HybridSN_V1_New.ipynb

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      "version": "3.6.8"
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    "colab": {
      "name": "HybridSN_V1.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "ImXShO0dvisc",
        "wg3XlImlvitn"
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      "gpuType": "T4"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install np_utils"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R5vXE1hBC46Z",
        "outputId": "f07bf685-8855-4836-ae49-66baba9f8596"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting np_utils\n",
            "  Downloading np_utils-0.6.0.tar.gz (61 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.0/62.0 kB\u001b[0m \u001b[31m955.4 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy>=1.0 in /usr/local/lib/python3.10/dist-packages (from np_utils) (1.23.5)\n",
            "Building wheels for collected packages: np_utils\n",
            "  Building wheel for np_utils (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for np_utils: filename=np_utils-0.6.0-py3-none-any.whl size=56439 sha256=13cdd5054d0c967ff63ebcc33b6c2c7549e9df42f5e0d9649918ffacae3ca589\n",
            "  Stored in directory: /root/.cache/pip/wheels/b6/c7/50/2307607f44366dd021209f660045f8d51cb976514d30be7cc7\n",
            "Successfully built np_utils\n",
            "Installing collected packages: np_utils\n",
            "Successfully installed np_utils-0.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYdqqSbxymLp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0bd41d3a-6d71-4eb0-a437-3a62062d61a5"
      },
      "source": [
        "!pip install spectral"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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[31m3.2 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) (1.23.5)\n",
            "Installing collected packages: spectral\n",
            "Successfully installed spectral-0.23.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEVP6x5CviqP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "4273ad2a-2781-4eb6-a8e7-e12fe15debe6"
      },
      "source": [
        "import keras\n",
        "import tensorflow as tf\n",
        "from keras.layers import Conv2D, Conv3D, Flatten, Dense, Reshape, BatchNormalization\n",
        "from keras.layers import Dropout, Input\n",
        "from keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "import np_utils\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score\n",
        "\n",
        "from operator import truediv\n",
        "\n",
        "from plotly.offline import init_notebook_mode\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy.io as sio\n",
        "import os\n",
        "import spectral\n",
        "\n",
        "init_notebook_mode(connected=True)\n",
        "%matplotlib inline"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-2.24.1.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "msb54AxTvsxl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7e2019d-63d5-486a-e5b1-c5dce4c1f507"
      },
      "source": [
        "if not (os.path.isfile('Indian_pines_corrected.mat')):\n",
        "  !wget http://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat\n",
        "if not (os.path.isfile('Indian_pines_gt.mat')):\n",
        "  !wget http://www.ehu.eus/ccwintco/uploads/c/c4/Indian_pines_gt.mat\n",
        "\n",
        "#if not (os.path.isfile('/content/Salinas_corrected.mat')):\n",
        "#  !wget https://github.com/gokriznastic/HybridSN/raw/master/data/Salinas_corrected.mat\n",
        "#if not (os.path.isfile('/content/Salinas_gt.mat')):\n",
        "#  !wget https://github.com/gokriznastic/HybridSN/raw/master/data/Salinas_gt.mat"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
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            "--2023-12-23 05:00:46--  http://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat\n",
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            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat [following]\n",
            "--2023-12-23 05:00:46--  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  2.18MB/s    in 2.6s    \n",
            "\n",
            "2023-12-23 05:00:49 (2.18 MB/s) - ‘Indian_pines_corrected.mat’ saved [5953527/5953527]\n",
            "\n",
            "URL transformed to HTTPS due to an HSTS policy\n",
            "--2023-12-23 05:00:49--  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",
            "\n",
            "Indian_pines_gt.mat 100%[===================>]   1.10K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-12-23 05:00:50 (58.4 MB/s) - ‘Indian_pines_gt.mat’ saved [1125/1125]\n",
            "\n"
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T0dGKcU9viqk"
      },
      "source": [
        "# Data Loading"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "taV2QK36viqm"
      },
      "source": [
        "## GLOBAL VARIABLES\n",
        "dataset = 'IP'\n",
        "test_ratio = 0.7\n",
        "windowSize = 25"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2S9wEYaviq3"
      },
      "source": [
        "def loadData(name):\n",
        "    data_path = os.path.join(os.getcwd(),'')\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 == '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",
        "\n",
        "    return data, labels"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2u6ACykXvirD"
      },
      "source": [
        "def splitTrainTestSet(X, y, testRatio, randomState=345):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=testRatio, random_state=randomState,\n",
        "                                                        stratify=y)\n",
        "    return X_train, X_test, y_train, y_test"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fN58ewFGvirP"
      },
      "source": [
        "def applyPCA(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"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Taj-YwjLvirZ"
      },
      "source": [
        "def padWithZeros(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"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VMotOvASvirk"
      },
      "source": [
        "def createImageCubes(X, y, windowSize=5, removeZeroLabels = True):\n",
        "    margin = int((windowSize - 1) / 2)\n",
        "    zeroPaddedX = padWithZeros(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"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WA1HHQR2virt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93f0f85c-b862-4237-fb8d-51772baa93af"
      },
      "source": [
        "X, y = loadData(dataset)\n",
        "\n",
        "X.shape, y.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((145, 145, 200), (145, 145))"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-t05nhFGvir0"
      },
      "source": [
        "K = X.shape[2]"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fLnJhFgZvir-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d73b86c-6b60-4189-9d36-a17ba455764b"
      },
      "source": [
        "K = 30 if dataset == 'IP' else 15\n",
        "X,pca = applyPCA(X,numComponents=K)\n",
        "\n",
        "X.shape"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(145, 145, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5VY0zJh-visG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a083ea4-15cc-44d1-d688-db5560d882e7"
      },
      "source": [
        "X, y = createImageCubes(X, y, windowSize=windowSize)\n",
        "\n",
        "X.shape, y.shape"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((10249, 25, 25, 30), (10249,))"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IHfhGxoxvisQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e48eaf6-3930-4051-fdf5-533419972899"
      },
      "source": [
        "Xtrain, Xtest, ytrain, ytest = splitTrainTestSet(X, y, test_ratio)\n",
        "\n",
        "Xtrain.shape, Xtest.shape, ytrain.shape, ytest.shape"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((3074, 25, 25, 30), (7175, 25, 25, 30), (3074,), (7175,))"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5DYJ7vX5visa"
      },
      "source": [
        "Xtrain, Xvalid, ytrain, yvalid = splitTrainTestSet(Xtrain, ytrain, 0.3333)\n",
        "\n",
        "Xtrain.shape, Xvalid.shape, ytrain.shape, yvalid.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ImXShO0dvisc"
      },
      "source": [
        "# Model and Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IoNjNFA_vise",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64dc0a30-2ae9-4310-a452-770ba8e8de6c"
      },
      "source": [
        "Xtrain = Xtrain.reshape(-1, windowSize, windowSize, K, 1)\n",
        "Xtrain.shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3074, 25, 25, 30, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7G1kSqbgvism",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8d907c4-43da-4a36-afca-8d427f145c83"
      },
      "source": [
        "ytrain = keras.utils.to_categorical(ytrain)\n",
        "ytrain.shape"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3074, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vrezb-7Rvisu"
      },
      "source": [
        "Xvalid = Xvalid.reshape(-1, windowSize, windowSize, K, 1)\n",
        "Xvalid.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7dGLRXMMvisx"
      },
      "source": [
        "yvalid = np_utils.to_categorical(yvalid)\n",
        "yvalid.shape"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aNz8CNqYvisz"
      },
      "source": [
        "S = windowSize\n",
        "L = K\n",
        "output_units = 9 if (dataset == 'PU' or dataset == 'PC') else 16"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uEecMnQYvis6"
      },
      "source": [
        "## input layer\n",
        "input_layer = Input((S, S, L, 1))\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",
        "#print(conv_layer3._keras_shape)\n",
        "conv3d_shape = conv_layer3.shape\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)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X_jkfy0evitB"
      },
      "source": [
        "# define the model with input layer and output layer\n",
        "model = Model(inputs=input_layer, outputs=output_layer)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMDUyRiivitH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8671551-3973-4276-bce4-35ba6dfa9dc2"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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, 16)                2064      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5122176 (19.54 MB)\n",
            "Trainable params: 5122176 (19.54 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n",
        "    initial_learning_rate=0.001,\n",
        "    decay_steps=10000,\n",
        "    decay_rate=0.9)\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)"
      ],
      "metadata": {
        "id": "rE6FZr11EYmn"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KEfKakAjvitN"
      },
      "source": [
        "# compiling the model\n",
        "#adam = Adam(learning_rate=0.001, decay=1e-06)\n",
        "model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ddDzXAfUvitU"
      },
      "source": [
        "# checkpoint\n",
        "filepath = \"best-model.hdf5\"\n",
        "checkpoint = ModelCheckpoint(filepath, monitor='acc', verbose=1, save_best_only=True, mode='max')\n",
        "callbacks_list = [checkpoint]"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENyt1ex5vitc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb1ed966-848e-4004-9e7d-c145c8d2470a"
      },
      "source": [
        "history = model.fit(x=Xtrain, y=ytrain, batch_size=256, epochs=100, callbacks=callbacks_list)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "13/13 [==============================] - ETA: 0s - loss: 2.9845 - accuracy: 0.1252"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 15s 241ms/step - loss: 2.9845 - accuracy: 0.1252\n",
            "Epoch 2/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 2.6978 - accuracy: 0.1234"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 2s 191ms/step - loss: 2.6980 - accuracy: 0.1233\n",
            "Epoch 3/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 2.5450 - accuracy: 0.1520"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 193ms/step - loss: 2.5448 - accuracy: 0.1519\n",
            "Epoch 4/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 2.4777 - accuracy: 0.1862"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 2s 190ms/step - loss: 2.4778 - accuracy: 0.1861\n",
            "Epoch 5/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 2.4223 - accuracy: 0.1995"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 2s 191ms/step - loss: 2.4229 - accuracy: 0.1994\n",
            "Epoch 6/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 2.3832 - accuracy: 0.2301"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 192ms/step - loss: 2.3835 - accuracy: 0.2300\n",
            "Epoch 7/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 2.1897 - accuracy: 0.2318"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 193ms/step - loss: 2.1897 - accuracy: 0.2316\n",
            "Epoch 8/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 1.9021 - accuracy: 0.3337"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 197ms/step - loss: 1.9020 - accuracy: 0.3334\n",
            "Epoch 9/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 1.5994 - accuracy: 0.4329"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 194ms/step - loss: 1.5994 - accuracy: 0.4327\n",
            "Epoch 10/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 1.3438 - accuracy: 0.5365"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 195ms/step - loss: 1.3434 - accuracy: 0.5364\n",
            "Epoch 11/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 1.1240 - accuracy: 0.6214"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 197ms/step - loss: 1.1250 - accuracy: 0.6210\n",
            "Epoch 12/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.8223 - accuracy: 0.7321"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 197ms/step - loss: 0.8222 - accuracy: 0.7323\n",
            "Epoch 13/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.5984 - accuracy: 0.8076"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 203ms/step - loss: 0.5983 - accuracy: 0.8074\n",
            "Epoch 14/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.5550 - accuracy: 0.8151"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.5551 - accuracy: 0.8152\n",
            "Epoch 15/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.3494 - accuracy: 0.8952"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.3492 - accuracy: 0.8953\n",
            "Epoch 16/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.2393 - accuracy: 0.9277"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.2400 - accuracy: 0.9275\n",
            "Epoch 17/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.4429 - accuracy: 0.8558"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.4429 - accuracy: 0.8556\n",
            "Epoch 18/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.3575 - accuracy: 0.8903"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 205ms/step - loss: 0.3574 - accuracy: 0.8904\n",
            "Epoch 19/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.2123 - accuracy: 0.9359"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 201ms/step - loss: 0.2121 - accuracy: 0.9359\n",
            "Epoch 20/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.1316 - accuracy: 0.9600"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 202ms/step - loss: 0.1315 - accuracy: 0.9600\n",
            "Epoch 21/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0785 - accuracy: 0.9792"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 203ms/step - loss: 0.0784 - accuracy: 0.9792\n",
            "Epoch 22/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0681 - accuracy: 0.9762"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 204ms/step - loss: 0.0681 - accuracy: 0.9763\n",
            "Epoch 23/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0775 - accuracy: 0.9769"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 205ms/step - loss: 0.0776 - accuracy: 0.9769\n",
            "Epoch 24/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0908 - accuracy: 0.9736"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 202ms/step - loss: 0.0907 - accuracy: 0.9736\n",
            "Epoch 25/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0708 - accuracy: 0.9805"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 202ms/step - loss: 0.0708 - accuracy: 0.9805\n",
            "Epoch 26/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0519 - accuracy: 0.9844"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 201ms/step - loss: 0.0518 - accuracy: 0.9844\n",
            "Epoch 27/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0475 - accuracy: 0.9837"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 202ms/step - loss: 0.0475 - accuracy: 0.9837\n",
            "Epoch 28/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0344 - accuracy: 0.9889"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 209ms/step - loss: 0.0344 - accuracy: 0.9889\n",
            "Epoch 29/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0397 - accuracy: 0.9883"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0397 - accuracy: 0.9883\n",
            "Epoch 30/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0264 - accuracy: 0.9909"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.0263 - accuracy: 0.9909\n",
            "Epoch 31/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0243 - accuracy: 0.9922"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 204ms/step - loss: 0.0243 - accuracy: 0.9922\n",
            "Epoch 32/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0260 - accuracy: 0.9928"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 203ms/step - loss: 0.0260 - accuracy: 0.9928\n",
            "Epoch 33/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0269 - accuracy: 0.9912"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0269 - accuracy: 0.9912\n",
            "Epoch 34/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0235 - accuracy: 0.9938"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 198ms/step - loss: 0.0235 - accuracy: 0.9938\n",
            "Epoch 35/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0177 - accuracy: 0.9945"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 198ms/step - loss: 0.0177 - accuracy: 0.9945\n",
            "Epoch 36/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0156 - accuracy: 0.9938"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 197ms/step - loss: 0.0155 - accuracy: 0.9938\n",
            "Epoch 37/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0176 - accuracy: 0.9945"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0176 - accuracy: 0.9945\n",
            "Epoch 38/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0139 - accuracy: 0.9958"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0139 - accuracy: 0.9958\n",
            "Epoch 39/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0110 - accuracy: 0.9964"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 197ms/step - loss: 0.0110 - accuracy: 0.9964\n",
            "Epoch 40/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0167 - accuracy: 0.9951"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 197ms/step - loss: 0.0167 - accuracy: 0.9951\n",
            "Epoch 41/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0142 - accuracy: 0.9951"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 198ms/step - loss: 0.0142 - accuracy: 0.9951\n",
            "Epoch 42/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0217 - accuracy: 0.9941"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0217 - accuracy: 0.9941\n",
            "Epoch 43/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0166 - accuracy: 0.9945"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.0166 - accuracy: 0.9945\n",
            "Epoch 44/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0167 - accuracy: 0.9951"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 202ms/step - loss: 0.0167 - accuracy: 0.9951\n",
            "Epoch 45/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0130 - accuracy: 0.9964"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 201ms/step - loss: 0.0130 - accuracy: 0.9964\n",
            "Epoch 46/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0157 - accuracy: 0.9941"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 198ms/step - loss: 0.0157 - accuracy: 0.9941\n",
            "Epoch 47/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0179 - accuracy: 0.9932"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 203ms/step - loss: 0.0178 - accuracy: 0.9932\n",
            "Epoch 48/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0105 - accuracy: 0.9971"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 201ms/step - loss: 0.0105 - accuracy: 0.9971\n",
            "Epoch 49/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0098 - accuracy: 0.9961"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0098 - accuracy: 0.9961\n",
            "Epoch 50/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0104 - accuracy: 0.9961"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0104 - accuracy: 0.9961\n",
            "Epoch 51/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0129 - accuracy: 0.9967"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.0128 - accuracy: 0.9967\n",
            "Epoch 52/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0110 - accuracy: 0.9971"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 202ms/step - loss: 0.0110 - accuracy: 0.9971\n",
            "Epoch 53/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0070 - accuracy: 0.9984"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 201ms/step - loss: 0.0070 - accuracy: 0.9984\n",
            "Epoch 54/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0109 - accuracy: 0.9971"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.0109 - accuracy: 0.9971\n",
            "Epoch 55/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0139 - accuracy: 0.9958"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.0139 - accuracy: 0.9958\n",
            "Epoch 56/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0173 - accuracy: 0.9958"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.0173 - accuracy: 0.9958\n",
            "Epoch 57/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0163 - accuracy: 0.9951"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 204ms/step - loss: 0.0163 - accuracy: 0.9951\n",
            "Epoch 58/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0332 - accuracy: 0.9932"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 201ms/step - loss: 0.0332 - accuracy: 0.9932\n",
            "Epoch 59/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0261 - accuracy: 0.9928"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.0261 - accuracy: 0.9928\n",
            "Epoch 60/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0163 - accuracy: 0.9961"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0163 - accuracy: 0.9961\n",
            "Epoch 61/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0121 - accuracy: 0.9961"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.0121 - accuracy: 0.9961\n",
            "Epoch 62/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0172 - accuracy: 0.9961"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 203ms/step - loss: 0.0172 - accuracy: 0.9961\n",
            "Epoch 63/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0127 - accuracy: 0.9974"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0127 - accuracy: 0.9974\n",
            "Epoch 64/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0069 - accuracy: 0.9984"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0069 - accuracy: 0.9984\n",
            "Epoch 65/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0100 - accuracy: 0.9971"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0100 - accuracy: 0.9971\n",
            "Epoch 66/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0091 - accuracy: 0.9977"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.0091 - accuracy: 0.9977\n",
            "Epoch 67/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0076 - accuracy: 0.9977"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 202ms/step - loss: 0.0076 - accuracy: 0.9977\n",
            "Epoch 68/100\n",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.0110 - accuracy: 0.9971\n",
            "Epoch 69/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0066 - accuracy: 0.9980"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0066 - accuracy: 0.9980\n",
            "Epoch 70/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0107 - accuracy: 0.9967"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0107 - accuracy: 0.9967\n",
            "Epoch 71/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0038 - accuracy: 0.9990"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0038 - accuracy: 0.9990\n",
            "Epoch 72/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0103 - accuracy: 0.9974"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 202ms/step - loss: 0.0103 - accuracy: 0.9974\n",
            "Epoch 73/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0062 - accuracy: 0.9980"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 198ms/step - loss: 0.0062 - accuracy: 0.9980\n",
            "Epoch 74/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0055 - accuracy: 0.9984"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 198ms/step - loss: 0.0055 - accuracy: 0.9984\n",
            "Epoch 75/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0113 - accuracy: 0.9987"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 198ms/step - loss: 0.0113 - accuracy: 0.9987\n",
            "Epoch 76/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0069 - accuracy: 0.9980"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0069 - accuracy: 0.9980\n",
            "Epoch 77/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0074 - accuracy: 0.9984"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 201ms/step - loss: 0.0074 - accuracy: 0.9984\n",
            "Epoch 78/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0078 - accuracy: 0.9971"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0078 - accuracy: 0.9971\n",
            "Epoch 79/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0094 - accuracy: 0.9971"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.0094 - accuracy: 0.9971\n",
            "Epoch 80/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0085 - accuracy: 0.9977"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 198ms/step - loss: 0.0085 - accuracy: 0.9977\n",
            "Epoch 81/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0051 - accuracy: 0.9984"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 201ms/step - loss: 0.0051 - accuracy: 0.9984\n",
            "Epoch 82/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0074 - accuracy: 0.9980"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 201ms/step - loss: 0.0074 - accuracy: 0.9980\n",
            "Epoch 83/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0082 - accuracy: 0.9977"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 198ms/step - loss: 0.0082 - accuracy: 0.9977\n",
            "Epoch 84/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0028 - accuracy: 0.9997"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0028 - accuracy: 0.9997\n",
            "Epoch 85/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0039 - accuracy: 0.9990"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.0039 - accuracy: 0.9990\n",
            "Epoch 86/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0029 - accuracy: 0.9993"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 204ms/step - loss: 0.0029 - accuracy: 0.9993\n",
            "Epoch 87/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0022 - accuracy: 0.9993"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 204ms/step - loss: 0.0022 - accuracy: 0.9993\n",
            "Epoch 88/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0044 - accuracy: 0.9984"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0044 - accuracy: 0.9984\n",
            "Epoch 89/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0058 - accuracy: 0.9984"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0058 - accuracy: 0.9984\n",
            "Epoch 90/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0049 - accuracy: 0.9984"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0049 - accuracy: 0.9984\n",
            "Epoch 91/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0073 - accuracy: 0.9967"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.0073 - accuracy: 0.9967\n",
            "Epoch 92/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0085 - accuracy: 0.9987"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.0085 - accuracy: 0.9987\n",
            "Epoch 93/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0091 - accuracy: 0.9987"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0091 - accuracy: 0.9987\n",
            "Epoch 94/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0092 - accuracy: 0.9964"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0092 - accuracy: 0.9964\n",
            "Epoch 95/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0061 - accuracy: 0.9980"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0061 - accuracy: 0.9980\n",
            "Epoch 96/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0087 - accuracy: 0.9987"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 200ms/step - loss: 0.0087 - accuracy: 0.9987\n",
            "Epoch 97/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0111 - accuracy: 0.9971"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 202ms/step - loss: 0.0111 - accuracy: 0.9971\n",
            "Epoch 98/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0101 - accuracy: 0.9964"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0101 - accuracy: 0.9964\n",
            "Epoch 99/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0075 - accuracy: 0.9977"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0075 - accuracy: 0.9977\n",
            "Epoch 100/100\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0079 - accuracy: 0.9977"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Can save best model only with acc available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r13/13 [==============================] - 3s 199ms/step - loss: 0.0079 - accuracy: 0.9977\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PfSP_f8ywJR1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76a9d6c5-3434-4bc4-c89e-554be120ab8f"
      },
      "source": [
        "model.save(\"best-model.hdf5\")"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning:\n",
            "\n",
            "You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wg3XlImlvitn"
      },
      "source": [
        "# Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKH6QFcmvitp"
      },
      "source": [
        "# load best weights\n",
        "model.load_weights(\"best-model.hdf5\")\n",
        "model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ynlGSBkvitv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e214c6c-2354-4019-ba01-4b2a8b05e12c"
      },
      "source": [
        "Xtest = Xtest.reshape(-1, windowSize, windowSize, K, 1)\n",
        "Xtest.shape"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7175, 25, 25, 30, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FrQBCdrqvit3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3522e89c-f5ae-41f7-a627-0b0733b0b47d"
      },
      "source": [
        "ytest = keras.utils.to_categorical(ytest)\n",
        "ytest.shape"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7175, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZDidpOOjvit-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b25fa221-83c7-4b6c-8e0a-5de12614f2b3"
      },
      "source": [
        "Y_pred_test = model.predict(Xtest)\n",
        "y_pred_test = np.argmax(Y_pred_test, axis=1)\n",
        "\n",
        "classification = classification_report(np.argmax(ytest, axis=1), y_pred_test)\n",
        "print(classification)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "225/225 [==============================] - 3s 10ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      1.00      0.98        32\n",
            "           1       1.00      1.00      1.00      1000\n",
            "           2       0.99      0.99      0.99       581\n",
            "           3       1.00      1.00      1.00       166\n",
            "           4       1.00      0.99      0.99       338\n",
            "           5       0.99      1.00      0.99       511\n",
            "           6       1.00      1.00      1.00        20\n",
            "           7       1.00      1.00      1.00       335\n",
            "           8       1.00      0.93      0.96        14\n",
            "           9       1.00      1.00      1.00       680\n",
            "          10       1.00      0.99      1.00      1719\n",
            "          11       0.99      1.00      1.00       415\n",
            "          12       0.97      1.00      0.99       143\n",
            "          13       1.00      1.00      1.00       886\n",
            "          14       1.00      1.00      1.00       270\n",
            "          15       0.97      0.95      0.96        65\n",
            "\n",
            "    accuracy                           1.00      7175\n",
            "   macro avg       0.99      0.99      0.99      7175\n",
            "weighted avg       1.00      1.00      1.00      7175\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fY39UA0nviuG"
      },
      "source": [
        "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": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_rNFvCZDviuM"
      },
      "source": [
        "def reports (X_test,y_test,name):\n",
        "    #start = time.time()\n",
        "    Y_pred = model.predict(X_test)\n",
        "    y_pred = np.argmax(Y_pred, axis=1)\n",
        "    #end = time.time()\n",
        "    #print(end - start)\n",
        "    if name == 'IP':\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",
        "    elif name == 'SA':\n",
        "        target_names = ['Brocoli_green_weeds_1','Brocoli_green_weeds_2','Fallow','Fallow_rough_plow','Fallow_smooth',\n",
        "                        'Stubble','Celery','Grapes_untrained','Soil_vinyard_develop','Corn_senesced_green_weeds',\n",
        "                        'Lettuce_romaine_4wk','Lettuce_romaine_5wk','Lettuce_romaine_6wk','Lettuce_romaine_7wk',\n",
        "                        'Vinyard_untrained','Vinyard_vertical_trellis']\n",
        "    elif name == 'PU':\n",
        "        target_names = ['Asphalt','Meadows','Gravel','Trees', 'Painted metal sheets','Bare Soil','Bitumen',\n",
        "                        'Self-Blocking Bricks','Shadows']\n",
        "\n",
        "    classification = classification_report(np.argmax(y_test, axis=1), y_pred, target_names=target_names)\n",
        "    oa = accuracy_score(np.argmax(y_test, axis=1), y_pred)\n",
        "    confusion = confusion_matrix(np.argmax(y_test, axis=1), y_pred)\n",
        "    each_acc, aa = AA_andEachClassAccuracy(confusion)\n",
        "    kappa = cohen_kappa_score(np.argmax(y_test, axis=1), y_pred)\n",
        "    score = model.evaluate(X_test, y_test, batch_size=32)\n",
        "    Test_Loss =  score[0]*100\n",
        "    Test_accuracy = score[1]*100\n",
        "\n",
        "    return classification, confusion, Test_Loss, Test_accuracy, oa*100, each_acc*100, aa*100, kappa*100"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6eDf1WHviuS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e4252d3-b683-45ac-d1e3-469e96473191"
      },
      "source": [
        "classification, confusion, Test_loss, Test_accuracy, oa, each_acc, aa, kappa = reports(Xtest,ytest,dataset)\n",
        "classification = str(classification)\n",
        "confusion = str(confusion)\n",
        "file_name = \"classification_report.txt\"\n",
        "\n",
        "with open(file_name, 'w') as x_file:\n",
        "    x_file.write('{} Test loss (%)'.format(Test_loss))\n",
        "    x_file.write('\\n')\n",
        "    x_file.write('{} Test accuracy (%)'.format(Test_accuracy))\n",
        "    x_file.write('\\n')\n",
        "    x_file.write('\\n')\n",
        "    x_file.write('{} Kappa accuracy (%)'.format(kappa))\n",
        "    x_file.write('\\n')\n",
        "    x_file.write('{} Overall accuracy (%)'.format(oa))\n",
        "    x_file.write('\\n')\n",
        "    x_file.write('{} Average accuracy (%)'.format(aa))\n",
        "    x_file.write('\\n')\n",
        "    x_file.write('\\n')\n",
        "    x_file.write('{}'.format(classification))\n",
        "    x_file.write('\\n')\n",
        "    x_file.write('{}'.format(confusion))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "225/225 [==============================] - 2s 10ms/step\n",
            "225/225 [==============================] - 3s 11ms/step - loss: 0.0171 - accuracy: 0.9961\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bftzj-2_viuY"
      },
      "source": [
        "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",
        "\n",
        "    return patch"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBWs8kmzviue"
      },
      "source": [
        "# load the original image\n",
        "X, y = loadData(dataset)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFsdbo9Aviuk"
      },
      "source": [
        "height = y.shape[0]\n",
        "width = y.shape[1]\n",
        "PATCH_SIZE = windowSize\n",
        "numComponents = K"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J9dc-buXviup"
      },
      "source": [
        "X,pca = applyPCA(X, numComponents=numComponents)"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eq14Gv4rvius"
      },
      "source": [
        "X = padWithZeros(X, PATCH_SIZE//2)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBtYMNhWviu0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12e66209-96d7-4372-c5b8-1c345d87fdb4"
      },
      "source": [
        "# calculate the predicted image\n",
        "outputs = np.zeros((height,width))\n",
        "for i in range(height):\n",
        "    for j in range(width):\n",
        "        target = int(y[i,j])\n",
        "        if target == 0 :\n",
        "            continue\n",
        "        else :\n",
        "            image_patch=Patch(X,i,j)\n",
        "            X_test_image = image_patch.reshape(1,image_patch.shape[0],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"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2H_WBEHEviu5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "outputId": "ecffa70d-c64c-4065-f532-6473037f371d"
      },
      "source": [
        "ground_truth = spectral.imshow(classes = y,figsize =(7,7))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/spectral/graphics/spypylab.py:796: UserWarning:\n",
            "\n",
            "Failed to create RectangleSelector object. Interactive pixel class labeling will be unavailable.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TIXpiUnavivA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        },
        "outputId": "10a66c36-b449-4d8f-9777-f1a8a843e990"
      },
      "source": [
        "predict_image = spectral.imshow(classes = outputs.astype(int),figsize =(7,7))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nqc0ygfYvivI"
      },
      "source": [
        "spectral.save_rgb(\"predictions.jpg\", outputs.astype(int), colors=spectral.spy_colors)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNgBjynzvivN"
      },
      "source": [
        "spectral.save_rgb(str(dataset)+\"_ground_truth.jpg\", y, colors=spectral.spy_colors)"
      ]
    }
  ]
}