None, 10, 10, 576)  55296       block_11_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_12_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_12_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise (DepthwiseCo (None, 10, 10, 576)  5184        block_12_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_BN (BatchNor (None, 10, 10, 576)  2304        block_12_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_relu (ReLU)  (None, 10, 10, 576)  0           block_12_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project (Conv2D)       (None, 10, 10, 96)   55296       block_12_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project_BN (BatchNorma (None, 10, 10, 96)   384         block_12_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_12_add (Add)              (None, 10, 10, 96)   0           block_11_add[0][0]               \n",
            "                                                                 block_12_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand (Conv2D)        (None, 10, 10, 576)  55296       block_12_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_13_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_13_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_pad (ZeroPadding2D)    (None, 11, 11, 576)  0           block_13_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise (DepthwiseCo (None, 5, 5, 576)    5184        block_13_pad[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_BN (BatchNor (None, 5, 5, 576)    2304        block_13_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_relu (ReLU)  (None, 5, 5, 576)    0           block_13_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project (Conv2D)       (None, 5, 5, 160)    92160       block_13_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project_BN (BatchNorma (None, 5, 5, 160)    640         block_13_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand (Conv2D)        (None, 5, 5, 960)    153600      block_13_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_14_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_14_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_14_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_14_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_14_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project (Conv2D)       (None, 5, 5, 160)    153600      block_14_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project_BN (BatchNorma (None, 5, 5, 160)    640         block_14_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_add (Add)              (None, 5, 5, 160)    0           block_13_project_BN[0][0]        \n",
            "                                                                 block_14_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand (Conv2D)        (None, 5, 5, 960)    153600      block_14_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_15_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_15_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_15_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_15_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_15_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project (Conv2D)       (None, 5, 5, 160)    153600      block_15_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project_BN (BatchNorma (None, 5, 5, 160)    640         block_15_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_15_add (Add)              (None, 5, 5, 160)    0           block_14_add[0][0]               \n",
            "                                                                 block_15_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand (Conv2D)        (None, 5, 5, 960)    153600      block_15_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_16_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_16_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_16_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_16_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_16_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project (Conv2D)       (None, 5, 5, 320)    307200      block_16_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project_BN (BatchNorma (None, 5, 5, 320)    1280        block_16_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1 (Conv2D)                 (None, 5, 5, 1280)   409600      block_16_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1_bn (BatchNormalization)  (None, 5, 5, 1280)   5120        Conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "out_relu (ReLU)                 (None, 5, 5, 1280)   0           Conv_1_bn[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_1 (Glo (None, 1280)         0           out_relu[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 1280)         0           global_average_pooling2d_1[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 105)          134505      dropout_1[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 2,392,489\n",
            "Trainable params: 2,358,377\n",
            "Non-trainable params: 34,112\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91KsMjw3f4YS",
        "outputId": "a7fc7044-50a8-4810-fa7d-a31a6d81c24d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "train_datagen = ImageDataGenerator(rescale=1/255,\n",
        "                                 shear_range=0.2,\n",
        "                                 zoom_range=0.2,\n",
        "                                 horizontal_flip=True,\n",
        "                                 rotation_range=40,\n",
        "                                 width_shift_range=0.1,\n",
        "                                 height_shift_range=0.1)\n",
        "                                 \n",
        "test_datagen = ImageDataGenerator(rescale=1/255)\n",
        "\n",
        "train_generator=train_datagen.flow_from_directory(train_dir,\n",
        "                                                  target_size=(rows,cols),\n",
        "                                                  class_mode='categorical')\n",
        "\n",
        "test_generator=test_datagen.flow_from_directory(test_dir,\n",
        "                                                target_size=(rows,cols),\n",
        "                                                class_mode='categorical')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 14084 images belonging to 105 classes.\n",
            "Found 3463 images belonging to 105 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UY0xcxrNf4YU",
        "outputId": "b5d5e66d-8f3c-4428-f9a8-4063e740482e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "result=model.fit(train_generator,\n",
        "                 validation_data=test_generator,\n",
        "                 epochs=50,\n",
        "                 callbacks=[callbacks],\n",
        "                 verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "  2/441 [..............................] - ETA: 21s - loss: 6.7485 - accuracy: 0.0000e+00WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0379s vs `on_train_batch_end` time: 0.0594s). Check your callbacks.\n",
            "441/441 [==============================] - 113s 257ms/step - loss: 4.9971 - accuracy: 0.0153 - val_loss: 4.8716 - val_accuracy: 0.0248\n",
            "Epoch 2/50\n",
            "441/441 [==============================] - 111s 252ms/step - loss: 4.5391 - accuracy: 0.0258 - val_loss: 4.4199 - val_accuracy: 0.0393\n",
            "Epoch 3/50\n",
            "441/441 [==============================] - 111s 251ms/step - loss: 4.2396 - accuracy: 0.0552 - val_loss: 4.0829 - val_accuracy: 0.0745\n",
            "Epoch 4/50\n",
            "441/441 [==============================] - 110s 250ms/step - loss: 3.8009 - accuracy: 0.1065 - val_loss: 3.3910 - val_accuracy: 0.1741\n",
            "Epoch 5/50\n",
            "441/441 [==============================] - 110s 249ms/step - loss: 3.3586 - accuracy: 0.1795 - val_loss: 2.8764 - val_accuracy: 0.2616\n",
            "Epoch 6/50\n",
            "441/441 [==============================] - 110s 250ms/step - loss: 2.9315 - accuracy: 0.2609 - val_loss: 2.5329 - val_accuracy: 0.3624\n",
            "Epoch 7/50\n",
            "441/441 [==============================] - 110s 250ms/step - loss: 2.5319 - accuracy: 0.3469 - val_loss: 2.3861 - val_accuracy: 0.3861\n",
            "Epoch 8/50\n",
            "441/441 [==============================] - 109s 247ms/step - loss: 2.2230 - accuracy: 0.4136 - val_loss: 1.8096 - val_accuracy: 0.5195\n",
            "Epoch 9/50\n",
            "441/441 [==============================] - 110s 248ms/step - loss: 1.9253 - accuracy: 0.4886 - val_loss: 1.6123 - val_accuracy: 0.5770\n",
            "Epoch 10/50\n",
            "441/441 [==============================] - 110s 248ms/step - loss: 1.6867 - accuracy: 0.5400 - val_loss: 1.4031 - val_accuracy: 0.6260\n",
            "Epoch 11/50\n",
            "441/441 [==============================] - 109s 247ms/step - loss: 1.5282 - accuracy: 0.5875 - val_loss: 1.3605 - val_accuracy: 0.6318\n",
            "Epoch 12/50\n",
            "441/441 [==============================] - 109s 248ms/step - loss: 1.3467 - accuracy: 0.6270 - val_loss: 1.3243 - val_accuracy: 0.6362\n",
            "Epoch 13/50\n",
            "441/441 [==============================] - 109s 248ms/step - loss: 1.1970 - accuracy: 0.6675 - val_loss: 1.4028 - val_accuracy: 0.6157\n",
            "Epoch 14/50\n",
            "441/441 [==============================] - 109s 247ms/step - loss: 1.0953 - accuracy: 0.6923 - val_loss: 1.3279 - val_accuracy: 0.6437\n",
            "Epoch 15/50\n",
            "441/441 [==============================] - 109s 248ms/step - loss: 0.9952 - accuracy: 0.7165 - val_loss: 1.0799 - val_accuracy: 0.6991\n",
            "Epoch 16/50\n",
            "441/441 [==============================] - 109s 246ms/step - loss: 0.9061 - accuracy: 0.7443 - val_loss: 1.0547 - val_accuracy: 0.7144\n",
            "Epoch 17/50\n",
            "441/441 [==============================] - 109s 247ms/step - loss: 0.8491 - accuracy: 0.7575 - val_loss: 0.8980 - val_accuracy: 0.7597\n",
            "Epoch 18/50\n",
            "441/441 [==============================] - 109s 247ms/step - loss: 0.7680 - accuracy: 0.7833 - val_loss: 0.8880 - val_accuracy: 0.7534\n",
            "Epoch 19/50\n",
            "441/441 [==============================] - 108s 246ms/step - loss: 0.7099 - accuracy: 0.8001 - val_loss: 0.8286 - val_accuracy: 0.7704\n",
            "Epoch 20/50\n",
            "441/441 [==============================] - 109s 247ms/step - loss: 0.6569 - accuracy: 0.8137 - val_loss: 0.8893 - val_accuracy: 0.7658\n",
            "Epoch 21/50\n",
            "441/441 [==============================] - 109s 247ms/step - loss: 0.6157 - accuracy: 0.8233 - val_loss: 0.7656 - val_accuracy: 0.7935\n",
            "Epoch 22/50\n",
            "441/441 [==============================] - 109s 246ms/step - loss: 0.5741 - accuracy: 0.8373 - val_loss: 0.6955 - val_accuracy: 0.8195\n",
            "Epoch 23/50\n",
            "441/441 [==============================] - 109s 247ms/step - loss: 0.5319 - accuracy: 0.8466 - val_loss: 0.7710 - val_accuracy: 0.8016\n",
            "Epoch 24/50\n",
            "441/441 [==============================] - 108s 246ms/step - loss: 0.4945 - accuracy: 0.8555 - val_loss: 0.8226 - val_accuracy: 0.7912\n",
            "Epoch 25/50\n",
            "441/441 [==============================] - 108s 246ms/step - loss: 0.4601 - accuracy: 0.8664 - val_loss: 0.7572 - val_accuracy: 0.7996\n",
            "Epoch 26/50\n",
            "441/441 [==============================] - 109s 248ms/step - loss: 0.4424 - accuracy: 0.8721 - val_loss: 0.7547 - val_accuracy: 0.8013\n",
            "Epoch 27/50\n",
            "441/441 [==============================] - 108s 246ms/step - loss: 0.4070 - accuracy: 0.8807 - val_loss: 0.6775 - val_accuracy: 0.8308\n",
            "Epoch 28/50\n",
            "441/441 [==============================] - 109s 246ms/step - loss: 0.3833 - accuracy: 0.8883 - val_loss: 0.6257 - val_accuracy: 0.8452\n",
            "Epoch 29/50\n",
            "441/441 [==============================] - 109s 247ms/step - loss: 0.3788 - accuracy: 0.8892 - val_loss: 0.6406 - val_accuracy: 0.8412\n",
            "Epoch 30/50\n",
            "441/441 [==============================] - 108s 246ms/step - loss: 0.3534 - accuracy: 0.8990 - val_loss: 0.6631 - val_accuracy: 0.8276\n",
            "Epoch 31/50\n",
            "441/441 [==============================] - 108s 246ms/step - loss: 0.3324 - accuracy: 0.9017 - val_loss: 0.6560 - val_accuracy: 0.8305\n",
            "Epoch 32/50\n",
            "441/441 [==============================] - 109s 247ms/step - loss: 0.3098 - accuracy: 0.9088 - val_loss: 0.6535 - val_accuracy: 0.8285\n",
            "Epoch 33/50\n",
            "441/441 [==============================] - 109s 247ms/step - loss: 0.3015 - accuracy: 0.9107 - val_loss: 0.7220 - val_accuracy: 0.8256\n",
            "Epoch 34/50\n",
            "441/441 [==============================] - 109s 246ms/step - loss: 0.2744 - accuracy: 0.9171 - val_loss: 0.8953 - val_accuracy: 0.7860\n",
            "Epoch 35/50\n",
            "441/441 [==============================] - 109s 247ms/step - loss: 0.2666 - accuracy: 0.9205 - val_loss: 0.5973 - val_accuracy: 0.8522\n",
            "Epoch 36/50\n",
            "441/441 [==============================] - 108s 245ms/step - loss: 0.2678 - accuracy: 0.9186 - val_loss: 0.6501 - val_accuracy: 0.8371\n",
            "Epoch 37/50\n",
            "441/441 [==============================] - 108s 244ms/step - loss: 0.2664 - accuracy: 0.9184 - val_loss: 0.6372 - val_accuracy: 0.8420\n",
            "Epoch 38/50\n",
            "441/441 [==============================] - 109s 247ms/step - loss: 0.2500 - accuracy: 0.9251 - val_loss: 0.6445 - val_accuracy: 0.8423\n",
            "Epoch 39/50\n",
            "441/441 [==============================] - 108s 245ms/step - loss: 0.2181 - accuracy: 0.9325 - val_loss: 0.6723 - val_accuracy: 0.8377\n",
            "Epoch 40/50\n",
            "441/441 [==============================] - 109s 247ms/step - loss: 0.2346 - accuracy: 0.9301 - val_loss: 0.7005 - val_accuracy: 0.8354\n",
            "Epoch 41/50\n",
            "441/441 [==============================] - 108s 246ms/step - loss: 0.2132 - accuracy: 0.9364 - val_loss: 0.7131 - val_accuracy: 0.8374\n",
            "Epoch 42/50\n",
            "441/441 [==============================] - 108s 245ms/step - loss: 0.2096 - accuracy: 0.9371 - val_loss: 0.7707 - val_accuracy: 0.8337\n",
            "Epoch 43/50\n",
            "441/441 [==============================] - 109s 246ms/step - loss: 0.1943 - accuracy: 0.9383 - val_loss: 0.6676 - val_accuracy: 0.8472\n",
            "Epoch 44/50\n",
            "441/441 [==============================] - 108s 244ms/step - loss: 0.1978 - accuracy: 0.9392 - val_loss: 0.6401 - val_accuracy: 0.8585\n",
            "Epoch 45/50\n",
            "441/441 [==============================] - 108s 244ms/step - loss: 0.1891 - accuracy: 0.9415 - val_loss: 0.8777 - val_accuracy: 0.8166\n",
            "Epoch 46/50\n",
            "441/441 [==============================] - 108s 246ms/step - loss: 0.1852 - accuracy: 0.9436 - val_loss: 0.6283 - val_accuracy: 0.8548\n",
            "Epoch 47/50\n",
            "441/441 [==============================] - 108s 245ms/step - loss: 0.1740 - accuracy: 0.9460 - val_loss: 0.7038 - val_accuracy: 0.8418\n",
            "Epoch 48/50\n",
            "441/441 [==============================] - 108s 245ms/step - loss: 0.1657 - accuracy: 0.9487 - val_loss: 0.6674 - val_accuracy: 0.8504\n",
            "Epoch 49/50\n",
            "441/441 [==============================] - 108s 246ms/step - loss: 0.1618 - accuracy: 0.9516 - val_loss: 0.6512 - val_accuracy: 0.8553\n",
            "Epoch 50/50\n",
            "441/441 [==============================] - 108s 244ms/step - loss: 0.1537 - accuracy: 0.9536 - val_loss: 0.6469 - val_accuracy: 0.8599\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P92mjrgZf4YX",
        "outputId": "af9be1de-ccd8-4fe8-eb3b-74f0bb96d3c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "acc = result.history['accuracy']\n",
        "val_acc = result.history['val_accuracy']\n",
        "\n",
        "loss = result.history['loss']\n",
        "val_loss = result.history['val_loss']\n",
        "\n",
        "epochs_range = range(50)\n",
        "\n",
        "plt.figure(figsize=(9, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMPBoMiOf4YZ",
        "outputId": "13e7165e-81fb-4538-ff71-82701dd8f18f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "path=\"/images (8).jpg\"  #Path to the target image to be predicted. \n",
        "\n",
        "classes=train_generator.class_indices\n",
        "class_names=list(classes.keys())   #List of the class names\n",
        "img = tf.keras.preprocessing.image.load_img(\n",
        "    path, target_size=(rows, cols)\n",
        ")\n",
        "\n",
        "img_array = tf.keras.preprocessing.image.img_to_array(img)\n",
        "img_array = tf.expand_dims(img_array, 0) # Create a batch\n",
        "img_array=img_array/255.\n",
        "\n",
        "score = model.predict(img_array)\n",
        "\n",
        "print(\n",
        "    \"The Person in the Image is {} .\"\n",
        "    .format(class_names[np.argmax(score)][5:].title())\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Person in the Image is Robert Downey Jr .\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}