3D_CNN_HSI_classification.ipynb

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      "cell_type": "code",
      "metadata": {
        "id": "t7gJbsbXY9_-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
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        "outputId": "49b4f014-c572-41f4-9476-110d40051b13"
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      "source": [
        "# Colab Requirements\n",
        "!pip install spectral\n",
        "#import tensorflow.compat.v1 as tf\n",
        "#tf.disable_v2_behavior()\n",
        "## Basics\n",
        "import gc\n",
        "gc.collect()\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import time\n",
        "import numpy\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from operator import truediv\n",
        "import scipy.io as sio\n",
        "import os\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import spectral\n",
        "## Ploting\n",
        "from plotly.offline import init_notebook_mode\n",
        "import plotly.graph_objs as go\n",
        "from matplotlib.pyplot import cm\n",
        "import matplotlib.pyplot as plt\n",
        "init_notebook_mode(connected=True)\n",
        "%matplotlib inline\n",
        "## Sklearn \n",
        "import sklearn as sk\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score\n",
        "from sklearn.decomposition import IncrementalPCA\n",
        "## Deep Model\n",
        "import keras\n",
        "import h5py\n",
        "from keras.layers import Dropout, Input, Conv2D, Conv3D, MaxPool3D, Flatten, Dense, Reshape, BatchNormalization\n",
        "from plotly.offline import iplot, init_notebook_mode\n",
        "from keras.losses import categorical_crossentropy\n",
        "from keras.optimizers import Adadelta\n",
        "from keras.models import Sequential, Model\n",
        "from keras.utils import np_utils\n",
        "from keras.optimizers import Adam\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "## Mounting Colab\n",
        "from google.colab import drive\n",
        "from google.colab import files\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "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[K     |████████████████████████████████| 212 kB 13.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from spectral) (1.21.6)\n",
            "Installing collected packages: spectral\n",
            "Successfully installed spectral-0.23.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
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              "        require.undef(\"plotly\");\n",
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              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-2.8.3.min']\n",
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              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
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              "        }\n",
              "        </script>\n",
              "        "
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D68mlgj2aM7d"
      },
      "source": [
        "## Split Training, Validation and Test Sets\n",
        "def SplitTr_Te(HSI, GT, TeRatio, randomState=345):\n",
        "    Tr, Te, TrC, TeC = train_test_split(HSI, GT, test_size=TeRatio, random_state=randomState, stratify=GT)\n",
        "    return Tr, Te, TrC, TeC"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w74K-4bCcLdf"
      },
      "source": [
        "## Dimensional Reduction Method\n",
        "def DL_Method(HSI, numComponents = 75):\n",
        "    RHSI = np.reshape(HSI, (-1, HSI.shape[2]))\n",
        "    n_batches = 256\n",
        "    inc_pca = IncrementalPCA(n_components=numComponents)\n",
        "    for X_batch in np.array_split(RHSI, n_batches):\n",
        "        inc_pca.partial_fit(X_batch)\n",
        "    X_ipca = inc_pca.transform(RHSI)\n",
        "    RHSI = np.reshape(X_ipca, (HSI.shape[0],HSI.shape[1], numComponents))\n",
        "    return RHSI"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-ofefUccq_y"
      },
      "source": [
        "## Padding\n",
        "def ZeroPad(HSI, margin=2):\n",
        "    NHSI = np.zeros((HSI.shape[0] + 2 * margin, HSI.shape[1] + 2* margin, HSI.shape[2]))\n",
        "    x_offset = margin\n",
        "    y_offset = margin\n",
        "    NHSI[x_offset:HSI.shape[0] + x_offset, y_offset:HSI.shape[1] + y_offset, :] = HSI\n",
        "    return NHSI"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uecYttGYc4V2"
      },
      "source": [
        "## Spatial Patches in 3D\n",
        "def HSICubes(HSI, GT, WinSize=5, removeZeroLabels = True):\n",
        "    margin = int((WinSize - 1) / 2)\n",
        "    zeroPaddedX = ZeroPad(HSI, margin=margin)\n",
        "    # split patches\n",
        "    patchesData = np.zeros((HSI.shape[0] * HSI.shape[1], WinSize, WinSize, HSI.shape[2]))\n",
        "    patchesLabels = np.zeros((HSI.shape[0] * HSI.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] = GT[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"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_IqpDFP68Nh4"
      },
      "source": [
        "## Compute Per Class Accuacy form Confusion Matrix\n",
        "def AA_andEachClassAccuracy(confusion_matrix):\n",
        "    counter = confusion_matrix.shape[0]\n",
        "    list_diag = np.diag(confusion_matrix)\n",
        "    list_raw_sum = np.sum(confusion_matrix, axis=1)\n",
        "    each_acc = np.nan_to_num(truediv(list_diag, list_raw_sum))\n",
        "    average_acc = np.mean(each_acc)\n",
        "    return each_acc, average_acc"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UryXYl9D8OrF"
      },
      "source": [
        "## Assigning Class Labels and Computing the Test Accuracy\n",
        "def reports(Te,TeC):\n",
        "    start = time.time()\n",
        "    Y_pred = model.predict(Te)\n",
        "    y_pred = np.argmax(Y_pred, axis=1)\n",
        "    end = time.time()\n",
        "    total = end - start\n",
        "    target_names = ['Alfalfa', 'Corn-notill', 'Corn-mintill', 'Corn'\n",
        "                        ,'Grass-pasture', 'Grass-trees', 'Grass-pasture-mowed', \n",
        "                        'Hay-windrowed', 'Oats', 'Soybean-notill', 'Soybean-mintill',\n",
        "                        'Soybean-clean', 'Wheat', 'Woods', 'Buildings-Grass-Trees-Drives',\n",
        "                        'Stone-Steel-Towers']\n",
        "    classification = classification_report(np.argmax(TeC, axis=1), y_pred, target_names=target_names)\n",
        "    oa = accuracy_score(np.argmax(TeC, axis=1), y_pred)\n",
        "    confusion = confusion_matrix(np.argmax(TeC, axis=1), y_pred)\n",
        "    each_acc, aa = AA_andEachClassAccuracy(confusion)\n",
        "    kappa = cohen_kappa_score(np.argmax(TeC, axis=1), y_pred)\n",
        "    score = model.evaluate(Te, TeC, batch_size=32)\n",
        "    Test_Loss =  score[0]*100\n",
        "    Test_accuracy = score[1]*100\n",
        "    return classification, confusion, Test_Loss, Test_accuracy, oa*100, each_acc*100, aa*100, kappa*100, target_names, y_pred, total"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dYJmZZGz8dIk"
      },
      "source": [
        "## Compute the Patch to Prepare for Ground Truths\n",
        "def Patch(data,height_index,width_index):\n",
        "    height_slice = slice(height_index, height_index+PATCH_SIZE)\n",
        "    width_slice = slice(width_index, width_index+PATCH_SIZE)\n",
        "    patch = data[height_slice, width_slice, :]\n",
        "    return patch"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## load the dataset"
      ],
      "metadata": {
        "id": "w9LLf_VaLpww"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget \"http:https://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat\"\n",
        "\n",
        "\n",
        "!wget \"http:https://www.ehu.eus/ccwintco/uploads/c/c4/Indian_pines_gt.mat\"\n",
        "!wget \"http:https://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iEFpScn5Loci",
        "outputId": "e273892b-b302-442a-c961-d820f1190d05"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-12-27 06:04:21--  http:https://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat\n",
            "Resolving www.ehu.eus (www.ehu.eus)... 158.227.0.65, 2001:720:1410::65\n",
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            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
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            "--2022-12-27 06:04:21--  https://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat\n",
            "Connecting to www.ehu.eus (www.ehu.eus)|158.227.0.65|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5953527 (5.7M)\n",
            "Saving to: ‘Indian_pines_corrected.mat’\n",
            "\n",
            "Indian_pines_correc 100%[===================>]   5.68M  3.48MB/s    in 1.6s    \n",
            "\n",
            "2022-12-27 06:04:23 (3.48 MB/s) - ‘Indian_pines_corrected.mat’ saved [5953527/5953527]\n",
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            "URL transformed to HTTPS due to an HSTS policy\n",
            "--2022-12-27 06:04:23--  https://www.ehu.eus/ccwintco/uploads/c/c4/Indian_pines_gt.mat\n",
            "Resolving www.ehu.eus (www.ehu.eus)... 158.227.0.65, 2001:720:1410::65\n",
            "Connecting to www.ehu.eus (www.ehu.eus)|158.227.0.65|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1125 (1.1K)\n",
            "Saving to: ‘Indian_pines_gt.mat’\n",
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            "Indian_pines_gt.mat 100%[===================>]   1.10K  --.-KB/s    in 0s      \n",
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            "--2022-12-27 06:04:23--  https://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat\n",
            "Resolving www.ehu.eus (www.ehu.eus)... 158.227.0.65, 2001:720:1410::65\n",
            "Connecting to www.ehu.eus (www.ehu.eus)|158.227.0.65|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5953527 (5.7M)\n",
            "Saving to: ‘Indian_pines_corrected.mat.1’\n",
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            "Indian_pines_correc 100%[===================>]   5.68M  3.01MB/s    in 1.9s    \n",
            "\n",
            "2022-12-27 06:04:25 (3.01 MB/s) - ‘Indian_pines_corrected.mat.1’ saved [5953527/5953527]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KNRuSVuF71xQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "49cc8b90-98f5-482a-9d1e-ea153be46c9c"
      },
      "source": [
        "## Loading Dataset\n",
        "## There is a typo in the paper on page#4 Table#4, the training samples are not 10%, the reported accuracies are obtained with almost 30~40% training samples. \n",
        "data_path = os.path.join(os.getcwd(),'/content/')\n",
        "HSI = sio.loadmat(os.path.join(data_path, 'Indian_pines_corrected.mat'))['indian_pines_corrected']\n",
        "GT = labels = sio.loadmat(os.path.join(data_path, 'Indian_pines_gt.mat'))['indian_pines_gt']\n",
        "HSI.shape, GT.shape\n",
        "## Reduce the Dimensionality \n",
        "HSI = DL_Method(HSI, numComponents=20)\n",
        "HSI.shape\n",
        "## Create Image Cubes for Model Building\n",
        "HSI, GT = HSICubes(HSI, GT, WinSize=11)\n",
        "HSI.shape, GT.shape\n",
        "## Split Train and Test sets\n",
        "Tr, Te, TrC, TeC = SplitTr_Te(HSI, GT, 0.40)\n",
        "Tr.shape, Te.shape, TrC.shape, TeC.shape\n",
        "## Split Train and Validation\n",
        "Tr, Tv, TrC, TvC = SplitTr_Te(Tr, TrC, 0.30)\n",
        "Tr.shape, Tv.shape, TrC.shape, TvC.shape\n",
        "## Model Pre requsites\n",
        "Tr = Tr.reshape(-1, 11, 11, 20, 1)\n",
        "TrC = np_utils.to_categorical(TrC)\n",
        "Tv = Tv.reshape(-1, 11, 11, 20, 1) \n",
        "TvC = np_utils.to_categorical(TvC)\n",
        "Tr.shape, TrC.shape, Tv.shape, TvC.shape\n",
        "## Model Structure \n",
        "## Input layer\n",
        "input_layer = Input((11, 11, 20, 1))\n",
        "## 3D 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",
        "conv_layer4 = Conv3D(filters=64, kernel_size=(3, 3, 3), activation='relu')(conv_layer3)\n",
        "## Faltten 3D Convolutional Layer\n",
        "flatten_layer = Flatten()(conv_layer4)\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=16, activation='softmax')(dense_layer2)\n",
        "\n",
        "# define the model with input layer and output layer\n",
        "model = Model(inputs=input_layer, outputs=output_layer)\n",
        "model.summary()\n",
        "# compiling the model\n",
        "adam = Adam(lr=0.001, decay=1e-06)\n",
        "model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
        "## Training Time and Fit the Model \n",
        "start = time.time()\n",
        "history = model.fit(x=Tr, y=TrC, batch_size=256, epochs=50, validation_data=(Tv, TvC))\n",
        "end = time.time()\n",
        "Tr_Time = end - start\n",
        "## Plot Training and Validation loss and Accuracy \n",
        "plt.figure(figsize=(7,7))\n",
        "plt.rcParams.update({'font.size': 18})\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.title('Training and Validation loss')\n",
        "plt.legend(['Training','Validation'], loc='upper right')\n",
        "plt.savefig(\"IP_loss.eps\")\n",
        "plt.show()\n",
        "## Plot Training and Validation Accuracy\n",
        "plt.figure(figsize=(7,7))\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.title('Training and Validation Accuracy')\n",
        "plt.legend(['Training','Validation'])\n",
        "plt.savefig(\"IP_Accuracy.eps\")\n",
        "plt.show()\n",
        "## Testing Phase\n",
        "model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
        "## Reshape Test Data\n",
        "Te = Te.reshape(-1, 11, 11, 20, 1)\n",
        "TeC = np_utils.to_categorical(TeC)\n",
        "Te.shape, TeC.shape\n",
        "## Computing and Writing the Accuacy in .txt file\n",
        "classification,confusion,Test_loss,Test_accuracy,oa,each_acc,aa,kappa,target_names,y_pred,Time = reports(Te,TeC)\n",
        "classification = str(classification)\n",
        "confusion = str(confusion)\n",
        "print(classification), print(Test_accuracy), print(oa), print(aa), print(Tr_Time), print(Time)\n",
        "## Draw Confusion Matrix\n",
        "confusion = confusion_matrix(np.argmax(TeC, axis=1), y_pred, labels=np.unique(np.argmax(TeC, axis=1)))\n",
        "cm_sum = np.sum(confusion, axis=1, keepdims=True)\n",
        "cm_perc = confusion / cm_sum.astype(float) * 100\n",
        "annot = np.empty_like(confusion).astype(str)\n",
        "nrows, ncols = confusion.shape\n",
        "for i in range(nrows):\n",
        "    for j in range(ncols):\n",
        "        c = confusion[i, j]\n",
        "        p = cm_perc[i, j]\n",
        "        if i == j:\n",
        "            s = cm_sum[i]\n",
        "            annot[i, j] = '%.1f%%\\n%d/%d' % (p, c, s)\n",
        "        elif c == 0:\n",
        "            annot[i, j] = ''\n",
        "        else:\n",
        "            annot[i, j] = '%.1f%%\\n%d' % (p, c)\n",
        "cm = pd.DataFrame(confusion, index=np.unique(target_names), columns=np.unique(target_names))\n",
        "cm.index.name = 'Actual'\n",
        "cm.columns.name = 'Predicted'\n",
        "fig, ax = plt.subplots(figsize=(15,10))\n",
        "plt.rcParams.update({'font.size': 12})\n",
        "sns.heatmap(cm, cmap= \"YlGnBu\", annot=annot, fmt='', ax=ax)\n",
        "## Loading Dataset\n",
        "data_path = os.path.join(os.getcwd(),'/content/')\n",
        "HSI = sio.loadmat(os.path.join(data_path, 'Indian_pines_corrected.mat'))['indian_pines_corrected']\n",
        "GT = labels = sio.loadmat(os.path.join(data_path, 'Indian_pines_gt.mat'))['indian_pines_gt']\n",
        "## Check the Dimensions of HSI\n",
        "height = GT.shape[0]\n",
        "width = GT.shape[1]\n",
        "PATCH_SIZE = 11\n",
        "## Dimensional Reduction and zero padding\n",
        "HSI = DL_Method(HSI, numComponents=20)\n",
        "HSI.shape\n",
        "HSI = ZeroPad(HSI, 11//2)\n",
        "## Calculate the predicted Ground Truths\n",
        "outputs = np.zeros((height,width))\n",
        "for i in range(height):\n",
        "    for j in range(width):\n",
        "        target = int(GT[i,j])\n",
        "        if target == 0 :\n",
        "            continue\n",
        "        else :\n",
        "            image_patch=Patch(HSI,i,j)\n",
        "            X_test_image = image_patch.reshape(1,image_patch.shape[0],\n",
        "                                               image_patch.shape[1], image_patch.shape[2], 1).astype('float32')                                   \n",
        "            prediction = (model.predict(X_test_image))\n",
        "            prediction = np.argmax(prediction, axis=1)\n",
        "            outputs[i][j] = prediction+1\n",
        "## Show Ground Truths\n",
        "ground_truth = spectral.imshow(classes = GT,figsize =(7,7))\n",
        "predict_image = spectral.imshow(classes = outputs.astype(int),figsize =(7,7))\n",
        "spectral.save_rgb(\"predictions.png\", outputs.astype(int), colors=spectral.spy_colors)\n",
        "spectral.save_rgb(\"ground_truth.png\", GT, colors=spectral.spy_colors)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 11, 11, 20, 1)]   0         \n",
            "                                                                 \n",
            " conv3d_4 (Conv3D)           (None, 9, 9, 14, 8)       512       \n",
            "                                                                 \n",
            " conv3d_5 (Conv3D)           (None, 7, 7, 10, 16)      5776      \n",
            "                                                                 \n",
            " conv3d_6 (Conv3D)           (None, 5, 5, 8, 32)       13856     \n",
            "                                                                 \n",
            " conv3d_7 (Conv3D)           (None, 3, 3, 6, 64)       55360     \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 3456)              0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 256)               884992    \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 16)                2064      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 995,456\n",
            "Trainable params: 995,456\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "17/17 [==============================] - 1s 37ms/step - loss: 16.1560 - accuracy: 0.1668 - val_loss: 2.3817 - val_accuracy: 0.2949\n",
            "Epoch 2/50\n",
            "17/17 [==============================] - 0s 20ms/step - loss: 2.1181 - accuracy: 0.3548 - val_loss: 1.7630 - val_accuracy: 0.4488\n",
            "Epoch 3/50\n",
            "17/17 [==============================] - 0s 19ms/step - loss: 1.6979 - accuracy: 0.4596 - val_loss: 1.3283 - val_accuracy: 0.5659\n",
            "Epoch 4/50\n",
            "17/17 [==============================] - 0s 20ms/step - loss: 1.4062 - accuracy: 0.5376 - val_loss: 1.1355 - val_accuracy: 0.6211\n",
            "Epoch 5/50\n",
            "17/17 [==============================] - 0s 21ms/step - loss: 1.1926 - accuracy: 0.5918 - val_loss: 0.9934 - val_accuracy: 0.6493\n",
            "Epoch 6/50\n",
            "17/17 [==============================] - 0s 19ms/step - loss: 1.0474 - accuracy: 0.6499 - val_loss: 0.8490 - val_accuracy: 0.7024\n",
            "Epoch 7/50\n",
            "17/17 [==============================] - 0s 21ms/step - loss: 0.9313 - accuracy: 0.6849 - val_loss: 0.7398 - val_accuracy: 0.7528\n",
            "Epoch 8/50\n",
            "17/17 [==============================] - 0s 20ms/step - loss: 0.7841 - accuracy: 0.7263 - val_loss: 0.5943 - val_accuracy: 0.7973\n",
            "Epoch 9/50\n",
            "17/17 [==============================] - 0s 30ms/step - loss: 0.6532 - accuracy: 0.7772 - val_loss: 0.5299 - val_accuracy: 0.8217\n",
            "Epoch 10/50\n",
            "17/17 [==============================] - 1s 32ms/step - loss: 0.5667 - accuracy: 0.8041 - val_loss: 0.4676 - val_accuracy: 0.8412\n",
            "Epoch 11/50\n",
            "17/17 [==============================] - 0s 20ms/step - loss: 0.4787 - accuracy: 0.8346 - val_loss: 0.4505 - val_accuracy: 0.8509\n",
            "Epoch 12/50\n",
            "17/17 [==============================] - 0s 19ms/step - loss: 0.4010 - accuracy: 0.8636 - val_loss: 0.3457 - val_accuracy: 0.8829\n",
            "Epoch 13/50\n",
            "17/17 [==============================] - 0s 20ms/step - loss: 0.3185 - accuracy: 0.8929 - val_loss: 0.2875 - val_accuracy: 0.9068\n",
            "Epoch 14/50\n",
            "17/17 [==============================] - 0s 21ms/step - loss: 0.2923 - accuracy: 0.9066 - val_loss: 0.2962 - val_accuracy: 0.9073\n",
            "Epoch 15/50\n",
            "17/17 [==============================] - 0s 20ms/step - loss: 0.2453 - accuracy: 0.9191 - val_loss: 0.2376 - val_accuracy: 0.9192\n",
            "Epoch 16/50\n",
            "17/17 [==============================] - 0s 20ms/step - loss: 0.2181 - accuracy: 0.9243 - val_loss: 0.1913 - val_accuracy: 0.9339\n",
            "Epoch 17/50\n",
            "17/17 [==============================] - 0s 21ms/step - loss: 0.1773 - accuracy: 0.9431 - val_loss: 0.2377 - val_accuracy: 0.9279\n",
            "Epoch 18/50\n",
            "17/17 [==============================] - 0s 19ms/step - loss: 0.1787 - accuracy: 0.9433 - val_loss: 0.1947 - val_accuracy: 0.9339\n",
            "Epoch 19/50\n",
            "17/17 [==============================] - 0s 20ms/step - loss: 0.1538 - accuracy: 0.9521 - val_loss: 0.1865 - val_accuracy: 0.9317\n",
            "Epoch 20/50\n",
            "17/17 [==============================] - 0s 19ms/step - loss: 0.1298 - accuracy: 0.9589 - val_loss: 0.1500 - val_accuracy: 0.9539\n",
            "Epoch 21/50\n",
            "17/17 [==============================] - 0s 22ms/step - loss: 0.1143 - accuracy: 0.9665 - val_loss: 0.1738 - val_accuracy: 0.9409\n",
            "Epoch 22/50\n",
            "17/17 [==============================] - 0s 21ms/step - loss: 0.1135 - accuracy: 0.9675 - val_loss: 0.1592 - val_accuracy: 0.9512\n",
            "Epoch 23/50\n",
            "17/17 [==============================] - 0s 21ms/step - loss: 0.0992 - accuracy: 0.9677 - val_loss: 0.1176 - val_accuracy: 0.9631\n",
            "Epoch 24/50\n",
            "17/17 [==============================] - 0s 20ms/step - loss: 0.0743 - accuracy: 0.9761 - val_loss: 0.1413 - val_accuracy: 0.9561\n",
            "Epoch 25/50\n",
            "17/17 [==============================] - 0s 20ms/step - loss: 0.1075 - accuracy: 0.9631 - val_loss: 0.1738 - val_accuracy: 0.9431\n",
            "Epoch 26/50\n",
            "17/17 [==============================] - 0s 19ms/step - loss: 0.0943 - accuracy: 0.9700 - val_loss: 0.1824 - val_accuracy: 0.9453\n",
            "Epoch 27/50\n",
            "17/17 [==============================] - 0s 19ms/step - loss: 0.1033 - accuracy: 0.9691 - val_loss: 0.0967 - val_accuracy: 0.9664\n",
            "Epoch 28/50\n",
            "17/17 [==============================] - 0s 21ms/step - loss: 0.0831 - accuracy: 0.9765 - val_loss: 0.1755 - val_accuracy: 0.9528\n",
            "Epoch 29/50\n",
            "17/17 [==============================] - 0s 19ms/step - loss: 0.0731 - accuracy: 0.9812 - val_loss: 0.1050 - val_accuracy: 0.9675\n",
            "Epoch 30/50\n",
            "17/17 [==============================] - 0s 19ms/step - loss: 0.0482 - accuracy: 0.9863 - val_loss: 0.1070 - val_accuracy: 0.9680\n",
            "Epoch 31/50\n",
            "17/17 [==============================] - 0s 19ms/step - loss: 0.0420 - accuracy: 0.9875 - val_loss: 0.1007 - val_accuracy: 0.9734\n",
            "Epoch 32/50\n",
            "17/17 [==============================] - 0s 20ms/step - loss: 0.0447 - accuracy: 0.9877 - val_loss: 0.0849 - val_accuracy: 0.9745\n",
            "Epoch 33/50\n",
            "17/17 [==============================] - 0s 19ms/step - loss: 0.0268 - accuracy: 0.9916 - val_loss: 0.1437 - val_accuracy: 0.9653\n",
            "Epoch 34/50\n",
            "17/17 [==============================] - 0s 21ms/step - loss: 0.0421 - accuracy: 0.9891 - val_loss: 0.1122 - val_accuracy: 0.9680\n",
            "Epoch 35/50\n",
            "17/17 [==============================] - 0s 21ms/step - loss: 0.0434 - accuracy: 0.9893 - val_loss: 0.1003 - val_accuracy: 0.9713\n",
            "Epoch 36/50\n",
            "17/17 [==============================] - 0s 21ms/step - loss: 0.0321 - accuracy: 0.9900 - val_loss: 0.0957 - val_accuracy: 0.9702\n",
            "Epoch 37/50\n",
            "17/17 [==============================] - 0s 20ms/step - loss: 0.0480 - accuracy: 0.9865 - val_loss: 0.1064 - val_accuracy: 0.9696\n",
            "Epoch 38/50\n",
            "17/17 [==============================] - 0s 19ms/step - loss: 0.0566 - accuracy: 0.9847 - val_loss: 0.1428 - val_accuracy: 0.9631\n",
            "Epoch 39/50\n",
            "17/17 [==============================] - 0s 21ms/step - loss: 0.0430 - accuracy: 0.9882 - val_loss: 0.0896 - val_accuracy: 0.9772\n",
            "Epoch 40/50\n",
            "17/17 [==============================] - 0s 20ms/step - loss: 0.0291 - accuracy: 0.9928 - val_loss: 0.1001 - val_accuracy: 0.9756\n",
            "Epoch 41/50\n",
            "17/17 [==============================] - 0s 19ms/step - loss: 0.0421 - accuracy: 0.9893 - val_loss: 0.1086 - val_accuracy: 0.9713\n",
            "Epoch 42/50\n",
            "17/17 [==============================] - 0s 20ms/step - loss: 0.0317 - accuracy: 0.9895 - val_loss: 0.0973 - val_accuracy: 0.9734\n",
            "Epoch 43/50\n",
            "17/17 [==============================] - 0s 20ms/step - loss: 0.0251 - accuracy: 0.9933 - val_loss: 0.0943 - val_accuracy: 0.9745\n",
            "Epoch 44/50\n",
            "17/17 [==============================] - 0s 19ms/step - loss: 0.0195 - accuracy: 0.9944 - val_loss: 0.1076 - val_accuracy: 0.9729\n",
            "Epoch 45/50\n",
            "17/17 [==============================] - 0s 21ms/step - loss: 0.0247 - accuracy: 0.9928 - val_loss: 0.1327 - val_accuracy: 0.9729\n",
            "Epoch 46/50\n",
            "17/17 [==============================] - 0s 19ms/step - loss: 0.0311 - accuracy: 0.9919 - val_loss: 0.1086 - val_accuracy: 0.9707\n",
            "Epoch 47/50\n",
            "17/17 [==============================] - 0s 19ms/step - loss: 0.0318 - accuracy: 0.9898 - val_loss: 0.1031 - val_accuracy: 0.9745\n",
            "Epoch 48/50\n",
            "17/17 [==============================] - 0s 21ms/step - loss: 0.0292 - accuracy: 0.9916 - val_loss: 0.0918 - val_accuracy: 0.9729\n",
            "Epoch 49/50\n",
            "17/17 [==============================] - 0s 20ms/step - loss: 0.0322 - accuracy: 0.9912 - val_loss: 0.0751 - val_accuracy: 0.9762\n",
            "Epoch 50/50\n",
            "17/17 [==============================] - 0s 21ms/step - loss: 0.0292 - accuracy: 0.9914 - val_loss: 0.1222 - val_accuracy: 0.9740\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
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            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 45ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 47ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 48ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 42ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 47ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 42ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 47ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uQuWTV16L2by"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}