hybrid_sn_v1_3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyO0nuxyQwYjY3+trCL/1o6i",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BohdanLiuisk/CSharp-Object-Oriented-Programming/blob/master/hybrid_sn_v1_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BdN2CKtkZ_8U",
        "outputId": "daca1b59-42bc-4bda-c70b-e8b4fac8b3c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting spectral\n",
            "  Downloading spectral-0.23.1-py3-none-any.whl (212 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.9/212.9 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from spectral) (1.22.4)\n",
            "Installing collected packages: spectral\n",
            "Successfully installed spectral-0.23.1\n"
          ]
        }
      ],
      "source": [
        "!pip install spectral"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from psutil import virtual_memory\n",
        "ram_gb = virtual_memory().total / 1e9\n",
        "print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
        "\n",
        "if ram_gb < 20:\n",
        "  print('Not using a high-RAM runtime')\n",
        "else:\n",
        "  print('You are using a high-RAM runtime!')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KzrOVJuxMqR4",
        "outputId": "aa205c76-e4d7-44c1-e71f-ebaf3c262e81"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your runtime has 27.3 gigabytes of available RAM\n",
            "\n",
            "You are using a high-RAM runtime!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "hsi_datasets_path = '/content/drive/My Drive/hsi_datasets'\n",
        "hsi_datasets_local_path = '/content/dataset'\n",
        "shutil.copytree(hsi_datasets_path, hsi_datasets_local_path)\n",
        "\n",
        "print(\"Current working directory:\", os.getcwd())\n",
        "print(\"Files in the working directory:\")\n",
        "os.listdir('.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PzBbVVnMf8Q8",
        "outputId": "a4df3c44-5ad9-4c04-f4e8-0db4f50ff651"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "Current working directory: /content\n",
            "Files in the working directory:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['.config', 'drive', 'dataset', 'sample_data']"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import scipy.io as sio\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "from tensorflow.keras.layers import Input, Conv3D, Conv2D, Dense, Flatten, Reshape, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from collections import Counter\n",
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "\n",
        "## GLOBAL VARIABLES\n",
        "DATASET = 'PU'\n",
        "TEST_RATIO = 0.7\n",
        "WINDOW_SIZE = 25\n",
        "DATA_PATH = '/content/dataset/'\n",
        "MODEL_PATH = \"/content/drive/MyDrive/trained_models/{}_hybrid_sn.h5\".format(DATASET)\n",
        "\n",
        "def load_data(name):\n",
        "    if name == 'IP':\n",
        "        data = sio.loadmat(os.path.join(DATA_PATH, 'Indian_pines_corrected.mat'))['indian_pines_corrected']\n",
        "        labels = sio.loadmat(os.path.join(DATA_PATH, 'Indian_pines_gt.mat'))['indian_pines_gt']\n",
        "    elif name == 'SA':\n",
        "        data = sio.loadmat(os.path.join(DATA_PATH, 'Salinas_corrected.mat'))['salinas_corrected']\n",
        "        labels = sio.loadmat(os.path.join(DATA_PATH, 'Salinas_gt.mat'))['salinas_gt']\n",
        "    elif name == 'SA_S':\n",
        "        data = sio.loadmat(os.path.join(DATA_PATH, 'SalinasA_corrected.mat'))['salinasA_corrected']\n",
        "        labels = sio.loadmat(os.path.join(DATA_PATH, 'SalinasA_gt.mat'))['salinasA_gt']\n",
        "    elif name == 'PU':\n",
        "        data = sio.loadmat(os.path.join(DATA_PATH, 'PaviaU.mat'))['paviaU']\n",
        "        labels = sio.loadmat(os.path.join(DATA_PATH, 'PaviaU_gt.mat'))['paviaU_gt']\n",
        "    return data, labels\n",
        "\n",
        "def apply_pca(X, numComponents=75):\n",
        "    newX = np.reshape(X, (-1, X.shape[2]))\n",
        "    pca = PCA(n_components=numComponents, whiten=True)\n",
        "    newX = pca.fit_transform(newX)\n",
        "    newX = np.reshape(newX, (X.shape[0],X.shape[1], numComponents))\n",
        "    return newX, pca\n",
        "\n",
        "def pad_with_zeros(X, margin=2):\n",
        "    newX = np.zeros((X.shape[0] + 2 * margin, X.shape[1] + 2* margin, X.shape[2]))\n",
        "    x_offset = margin\n",
        "    y_offset = margin\n",
        "    newX[x_offset:X.shape[0] + x_offset, y_offset:X.shape[1] + y_offset, :] = X\n",
        "    return newX\n",
        "\n",
        "def create_image_cubes(X, y, windowSize=5, removeZeroLabels = True):\n",
        "    margin = int((windowSize - 1) / 2)\n",
        "    zeroPaddedX = pad_with_zeros(X, margin=margin)\n",
        "    # split patches\n",
        "    patchesData = np.zeros((X.shape[0] * X.shape[1], windowSize, windowSize, X.shape[2]))\n",
        "    patchesLabels = np.zeros((X.shape[0] * X.shape[1]))\n",
        "    patchIndex = 0\n",
        "    for r in range(margin, zeroPaddedX.shape[0] - margin):\n",
        "        for c in range(margin, zeroPaddedX.shape[1] - margin):\n",
        "            patch = zeroPaddedX[r - margin:r + margin + 1, c - margin:c + margin + 1]   \n",
        "            patchesData[patchIndex, :, :, :] = patch\n",
        "            patchesLabels[patchIndex] = y[r-margin, c-margin]\n",
        "            patchIndex = patchIndex + 1\n",
        "    if removeZeroLabels:\n",
        "        patchesData = patchesData[patchesLabels>0,:,:,:]\n",
        "        patchesLabels = patchesLabels[patchesLabels>0]\n",
        "        patchesLabels -= 1\n",
        "    return patchesData, patchesLabels\n",
        "\n",
        "def plot_model_history(history):\n",
        "    # Plotting the Loss Curve\n",
        "    plt.figure(figsize=(5,5))\n",
        "    plt.grid() \n",
        "    plt.plot(history.history['loss'])\n",
        "    plt.plot(history.history['val_loss'])\n",
        "    plt.ylabel('Loss')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.legend(['Training','Validation'], loc='upper right')\n",
        "    plt.savefig(\"loss_curve.png\")\n",
        "    plt.show()\n",
        "\n",
        "    # Plotting the Accuracy Curve\n",
        "    plt.figure(figsize=(5,5))\n",
        "    plt.ylim(0,1.1)\n",
        "    plt.grid() \n",
        "    plt.plot(history.history['accuracy'])\n",
        "    plt.plot(history.history['val_accuracy'])\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.legend(['Training','Validation'])\n",
        "    plt.savefig(\"acc_curve.png\")\n",
        "    plt.show()\n",
        "\n",
        "def build_model(input_shape, output_units):\n",
        "    ## input layer\n",
        "    input_layer = Input(input_shape)\n",
        "\n",
        "    ## convolutional layers\n",
        "    conv_layer1 = Conv3D(filters=8, kernel_size=(3, 3, 7), activation='relu')(input_layer)\n",
        "    conv_layer2 = Conv3D(filters=16, kernel_size=(3, 3, 5), activation='relu')(conv_layer1)\n",
        "    conv_layer3 = Conv3D(filters=32, kernel_size=(3, 3, 3), activation='relu')(conv_layer2)\n",
        "\n",
        "    conv3d_shape = tf.keras.backend.int_shape(conv_layer3)\n",
        "    conv_layer3 = Reshape((conv3d_shape[1], conv3d_shape[2], conv3d_shape[3]*conv3d_shape[4]))(conv_layer3)\n",
        "    conv_layer4 = Conv2D(filters=64, kernel_size=(3,3), activation='relu')(conv_layer3)\n",
        "\n",
        "    flatten_layer = Flatten()(conv_layer4)\n",
        "\n",
        "    ## fully connected layers\n",
        "    dense_layer1 = Dense(units=256, activation='relu')(flatten_layer)\n",
        "    dense_layer1 = Dropout(0.4)(dense_layer1)\n",
        "    dense_layer2 = Dense(units=128, activation='relu')(dense_layer1)\n",
        "    dense_layer2 = Dropout(0.4)(dense_layer2)\n",
        "    output_layer = Dense(units=output_units, activation='softmax')(dense_layer2)\n",
        "\n",
        "    # Define the model and print the summary\n",
        "    model = Model(inputs=input_layer, outputs=output_layer)\n",
        "    model.summary()\n",
        "\n",
        "    return model\n",
        "\n",
        "def generate_image_cubes(X, y, windowSize=5, removeZeroLabels = True):\n",
        "    margin = int((windowSize - 1) / 2)\n",
        "    zeroPaddedX = pad_with_zeros(X, margin=margin)\n",
        "    # generate patches\n",
        "    for r in range(margin, zeroPaddedX.shape[0] - margin):\n",
        "        for c in range(margin, zeroPaddedX.shape[1] - margin):\n",
        "            patch = zeroPaddedX[r - margin:r + margin + 1, c - margin:c + margin + 1]   \n",
        "            patch_label = y[r-margin, c-margin]\n",
        "            if removeZeroLabels and patch_label > 0:\n",
        "                yield (patch, patch_label - 1)\n",
        "            elif not removeZeroLabels:\n",
        "                yield (patch, patch_label)\n",
        "\n",
        "def train_test_split_generator(data, labels, test_size=0.3):\n",
        "    splitter = StratifiedShuffleSplit(n_splits=1, test_size=test_size)\n",
        "    for train_index, test_index in splitter.split(data, labels):\n",
        "        yield data[train_index], data[test_index], labels[train_index], labels[test_index]\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    if not os.path.exists(MODEL_PATH):\n",
        "        X, y = load_data(DATASET)\n",
        "        K = 30\n",
        "        X, _ = apply_pca(X, numComponents=K)\n",
        "        patchesGenerator = generate_image_cubes(X, y, windowSize=WINDOW_SIZE)\n",
        "        X_patches = []\n",
        "        y_patches = []\n",
        "        for (patch, label) in patchesGenerator:\n",
        "            X_patches.append(patch)\n",
        "            y_patches.append(label)\n",
        "        X = np.array(X_patches)\n",
        "        y = np.array(y_patches)\n",
        "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_RATIO, stratify=y)\n",
        "        X_train = X_train.reshape(-1, WINDOW_SIZE, WINDOW_SIZE, K, 1)\n",
        "        print(\"Unique labels before to_categorical:\", np.unique(y_train))\n",
        "        le = LabelEncoder()\n",
        "        y_train_encoded = le.fit_transform(y_train)\n",
        "        y_train = to_categorical(y_train_encoded)\n",
        "        print(\"Unique labels after encoding:\", np.unique(y_train_encoded)) \n",
        "        # Determine the number of unique classes\n",
        "        output_units = len(np.unique(y_train_encoded))\n",
        "        model = build_model((WINDOW_SIZE, WINDOW_SIZE, K, 1), output_units)\n",
        "        #compiling the model\n",
        "        adam = tf.keras.optimizers.legacy.Adam(lr=0.001, decay=1e-06)\n",
        "        model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
        "        history = model.fit(x=X_train, y=y_train, batch_size=256, epochs=100, validation_split=0.2)\n",
        "        model.save(MODEL_PATH)\n",
        "        plot_model_history(history)\n",
        "    else:\n",
        "        print(f'Model {MODEL_PATH} already exists.')\n"
      ],
      "metadata": {
        "id": "o9f3zCX6f_-V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "740249f1-8611-4a93-ed82-12a1faccc0ea"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique labels before to_categorical: [0 1 2 3 4 5 6 7 8]\n",
            "Unique labels after encoding: [0 1 2 3 4 5 6 7 8]\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 25, 25, 30, 1)]   0         \n",
            "                                                                 \n",
            " conv3d (Conv3D)             (None, 23, 23, 24, 8)     512       \n",
            "                                                                 \n",
            " conv3d_1 (Conv3D)           (None, 21, 21, 20, 16)    5776      \n",
            "                                                                 \n",
            " conv3d_2 (Conv3D)           (None, 19, 19, 18, 32)    13856     \n",
            "                                                                 \n",
            " reshape (Reshape)           (None, 19, 19, 576)       0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 17, 17, 64)        331840    \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 18496)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               4735232   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 256)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 9)                 1161      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5,121,273\n",
            "Trainable params: 5,121,273\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/optimizers/legacy/adam.py:117: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super().__init__(name, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "41/41 [==============================] - 22s 225ms/step - loss: 2.0584 - accuracy: 0.3575 - val_loss: 1.4383 - val_accuracy: 0.4258\n",
            "Epoch 2/100\n",
            "41/41 [==============================] - 8s 198ms/step - loss: 1.0502 - accuracy: 0.5987 - val_loss: 0.5284 - val_accuracy: 0.8212\n",
            "Epoch 3/100\n",
            "41/41 [==============================] - 8s 200ms/step - loss: 0.4048 - accuracy: 0.8591 - val_loss: 0.2207 - val_accuracy: 0.9299\n",
            "Epoch 4/100\n",
            "41/41 [==============================] - 9s 218ms/step - loss: 0.1424 - accuracy: 0.9536 - val_loss: 0.0660 - val_accuracy: 0.9786\n",
            "Epoch 5/100\n",
            "41/41 [==============================] - 8s 205ms/step - loss: 0.0576 - accuracy: 0.9836 - val_loss: 0.0534 - val_accuracy: 0.9794\n",
            "Epoch 6/100\n",
            "41/41 [==============================] - 8s 207ms/step - loss: 0.0306 - accuracy: 0.9911 - val_loss: 0.0163 - val_accuracy: 0.9953\n",
            "Epoch 7/100\n",
            "41/41 [==============================] - 9s 208ms/step - loss: 0.0177 - accuracy: 0.9953 - val_loss: 0.0116 - val_accuracy: 0.9984\n",
            "Epoch 8/100\n",
            "41/41 [==============================] - 9s 208ms/step - loss: 0.0121 - accuracy: 0.9972 - val_loss: 0.0141 - val_accuracy: 0.9965\n",
            "Epoch 9/100\n",
            "41/41 [==============================] - 9s 208ms/step - loss: 0.0129 - accuracy: 0.9962 - val_loss: 0.0144 - val_accuracy: 0.9969\n",
            "Epoch 10/100\n",
            "41/41 [==============================] - 9s 209ms/step - loss: 0.0105 - accuracy: 0.9962 - val_loss: 0.0065 - val_accuracy: 0.9977\n",
            "Epoch 11/100\n",
            "41/41 [==============================] - 9s 211ms/step - loss: 0.0166 - accuracy: 0.9960 - val_loss: 0.0095 - val_accuracy: 0.9965\n",
            "Epoch 12/100\n",
            "41/41 [==============================] - 9s 215ms/step - loss: 0.0245 - accuracy: 0.9933 - val_loss: 0.0220 - val_accuracy: 0.9938\n",
            "Epoch 13/100\n",
            "41/41 [==============================] - 9s 218ms/step - loss: 0.0215 - accuracy: 0.9954 - val_loss: 0.0078 - val_accuracy: 0.9977\n",
            "Epoch 14/100\n",
            "41/41 [==============================] - 9s 222ms/step - loss: 0.0096 - accuracy: 0.9977 - val_loss: 0.0083 - val_accuracy: 0.9973\n",
            "Epoch 15/100\n",
            "41/41 [==============================] - 9s 227ms/step - loss: 0.0095 - accuracy: 0.9976 - val_loss: 0.0185 - val_accuracy: 0.9953\n",
            "Epoch 16/100\n",
            "41/41 [==============================] - 9s 226ms/step - loss: 0.0040 - accuracy: 0.9990 - val_loss: 0.0052 - val_accuracy: 0.9988\n",
            "Epoch 17/100\n",
            "41/41 [==============================] - 9s 222ms/step - loss: 0.0040 - accuracy: 0.9993 - val_loss: 0.0054 - val_accuracy: 0.9984\n",
            "Epoch 18/100\n",
            "41/41 [==============================] - 9s 219ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 0.0041 - val_accuracy: 0.9984\n",
            "Epoch 19/100\n",
            "41/41 [==============================] - 9s 219ms/step - loss: 0.0018 - accuracy: 0.9995 - val_loss: 0.0110 - val_accuracy: 0.9984\n",
            "Epoch 20/100\n",
            "41/41 [==============================] - 9s 218ms/step - loss: 0.0040 - accuracy: 0.9990 - val_loss: 0.0125 - val_accuracy: 0.9981\n",
            "Epoch 21/100\n",
            "41/41 [==============================] - 9s 219ms/step - loss: 8.2183e-04 - accuracy: 0.9998 - val_loss: 0.0014 - val_accuracy: 0.9996\n",
            "Epoch 22/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 0.0107 - val_accuracy: 0.9969\n",
            "Epoch 23/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0077 - accuracy: 0.9979 - val_loss: 0.0087 - val_accuracy: 0.9969\n",
            "Epoch 24/100\n",
            "41/41 [==============================] - 9s 222ms/step - loss: 0.0151 - accuracy: 0.9968 - val_loss: 0.0083 - val_accuracy: 0.9981\n",
            "Epoch 25/100\n",
            "41/41 [==============================] - 9s 222ms/step - loss: 0.0059 - accuracy: 0.9983 - val_loss: 0.0040 - val_accuracy: 0.9996\n",
            "Epoch 26/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0040 - accuracy: 0.9987 - val_loss: 0.0095 - val_accuracy: 0.9988\n",
            "Epoch 27/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0052 - accuracy: 0.9988 - val_loss: 0.0068 - val_accuracy: 0.9988\n",
            "Epoch 28/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0043 - accuracy: 0.9991 - val_loss: 0.0135 - val_accuracy: 0.9981\n",
            "Epoch 29/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0038 - accuracy: 0.9990 - val_loss: 0.0088 - val_accuracy: 0.9988\n",
            "Epoch 30/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 0.0087 - val_accuracy: 0.9992\n",
            "Epoch 31/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0025 - accuracy: 0.9992 - val_loss: 0.0091 - val_accuracy: 0.9988\n",
            "Epoch 32/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.0016 - val_accuracy: 0.9996\n",
            "Epoch 33/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 3.3342e-04 - val_accuracy: 1.0000\n",
            "Epoch 34/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0032 - accuracy: 0.9995 - val_loss: 0.0169 - val_accuracy: 0.9988\n",
            "Epoch 35/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0321 - accuracy: 0.9945 - val_loss: 0.0282 - val_accuracy: 0.9930\n",
            "Epoch 36/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0452 - accuracy: 0.9884 - val_loss: 0.0087 - val_accuracy: 0.9969\n",
            "Epoch 37/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0132 - accuracy: 0.9977 - val_loss: 0.0211 - val_accuracy: 0.9918\n",
            "Epoch 38/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0090 - accuracy: 0.9974 - val_loss: 0.0033 - val_accuracy: 0.9996\n",
            "Epoch 39/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0038 - accuracy: 0.9989 - val_loss: 0.0044 - val_accuracy: 0.9996\n",
            "Epoch 40/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0026 - accuracy: 0.9992 - val_loss: 0.0077 - val_accuracy: 0.9984\n",
            "Epoch 41/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0034 - accuracy: 0.9991 - val_loss: 9.3559e-04 - val_accuracy: 0.9996\n",
            "Epoch 42/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0023 - accuracy: 0.9996 - val_loss: 0.0079 - val_accuracy: 0.9988\n",
            "Epoch 43/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0036 - accuracy: 0.9993 - val_loss: 0.0027 - val_accuracy: 0.9996\n",
            "Epoch 44/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0022 - accuracy: 0.9996 - val_loss: 0.0012 - val_accuracy: 0.9996\n",
            "Epoch 45/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0084 - accuracy: 0.9984 - val_loss: 0.0060 - val_accuracy: 0.9992\n",
            "Epoch 46/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0064 - accuracy: 0.9983 - val_loss: 0.0032 - val_accuracy: 0.9996\n",
            "Epoch 47/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0053 - accuracy: 0.9989 - val_loss: 0.0026 - val_accuracy: 0.9992\n",
            "Epoch 48/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0077 - accuracy: 0.9985 - val_loss: 0.0035 - val_accuracy: 0.9988\n",
            "Epoch 49/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0037 - accuracy: 0.9988 - val_loss: 5.6190e-04 - val_accuracy: 0.9996\n",
            "Epoch 50/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 4.5595e-04 - accuracy: 0.9999 - val_loss: 5.5365e-04 - val_accuracy: 0.9996\n",
            "Epoch 51/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0024 - accuracy: 0.9993 - val_loss: 7.8391e-04 - val_accuracy: 0.9996\n",
            "Epoch 52/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0081 - val_accuracy: 0.9996\n",
            "Epoch 53/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 0.0059 - val_accuracy: 0.9988\n",
            "Epoch 54/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0023 - accuracy: 0.9993 - val_loss: 3.5810e-04 - val_accuracy: 1.0000\n",
            "Epoch 55/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0039 - accuracy: 0.9991 - val_loss: 2.6755e-04 - val_accuracy: 1.0000\n",
            "Epoch 56/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0032 - accuracy: 0.9990 - val_loss: 0.0050 - val_accuracy: 0.9988\n",
            "Epoch 57/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0032 - accuracy: 0.9990 - val_loss: 0.0078 - val_accuracy: 0.9988\n",
            "Epoch 58/100\n",
            "41/41 [==============================] - 9s 221ms/step - loss: 0.0025 - accuracy: 0.9994 - val_loss: 0.0096 - val_accuracy: 0.9992\n",
            "Epoch 59/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.0101 - val_accuracy: 0.9992\n",
            "Epoch 60/100\n",
            "41/41 [==============================] - 10s 235ms/step - loss: 9.1094e-04 - accuracy: 0.9996 - val_loss: 0.0100 - val_accuracy: 0.9996\n",
            "Epoch 61/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0025 - accuracy: 0.9993 - val_loss: 6.0526e-04 - val_accuracy: 0.9996\n",
            "Epoch 62/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 4.9183e-04 - accuracy: 0.9999 - val_loss: 0.0019 - val_accuracy: 0.9992\n",
            "Epoch 63/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 4.4316e-04 - accuracy: 0.9998 - val_loss: 0.0013 - val_accuracy: 0.9996\n",
            "Epoch 64/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0026 - accuracy: 0.9996 - val_loss: 0.0036 - val_accuracy: 0.9992\n",
            "Epoch 65/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 4.2552e-04 - accuracy: 0.9998 - val_loss: 9.1274e-04 - val_accuracy: 0.9996\n",
            "Epoch 66/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 8.2936e-04 - val_accuracy: 0.9996\n",
            "Epoch 67/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 5.0033e-04 - accuracy: 0.9998 - val_loss: 9.2295e-04 - val_accuracy: 0.9996\n",
            "Epoch 68/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 2.8640e-04 - accuracy: 1.0000 - val_loss: 2.0281e-04 - val_accuracy: 1.0000\n",
            "Epoch 69/100\n",
            "41/41 [==============================] - 9s 219ms/step - loss: 6.3822e-04 - accuracy: 0.9998 - val_loss: 5.4025e-05 - val_accuracy: 1.0000\n",
            "Epoch 70/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 2.1357e-04 - accuracy: 0.9999 - val_loss: 7.0067e-05 - val_accuracy: 1.0000\n",
            "Epoch 71/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 6.3013e-05 - accuracy: 1.0000 - val_loss: 1.1784e-04 - val_accuracy: 1.0000\n",
            "Epoch 72/100\n",
            "41/41 [==============================] - 9s 219ms/step - loss: 2.2048e-04 - accuracy: 0.9999 - val_loss: 9.9040e-05 - val_accuracy: 1.0000\n",
            "Epoch 73/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 4.0510e-04 - accuracy: 0.9998 - val_loss: 5.5833e-04 - val_accuracy: 0.9996\n",
            "Epoch 74/100\n",
            "41/41 [==============================] - 9s 219ms/step - loss: 2.5725e-04 - accuracy: 0.9999 - val_loss: 2.8280e-04 - val_accuracy: 1.0000\n",
            "Epoch 75/100\n",
            "41/41 [==============================] - 9s 220ms/step - loss: 1.8058e-04 - accuracy: 0.9999 - val_loss: 0.0014 - val_accuracy: 0.9996\n",
            "Epoch 76/100\n",
            "41/41 [==============================] - 9s 219ms/step - loss: 1.9669e-04 - accuracy: 0.9999 - val_loss: 0.0026 - val_accuracy: 0.9996\n",
            "Epoch 77/100\n",
            "41/41 [==============================] - 9s 219ms/step - loss: 1.5095e-04 - accuracy: 0.9999 - val_loss: 7.7932e-04 - val_accuracy: 0.9996\n",
            "Epoch 78/100\n",
            "41/41 [==============================] - 9s 219ms/step - loss: 3.2233e-05 - accuracy: 1.0000 - val_loss: 6.9546e-04 - val_accuracy: 0.9992\n",
            "Epoch 79/100\n",
            "41/41 [==============================] - 9s 219ms/step - loss: 6.1093e-05 - accuracy: 1.0000 - val_loss: 7.3625e-04 - val_accuracy: 0.9996\n",
            "Epoch 80/100\n",
            "41/41 [==============================] - 9s 219ms/step - loss: 3.9868e-04 - accuracy: 0.9999 - val_loss: 0.0020 - val_accuracy: 0.9996\n",
            "Epoch 81/100\n",
            "41/41 [==============================] - 9s 219ms/step - loss: 8.5473e-05 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9992\n",
            "Epoch 82/100\n",
            "41/41 [==============================] - 9s 219ms/step - loss: 0.0175 - accuracy: 0.9968 - val_loss: 0.0727 - val_accuracy: 0.9860\n",
            "Epoch 83/100\n",
            "41/41 [==============================] - 9s 219ms/step - loss: 0.1402 - accuracy: 0.9759 - val_loss: 0.0064 - val_accuracy: 0.9981\n",
            "Epoch 84/100\n",
            "41/41 [==============================] - 9s 218ms/step - loss: 0.0272 - accuracy: 0.9947 - val_loss: 0.0067 - val_accuracy: 0.9992\n",
            "Epoch 85/100\n",
            "41/41 [==============================] - 9s 219ms/step - loss: 0.0150 - accuracy: 0.9974 - val_loss: 0.0040 - val_accuracy: 0.9992\n",
            "Epoch 86/100\n",
            "41/41 [==============================] - 9s 219ms/step - loss: 0.0061 - accuracy: 0.9985 - val_loss: 0.0081 - val_accuracy: 0.9992\n",
            "Epoch 87/100\n",
            "41/41 [==============================] - 9s 218ms/step - loss: 0.0062 - accuracy: 0.9988 - val_loss: 0.0075 - val_accuracy: 0.9992\n",
            "Epoch 88/100\n",
            "41/41 [==============================] - 9s 218ms/step - loss: 0.0019 - accuracy: 0.9992 - val_loss: 0.0069 - val_accuracy: 0.9984\n",
            "Epoch 89/100\n",
            "41/41 [==============================] - 9s 218ms/step - loss: 0.0020 - accuracy: 0.9993 - val_loss: 0.0093 - val_accuracy: 0.9996\n",
            "Epoch 90/100\n",
            "41/41 [==============================] - 9s 219ms/step - loss: 0.0042 - accuracy: 0.9990 - val_loss: 0.0065 - val_accuracy: 0.9992\n",
            "Epoch 91/100\n",
            "41/41 [==============================] - 9s 218ms/step - loss: 0.0025 - accuracy: 0.9991 - val_loss: 0.0037 - val_accuracy: 0.9988\n",
            "Epoch 92/100\n",
            "41/41 [==============================] - 9s 218ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0033 - val_accuracy: 0.9996\n",
            "Epoch 93/100\n",
            "41/41 [==============================] - 9s 218ms/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 0.0026 - val_accuracy: 0.9992\n",
            "Epoch 94/100\n",
            "41/41 [==============================] - 9s 218ms/step - loss: 0.0018 - accuracy: 0.9993 - val_loss: 0.0024 - val_accuracy: 0.9992\n",
            "Epoch 95/100\n",
            "41/41 [==============================] - 9s 217ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.0026 - val_accuracy: 0.9992\n",
            "Epoch 96/100\n",
            "41/41 [==============================] - 9s 217ms/step - loss: 0.0035 - accuracy: 0.9991 - val_loss: 0.0138 - val_accuracy: 0.9984\n",
            "Epoch 97/100\n",
            "41/41 [==============================] - 9s 217ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9996\n",
            "Epoch 98/100\n",
            "41/41 [==============================] - 9s 217ms/step - loss: 3.3595e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9996\n",
            "Epoch 99/100\n",
            "41/41 [==============================] - 9s 216ms/step - loss: 2.3284e-04 - accuracy: 0.9999 - val_loss: 0.0060 - val_accuracy: 0.9996\n",
            "Epoch 100/100\n",
            "41/41 [==============================] - 9s 217ms/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 0.0062 - val_accuracy: 0.9996\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}