lgariv/covid_mask_classifier.ipynb Secret

## covid_mask_classifier.ipynb
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  "metadata": {
    "colab": {
      "name": "covid_mask_classifier.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "n8L6mJP2rYPo",
        "6tNd_IrAK6gD",
        "SqWjYpSJLLLT",
        "X_T_PuTsLZHE",
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        "cfudVogELyT5",
        "jBCoPoJML6m5"
      ],
      "authorship_tag": "ABX9TyOeePDpRItdn7E3+5ToQnR/",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/lgariv/284d811d184db3ced09ce571ea9716d4/covid_mask_classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n8L6mJP2rYPo"
      },
      "source": [
        "# **Download and prepare dataset**\r\n",
        "We want to have 2 main datasets to train the model with: 1 for the actual training, which the model make a guess against, and 1 for testing, to let the model know how good it did.\r\n",
        "\r\n",
        "For that we used 2 different complete datasets (each contains both 'train' and 'test' subsets)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6tNd_IrAK6gD"
      },
      "source": [
        "## First dataset\r\n",
        "Fetch from GitHub, crop from .csv, and sort in folders by class.\r\n",
        "\r\n",
        "---\r\n",
        "\r\n",
        "**Note:** *This cell will execute once if the target folder does not exist. To execute again, check `force_execute`*."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IstyD15a9Xub"
      },
      "source": [
        "Data structure:\n",
        "```\n",
        "/content/\n",
        "└───mask-dataset/\n",
        "    └───images/\n",
        "        ├───train/\n",
        "        │   ├───maksssksksss0.png\n",
        "        │   ├───maksssksksss1.png\n",
        "        │   ├─── • • •\n",
        "        │   ├───maksssksksss547.png\n",
        "        │   └───maksssksksss548.png\n",
        "        ├───test/\n",
        "        │   ├───maksssksksss683.png\n",
        "        │   ├───maksssksksss684.png\n",
        "        │   ├─── • • •\n",
        "        │   ├───maksssksksss851.png\n",
        "        │   └───maksssksksss852.png\n",
        "        ├───train_labels.csv\n",
        "        └───test_labels.csv\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6NvOSZ0tszgN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64073987-c494-4f12-b3e7-b917413b3274"
      },
      "source": [
        "#@title  { display-mode: \"form\" }\n",
        "force_execute = False #@param {type:\"boolean\"}\n",
        "import os\n",
        "exists = os.path.exists('/tmp/train') and os.path.exists('/tmp/test')\n",
        "print('' if force_execute or not exists else 'Path already exists')\n",
        "if force_execute or not exists:\n",
        "    # Improting Image class from PIL module \n",
        "    from PIL import Image \n",
        "    #from google.colab.patches import cv2_imshow\n",
        "    #import cv2\n",
        "    import numbers\n",
        "    import sys\n",
        "    import secrets\n",
        "    import numpy as np\n",
        "    import shutil\n",
        "\n",
        "    !git clone https://github.com/lgariv/mask-dataset.git\n",
        "\n",
        "    datasets = [\"mask-dataset/images/train_labels.csv\", \"mask-dataset/images/test_labels.csv\"]\n",
        "\n",
        "    !mkdir -p /tmp/train/with_mask\n",
        "    !mkdir -p /tmp/train/without_mask\n",
        "    # !mkdir -p /tmp/train/mask_weared_incorrect\n",
        "    !mkdir -p /tmp/test/with_mask\n",
        "    !mkdir -p /tmp/test/without_mask\n",
        "    # !mkdir -p /tmp/test/mask_weared_incorrect\n",
        "\n",
        "    train_lowest = 0\n",
        "    test_lowest = 0\n",
        "    relative_batch_size = 0\n",
        "\n",
        "    print(\"[Step 1] Processing datasets\")\n",
        "    for dataset in datasets:\n",
        "        rows = open(dataset).read().strip().split(\"\\n\")\n",
        "        rows.pop(0)\n",
        "\n",
        "        subset = 'train/' if ('train' in dataset) else 'test/'\n",
        "        print(f\"[      ] Processing {'training' if ('train' in subset) else 'testing'} dataset\")\n",
        "\n",
        "        with_mask_count = 1\n",
        "        without_mask_count = 1\n",
        "        # mask_weared_incorrect_count = 1\n",
        "        for row in rows:\n",
        "            row = row.split(\",\")\n",
        "            (filename, startX, startY, endX, endY, label) = [row[0], row[4], row[5], row[6], row[7], row[3]]\n",
        "\n",
        "            # Opens a image in RGB mode\n",
        "            im = Image.open(\"/content/mask-dataset/images/\" + subset + filename)\n",
        "\n",
        "            # Size of the image in pixels (size of orginal image)\n",
        "            # (This is not mandatory)\n",
        "            width, height = im.size\n",
        "\n",
        "            # Setting the points for cropped image\n",
        "            left = float(startX)\n",
        "            top = float(startY)\n",
        "            right = float(endX)\n",
        "            bottom = float(endY)\n",
        "\n",
        "            # Cropped image of above dimension\n",
        "            # (It will not change orginal image)\n",
        "            im1 = im.crop((left, top, right, bottom))\n",
        "            (width, height) = im1.size\n",
        "\n",
        "            if width > 10 and height > 10:\n",
        "                if label == 'with_mask':\n",
        "                    im1.save('/tmp/' + subset + 'with_mask/' + str(with_mask_count) + \".png\")\n",
        "                    with_mask_count+=1\n",
        "                # else:\n",
        "                elif label == 'without_mask':\n",
        "                    im1.save('/tmp/' + subset + 'without_mask/' + str(without_mask_count) + \".png\")\n",
        "                    without_mask_count+=1\n",
        "                # else:\n",
        "                #     im1.save('/tmp/' + subset + 'mask_weared_incorrect/' + str(mask_weared_incorrect_count) + \".png\")\n",
        "                #     mask_weared_incorrect_count+=1\n",
        "\n",
        "                # Shows the image in image viewer\n",
        "                #cv2_imshow(cv2.cvtColor(np.array(im1), cv2.COLOR_BGR2RGB))\n",
        "\n",
        "    #     counts = []\n",
        "    #     print(\"[      ] Checking for lowest count\")\n",
        "    #     for base, stupid, files in os.walk('/tmp/'):\n",
        "    #         subset_dir = f'{subset[:-1]}'+'\\\\' if os.name == 'nt' else subset # correct to back-slash if running on Windows system\n",
        "    #         if subset_dir in base:\n",
        "    #             count = 0\n",
        "    #             for file in files:\n",
        "    #                 count += 1\n",
        "    #             counts.append(count)\n",
        "    #     print(f\"[      ] counts is: {counts}\")\n",
        "    #     lowest = sorted(counts, key=lambda n: n if isinstance(n, numbers.Number) else sys.float_info.min, reverse=False)[0]\n",
        "    #     if ('train' in subset):\n",
        "    #         train_lowest = lowest\n",
        "    #     else:\n",
        "    #         test_lowest = lowest\n",
        "    #     print(f\"[      ] lowest is: {lowest}\")\n",
        "    #     for base, stupid, files in os.walk('/tmp/'):\n",
        "    #         if f'{subset[:-1]}'+'\\\\' in base:\n",
        "    #             count = 0\n",
        "    #             for file in files:\n",
        "    #                 count += 1\n",
        "    #             if count > lowest:\n",
        "    #                 amount_to_delete = count-lowest\n",
        "    #                 deleted = []\n",
        "    #                 for i in range(0, amount_to_delete):\n",
        "    #                     deleting = True\n",
        "    #                     while deleting:\n",
        "    #                         random_file = secrets.choice(files)\n",
        "    #                         if random_file not in deleted:\n",
        "    #                             deleted.append(random_file)\n",
        "    #                             random_file_path = os.path.join(base, random_file)\n",
        "    #                             os.remove(random_file_path)\n",
        "    #                             deleting = False\n",
        "\n",
        "    # relative_batch_size = np.gcd(train_lowest, test_lowest)\n",
        "    # do_it = 0\n",
        "    # for i in range(0, 5):\n",
        "    #     if relative_batch_size < 4:\n",
        "    #         do_it += 1\n",
        "    #         relative_batch_size = np.gcd(train_lowest, test_lowest)\n",
        "    #         if relative_batch_size > 4:\n",
        "    #             train_lowest += 1\n",
        "    #             test_lowest -= 1\n",
        "    #             relative_batch_size = 4\n",
        "\n",
        "    # print(f'[      ] train_lowest: {train_lowest}, test_lowest: {test_lowest}')\n",
        "    # print(f'[      ] relative_batch_size: {relative_batch_size}')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Cloning into 'mask-dataset'...\n",
            "remote: Enumerating objects: 32, done.\u001b[K\n",
            "remote: Counting objects: 100% (32/32), done.\u001b[K\n",
            "remote: Compressing objects: 100% (30/30), done.\u001b[K\n",
            "remote: Total 1762 (delta 11), reused 15 (delta 2), pack-reused 1730\u001b[K\n",
            "Receiving objects: 100% (1762/1762), 394.72 MiB | 50.05 MiB/s, done.\n",
            "Resolving deltas: 100% (862/862), done.\n",
            "Checking out files: 100% (3416/3416), done.\n",
            "[Step 1] Processing datasets\n",
            "[      ] Processing training dataset\n",
            "[      ] Processing testing dataset\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qa3YJnw2xe-Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b14d640-5d0f-4223-c22e-352ecaf65914"
      },
      "source": [
        "#@title Count sorted examples\r\n",
        "train_mask = len(os.listdir(\"/tmp/train/with_mask\"))\r\n",
        "train_no_mask = len(os.listdir(\"/tmp/train/without_mask\"))\r\n",
        "print(f'/tmp/train/with_mask/: {train_mask}\\n/tmp/train/without_mask/: {train_no_mask}\\n')\r\n",
        "\r\n",
        "test_mask = len(os.listdir(\"/tmp/test/with_mask\"))\r\n",
        "test_no_mask = len(os.listdir(\"/tmp/test/without_mask\"))\r\n",
        "print(f'/tmp/test/with_mask/: {test_mask}\\n/tmp/test/without_mask/: {test_no_mask}\\n')\r\n",
        "\r\n",
        "print(f'combined: {train_mask+train_no_mask+test_mask+test_no_mask}')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/tmp/train/with_mask/: 2243\n",
            "/tmp/train/without_mask/: 487\n",
            "\n",
            "/tmp/test/with_mask/: 551\n",
            "/tmp/test/without_mask/: 83\n",
            "\n",
            "combined: 3364\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SqWjYpSJLLLT"
      },
      "source": [
        "## Second dataset\r\n",
        "Fetch from GitHub, and sort in folders by class from .csv file.\r\n",
        "\r\n",
        "---\r\n",
        "\r\n",
        "**Note:** *This cell will execute once if the target folder does not exist. To execute again, check `force_execute`*."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9LCYIAq-Jn7"
      },
      "source": [
        "Data structure:\n",
        "```\n",
        "/content/\n",
        "└───observations/\n",
        "    └───experiements/\n",
        "        └───dest_folder/\n",
        "            ├───train/\n",
        "            │   ├───with_mask/\n",
        "            │   │   └───**images containing people wearing masks**\n",
        "            │   └───without_mask/\n",
        "            │       └───**images containing people not wearing masks**\n",
        "            ├───test/\n",
        "            │   ├───with_mask/\n",
        "            │   │   └───**images containing people wearing masks**\n",
        "            │   └───without_mask/\n",
        "            │       └───**images containing people not wearing masks**\n",
        "            ├───val/\n",
        "            │   ├───with_mask/\n",
        "            │   │   └───**images containing people wearing masks**\n",
        "            │   └───without_mask/\n",
        "            │       └───**images containing people not wearing masks**\n",
        "            ├───train.csv\n",
        "            └───test.csv\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jnBIQRr2ww1l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe55eab9-ad45-4286-d583-5e6efbb0a2e8"
      },
      "source": [
        "#@title  { display-mode: \"form\" }\n",
        "force_execute = False #@param {type:\"boolean\"}\n",
        "import os\n",
        "exists = os.path.exists('/tmp/train') and os.path.exists('/tmp/test') and os.path.exists('/content/observations')\n",
        "print('' if force_execute or not exists else 'Path already exists')\n",
        "if force_execute or not exists:\n",
        "    # Improting Image class from PIL module \n",
        "    from PIL import Image \n",
        "    #from google.colab.patches import cv2_imshow\n",
        "    #import cv2\n",
        "    import numbers\n",
        "    import sys\n",
        "    import secrets\n",
        "    import numpy as np\n",
        "    import shutil\n",
        "\n",
        "    !git clone https://github.com/prajnasb/observations.git\n",
        "\n",
        "    datasets = [\"observations/experiements/dest_folder/train.csv\", \"observations/experiements/dest_folder/test.csv\"]\n",
        "\n",
        "    train_lowest = 0\n",
        "    test_lowest = 0\n",
        "    relative_batch_size = 0\n",
        "\n",
        "    print(\"[Step 1] Processing datasets\")\n",
        "    for dataset in datasets:\n",
        "        rows = open(dataset).read().strip().split(\"\\n\")\n",
        "        rows.pop(0)\n",
        "\n",
        "        subset = 'train/' if ('train' in dataset) else 'test/'\n",
        "        print(f\"[      ] Processing {'training' if ('train' in subset) else 'testing'} dataset\")\n",
        "\n",
        "        with_mask_count = len(os.listdir(\"/tmp/\"+subset+\"with_mask\"))+1\n",
        "        without_mask_count = len(os.listdir(\"/tmp/\"+subset+\"without_mask\"))+1\n",
        "        # mask_weared_incorrect_count = 1\n",
        "        for row in rows:\n",
        "            row = row.split(\",\")\n",
        "            (filename, label) = row\n",
        "\n",
        "            # Opens a image in RGB mode\n",
        "            im = Image.open(\"/content/observations/experiements/dest_folder/\" + subset + f\"{label}/\" + filename)\n",
        "\n",
        "            if label == 'with_mask':\n",
        "                im.save('/tmp/' + subset + 'with_mask/' + str(with_mask_count) + \".png\", format=\"PNG\")\n",
        "                with_mask_count+=1\n",
        "            # else:\n",
        "            elif label == 'without_mask':\n",
        "                im.save('/tmp/' + subset + 'without_mask/' + str(without_mask_count) + \".png\", format=\"PNG\")\n",
        "                without_mask_count+=1\n",
        "            # else:\n",
        "            #     im.save('/tmp/' + subset + 'mask_weared_incorrect/' + str(mask_weared_incorrect_count) + \".png\")\n",
        "            #     mask_weared_incorrect_count+=1\n",
        "\n",
        "            # Shows the image in image viewer\n",
        "            #cv2_imshow(cv2.cvtColor(np.array(im1), cv2.COLOR_BGR2RGB))\n",
        "\n",
        "    #     counts = []\n",
        "    #     print(\"[      ] Checking for lowest count\")\n",
        "    #     for base, stupid, files in os.walk('/tmp/'):\n",
        "    #         subset_dir = f'{subset[:-1]}'+'\\\\' if os.name == 'nt' else subset # correct to back-slash if running on Windows system\n",
        "    #         if subset_dir in base:\n",
        "    #             count = 0\n",
        "    #             for file in files:\n",
        "    #                 count += 1\n",
        "    #             counts.append(count)\n",
        "    #     print(f\"[      ] counts is: {counts}\")\n",
        "    #     lowest = sorted(counts, key=lambda n: n if isinstance(n, numbers.Number) else sys.float_info.min, reverse=False)[0]\n",
        "    #     if ('train' in subset):\n",
        "    #         train_lowest = lowest\n",
        "    #     else:\n",
        "    #         test_lowest = lowest\n",
        "    #     print(f\"[      ] lowest is: {lowest}\")\n",
        "    #     for base, stupid, files in os.walk('/tmp/'):\n",
        "    #         if f'{subset[:-1]}'+'\\\\' in base:\n",
        "    #             count = 0\n",
        "    #             for file in files:\n",
        "    #                 count += 1\n",
        "    #             if count > lowest:\n",
        "    #                 amount_to_delete = count-lowest\n",
        "    #                 deleted = []\n",
        "    #                 for i in range(0, amount_to_delete):\n",
        "    #                     deleting = True\n",
        "    #                     while deleting:\n",
        "    #                         random_file = secrets.choice(files)\n",
        "    #                         if random_file not in deleted:\n",
        "    #                             deleted.append(random_file)\n",
        "    #                             random_file_path = os.path.join(base, random_file)\n",
        "    #                             os.remove(random_file_path)\n",
        "    #                             deleting = False\n",
        "\n",
        "    # relative_batch_size = np.gcd(train_lowest, test_lowest)\n",
        "    # do_it = 0\n",
        "    # for i in range(0, 5):\n",
        "    #     if relative_batch_size < 4:\n",
        "    #         do_it += 1\n",
        "    #         relative_batch_size = np.gcd(train_lowest, test_lowest)\n",
        "    #         if relative_batch_size > 4:\n",
        "    #             train_lowest += 1\n",
        "    #             test_lowest -= 1\n",
        "    #             relative_batch_size = 4\n",
        "\n",
        "    # print(f'[      ] train_lowest: {train_lowest}, test_lowest: {test_lowest}')\n",
        "    # print(f'[      ] relative_batch_size: {relative_batch_size}')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Cloning into 'observations'...\n",
            "remote: Enumerating objects: 34, done.\u001b[K\n",
            "remote: Counting objects: 100% (34/34), done.\u001b[K\n",
            "remote: Compressing objects: 100% (33/33), done.\u001b[K\n",
            "remote: Total 1638 (delta 9), reused 0 (delta 0), pack-reused 1604\u001b[K\n",
            "Receiving objects: 100% (1638/1638), 75.94 MiB | 45.63 MiB/s, done.\n",
            "Resolving deltas: 100% (20/20), done.\n",
            "[Step 1] Processing datasets\n",
            "[      ] Processing training dataset\n",
            "[      ] Processing testing dataset\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BffiC7U05--e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "outputId": "b612fccc-a7fe-40f3-f16c-79ace4cf94b1"
      },
      "source": [
        "#@title Count sorted examples\r\n",
        "train_mask = len(os.listdir(\"/tmp/train/with_mask\"))\r\n",
        "train_no_mask = len(os.listdir(\"/tmp/train/without_mask\"))\r\n",
        "print(f'/tmp/train/with_mask/: {train_mask}\\n/tmp/train/without_mask/: {train_no_mask}\\n')\r\n",
        "\r\n",
        "test_mask = len(os.listdir(\"/tmp/test/with_mask\"))\r\n",
        "test_no_mask = len(os.listdir(\"/tmp/test/without_mask\"))\r\n",
        "print(f'/tmp/test/with_mask/: {test_mask}\\n/tmp/test/without_mask/: {test_no_mask}\\n')\r\n",
        "\r\n",
        "print(f'combined: {train_mask+train_no_mask+test_mask+test_no_mask}')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/tmp/train/with_mask/: 2832\n",
            "/tmp/train/without_mask/: 1076\n",
            "\n",
            "/tmp/test/with_mask/: 648\n",
            "/tmp/test/without_mask/: 180\n",
            "\n",
            "combined: 4736\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_DqpNnJLUV2"
      },
      "source": [
        "# **Augmentation**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X_T_PuTsLZHE"
      },
      "source": [
        "## Defining augmentation functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZL9TLVL3JBN"
      },
      "source": [
        "import os\r\n",
        "import tensorflow as tf\r\n",
        "import PIL\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H8FnAheNtaiY"
      },
      "source": [
        "### Flip Augmentation\r\n",
        "Randomally flipping horizontaly and vertically."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UYV4Qf0lFX-L"
      },
      "source": [
        "def augment_flip(image: tf.Tensor) -> tf.Tensor:\n",
        "    print(image.shape)\n",
        "    image = tf.image.random_flip_left_right(image)\n",
        "    image = tf.image.random_flip_up_down(image)\n",
        "    return image"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-_8eviHt69p"
      },
      "source": [
        "### Color Augmentation\r\n",
        "Randomally apply hue, saturation, brightness, and contrast filters to input images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0lvpPwy8FgXB"
      },
      "source": [
        "def augment_color(image: tf.Tensor) -> tf.Tensor:\n",
        "    image = tf.image.random_hue(image, max_delta=0.08)\n",
        "    image = tf.image.random_saturation(image, lower=0.7, upper=1.3)\n",
        "    image = tf.image.random_brightness(image, 0.05)\n",
        "    image = tf.image.random_contrast(image, lower=0.8, upper=1)\n",
        "    image = tf.clip_by_value(image, clip_value_min=0, clip_value_max=1)\n",
        "    return image"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-hoNR8VuhUl"
      },
      "source": [
        "### Rotation Augmentation\r\n",
        "Randomally rotate input image by 0, 90, 180, or 270 degrees."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQfom8cxFiac"
      },
      "source": [
        "def augment_rotation(image: tf.Tensor) -> tf.Tensor:\n",
        "    # Rotate 0, 90, 180, 270 degrees\n",
        "    return tf.image.rot90(\n",
        "        image,\n",
        "        tf.random.uniform(shape=[], minval=0, maxval=4, dtype=tf.int32)\n",
        "    )"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rlYeO4pUvhXt"
      },
      "source": [
        "### Color inversion\r\n",
        "Invert pixel values in randomally chosen images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MmnxF4XMFkAC"
      },
      "source": [
        "def augment_inversion(image: tf.Tensor) -> tf.Tensor:\n",
        "    random = tf.random.uniform(shape=[], minval=0, maxval=1)\n",
        "    if random > 0.5:\n",
        "        image = tf.math.multiply(image, -1)\n",
        "        image = tf.math.add(image, 1)\n",
        "    return image"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sZd41h1gb5_v"
      },
      "source": [
        "### Zoom Augmentation\r\n",
        "Applies cropping of a randomized factor between 1% to 20%. Applies to a maximum of 50% of the images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIKdM07_FlnM"
      },
      "source": [
        "def augment_zoom(image: tf.Tensor, min_zoom=0.8, max_zoom=1.0) -> tf.Tensor:\n",
        "    image_width, image_height, image_colors = image.shape\n",
        "    crop_size = (image_width, image_height)\n",
        "\n",
        "    # Generate crop settings, ranging from a 1% to 20% crop.\n",
        "    scales = list(np.arange(min_zoom, max_zoom, 0.01))\n",
        "    boxes = np.zeros((len(scales), 4))\n",
        "\n",
        "    for i, scale in enumerate(scales):\n",
        "        x1 = y1 = 0.5 - (0.5 * scale)\n",
        "        x2 = y2 = 0.5 + (0.5 * scale)\n",
        "        boxes[i] = [x1, y1, x2, y2]\n",
        "\n",
        "    def random_crop(img):\n",
        "        # Create different crops for an image\n",
        "        print(type(img))\n",
        "        crops = tf.image.crop_and_resize(\n",
        "            [img],\n",
        "            boxes=boxes,\n",
        "            box_indices=np.zeros(len(scales)),\n",
        "            crop_size=crop_size\n",
        "        )\n",
        "        # Return a random crop\n",
        "        return crops[tf.random.uniform(shape=[], minval=0, maxval=len(scales), dtype=tf.int32)]\n",
        "\n",
        "    choice = tf.random.uniform(shape=[], minval=0., maxval=1., dtype=tf.float32)\n",
        "\n",
        "    # Only apply cropping 50% of the time\n",
        "    return tf.cond(choice < 0.5, lambda: image, lambda: random_crop(image))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vIC7qz7wdxrl"
      },
      "source": [
        "### Apply Augmentations\r\n",
        "Apply all augmentations to a given pair of image & label"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LmrRTLgvFre9"
      },
      "source": [
        "def augment_data(image, label):\n",
        "    image = augment_flip(image)\n",
        "    image = augment_color(image)\n",
        "    image = augment_rotation(image)\n",
        "    image = augment_zoom(image)\n",
        "    image = augment_inversion(image)\n",
        "    return image, label"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9tmJTiCxLhow"
      },
      "source": [
        "## Creating generator from directories"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b9lYIhqylUn2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "0d16ea97-c50a-4b20-e231-b9c5b1e8b487"
      },
      "source": [
        "TRAINING_DIR = \"/tmp/train/\"\r\n",
        "VALIDATION_DIR = \"/tmp/test/\"\r\n",
        "\r\n",
        "from tensorflow import keras\r\n",
        "from keras.preprocessing.image import ImageDataGenerator\r\n",
        "\r\n",
        "training_datagen = ImageDataGenerator(\r\n",
        "    rescale = 1./255,\r\n",
        "    rotation_range=40,\r\n",
        "    width_shift_range=0.2,\r\n",
        "    height_shift_range=0.2,\r\n",
        "    shear_range=0.2,\r\n",
        "    zoom_range=0.2,\r\n",
        "    horizontal_flip=True,\r\n",
        "    fill_mode='nearest'\r\n",
        "    )\r\n",
        "\r\n",
        "validation_datagen = ImageDataGenerator(rescale = 1./255)\r\n",
        "\r\n",
        "def make_train_generator():\r\n",
        "    return training_datagen.flow_from_directory(TRAINING_DIR,target_size=(150, 150), class_mode='categorical', batch_size=32)\r\n",
        "\r\n",
        "def make_test_generator():\r\n",
        "    return validation_datagen.flow_from_directory(VALIDATION_DIR,target_size=(150, 150), class_mode='categorical', batch_size=32)\r\n",
        "\r\n",
        "train_dataset_raw = tf.data.Dataset.from_generator(make_train_generator,\r\n",
        "                                                   output_signature=(\r\n",
        "                                                       tf.TensorSpec(shape=(None, 150, 150, 3), dtype=tf.float32),\r\n",
        "                                                       tf.TensorSpec(shape=(None, 2), dtype=tf.float32))\r\n",
        "                                                   )\r\n",
        "test_dataset_raw = tf.data.Dataset.from_generator(make_test_generator,\r\n",
        "                                                  output_signature=(\r\n",
        "                                                      tf.TensorSpec(shape=(None, 150, 150, 3), dtype=tf.float32),\r\n",
        "                                                      tf.TensorSpec(shape=(None, 2), dtype=tf.float32))\r\n",
        "                                                  )\r\n",
        "\r\n",
        "train_dataset_raw_2 = tf.keras.preprocessing.image_dataset_from_directory(\r\n",
        "\tTRAINING_DIR,\r\n",
        "    labels='inferred',\r\n",
        "    label_mode='categorical',\r\n",
        "    batch_size=1,\r\n",
        "\timage_size=(150, 150)\r\n",
        "    )\r\n",
        "\r\n",
        "\r\n",
        "test_dataset_raw_2 = tf.keras.preprocessing.image_dataset_from_directory(\r\n",
        "\tVALIDATION_DIR,\r\n",
        "    labels='inferred',\r\n",
        "    label_mode='categorical',\r\n",
        "    batch_size=1,\r\n",
        "\timage_size=(150, 150)\r\n",
        "    )\r\n",
        "\r\n",
        "from sklearn import preprocessing\r\n",
        "def label_from_categorical(labels):\r\n",
        "    if len(labels) > 1:\r\n",
        "        for i in range(len(labels)):\r\n",
        "            if labels[int(i)]:\r\n",
        "                return train_dataset_raw_2.class_names[i]\r\n",
        "    else:\r\n",
        "        print(labels)\r\n",
        "        return train_dataset_raw_2.class_names[labels]\r\n",
        "\r\n",
        "print(train_dataset_raw)\r\n",
        "print(train_dataset_raw_2)\r\n",
        "\r\n",
        "plt.figure(figsize=(12, 12))\r\n",
        "plot_index = 0\r\n",
        "for features in train_dataset_raw.take(8):\r\n",
        "        (image, label) = features\r\n",
        "        plot_index += 1\r\n",
        "        plt.subplot(3, 4, plot_index)\r\n",
        "        # plt.axis('Off')\r\n",
        "        label = label_from_categorical(label[0])\r\n",
        "        plt.title('Label: %s' % label)\r\n",
        "        plt.imshow(image.numpy()[0].reshape(150,150,3))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 3908 files belonging to 2 classes.\n",
            "Found 828 files belonging to 2 classes.\n",
            "<FlatMapDataset shapes: ((None, 150, 150, 3), (None, 2)), types: (tf.float32, tf.float32)>\n",
            "<BatchDataset shapes: ((None, 150, 150, 3), (None, 2)), types: (tf.float32, tf.float32)>\n",
            "Found 3908 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cfudVogELyT5"
      },
      "source": [
        "## Formatting and caching datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOofOvC9fVXq"
      },
      "source": [
        "def format_example(image, label):\n",
        "    # label = tf.cast(label[0], tf.int64)\n",
        "    label = tf.cast(label, tf.int64)\n",
        "    if len(label.shape) is 2:\n",
        "        label = label[0]\n",
        "    # Make image color values to be float.\n",
        "    image = tf.cast(image, tf.float32)\n",
        "    # Make image color values to be in [0..1] range.\n",
        "    image = image / 255.\n",
        "    # Make sure that image has a right size\n",
        "    image = tf.image.resize(image, [150, 150])\n",
        "    if len(image.shape) is 4:\n",
        "        image = image[0]\n",
        "    return image, label"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZYg2NvHmg5fR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b852d9c-0564-42ca-df03-412db0c7041a"
      },
      "source": [
        "train_dataset = train_dataset_raw_2.map(format_example).cache()\r\n",
        "test_dataset = test_dataset_raw_2.map(format_example).cache()\r\n",
        "\r\n",
        "count = tf.data.experimental.cardinality(train_dataset)\r\n",
        "print(count)\r\n",
        "\r\n",
        "print(train_dataset_raw_2)\r\n",
        "print(test_dataset_raw_2)\r\n",
        "print(train_dataset)\r\n",
        "print(test_dataset)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(3908, shape=(), dtype=int64)\n",
            "<BatchDataset shapes: ((None, 150, 150, 3), (None, 2)), types: (tf.float32, tf.float32)>\n",
            "<BatchDataset shapes: ((None, 150, 150, 3), (None, 2)), types: (tf.float32, tf.float32)>\n",
            "<CacheDataset shapes: ((150, 150, 3), (2,)), types: (tf.float32, tf.int64)>\n",
            "<CacheDataset shapes: ((150, 150, 3), (2,)), types: (tf.float32, tf.int64)>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d0QPUYQfLt6R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1fdc44e-ce54-47d5-ce4d-17fa20116618"
      },
      "source": [
        "# Explore what values are used to represent the image. \n",
        "(first_image, first_label) = list(train_dataset.take(1))[0]\n",
        "print('Label:', first_label.numpy())\n",
        "print('Image shape:', first_image.numpy().shape)\n",
        "# print(first_image.numpy())"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Label: [0 1]\n",
            "Image shape: (150, 150, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y_MA0RMTLt5s"
      },
      "source": [
        "def preview_dataset(dataset):\n",
        "    plt.figure(figsize=(12, 12))\n",
        "    plot_index = 0\n",
        "    for features in dataset.take(8):\n",
        "        (image, label) = features\n",
        "        plot_index += 1\n",
        "        plt.subplot(3, 4, plot_index)\n",
        "        # plt.axis('Off')\n",
        "        label = label_from_categorical(label.numpy())\n",
        "        plt.title('Label: %s' % label)\n",
        "        plt.imshow(image.numpy())"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ce0c2tI7tFby",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "8034ee85-fff9-467b-92dc-6e4de4ff1548"
      },
      "source": [
        "preview_dataset(train_dataset)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jBCoPoJML6m5"
      },
      "source": [
        "## Applying augmentations to the training dataset\r\n",
        "Applying augmentations, shuffling, and prefetching the training dataset, then batching both datasets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VSLjd7ajFtub",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5237340-174d-4304-8959-3e956e67a30f"
      },
      "source": [
        "dataset_train_augmented = train_dataset.map(\r\n",
        "    augment_data,\r\n",
        "    num_parallel_calls=tf.data.AUTOTUNE\r\n",
        ")\r\n",
        "\r\n",
        "BATCH_SIZE = 128\r\n",
        "\r\n",
        "import glob\r\n",
        "train_dataset_image_count = len(tf.data.Dataset.list_files(glob.glob(TRAINING_DIR+'*/*')))\r\n",
        "# print(train_dataset_image_count)\r\n",
        "\r\n",
        "dataset_train_augmented_shuffled = dataset_train_augmented.shuffle(\r\n",
        "    reshuffle_each_iteration=True,\r\n",
        "    buffer_size=train_dataset_image_count\r\n",
        ")\r\n",
        "\r\n",
        "dataset_train_augmented_shuffled_batch = dataset_train_augmented_shuffled.batch(\r\n",
        "    batch_size=BATCH_SIZE\r\n",
        ")\r\n",
        "\r\n",
        "# Prefetch will enable the input pipeline to asynchronously fetch batches while your model is training.\r\n",
        "dataset_train_augmented_shuffled_batch = dataset_train_augmented_shuffled_batch.prefetch(\r\n",
        "    buffer_size=tf.data.AUTOTUNE\r\n",
        ")\r\n",
        "\r\n",
        "dataset_test_shuffled_batch = test_dataset.batch(BATCH_SIZE)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(150, 150, 3)\n",
            "<class 'tensorflow.python.framework.ops.Tensor'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TiXw2QMxLt8K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2078d1b9-188b-4e10-99a8-b1b8092dc177"
      },
      "source": [
        "print(dataset_train_augmented)\n",
        "print(dataset_train_augmented_shuffled_batch)\n",
        "print(test_dataset)\n",
        "print(dataset_test_shuffled_batch)\n",
        "\n",
        "# print(\"epoch 1:\")\n",
        "# for elem in list(dataset_train_augmented_shuffled_batch.take(1))[0]:\n",
        "#     print(elem.numpy()[0])\n",
        "# print(\"epoch 2:\")\n",
        "# for elem in list(dataset_train_augmented_shuffled_batch.take(1))[0]:\n",
        "#     print(elem.numpy()[0])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<ParallelMapDataset shapes: ((150, 150, 3), (2,)), types: (tf.float32, tf.int64)>\n",
            "<PrefetchDataset shapes: ((None, 150, 150, 3), (None, 2)), types: (tf.float32, tf.int64)>\n",
            "<CacheDataset shapes: ((150, 150, 3), (2,)), types: (tf.float32, tf.int64)>\n",
            "<BatchDataset shapes: ((None, 150, 150, 3), (None, 2)), types: (tf.float32, tf.int64)>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwbNSq9S01Qh"
      },
      "source": [
        "print_augmentation_results = False #@param {type:\"boolean\"}\n",
        "if print_augmentation_results:\n",
        "    plt.figure(figsize=(12, 12))\n",
        "    plot_index = 0\n",
        "    for features in dataset_train_augmented_shuffled_batch.take(1):\n",
        "        (images_from_batch, labels_from_batch) = features\n",
        "        for i in range(8):\n",
        "            image = images_from_batch[i]\n",
        "            label = labels_from_batch[i]\n",
        "            plot_index += 1\n",
        "            plt.subplot(3, 4, plot_index)\n",
        "            plt.axis('Off')\n",
        "            label = label_from_categorical(label.numpy())\n",
        "            plt.title('Label: %s' % label)\n",
        "            plt.imshow(image.numpy())"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-qCEOd9ZyRsC"
      },
      "source": [
        "# **Configure and train the model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XpkaEotPpOIv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "851b67cd-dd28-4383-9cb1-6c8f52c74f36"
      },
      "source": [
        "#@title main model { display-mode: \"form\" }\n",
        "optimizer = \"Adadelta\" #@param [\"RMSprop\", \"Adam\", \"Adadelta\", \"SGD\", \"Nadam\"]\n",
        "modify_learning_rate = False #@param {type:\"boolean\"}\n",
        "learning_rate = 0.001 #@param {type:\"number\"}\n",
        "%matplotlib inline\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "\n",
        "r\"\"\"\n",
        "import plaidml.keras\n",
        "plaidml.tf.keras.install_backend()\n",
        "\n",
        "os.environ[\"KERAS_BACKEND\"] = \"plaidml.tf.keras.backend\"\n",
        "\n",
        "# use plaidml to run on Radeon GPU (If using on Mac for example)\n",
        "\"\"\"\n",
        "from keras import backend as K\n",
        "\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "import keras_preprocessing\n",
        "from keras_preprocessing import image\n",
        "from keras_preprocessing.image import ImageDataGenerator\n",
        "from keras.optimizers import RMSprop, Adam, Adadelta, SGD, Nadam\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "    # Note the input shape is the desired size of the image 150x150 with 3 bytes color\n",
        "    # This is the first convolution\n",
        "    keras.layers.Conv2D(64, (3,3), input_shape=(150, 150, 3)),\n",
        "    keras.layers.Activation('relu'),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    keras.layers.MaxPooling2D(2,2),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    # The second convolution\n",
        "    keras.layers.Conv2D(64, (3,3)),\n",
        "    keras.layers.Activation('relu'),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    keras.layers.MaxPooling2D(2,2),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    # The third convolution\n",
        "    keras.layers.Conv2D(128, (3,3)),\n",
        "    keras.layers.Activation('relu'),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    keras.layers.MaxPooling2D(2,2),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    # The fourth convolution\n",
        "    keras.layers.Conv2D(128, (3,3)),\n",
        "    keras.layers.Activation('relu'),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    keras.layers.MaxPooling2D(2,2),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    # Flatten the results to feed into a DNN\n",
        "    tf.keras.layers.Flatten(),\n",
        "    # tf.keras.layers.Dropout(0.5),\n",
        "    # 512 neuron hidden layer\n",
        "    tf.keras.layers.Dense(512, activation='relu'),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.Dense(2, activation='softmax')\n",
        "])\n",
        "\n",
        "model.summary()\n",
        "\n",
        "early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss',patience=6)\n",
        "\n",
        "switch = {\"RMSprop\" : RMSprop(lr=learning_rate) if modify_learning_rate else RMSprop(),\n",
        "    \"Adam\" : Adam(lr=learning_rate) if modify_learning_rate else Adam(),\n",
        "    \"Adadelta\" : Adadelta(lr=learning_rate) if modify_learning_rate else Adadelta(),\n",
        "    \"SGD\" : SGD(lr=learning_rate) if modify_learning_rate else SGD(),\n",
        "    \"Nadam\" : Nadam(lr=learning_rate) if modify_learning_rate else Nadam()\n",
        "    }\n",
        "\n",
        "optimizer = switch.get(optimizer)\n",
        "model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(dataset_train_augmented_shuffled_batch,\n",
        "                    epochs=300,\n",
        "                    # steps_per_epoch=20,\n",
        "                    validation_data = dataset_test_shuffled_batch,\n",
        "                    # callbacks=[early_stopping],\n",
        "                    validation_steps=3,\n",
        "                    use_multiprocessing=True,\n",
        "                    verbose = 1)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 148, 148, 64)      1792      \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 148, 148, 64)      0         \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 148, 148, 64)      256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 74, 74, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 74, 74, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 72, 72, 64)        36928     \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 72, 72, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 72, 72, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 36, 36, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 36, 36, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 34, 34, 128)       73856     \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 34, 34, 128)       0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 34, 34, 128)       512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 17, 17, 128)       0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 17, 17, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 15, 15, 128)       147584    \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 15, 15, 128)       0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 15, 15, 128)       512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 7, 7, 128)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 7, 7, 128)         512       \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 512)               3211776   \n",
            "_________________________________________________________________\n",
            "batch_normalization_8 (Batch (None, 512)               2048      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 2)                 1026      \n",
            "=================================================================\n",
            "Total params: 3,478,082\n",
            "Trainable params: 3,475,522\n",
            "Non-trainable params: 2,560\n",
            "_________________________________________________________________\n",
            "Epoch 1/300\n",
            "31/31 [==============================] - 34s 221ms/step - loss: 0.9366 - accuracy: 0.5425 - val_loss: 0.6750 - val_accuracy: 0.7917\n",
            "Epoch 2/300\n",
            "31/31 [==============================] - 19s 189ms/step - loss: 0.8598 - accuracy: 0.5947 - val_loss: 0.6690 - val_accuracy: 0.7760\n",
            "Epoch 3/300\n",
            "31/31 [==============================] - 19s 189ms/step - loss: 0.8540 - accuracy: 0.6009 - val_loss: 0.6781 - val_accuracy: 0.7526\n",
            "Epoch 4/300\n",
            "31/31 [==============================] - 19s 190ms/step - loss: 0.7712 - accuracy: 0.6242 - val_loss: 0.6646 - val_accuracy: 0.7604\n",
            "Epoch 5/300\n",
            "31/31 [==============================] - 19s 190ms/step - loss: 0.7253 - accuracy: 0.6563 - val_loss: 0.6481 - val_accuracy: 0.7760\n",
            "Epoch 6/300\n",
            "31/31 [==============================] - 20s 192ms/step - loss: 0.7556 - accuracy: 0.6469 - val_loss: 0.6266 - val_accuracy: 0.7734\n",
            "Epoch 7/300\n",
            "31/31 [==============================] - 19s 192ms/step - loss: 0.6926 - accuracy: 0.6729 - val_loss: 0.5985 - val_accuracy: 0.7865\n",
            "Epoch 8/300\n",
            "31/31 [==============================] - 19s 193ms/step - loss: 0.6932 - accuracy: 0.6956 - val_loss: 0.5739 - val_accuracy: 0.7891\n",
            "Epoch 9/300\n",
            "31/31 [==============================] - 19s 193ms/step - loss: 0.7067 - accuracy: 0.6791 - val_loss: 0.5591 - val_accuracy: 0.7917\n",
            "Epoch 10/300\n",
            "31/31 [==============================] - 19s 193ms/step - loss: 0.6438 - accuracy: 0.7183 - val_loss: 0.5494 - val_accuracy: 0.7891\n",
            "Epoch 11/300\n",
            "31/31 [==============================] - 19s 194ms/step - loss: 0.6315 - accuracy: 0.7158 - val_loss: 0.5326 - val_accuracy: 0.7865\n",
            "Epoch 12/300\n",
            "31/31 [==============================] - 20s 196ms/step - loss: 0.6240 - accuracy: 0.7225 - val_loss: 0.5222 - val_accuracy: 0.7917\n",
            "Epoch 13/300\n",
            "31/31 [==============================] - 19s 192ms/step - loss: 0.6353 - accuracy: 0.7259 - val_loss: 0.5042 - val_accuracy: 0.7969\n",
            "Epoch 14/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.6046 - accuracy: 0.7382 - val_loss: 0.4777 - val_accuracy: 0.8125\n",
            "Epoch 15/300\n",
            "31/31 [==============================] - 19s 196ms/step - loss: 0.6206 - accuracy: 0.7266 - val_loss: 0.4597 - val_accuracy: 0.8151\n",
            "Epoch 16/300\n",
            "31/31 [==============================] - 19s 195ms/step - loss: 0.5893 - accuracy: 0.7334 - val_loss: 0.4448 - val_accuracy: 0.8203\n",
            "Epoch 17/300\n",
            "31/31 [==============================] - 20s 196ms/step - loss: 0.5558 - accuracy: 0.7515 - val_loss: 0.4293 - val_accuracy: 0.8125\n",
            "Epoch 18/300\n",
            "31/31 [==============================] - 19s 197ms/step - loss: 0.5767 - accuracy: 0.7481 - val_loss: 0.4207 - val_accuracy: 0.8229\n",
            "Epoch 19/300\n",
            "31/31 [==============================] - 19s 198ms/step - loss: 0.5686 - accuracy: 0.7598 - val_loss: 0.4143 - val_accuracy: 0.8411\n",
            "Epoch 20/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.5711 - accuracy: 0.7624 - val_loss: 0.4128 - val_accuracy: 0.8333\n",
            "Epoch 21/300\n",
            "31/31 [==============================] - 20s 195ms/step - loss: 0.5548 - accuracy: 0.7581 - val_loss: 0.4063 - val_accuracy: 0.8438\n",
            "Epoch 22/300\n",
            "31/31 [==============================] - 19s 196ms/step - loss: 0.5376 - accuracy: 0.7816 - val_loss: 0.4084 - val_accuracy: 0.8125\n",
            "Epoch 23/300\n",
            "31/31 [==============================] - 19s 196ms/step - loss: 0.5005 - accuracy: 0.7948 - val_loss: 0.4154 - val_accuracy: 0.8177\n",
            "Epoch 24/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.5241 - accuracy: 0.7785 - val_loss: 0.4163 - val_accuracy: 0.8203\n",
            "Epoch 25/300\n",
            "31/31 [==============================] - 21s 201ms/step - loss: 0.5347 - accuracy: 0.7873 - val_loss: 0.4156 - val_accuracy: 0.8229\n",
            "Epoch 26/300\n",
            "31/31 [==============================] - 20s 196ms/step - loss: 0.5156 - accuracy: 0.7817 - val_loss: 0.4177 - val_accuracy: 0.8255\n",
            "Epoch 27/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.5124 - accuracy: 0.7980 - val_loss: 0.4201 - val_accuracy: 0.8255\n",
            "Epoch 28/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.4873 - accuracy: 0.7928 - val_loss: 0.4202 - val_accuracy: 0.8203\n",
            "Epoch 29/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.4953 - accuracy: 0.7897 - val_loss: 0.4246 - val_accuracy: 0.8203\n",
            "Epoch 30/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.5061 - accuracy: 0.7911 - val_loss: 0.4102 - val_accuracy: 0.8411\n",
            "Epoch 31/300\n",
            "31/31 [==============================] - 20s 196ms/step - loss: 0.5060 - accuracy: 0.7916 - val_loss: 0.4125 - val_accuracy: 0.8333\n",
            "Epoch 32/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.4787 - accuracy: 0.8097 - val_loss: 0.4004 - val_accuracy: 0.8385\n",
            "Epoch 33/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.4798 - accuracy: 0.8057 - val_loss: 0.4032 - val_accuracy: 0.8333\n",
            "Epoch 34/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.4665 - accuracy: 0.8124 - val_loss: 0.4009 - val_accuracy: 0.8359\n",
            "Epoch 35/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.4721 - accuracy: 0.8132 - val_loss: 0.3912 - val_accuracy: 0.8438\n",
            "Epoch 36/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.4541 - accuracy: 0.8128 - val_loss: 0.3885 - val_accuracy: 0.8411\n",
            "Epoch 37/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.4532 - accuracy: 0.8213 - val_loss: 0.3823 - val_accuracy: 0.8411\n",
            "Epoch 38/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.4661 - accuracy: 0.8265 - val_loss: 0.3776 - val_accuracy: 0.8385\n",
            "Epoch 39/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.4389 - accuracy: 0.8239 - val_loss: 0.3658 - val_accuracy: 0.8620\n",
            "Epoch 40/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.4431 - accuracy: 0.8231 - val_loss: 0.3646 - val_accuracy: 0.8594\n",
            "Epoch 41/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.4435 - accuracy: 0.8287 - val_loss: 0.3595 - val_accuracy: 0.8646\n",
            "Epoch 42/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.4418 - accuracy: 0.8276 - val_loss: 0.3486 - val_accuracy: 0.8750\n",
            "Epoch 43/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.4369 - accuracy: 0.8302 - val_loss: 0.3419 - val_accuracy: 0.8854\n",
            "Epoch 44/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.4302 - accuracy: 0.8251 - val_loss: 0.3377 - val_accuracy: 0.8880\n",
            "Epoch 45/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.4268 - accuracy: 0.8430 - val_loss: 0.3352 - val_accuracy: 0.8880\n",
            "Epoch 46/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.4539 - accuracy: 0.8130 - val_loss: 0.3285 - val_accuracy: 0.8880\n",
            "Epoch 47/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.4163 - accuracy: 0.8371 - val_loss: 0.3347 - val_accuracy: 0.8854\n",
            "Epoch 48/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.4344 - accuracy: 0.8240 - val_loss: 0.3348 - val_accuracy: 0.8854\n",
            "Epoch 49/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.4274 - accuracy: 0.8404 - val_loss: 0.3302 - val_accuracy: 0.8932\n",
            "Epoch 50/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.3951 - accuracy: 0.8377 - val_loss: 0.3233 - val_accuracy: 0.8906\n",
            "Epoch 51/300\n",
            "31/31 [==============================] - 20s 196ms/step - loss: 0.3867 - accuracy: 0.8437 - val_loss: 0.3214 - val_accuracy: 0.8958\n",
            "Epoch 52/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.3800 - accuracy: 0.8580 - val_loss: 0.3122 - val_accuracy: 0.9036\n",
            "Epoch 53/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.3871 - accuracy: 0.8501 - val_loss: 0.3133 - val_accuracy: 0.9036\n",
            "Epoch 54/300\n",
            "31/31 [==============================] - 20s 196ms/step - loss: 0.4041 - accuracy: 0.8348 - val_loss: 0.3096 - val_accuracy: 0.9036\n",
            "Epoch 55/300\n",
            "31/31 [==============================] - 20s 196ms/step - loss: 0.3952 - accuracy: 0.8457 - val_loss: 0.3032 - val_accuracy: 0.9062\n",
            "Epoch 56/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.4150 - accuracy: 0.8549 - val_loss: 0.2977 - val_accuracy: 0.9062\n",
            "Epoch 57/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3900 - accuracy: 0.8442 - val_loss: 0.2933 - val_accuracy: 0.9089\n",
            "Epoch 58/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3786 - accuracy: 0.8490 - val_loss: 0.2999 - val_accuracy: 0.9089\n",
            "Epoch 59/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.4081 - accuracy: 0.8428 - val_loss: 0.2985 - val_accuracy: 0.9062\n",
            "Epoch 60/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.4026 - accuracy: 0.8466 - val_loss: 0.2905 - val_accuracy: 0.9089\n",
            "Epoch 61/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.4006 - accuracy: 0.8463 - val_loss: 0.2830 - val_accuracy: 0.9062\n",
            "Epoch 62/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3733 - accuracy: 0.8617 - val_loss: 0.2832 - val_accuracy: 0.9089\n",
            "Epoch 63/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.3681 - accuracy: 0.8581 - val_loss: 0.2846 - val_accuracy: 0.9115\n",
            "Epoch 64/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.3900 - accuracy: 0.8514 - val_loss: 0.2805 - val_accuracy: 0.9115\n",
            "Epoch 65/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.3851 - accuracy: 0.8482 - val_loss: 0.2737 - val_accuracy: 0.9089\n",
            "Epoch 66/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.3737 - accuracy: 0.8635 - val_loss: 0.2766 - val_accuracy: 0.9062\n",
            "Epoch 67/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3685 - accuracy: 0.8592 - val_loss: 0.2790 - val_accuracy: 0.9036\n",
            "Epoch 68/300\n",
            "31/31 [==============================] - 20s 201ms/step - loss: 0.3799 - accuracy: 0.8528 - val_loss: 0.2723 - val_accuracy: 0.9062\n",
            "Epoch 69/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3873 - accuracy: 0.8593 - val_loss: 0.2714 - val_accuracy: 0.9062\n",
            "Epoch 70/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3613 - accuracy: 0.8604 - val_loss: 0.2671 - val_accuracy: 0.9089\n",
            "Epoch 71/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.3518 - accuracy: 0.8635 - val_loss: 0.2706 - val_accuracy: 0.9062\n",
            "Epoch 72/300\n",
            "31/31 [==============================] - 20s 201ms/step - loss: 0.3720 - accuracy: 0.8576 - val_loss: 0.2677 - val_accuracy: 0.9089\n",
            "Epoch 73/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3739 - accuracy: 0.8572 - val_loss: 0.2635 - val_accuracy: 0.9115\n",
            "Epoch 74/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.3716 - accuracy: 0.8577 - val_loss: 0.2598 - val_accuracy: 0.9115\n",
            "Epoch 75/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3457 - accuracy: 0.8707 - val_loss: 0.2603 - val_accuracy: 0.9115\n",
            "Epoch 76/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3447 - accuracy: 0.8682 - val_loss: 0.2605 - val_accuracy: 0.9089\n",
            "Epoch 77/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3370 - accuracy: 0.8708 - val_loss: 0.2661 - val_accuracy: 0.9089\n",
            "Epoch 78/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3536 - accuracy: 0.8674 - val_loss: 0.2574 - val_accuracy: 0.9089\n",
            "Epoch 79/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3686 - accuracy: 0.8637 - val_loss: 0.2569 - val_accuracy: 0.9115\n",
            "Epoch 80/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3561 - accuracy: 0.8644 - val_loss: 0.2547 - val_accuracy: 0.9115\n",
            "Epoch 81/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3492 - accuracy: 0.8671 - val_loss: 0.2567 - val_accuracy: 0.9141\n",
            "Epoch 82/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3663 - accuracy: 0.8640 - val_loss: 0.2513 - val_accuracy: 0.9141\n",
            "Epoch 83/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3199 - accuracy: 0.8795 - val_loss: 0.2479 - val_accuracy: 0.9115\n",
            "Epoch 84/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.3361 - accuracy: 0.8766 - val_loss: 0.2471 - val_accuracy: 0.9115\n",
            "Epoch 85/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3230 - accuracy: 0.8809 - val_loss: 0.2454 - val_accuracy: 0.9141\n",
            "Epoch 86/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.3266 - accuracy: 0.8834 - val_loss: 0.2412 - val_accuracy: 0.9167\n",
            "Epoch 87/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.3301 - accuracy: 0.8773 - val_loss: 0.2429 - val_accuracy: 0.9167\n",
            "Epoch 88/300\n",
            "31/31 [==============================] - 21s 200ms/step - loss: 0.3462 - accuracy: 0.8668 - val_loss: 0.2426 - val_accuracy: 0.9115\n",
            "Epoch 89/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.3216 - accuracy: 0.8821 - val_loss: 0.2440 - val_accuracy: 0.9167\n",
            "Epoch 90/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3421 - accuracy: 0.8718 - val_loss: 0.2368 - val_accuracy: 0.9141\n",
            "Epoch 91/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.3349 - accuracy: 0.8718 - val_loss: 0.2357 - val_accuracy: 0.9167\n",
            "Epoch 92/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3337 - accuracy: 0.8771 - val_loss: 0.2377 - val_accuracy: 0.9167\n",
            "Epoch 93/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3367 - accuracy: 0.8716 - val_loss: 0.2424 - val_accuracy: 0.9167\n",
            "Epoch 94/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3327 - accuracy: 0.8756 - val_loss: 0.2447 - val_accuracy: 0.9141\n",
            "Epoch 95/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.3348 - accuracy: 0.8763 - val_loss: 0.2338 - val_accuracy: 0.9167\n",
            "Epoch 96/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3108 - accuracy: 0.8711 - val_loss: 0.2335 - val_accuracy: 0.9167\n",
            "Epoch 97/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3348 - accuracy: 0.8736 - val_loss: 0.2337 - val_accuracy: 0.9167\n",
            "Epoch 98/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3235 - accuracy: 0.8810 - val_loss: 0.2321 - val_accuracy: 0.9141\n",
            "Epoch 99/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3137 - accuracy: 0.8813 - val_loss: 0.2334 - val_accuracy: 0.9141\n",
            "Epoch 100/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.3081 - accuracy: 0.8865 - val_loss: 0.2317 - val_accuracy: 0.9167\n",
            "Epoch 101/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3138 - accuracy: 0.8839 - val_loss: 0.2292 - val_accuracy: 0.9141\n",
            "Epoch 102/300\n",
            "31/31 [==============================] - 20s 201ms/step - loss: 0.3160 - accuracy: 0.8830 - val_loss: 0.2296 - val_accuracy: 0.9115\n",
            "Epoch 103/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.3131 - accuracy: 0.8902 - val_loss: 0.2309 - val_accuracy: 0.9141\n",
            "Epoch 104/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3400 - accuracy: 0.8716 - val_loss: 0.2240 - val_accuracy: 0.9115\n",
            "Epoch 105/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3251 - accuracy: 0.8803 - val_loss: 0.2205 - val_accuracy: 0.9193\n",
            "Epoch 106/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3076 - accuracy: 0.8840 - val_loss: 0.2236 - val_accuracy: 0.9141\n",
            "Epoch 107/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2970 - accuracy: 0.8859 - val_loss: 0.2221 - val_accuracy: 0.9167\n",
            "Epoch 108/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3336 - accuracy: 0.8711 - val_loss: 0.2198 - val_accuracy: 0.9167\n",
            "Epoch 109/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3118 - accuracy: 0.8872 - val_loss: 0.2217 - val_accuracy: 0.9193\n",
            "Epoch 110/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3154 - accuracy: 0.8825 - val_loss: 0.2219 - val_accuracy: 0.9167\n",
            "Epoch 111/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3064 - accuracy: 0.8919 - val_loss: 0.2189 - val_accuracy: 0.9193\n",
            "Epoch 112/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.3126 - accuracy: 0.8865 - val_loss: 0.2134 - val_accuracy: 0.9219\n",
            "Epoch 113/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3161 - accuracy: 0.8798 - val_loss: 0.2100 - val_accuracy: 0.9193\n",
            "Epoch 114/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3108 - accuracy: 0.8914 - val_loss: 0.2118 - val_accuracy: 0.9245\n",
            "Epoch 115/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3130 - accuracy: 0.8794 - val_loss: 0.2071 - val_accuracy: 0.9219\n",
            "Epoch 116/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3125 - accuracy: 0.8939 - val_loss: 0.2027 - val_accuracy: 0.9271\n",
            "Epoch 117/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3049 - accuracy: 0.8895 - val_loss: 0.2096 - val_accuracy: 0.9219\n",
            "Epoch 118/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3148 - accuracy: 0.8908 - val_loss: 0.2069 - val_accuracy: 0.9245\n",
            "Epoch 119/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3149 - accuracy: 0.8797 - val_loss: 0.2032 - val_accuracy: 0.9245\n",
            "Epoch 120/300\n",
            "31/31 [==============================] - 20s 201ms/step - loss: 0.2915 - accuracy: 0.8961 - val_loss: 0.2107 - val_accuracy: 0.9219\n",
            "Epoch 121/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.3032 - accuracy: 0.8878 - val_loss: 0.2089 - val_accuracy: 0.9245\n",
            "Epoch 122/300\n",
            "31/31 [==============================] - 20s 201ms/step - loss: 0.2763 - accuracy: 0.9009 - val_loss: 0.2095 - val_accuracy: 0.9245\n",
            "Epoch 123/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2986 - accuracy: 0.8862 - val_loss: 0.2092 - val_accuracy: 0.9219\n",
            "Epoch 124/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3063 - accuracy: 0.8978 - val_loss: 0.2103 - val_accuracy: 0.9219\n",
            "Epoch 125/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2965 - accuracy: 0.8938 - val_loss: 0.2092 - val_accuracy: 0.9167\n",
            "Epoch 126/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2745 - accuracy: 0.9026 - val_loss: 0.2078 - val_accuracy: 0.9219\n",
            "Epoch 127/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2916 - accuracy: 0.8950 - val_loss: 0.2103 - val_accuracy: 0.9193\n",
            "Epoch 128/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2998 - accuracy: 0.8890 - val_loss: 0.2073 - val_accuracy: 0.9219\n",
            "Epoch 129/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2980 - accuracy: 0.8942 - val_loss: 0.2074 - val_accuracy: 0.9245\n",
            "Epoch 130/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2858 - accuracy: 0.9015 - val_loss: 0.2072 - val_accuracy: 0.9245\n",
            "Epoch 131/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2687 - accuracy: 0.9040 - val_loss: 0.2022 - val_accuracy: 0.9245\n",
            "Epoch 132/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2882 - accuracy: 0.8941 - val_loss: 0.2016 - val_accuracy: 0.9297\n",
            "Epoch 133/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.3170 - accuracy: 0.8822 - val_loss: 0.2016 - val_accuracy: 0.9297\n",
            "Epoch 134/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2940 - accuracy: 0.8861 - val_loss: 0.2013 - val_accuracy: 0.9245\n",
            "Epoch 135/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2871 - accuracy: 0.9009 - val_loss: 0.2033 - val_accuracy: 0.9297\n",
            "Epoch 136/300\n",
            "31/31 [==============================] - 20s 201ms/step - loss: 0.2920 - accuracy: 0.8957 - val_loss: 0.2003 - val_accuracy: 0.9271\n",
            "Epoch 137/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2661 - accuracy: 0.8973 - val_loss: 0.2007 - val_accuracy: 0.9323\n",
            "Epoch 138/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2747 - accuracy: 0.9004 - val_loss: 0.1949 - val_accuracy: 0.9271\n",
            "Epoch 139/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2744 - accuracy: 0.8944 - val_loss: 0.1982 - val_accuracy: 0.9297\n",
            "Epoch 140/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2894 - accuracy: 0.8932 - val_loss: 0.1982 - val_accuracy: 0.9349\n",
            "Epoch 141/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2716 - accuracy: 0.8971 - val_loss: 0.1936 - val_accuracy: 0.9323\n",
            "Epoch 142/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2790 - accuracy: 0.8968 - val_loss: 0.1932 - val_accuracy: 0.9349\n",
            "Epoch 143/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2786 - accuracy: 0.8960 - val_loss: 0.1922 - val_accuracy: 0.9323\n",
            "Epoch 144/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2818 - accuracy: 0.8993 - val_loss: 0.1934 - val_accuracy: 0.9323\n",
            "Epoch 145/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2726 - accuracy: 0.9044 - val_loss: 0.1939 - val_accuracy: 0.9297\n",
            "Epoch 146/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2659 - accuracy: 0.9106 - val_loss: 0.1931 - val_accuracy: 0.9323\n",
            "Epoch 147/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2642 - accuracy: 0.9090 - val_loss: 0.1971 - val_accuracy: 0.9297\n",
            "Epoch 148/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2788 - accuracy: 0.9127 - val_loss: 0.1950 - val_accuracy: 0.9323\n",
            "Epoch 149/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2530 - accuracy: 0.9085 - val_loss: 0.1906 - val_accuracy: 0.9349\n",
            "Epoch 150/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2728 - accuracy: 0.8974 - val_loss: 0.1926 - val_accuracy: 0.9297\n",
            "Epoch 151/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2734 - accuracy: 0.9000 - val_loss: 0.1931 - val_accuracy: 0.9323\n",
            "Epoch 152/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2697 - accuracy: 0.9028 - val_loss: 0.1896 - val_accuracy: 0.9323\n",
            "Epoch 153/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2683 - accuracy: 0.9090 - val_loss: 0.1885 - val_accuracy: 0.9349\n",
            "Epoch 154/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2716 - accuracy: 0.9004 - val_loss: 0.1921 - val_accuracy: 0.9323\n",
            "Epoch 155/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2698 - accuracy: 0.9020 - val_loss: 0.1878 - val_accuracy: 0.9349\n",
            "Epoch 156/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2734 - accuracy: 0.9078 - val_loss: 0.1874 - val_accuracy: 0.9349\n",
            "Epoch 157/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2679 - accuracy: 0.9147 - val_loss: 0.1846 - val_accuracy: 0.9349\n",
            "Epoch 158/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2842 - accuracy: 0.8929 - val_loss: 0.1865 - val_accuracy: 0.9349\n",
            "Epoch 159/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2587 - accuracy: 0.9116 - val_loss: 0.1847 - val_accuracy: 0.9349\n",
            "Epoch 160/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2650 - accuracy: 0.9095 - val_loss: 0.1812 - val_accuracy: 0.9375\n",
            "Epoch 161/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2651 - accuracy: 0.9065 - val_loss: 0.1814 - val_accuracy: 0.9349\n",
            "Epoch 162/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2786 - accuracy: 0.8939 - val_loss: 0.1849 - val_accuracy: 0.9349\n",
            "Epoch 163/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2682 - accuracy: 0.9031 - val_loss: 0.1863 - val_accuracy: 0.9349\n",
            "Epoch 164/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2647 - accuracy: 0.9103 - val_loss: 0.1852 - val_accuracy: 0.9349\n",
            "Epoch 165/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2647 - accuracy: 0.9040 - val_loss: 0.1834 - val_accuracy: 0.9323\n",
            "Epoch 166/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2593 - accuracy: 0.9157 - val_loss: 0.1833 - val_accuracy: 0.9375\n",
            "Epoch 167/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2679 - accuracy: 0.9027 - val_loss: 0.1795 - val_accuracy: 0.9349\n",
            "Epoch 168/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2578 - accuracy: 0.9110 - val_loss: 0.1768 - val_accuracy: 0.9375\n",
            "Epoch 169/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2514 - accuracy: 0.9097 - val_loss: 0.1769 - val_accuracy: 0.9375\n",
            "Epoch 170/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2489 - accuracy: 0.9133 - val_loss: 0.1723 - val_accuracy: 0.9401\n",
            "Epoch 171/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2770 - accuracy: 0.9050 - val_loss: 0.1809 - val_accuracy: 0.9375\n",
            "Epoch 172/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2555 - accuracy: 0.9103 - val_loss: 0.1773 - val_accuracy: 0.9375\n",
            "Epoch 173/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2460 - accuracy: 0.9126 - val_loss: 0.1757 - val_accuracy: 0.9427\n",
            "Epoch 174/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2635 - accuracy: 0.9058 - val_loss: 0.1749 - val_accuracy: 0.9427\n",
            "Epoch 175/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2592 - accuracy: 0.9153 - val_loss: 0.1748 - val_accuracy: 0.9427\n",
            "Epoch 176/300\n",
            "31/31 [==============================] - 20s 202ms/step - loss: 0.2404 - accuracy: 0.9181 - val_loss: 0.1780 - val_accuracy: 0.9427\n",
            "Epoch 177/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2460 - accuracy: 0.9169 - val_loss: 0.1771 - val_accuracy: 0.9401\n",
            "Epoch 178/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2475 - accuracy: 0.9101 - val_loss: 0.1799 - val_accuracy: 0.9375\n",
            "Epoch 179/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2519 - accuracy: 0.9126 - val_loss: 0.1783 - val_accuracy: 0.9375\n",
            "Epoch 180/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2588 - accuracy: 0.9118 - val_loss: 0.1716 - val_accuracy: 0.9401\n",
            "Epoch 181/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2423 - accuracy: 0.9156 - val_loss: 0.1696 - val_accuracy: 0.9427\n",
            "Epoch 182/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2348 - accuracy: 0.9249 - val_loss: 0.1727 - val_accuracy: 0.9427\n",
            "Epoch 183/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2399 - accuracy: 0.9099 - val_loss: 0.1783 - val_accuracy: 0.9375\n",
            "Epoch 184/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2606 - accuracy: 0.9140 - val_loss: 0.1756 - val_accuracy: 0.9375\n",
            "Epoch 185/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2404 - accuracy: 0.9179 - val_loss: 0.1736 - val_accuracy: 0.9401\n",
            "Epoch 186/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2498 - accuracy: 0.9151 - val_loss: 0.1716 - val_accuracy: 0.9401\n",
            "Epoch 187/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2714 - accuracy: 0.9049 - val_loss: 0.1717 - val_accuracy: 0.9453\n",
            "Epoch 188/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2558 - accuracy: 0.9060 - val_loss: 0.1742 - val_accuracy: 0.9375\n",
            "Epoch 189/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2506 - accuracy: 0.9139 - val_loss: 0.1735 - val_accuracy: 0.9401\n",
            "Epoch 190/300\n",
            "31/31 [==============================] - 20s 196ms/step - loss: 0.2325 - accuracy: 0.9137 - val_loss: 0.1725 - val_accuracy: 0.9401\n",
            "Epoch 191/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2363 - accuracy: 0.9258 - val_loss: 0.1711 - val_accuracy: 0.9427\n",
            "Epoch 192/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2698 - accuracy: 0.9092 - val_loss: 0.1719 - val_accuracy: 0.9427\n",
            "Epoch 193/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2414 - accuracy: 0.9187 - val_loss: 0.1727 - val_accuracy: 0.9401\n",
            "Epoch 194/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2439 - accuracy: 0.9114 - val_loss: 0.1718 - val_accuracy: 0.9453\n",
            "Epoch 195/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2426 - accuracy: 0.9057 - val_loss: 0.1714 - val_accuracy: 0.9401\n",
            "Epoch 196/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2241 - accuracy: 0.9157 - val_loss: 0.1693 - val_accuracy: 0.9427\n",
            "Epoch 197/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2321 - accuracy: 0.9244 - val_loss: 0.1692 - val_accuracy: 0.9427\n",
            "Epoch 198/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2439 - accuracy: 0.9127 - val_loss: 0.1677 - val_accuracy: 0.9453\n",
            "Epoch 199/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2273 - accuracy: 0.9164 - val_loss: 0.1682 - val_accuracy: 0.9453\n",
            "Epoch 200/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2412 - accuracy: 0.9215 - val_loss: 0.1653 - val_accuracy: 0.9479\n",
            "Epoch 201/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2554 - accuracy: 0.9109 - val_loss: 0.1639 - val_accuracy: 0.9479\n",
            "Epoch 202/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2328 - accuracy: 0.9183 - val_loss: 0.1701 - val_accuracy: 0.9427\n",
            "Epoch 203/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2386 - accuracy: 0.9211 - val_loss: 0.1670 - val_accuracy: 0.9453\n",
            "Epoch 204/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2384 - accuracy: 0.9223 - val_loss: 0.1654 - val_accuracy: 0.9453\n",
            "Epoch 205/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2432 - accuracy: 0.9173 - val_loss: 0.1672 - val_accuracy: 0.9401\n",
            "Epoch 206/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2243 - accuracy: 0.9231 - val_loss: 0.1627 - val_accuracy: 0.9505\n",
            "Epoch 207/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2236 - accuracy: 0.9220 - val_loss: 0.1653 - val_accuracy: 0.9479\n",
            "Epoch 208/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2411 - accuracy: 0.9191 - val_loss: 0.1650 - val_accuracy: 0.9479\n",
            "Epoch 209/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2425 - accuracy: 0.9088 - val_loss: 0.1670 - val_accuracy: 0.9479\n",
            "Epoch 210/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2254 - accuracy: 0.9240 - val_loss: 0.1653 - val_accuracy: 0.9427\n",
            "Epoch 211/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2353 - accuracy: 0.9196 - val_loss: 0.1630 - val_accuracy: 0.9479\n",
            "Epoch 212/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2091 - accuracy: 0.9283 - val_loss: 0.1638 - val_accuracy: 0.9479\n",
            "Epoch 213/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2369 - accuracy: 0.9171 - val_loss: 0.1643 - val_accuracy: 0.9479\n",
            "Epoch 214/300\n",
            "31/31 [==============================] - 20s 196ms/step - loss: 0.2451 - accuracy: 0.9200 - val_loss: 0.1624 - val_accuracy: 0.9479\n",
            "Epoch 215/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2383 - accuracy: 0.9161 - val_loss: 0.1621 - val_accuracy: 0.9479\n",
            "Epoch 216/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2300 - accuracy: 0.9174 - val_loss: 0.1636 - val_accuracy: 0.9479\n",
            "Epoch 217/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2283 - accuracy: 0.9169 - val_loss: 0.1630 - val_accuracy: 0.9479\n",
            "Epoch 218/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2330 - accuracy: 0.9238 - val_loss: 0.1650 - val_accuracy: 0.9479\n",
            "Epoch 219/300\n",
            "31/31 [==============================] - 19s 198ms/step - loss: 0.2410 - accuracy: 0.9188 - val_loss: 0.1667 - val_accuracy: 0.9427\n",
            "Epoch 220/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2417 - accuracy: 0.9139 - val_loss: 0.1636 - val_accuracy: 0.9479\n",
            "Epoch 221/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2304 - accuracy: 0.9222 - val_loss: 0.1641 - val_accuracy: 0.9479\n",
            "Epoch 222/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2135 - accuracy: 0.9278 - val_loss: 0.1548 - val_accuracy: 0.9531\n",
            "Epoch 223/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2265 - accuracy: 0.9272 - val_loss: 0.1615 - val_accuracy: 0.9453\n",
            "Epoch 224/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2383 - accuracy: 0.9140 - val_loss: 0.1580 - val_accuracy: 0.9479\n",
            "Epoch 225/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2194 - accuracy: 0.9307 - val_loss: 0.1608 - val_accuracy: 0.9453\n",
            "Epoch 226/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2222 - accuracy: 0.9194 - val_loss: 0.1582 - val_accuracy: 0.9453\n",
            "Epoch 227/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2294 - accuracy: 0.9217 - val_loss: 0.1638 - val_accuracy: 0.9453\n",
            "Epoch 228/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2269 - accuracy: 0.9197 - val_loss: 0.1610 - val_accuracy: 0.9453\n",
            "Epoch 229/300\n",
            "31/31 [==============================] - 20s 201ms/step - loss: 0.2202 - accuracy: 0.9244 - val_loss: 0.1615 - val_accuracy: 0.9453\n",
            "Epoch 230/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2261 - accuracy: 0.9188 - val_loss: 0.1609 - val_accuracy: 0.9453\n",
            "Epoch 231/300\n",
            "31/31 [==============================] - 20s 196ms/step - loss: 0.2158 - accuracy: 0.9344 - val_loss: 0.1595 - val_accuracy: 0.9453\n",
            "Epoch 232/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2178 - accuracy: 0.9251 - val_loss: 0.1575 - val_accuracy: 0.9453\n",
            "Epoch 233/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2277 - accuracy: 0.9241 - val_loss: 0.1593 - val_accuracy: 0.9453\n",
            "Epoch 234/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2277 - accuracy: 0.9212 - val_loss: 0.1573 - val_accuracy: 0.9479\n",
            "Epoch 235/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2259 - accuracy: 0.9194 - val_loss: 0.1598 - val_accuracy: 0.9479\n",
            "Epoch 236/300\n",
            "31/31 [==============================] - 19s 198ms/step - loss: 0.2228 - accuracy: 0.9210 - val_loss: 0.1571 - val_accuracy: 0.9505\n",
            "Epoch 237/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2421 - accuracy: 0.9092 - val_loss: 0.1558 - val_accuracy: 0.9453\n",
            "Epoch 238/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2040 - accuracy: 0.9272 - val_loss: 0.1580 - val_accuracy: 0.9453\n",
            "Epoch 239/300\n",
            "31/31 [==============================] - 19s 198ms/step - loss: 0.2063 - accuracy: 0.9273 - val_loss: 0.1571 - val_accuracy: 0.9479\n",
            "Epoch 240/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2110 - accuracy: 0.9293 - val_loss: 0.1566 - val_accuracy: 0.9453\n",
            "Epoch 241/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2429 - accuracy: 0.9107 - val_loss: 0.1550 - val_accuracy: 0.9453\n",
            "Epoch 242/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2166 - accuracy: 0.9190 - val_loss: 0.1571 - val_accuracy: 0.9453\n",
            "Epoch 243/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2082 - accuracy: 0.9257 - val_loss: 0.1557 - val_accuracy: 0.9453\n",
            "Epoch 244/300\n",
            "31/31 [==============================] - 19s 196ms/step - loss: 0.2177 - accuracy: 0.9293 - val_loss: 0.1558 - val_accuracy: 0.9505\n",
            "Epoch 245/300\n",
            "31/31 [==============================] - 20s 202ms/step - loss: 0.2090 - accuracy: 0.9299 - val_loss: 0.1548 - val_accuracy: 0.9505\n",
            "Epoch 246/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2154 - accuracy: 0.9280 - val_loss: 0.1560 - val_accuracy: 0.9479\n",
            "Epoch 247/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2038 - accuracy: 0.9332 - val_loss: 0.1533 - val_accuracy: 0.9479\n",
            "Epoch 248/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2048 - accuracy: 0.9334 - val_loss: 0.1568 - val_accuracy: 0.9479\n",
            "Epoch 249/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2054 - accuracy: 0.9320 - val_loss: 0.1544 - val_accuracy: 0.9479\n",
            "Epoch 250/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2159 - accuracy: 0.9296 - val_loss: 0.1539 - val_accuracy: 0.9479\n",
            "Epoch 251/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2050 - accuracy: 0.9328 - val_loss: 0.1540 - val_accuracy: 0.9479\n",
            "Epoch 252/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2173 - accuracy: 0.9253 - val_loss: 0.1553 - val_accuracy: 0.9505\n",
            "Epoch 253/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2104 - accuracy: 0.9277 - val_loss: 0.1539 - val_accuracy: 0.9505\n",
            "Epoch 254/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2265 - accuracy: 0.9244 - val_loss: 0.1550 - val_accuracy: 0.9479\n",
            "Epoch 255/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2068 - accuracy: 0.9260 - val_loss: 0.1542 - val_accuracy: 0.9505\n",
            "Epoch 256/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.1940 - accuracy: 0.9385 - val_loss: 0.1529 - val_accuracy: 0.9505\n",
            "Epoch 257/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2092 - accuracy: 0.9225 - val_loss: 0.1528 - val_accuracy: 0.9505\n",
            "Epoch 258/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2081 - accuracy: 0.9269 - val_loss: 0.1511 - val_accuracy: 0.9531\n",
            "Epoch 259/300\n",
            "31/31 [==============================] - 19s 200ms/step - loss: 0.2028 - accuracy: 0.9347 - val_loss: 0.1505 - val_accuracy: 0.9531\n",
            "Epoch 260/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2060 - accuracy: 0.9337 - val_loss: 0.1504 - val_accuracy: 0.9531\n",
            "Epoch 261/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2007 - accuracy: 0.9320 - val_loss: 0.1501 - val_accuracy: 0.9531\n",
            "Epoch 262/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2127 - accuracy: 0.9279 - val_loss: 0.1537 - val_accuracy: 0.9531\n",
            "Epoch 263/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2130 - accuracy: 0.9268 - val_loss: 0.1522 - val_accuracy: 0.9531\n",
            "Epoch 264/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2019 - accuracy: 0.9303 - val_loss: 0.1519 - val_accuracy: 0.9531\n",
            "Epoch 265/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2192 - accuracy: 0.9249 - val_loss: 0.1490 - val_accuracy: 0.9531\n",
            "Epoch 266/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2126 - accuracy: 0.9305 - val_loss: 0.1471 - val_accuracy: 0.9479\n",
            "Epoch 267/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2045 - accuracy: 0.9309 - val_loss: 0.1485 - val_accuracy: 0.9479\n",
            "Epoch 268/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2091 - accuracy: 0.9328 - val_loss: 0.1466 - val_accuracy: 0.9531\n",
            "Epoch 269/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2106 - accuracy: 0.9306 - val_loss: 0.1488 - val_accuracy: 0.9505\n",
            "Epoch 270/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.1866 - accuracy: 0.9407 - val_loss: 0.1506 - val_accuracy: 0.9531\n",
            "Epoch 271/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.1905 - accuracy: 0.9348 - val_loss: 0.1483 - val_accuracy: 0.9531\n",
            "Epoch 272/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2093 - accuracy: 0.9293 - val_loss: 0.1492 - val_accuracy: 0.9531\n",
            "Epoch 273/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.1903 - accuracy: 0.9412 - val_loss: 0.1496 - val_accuracy: 0.9531\n",
            "Epoch 274/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2062 - accuracy: 0.9296 - val_loss: 0.1495 - val_accuracy: 0.9531\n",
            "Epoch 275/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2017 - accuracy: 0.9342 - val_loss: 0.1500 - val_accuracy: 0.9505\n",
            "Epoch 276/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2155 - accuracy: 0.9294 - val_loss: 0.1487 - val_accuracy: 0.9505\n",
            "Epoch 277/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2172 - accuracy: 0.9210 - val_loss: 0.1486 - val_accuracy: 0.9505\n",
            "Epoch 278/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2057 - accuracy: 0.9239 - val_loss: 0.1465 - val_accuracy: 0.9505\n",
            "Epoch 279/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.1886 - accuracy: 0.9365 - val_loss: 0.1483 - val_accuracy: 0.9531\n",
            "Epoch 280/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2007 - accuracy: 0.9357 - val_loss: 0.1495 - val_accuracy: 0.9479\n",
            "Epoch 281/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.1899 - accuracy: 0.9359 - val_loss: 0.1484 - val_accuracy: 0.9479\n",
            "Epoch 282/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2075 - accuracy: 0.9323 - val_loss: 0.1479 - val_accuracy: 0.9479\n",
            "Epoch 283/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2011 - accuracy: 0.9374 - val_loss: 0.1476 - val_accuracy: 0.9479\n",
            "Epoch 284/300\n",
            "31/31 [==============================] - 19s 201ms/step - loss: 0.2167 - accuracy: 0.9240 - val_loss: 0.1492 - val_accuracy: 0.9479\n",
            "Epoch 285/300\n",
            "31/31 [==============================] - 19s 198ms/step - loss: 0.2072 - accuracy: 0.9271 - val_loss: 0.1478 - val_accuracy: 0.9479\n",
            "Epoch 286/300\n",
            "31/31 [==============================] - 20s 199ms/step - loss: 0.2077 - accuracy: 0.9249 - val_loss: 0.1436 - val_accuracy: 0.9531\n",
            "Epoch 287/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2045 - accuracy: 0.9352 - val_loss: 0.1440 - val_accuracy: 0.9531\n",
            "Epoch 288/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.1922 - accuracy: 0.9334 - val_loss: 0.1439 - val_accuracy: 0.9557\n",
            "Epoch 289/300\n",
            "31/31 [==============================] - 19s 198ms/step - loss: 0.1918 - accuracy: 0.9355 - val_loss: 0.1418 - val_accuracy: 0.9531\n",
            "Epoch 290/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.1990 - accuracy: 0.9300 - val_loss: 0.1431 - val_accuracy: 0.9531\n",
            "Epoch 291/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.2023 - accuracy: 0.9333 - val_loss: 0.1462 - val_accuracy: 0.9531\n",
            "Epoch 292/300\n",
            "31/31 [==============================] - 19s 199ms/step - loss: 0.1924 - accuracy: 0.9394 - val_loss: 0.1401 - val_accuracy: 0.9557\n",
            "Epoch 293/300\n",
            "31/31 [==============================] - 20s 197ms/step - loss: 0.1958 - accuracy: 0.9362 - val_loss: 0.1387 - val_accuracy: 0.9557\n",
            "Epoch 294/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.1929 - accuracy: 0.9345 - val_loss: 0.1415 - val_accuracy: 0.9531\n",
            "Epoch 295/300\n",
            "31/31 [==============================] - 19s 198ms/step - loss: 0.1903 - accuracy: 0.9394 - val_loss: 0.1411 - val_accuracy: 0.9531\n",
            "Epoch 296/300\n",
            "31/31 [==============================] - 20s 198ms/step - loss: 0.2032 - accuracy: 0.9303 - val_loss: 0.1395 - val_accuracy: 0.9531\n",
            "Epoch 297/300\n",
            "31/31 [==============================] - 19s 197ms/step - loss: 0.2079 - accuracy: 0.9227 - val_loss: 0.1423 - val_accuracy: 0.9531\n",
            "Epoch 298/300\n",
            "31/31 [==============================] - 20s 200ms/step - loss: 0.2133 - accuracy: 0.9261 - val_loss: 0.1409 - val_accuracy: 0.9531\n",
            "Epoch 299/300\n",
            "31/31 [==============================] - 19s 197ms/step - loss: 0.2110 - accuracy: 0.9294 - val_loss: 0.1395 - val_accuracy: 0.9531\n",
            "Epoch 300/300\n",
            "31/31 [==============================] - 19s 197ms/step - loss: 0.1867 - accuracy: 0.9371 - val_loss: 0.1367 - val_accuracy: 0.9583\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S81c54_Lnil6"
      },
      "source": [
        "#@title Export Model as File { display-mode: \"form\" }\n",
        "Execute = False #@param {type:\"boolean\"}\n",
        "if Execute:\n",
        "    TF_Model_Name = 'mask_detection_classifier' #@param {type:\"string\"}\n",
        "    model_type = 'Keras' #@param ['Keras', 'TensorFlow Lite']\n",
        "    download = False #@param {type:\"boolean\"}\n",
        "    from google.colab import files\n",
        "    import tensorflow as tf\n",
        "    model.save(TF_Model_Name + \".h5\")\n",
        "    if model_type == 'TensorFlow Lite':\n",
        "        converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "        tflite_model = converter.convert()\n",
        "\n",
        "        with open(TF_Model_Name+'.tflite', 'wb') as f:\n",
        "            f.write(tflite_model)\n",
        "\n",
        "        if download:\n",
        "            files.download(f'{TF_Model_Name}.tflite')\n",
        "    else:\n",
        "        if download:\n",
        "            files.download(f'{TF_Model_Name}.h5')"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3zYQJ6gXu2Ig"
      },
      "source": [
        "# **Review**\r\n",
        "Compare training accuracy and validation accuracy in a graphical way, and evaluate the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jciviDb8yRCz",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "e81a6293-f463-4451-a3c6-1bdea6e5f394"
      },
      "source": [
        "#@title Compare training and validation accuracy over ephocs\n",
        "import matplotlib.pyplot as plt\n",
        "try:\n",
        "    acc = history.history['acc']\n",
        "    val_acc = history.history['val_acc']\n",
        "except:\n",
        "    acc = history.history['accuracy']\n",
        "    val_acc = history.history['val_accuracy']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(len(acc))\n",
        "\n",
        "plt.plot(epochs, acc, label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend(loc=0)\n",
        "plt.figure()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0wI9V6bXW_p"
      },
      "source": [
        "## Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "kS4nCkmL8rR3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        },
        "outputId": "aa5c30a6-9791-4e71-dd7b-107569b9fd85"
      },
      "source": [
        "#@title random photos from the unused \"validation\" subset.\n",
        "import random\n",
        "from IPython.display import Image\n",
        "\n",
        "with_mask_val = '/content/observations/experiements/dest_folder/val/with_mask'\n",
        "without_mask_val = '/content/observations/experiements/dest_folder/val/without_mask'\n",
        "\n",
        "val_set = [with_mask_val, without_mask_val]\n",
        "\n",
        "for fn in val_set:\n",
        "    # predicting images\n",
        "    path = f'{fn}/{random.choice(os.listdir(fn))}'\n",
        "    img = image.load_img(path, target_size=(150, 150))\n",
        "    x = image.img_to_array(img)\n",
        "    x = np.expand_dims(x, axis=0)\n",
        "\n",
        "    images = np.vstack([x])\n",
        "    classes = model.predict(images, batch_size=10)\n",
        "    display(Image(path))\n",
        "    print(path)\n",
        "    print(classes)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/content/observations/experiements/dest_folder/val/with_mask/321-with-mask.jpg\n",
            "[[1. 0.]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/content/observations/experiements/dest_folder/val/without_mask/205.jpg\n",
            "[[0. 1.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZABJp7T3VLCU"
      },
      "source": [
        "#@title Upload Image to Check { display-mode: \"form\" }\n",
        "check = False #@param {type:\"boolean\"}\n",
        "\n",
        "if check:\n",
        "    import numpy as np\n",
        "    from google.colab import files\n",
        "    from keras.preprocessing import image\n",
        "\n",
        "    uploaded = files.upload()\n",
        "\n",
        "    for fn in uploaded.keys():\n",
        "\n",
        "        # predicting images\n",
        "        path = fn\n",
        "        img = image.load_img(path, target_size=(150, 150))\n",
        "        x = image.img_to_array(img)\n",
        "        x = np.expand_dims(x, axis=0)\n",
        "\n",
        "        images = np.vstack([x])\n",
        "        classes = model.predict(images, batch_size=10, verbose=1)\n",
        "        print(fn)\n",
        "        print(classes)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oOnxH6iGXr_1"
      },
      "source": [
        "### Test with webcam"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hfR-Q0u0JFGc",
        "cellView": "form"
      },
      "source": [
        "check = False #@param {type:\"boolean\"}\n",
        "if check:\n",
        "    \"\"\"Javascript function define\"\"\"\n",
        "    from IPython.display import display, Javascript\n",
        "    from google.colab.output import eval_js\n",
        "    from base64 import b64decode\n",
        "\n",
        "    def take_photo(filename='photo.jpg', quality=0.8):\n",
        "        js = Javascript('''\n",
        "        async function takePhoto(quality) {\n",
        "            const div = document.createElement('div');\n",
        "            const capture = document.createElement('button');\n",
        "            capture.textContent = 'Capture';\n",
        "            div.appendChild(capture);\n",
        "\n",
        "            const video = document.createElement('video');\n",
        "            video.style.display = 'block';\n",
        "            const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
        "\n",
        "            document.body.appendChild(div);\n",
        "            div.appendChild(video);\n",
        "            video.srcObject = stream;\n",
        "            await video.play();\n",
        "\n",
        "            // Resize the output to fit the video element.\n",
        "            google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
        "\n",
        "            // Wait for Capture to be clicked.\n",
        "            await new Promise((resolve) => capture.onclick = resolve);\n",
        "\n",
        "            const canvas = document.createElement('canvas');\n",
        "            canvas.width = video.videoWidth;\n",
        "            canvas.height = video.videoHeight;\n",
        "            canvas.getContext('2d').drawImage(video, 0, 0);\n",
        "            stream.getVideoTracks()[0].stop();\n",
        "            div.remove();\n",
        "            return canvas.toDataURL('image/jpeg', quality);\n",
        "        }\n",
        "        ''')\n",
        "        display(js)\n",
        "        data = eval_js('takePhoto({})'.format(quality))\n",
        "        binary = b64decode(data.split(',')[1])\n",
        "        with open(filename, 'wb') as f:\n",
        "            f.write(binary)\n",
        "        return filename\n",
        "\n",
        "    \"\"\"try to execute Javascript function\"\"\"\n",
        "    from IPython.display import Image\n",
        "    try:\n",
        "        filename = take_photo()\n",
        "        print('Saved to {}'.format(filename))\n",
        "        \n",
        "        # Show the image which was just taken.\n",
        "        display(Image(filename))\n",
        "    except Exception as err:\n",
        "        # Errors will be thrown if the user does not have a webcam or if they do not\n",
        "        # grant the page permission to access it.\n",
        "        print(str(err))\n",
        "    \n",
        "    \"\"\"predicting image class using trained model\"\"\"\n",
        "    try:\n",
        "        path = filename\n",
        "        img = image.load_img(path, target_size=(150, 150))\n",
        "        x = image.img_to_array(img)\n",
        "        x = np.expand_dims(x, axis=0)\n",
        "        images = np.vstack([x])\n",
        "        classes = model.predict(images, batch_size=10, verbose=1)\n",
        "        print(path)\n",
        "        print(classes)\n",
        "    except:\n",
        "        pass"
      ],
      "execution_count": 26,
      "outputs": []
    }
  ]
}