MNIST_CNN.ipynb

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      "name": "MNIST CNN.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d8crIC3Y3njG"
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      "source": [
        "#Section 1\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ITwO9SL8fh4N"
      },
      "source": [
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D , Activation, Flatten , Dense, MaxPooling2D\n",
        "from tensorflow.keras.layers import Dropout\n",
        "\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.datasets import mnist\n",
        "from keras.utils import np_utils, to_categorical\n",
        "from sklearn.model_selection import KFold"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G8BOiwKlop_a"
      },
      "source": [
        "def load_data():\n",
        "    \n",
        "    return mnist.load_data()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVciDIoaqny_"
      },
      "source": [
        "def prepare_data(train_images, train_labels, test_images, test_labels):\n",
        "\n",
        "    x_train = train_images.reshape(60000,28,28,1)\n",
        "\n",
        "    x_test = test_images.reshape(10000,28,28,1)\n",
        "\n",
        "    #one hot encoding\n",
        "    y_train =  to_categorical(train_labels) \n",
        "    y_test = to_categorical(test_labels)\n",
        "\n",
        "    return (x_train, y_train, x_test, y_test )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AXM3H9Yvwq-l"
      },
      "source": [
        "def model(x_train, y_train, x_test, y_test ):\n",
        "    \n",
        "    kfold = KFold(n_splits=3, shuffle=True)\n",
        "    fold_no = 0\n",
        "    history = ''\n",
        "    \n",
        "    # Define per-fold score containers\n",
        "    for train_index, test_index in kfold.split(x_train):\n",
        "        print('------------------------------------------------------------------------')\n",
        "        print(f'Training for fold {fold_no} ...')\n",
        "\n",
        "        model = Sequential()\n",
        "\n",
        "        model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', padding='valid', input_shape=(28,28,1)))\n",
        "        model.add(BatchNormalization())\n",
        "        model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "        model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', padding='valid'))\n",
        "        model.add(BatchNormalization())\n",
        "        model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "        model.add(Flatten())\n",
        "        model.add(Dense(64, activation='relu'))\n",
        "        model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "        model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "        \n",
        "        history = model.fit(x_train[train_index], y_train[train_index],\n",
        "                batch_size=2048, epochs=10, verbose=2,\n",
        "                validation_data=(x_train[test_index], y_train[test_index]))\n",
        "        \n",
        "        \n",
        "        scores = model.evaluate(x_train[test_index], y_train[test_index], verbose=0)\n",
        "        print(f'Score for fold {fold_no}: {model.metrics_names[0]} of {scores[0]}; {model.metrics_names[1]} of {scores[1]*100}%')\n",
        "\n",
        "        fold_no += 1\n",
        "    \n",
        "    model.summary()\n",
        "\n",
        "    score = model.evaluate(x_test, y_test)\n",
        "    print('Test loss :', score[0])\n",
        "    print('Test accuracy:', score[1] * 100)\n",
        "\n",
        "    return history"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GCzZHF3yJC4x"
      },
      "source": [
        "def plot(history):\n",
        "    plt.plot(history.history['accuracy'])\n",
        "    plt.plot(history.history['val_accuracy'])\n",
        "    plt.title('model accuracy')\n",
        "    plt.ylabel('accuracy')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(['train', 'test'], loc='upper left')\n",
        "    plt.show()\n",
        "    # summarize history for loss\n",
        "    plt.plot(history.history['loss'])\n",
        "    plt.plot(history.history['val_loss'])\n",
        "    plt.title('model loss')\n",
        "    plt.ylabel('loss')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(['train', 'test'], loc='upper left')\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7K4I3_XOTOA"
      },
      "source": [
        "def main():\n",
        "    (train_images, train_labels), (test_images, test_labels) = load_data()\n",
        "   \n",
        "    x_train, y_train, x_test, y_test = prepare_data(train_images, train_labels, test_images, test_labels)\n",
        "\n",
        "    history = model(x_train, y_train, x_test, y_test)\n",
        "    \n",
        "    plot(history)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1WDua5JAOaLJ",
        "outputId": "e32d9aaa-a264-4bd8-a74b-4d5e50df0c8d"
      },
      "source": [
        "main()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 0 ...\n",
            "Epoch 1/10\n",
            "20/20 - 9s - loss: 1.1231 - accuracy: 0.6632 - val_loss: 1.7051 - val_accuracy: 0.7250\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.2246 - accuracy: 0.9323 - val_loss: 1.0008 - val_accuracy: 0.8038\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.1344 - accuracy: 0.9594 - val_loss: 0.4424 - val_accuracy: 0.8837\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.0961 - accuracy: 0.9702 - val_loss: 0.2530 - val_accuracy: 0.9259\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.0751 - accuracy: 0.9773 - val_loss: 0.1413 - val_accuracy: 0.9567\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.0617 - accuracy: 0.9815 - val_loss: 0.1024 - val_accuracy: 0.9684\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.0521 - accuracy: 0.9844 - val_loss: 0.0894 - val_accuracy: 0.9719\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.0445 - accuracy: 0.9867 - val_loss: 0.0764 - val_accuracy: 0.9762\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.0390 - accuracy: 0.9887 - val_loss: 0.0608 - val_accuracy: 0.9807\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.0339 - accuracy: 0.9905 - val_loss: 0.0577 - val_accuracy: 0.9814\n",
            "Score for fold 0: loss of 0.05767666548490524; accuracy of 98.14000129699707%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 1 ...\n",
            "Epoch 1/10\n",
            "20/20 - 1s - loss: 1.0149 - accuracy: 0.6824 - val_loss: 2.0344 - val_accuracy: 0.6449\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.2224 - accuracy: 0.9327 - val_loss: 0.9118 - val_accuracy: 0.7949\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.1357 - accuracy: 0.9598 - val_loss: 0.5169 - val_accuracy: 0.8652\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.1002 - accuracy: 0.9706 - val_loss: 0.2144 - val_accuracy: 0.9370\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.0795 - accuracy: 0.9762 - val_loss: 0.1556 - val_accuracy: 0.9516\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.0665 - accuracy: 0.9803 - val_loss: 0.0927 - val_accuracy: 0.9708\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.0572 - accuracy: 0.9836 - val_loss: 0.0804 - val_accuracy: 0.9740\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.0489 - accuracy: 0.9859 - val_loss: 0.0617 - val_accuracy: 0.9813\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.0424 - accuracy: 0.9885 - val_loss: 0.0579 - val_accuracy: 0.9816\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.0376 - accuracy: 0.9901 - val_loss: 0.0523 - val_accuracy: 0.9834\n",
            "Score for fold 1: loss of 0.05226318910717964; accuracy of 98.33999872207642%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 2 ...\n",
            "Epoch 1/10\n",
            "20/20 - 1s - loss: 1.0224 - accuracy: 0.6812 - val_loss: 1.3041 - val_accuracy: 0.7643\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.2064 - accuracy: 0.9398 - val_loss: 0.7236 - val_accuracy: 0.8330\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.1235 - accuracy: 0.9638 - val_loss: 0.3014 - val_accuracy: 0.9114\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.0918 - accuracy: 0.9739 - val_loss: 0.2268 - val_accuracy: 0.9324\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.0746 - accuracy: 0.9787 - val_loss: 0.1463 - val_accuracy: 0.9578\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.0623 - accuracy: 0.9821 - val_loss: 0.1056 - val_accuracy: 0.9678\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.0532 - accuracy: 0.9854 - val_loss: 0.0844 - val_accuracy: 0.9746\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.0466 - accuracy: 0.9875 - val_loss: 0.0753 - val_accuracy: 0.9770\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.0410 - accuracy: 0.9887 - val_loss: 0.0664 - val_accuracy: 0.9790\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.0369 - accuracy: 0.9898 - val_loss: 0.0615 - val_accuracy: 0.9809\n",
            "Score for fold 2: loss of 0.0614907406270504; accuracy of 98.089998960495%\n",
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_4 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 26, 26, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 11, 11, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 11, 11, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 5, 5, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 800)               0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 64)                51264     \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 61,738\n",
            "Trainable params: 61,610\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0522 - accuracy: 0.9840\n",
            "Test loss : 0.05220393091440201\n",
            "Test accuracy: 98.4000027179718\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m_Cin_Fo24ga"
      },
      "source": [
        "#noisy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vtx466CKzsbp"
      },
      "source": [
        "def show_result(x_test_noisy, test_images):\n",
        "    n = 10\n",
        "    for i in range(n):\n",
        "        # Display original\n",
        "        ax = plt.subplot(2, n, i + 1)\n",
        "        plt.imshow(test_images[i].reshape(28, 28))\n",
        "        plt.gray()\n",
        "        ax.get_xaxis().set_visible(False)\n",
        "        ax.get_yaxis().set_visible(False)\n",
        "\n",
        "        # Display noisy \n",
        "        ax = plt.subplot(2, n, i + 1)\n",
        "        plt.imshow(x_test_noisy[i].reshape(28, 28))\n",
        "        plt.gray()\n",
        "        ax.get_xaxis().set_visible(False)\n",
        "        ax.get_yaxis().set_visible(False)\n",
        "\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Df3F4m8Y2gC5"
      },
      "source": [
        "def add_noise(x_train, x_test, noise_factor_train, noise_factor_test):\n",
        "\n",
        "    x_train_noisy = x_train + noise_factor_train * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape) \n",
        "    x_test_noisy = x_test + noise_factor_test * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape) \n",
        "\n",
        "    x_train_noisy = np.clip(x_train_noisy, 0., 1.)\n",
        "    x_test_noisy = np.clip(x_test_noisy, 0., 1.)\n",
        "\n",
        "    return x_train_noisy, x_test_noisy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jV9zlqeV2kwP"
      },
      "source": [
        "def main_noisy(noise_factor_train = 0.4, noise_factor_test = 0.7):\n",
        "\n",
        "    (train_images, train_labels), (test_images, test_labels) = load_data()\n",
        "\n",
        "    x_train, y_train, x_test, y_test = prepare_data(train_images, train_labels, test_images, test_labels)\n",
        "    \n",
        "    x_train_noisy, x_test_noisy = add_noise(x_train, x_test, noise_factor_train, noise_factor_test)\n",
        "\n",
        "    history = model(x_train_noisy, y_train, x_test_noisy, y_test)\n",
        "    \n",
        "    show_result(x_train_noisy, x_test_noisy)\n",
        "\n",
        "    plot(history)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oyhiqJVF2v83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8262028c-ebc8-4b2d-8c3c-cd72b6daab42"
      },
      "source": [
        "main_noisy(noise_factor_train = 0.9, noise_factor_test = 0.9)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for fold 0 ...\n",
            "Epoch 1/10\n",
            "20/20 - 1s - loss: 1.5553 - accuracy: 0.4979 - val_loss: 2.0569 - val_accuracy: 0.4635\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.4869 - accuracy: 0.8468 - val_loss: 1.8993 - val_accuracy: 0.5407\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.3155 - accuracy: 0.9019 - val_loss: 2.0082 - val_accuracy: 0.2199\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.2374 - accuracy: 0.9273 - val_loss: 2.1512 - val_accuracy: 0.1331\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.1908 - accuracy: 0.9418 - val_loss: 2.2683 - val_accuracy: 0.1125\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.1595 - accuracy: 0.9512 - val_loss: 2.3477 - val_accuracy: 0.1089\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.1370 - accuracy: 0.9589 - val_loss: 2.3568 - val_accuracy: 0.1160\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.1191 - accuracy: 0.9640 - val_loss: 2.3487 - val_accuracy: 0.1330\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.1046 - accuracy: 0.9690 - val_loss: 2.3087 - val_accuracy: 0.1648\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.0937 - accuracy: 0.9723 - val_loss: 2.2172 - val_accuracy: 0.2058\n",
            "Score for fold 0: loss of 2.217235803604126; accuracy of 20.57500034570694%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 1 ...\n",
            "Epoch 1/10\n",
            "20/20 - 2s - loss: 1.5875 - accuracy: 0.4937 - val_loss: 2.1050 - val_accuracy: 0.4157\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.5559 - accuracy: 0.8242 - val_loss: 2.0201 - val_accuracy: 0.2291\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.3621 - accuracy: 0.8880 - val_loss: 2.1994 - val_accuracy: 0.2508\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.2746 - accuracy: 0.9154 - val_loss: 2.4640 - val_accuracy: 0.1433\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.2208 - accuracy: 0.9330 - val_loss: 2.7801 - val_accuracy: 0.1133\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.1840 - accuracy: 0.9433 - val_loss: 3.0607 - val_accuracy: 0.1123\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.1564 - accuracy: 0.9519 - val_loss: 3.2367 - val_accuracy: 0.1123\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.1358 - accuracy: 0.9581 - val_loss: 3.2046 - val_accuracy: 0.1123\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.1185 - accuracy: 0.9641 - val_loss: 3.2107 - val_accuracy: 0.1123\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.1056 - accuracy: 0.9687 - val_loss: 3.1479 - val_accuracy: 0.1124\n",
            "Score for fold 1: loss of 3.147949695587158; accuracy of 11.240000277757645%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 2 ...\n",
            "Epoch 1/10\n",
            "20/20 - 2s - loss: 1.8281 - accuracy: 0.4321 - val_loss: 2.1576 - val_accuracy: 0.4365\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.5955 - accuracy: 0.8120 - val_loss: 2.1570 - val_accuracy: 0.1445\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.3513 - accuracy: 0.8898 - val_loss: 2.4532 - val_accuracy: 0.1094\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.2543 - accuracy: 0.9212 - val_loss: 2.8008 - val_accuracy: 0.1037\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.2011 - accuracy: 0.9380 - val_loss: 3.0349 - val_accuracy: 0.1303\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.1668 - accuracy: 0.9488 - val_loss: 3.1951 - val_accuracy: 0.1576\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.1447 - accuracy: 0.9561 - val_loss: 3.3257 - val_accuracy: 0.1669\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.1265 - accuracy: 0.9621 - val_loss: 3.3349 - val_accuracy: 0.1753\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.1136 - accuracy: 0.9658 - val_loss: 3.3218 - val_accuracy: 0.1936\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.1024 - accuracy: 0.9695 - val_loss: 3.2817 - val_accuracy: 0.2086\n",
            "Score for fold 2: loss of 3.281740665435791; accuracy of 20.864999294281006%\n",
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_22 (Conv2D)           (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "batch_normalization_22 (Batc (None, 26, 26, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_22 (MaxPooling (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_23 (Conv2D)           (None, 11, 11, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_23 (Batc (None, 11, 11, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_23 (MaxPooling (None, 5, 5, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_11 (Flatten)         (None, 800)               0         \n",
            "_________________________________________________________________\n",
            "dense_22 (Dense)             (None, 64)                51264     \n",
            "_________________________________________________________________\n",
            "dense_23 (Dense)             (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 61,738\n",
            "Trainable params: 61,610\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 3.2706 - accuracy: 0.2077\n",
            "Test loss : 3.270639181137085\n",
            "Test accuracy: 20.76999992132187\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
            "  if sys.path[0] == '':\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTU36mG72_Fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ae4e6528-d1ff-4940-a41f-71a9899d6e3d"
      },
      "source": [
        "main_noisy(noise_factor_train = 0.7, noise_factor_test = 0.7)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for fold 0 ...\n",
            "Epoch 1/10\n",
            "20/20 - 2s - loss: 1.7678 - accuracy: 0.4516 - val_loss: 2.1585 - val_accuracy: 0.2768\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.5958 - accuracy: 0.8130 - val_loss: 2.2563 - val_accuracy: 0.1392\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.3620 - accuracy: 0.8881 - val_loss: 2.6037 - val_accuracy: 0.1529\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.2705 - accuracy: 0.9174 - val_loss: 2.9424 - val_accuracy: 0.1063\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.2126 - accuracy: 0.9361 - val_loss: 3.2181 - val_accuracy: 0.1015\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.1742 - accuracy: 0.9482 - val_loss: 3.4349 - val_accuracy: 0.1011\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.1464 - accuracy: 0.9567 - val_loss: 3.5563 - val_accuracy: 0.1019\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.1268 - accuracy: 0.9624 - val_loss: 3.6128 - val_accuracy: 0.1089\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.1115 - accuracy: 0.9668 - val_loss: 3.6480 - val_accuracy: 0.1127\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.0983 - accuracy: 0.9711 - val_loss: 3.6232 - val_accuracy: 0.1239\n",
            "Score for fold 0: loss of 3.6232128143310547; accuracy of 12.389999628067017%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 1 ...\n",
            "Epoch 1/10\n",
            "20/20 - 1s - loss: 1.6768 - accuracy: 0.4789 - val_loss: 2.0717 - val_accuracy: 0.4128\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.5216 - accuracy: 0.8390 - val_loss: 1.9909 - val_accuracy: 0.3851\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.3426 - accuracy: 0.8967 - val_loss: 2.4635 - val_accuracy: 0.1004\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.2664 - accuracy: 0.9196 - val_loss: 3.4281 - val_accuracy: 0.0996\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.2170 - accuracy: 0.9350 - val_loss: 4.4682 - val_accuracy: 0.0996\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.1818 - accuracy: 0.9459 - val_loss: 5.1587 - val_accuracy: 0.0996\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.1547 - accuracy: 0.9536 - val_loss: 5.5549 - val_accuracy: 0.0996\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.1355 - accuracy: 0.9595 - val_loss: 5.6414 - val_accuracy: 0.0996\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.1198 - accuracy: 0.9641 - val_loss: 5.5026 - val_accuracy: 0.0996\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.1080 - accuracy: 0.9678 - val_loss: 5.3341 - val_accuracy: 0.0996\n",
            "Score for fold 1: loss of 5.334078788757324; accuracy of 9.955000132322311%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 2 ...\n",
            "Epoch 1/10\n",
            "20/20 - 1s - loss: 1.4828 - accuracy: 0.5324 - val_loss: 2.0857 - val_accuracy: 0.4500\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.4598 - accuracy: 0.8573 - val_loss: 2.0324 - val_accuracy: 0.1343\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.3014 - accuracy: 0.9087 - val_loss: 2.2071 - val_accuracy: 0.0971\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.2278 - accuracy: 0.9313 - val_loss: 2.3977 - val_accuracy: 0.0971\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.1818 - accuracy: 0.9456 - val_loss: 2.5239 - val_accuracy: 0.0971\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.1514 - accuracy: 0.9545 - val_loss: 2.5977 - val_accuracy: 0.0971\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.1311 - accuracy: 0.9605 - val_loss: 2.6100 - val_accuracy: 0.0971\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.1133 - accuracy: 0.9662 - val_loss: 2.6090 - val_accuracy: 0.0971\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.1004 - accuracy: 0.9699 - val_loss: 2.5891 - val_accuracy: 0.0971\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.0901 - accuracy: 0.9739 - val_loss: 2.5192 - val_accuracy: 0.0975\n",
            "Score for fold 2: loss of 2.51920223236084; accuracy of 9.754999727010727%\n",
            "Model: \"sequential_14\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_28 (Conv2D)           (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "batch_normalization_28 (Batc (None, 26, 26, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_28 (MaxPooling (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_29 (Conv2D)           (None, 11, 11, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_29 (Batc (None, 11, 11, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_29 (MaxPooling (None, 5, 5, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_14 (Flatten)         (None, 800)               0         \n",
            "_________________________________________________________________\n",
            "dense_28 (Dense)             (None, 64)                51264     \n",
            "_________________________________________________________________\n",
            "dense_29 (Dense)             (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 61,738\n",
            "Trainable params: 61,610\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.5017 - accuracy: 0.0975\n",
            "Test loss : 2.501748561859131\n",
            "Test accuracy: 9.749999642372131\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
            "  if sys.path[0] == '':\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bi6Jol5j5DG0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fd7b29cc-aa62-4299-f9cd-503bc763e4b8"
      },
      "source": [
        "main_noisy(noise_factor_train = 0.3, noise_factor_test = 0.3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for fold 0 ...\n",
            "Epoch 1/10\n",
            "20/20 - 1s - loss: 1.2154 - accuracy: 0.6241 - val_loss: 2.0036 - val_accuracy: 0.7078\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.3103 - accuracy: 0.9058 - val_loss: 1.8534 - val_accuracy: 0.6707\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.1882 - accuracy: 0.9436 - val_loss: 1.9106 - val_accuracy: 0.3862\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.1388 - accuracy: 0.9585 - val_loss: 1.9622 - val_accuracy: 0.2686\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.1102 - accuracy: 0.9677 - val_loss: 2.0010 - val_accuracy: 0.2350\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.0923 - accuracy: 0.9729 - val_loss: 2.0050 - val_accuracy: 0.2542\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.0789 - accuracy: 0.9772 - val_loss: 1.9937 - val_accuracy: 0.2901\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.0693 - accuracy: 0.9801 - val_loss: 1.9171 - val_accuracy: 0.3379\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.0616 - accuracy: 0.9822 - val_loss: 1.8638 - val_accuracy: 0.3697\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.0538 - accuracy: 0.9844 - val_loss: 1.7491 - val_accuracy: 0.4029\n",
            "Score for fold 0: loss of 1.7491474151611328; accuracy of 40.29000103473663%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 1 ...\n",
            "Epoch 1/10\n",
            "20/20 - 1s - loss: 1.1441 - accuracy: 0.6360 - val_loss: 1.9892 - val_accuracy: 0.5076\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.2934 - accuracy: 0.9088 - val_loss: 2.0392 - val_accuracy: 0.1957\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.1877 - accuracy: 0.9421 - val_loss: 2.2058 - val_accuracy: 0.0978\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.1436 - accuracy: 0.9555 - val_loss: 2.3726 - val_accuracy: 0.0971\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.1179 - accuracy: 0.9646 - val_loss: 2.5329 - val_accuracy: 0.0971\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.1000 - accuracy: 0.9704 - val_loss: 2.6285 - val_accuracy: 0.0971\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.0859 - accuracy: 0.9755 - val_loss: 2.6666 - val_accuracy: 0.0971\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.0751 - accuracy: 0.9783 - val_loss: 2.6774 - val_accuracy: 0.0972\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.0662 - accuracy: 0.9814 - val_loss: 2.5941 - val_accuracy: 0.0975\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.0589 - accuracy: 0.9833 - val_loss: 2.4997 - val_accuracy: 0.0989\n",
            "Score for fold 1: loss of 2.499666690826416; accuracy of 9.889999777078629%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 2 ...\n",
            "Epoch 1/10\n",
            "20/20 - 1s - loss: 1.1654 - accuracy: 0.6323 - val_loss: 2.0150 - val_accuracy: 0.5133\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.2897 - accuracy: 0.9100 - val_loss: 1.8627 - val_accuracy: 0.7129\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.1855 - accuracy: 0.9425 - val_loss: 1.9020 - val_accuracy: 0.4904\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.1384 - accuracy: 0.9579 - val_loss: 1.9510 - val_accuracy: 0.2088\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.1111 - accuracy: 0.9668 - val_loss: 2.0177 - val_accuracy: 0.1511\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.0941 - accuracy: 0.9719 - val_loss: 2.0667 - val_accuracy: 0.1388\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.0817 - accuracy: 0.9762 - val_loss: 2.1340 - val_accuracy: 0.1196\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.0717 - accuracy: 0.9794 - val_loss: 2.1232 - val_accuracy: 0.1336\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.0640 - accuracy: 0.9816 - val_loss: 2.0753 - val_accuracy: 0.1498\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.0566 - accuracy: 0.9843 - val_loss: 2.0240 - val_accuracy: 0.1660\n",
            "Score for fold 2: loss of 2.024044990539551; accuracy of 16.599999368190765%\n",
            "Model: \"sequential_17\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_34 (Conv2D)           (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "batch_normalization_34 (Batc (None, 26, 26, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_34 (MaxPooling (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_35 (Conv2D)           (None, 11, 11, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_35 (Batc (None, 11, 11, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_35 (MaxPooling (None, 5, 5, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_17 (Flatten)         (None, 800)               0         \n",
            "_________________________________________________________________\n",
            "dense_34 (Dense)             (None, 64)                51264     \n",
            "_________________________________________________________________\n",
            "dense_35 (Dense)             (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 61,738\n",
            "Trainable params: 61,610\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.0209 - accuracy: 0.1629\n",
            "Test loss : 2.020909070968628\n",
            "Test accuracy: 16.290000081062317\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
            "  if sys.path[0] == '':\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q112ZZAd5IYj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "39aca028-d28b-4939-8eb5-63eacf4f665b"
      },
      "source": [
        "main_noisy(noise_factor_train = 0.4, noise_factor_test = 0.7)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for fold 0 ...\n",
            "Epoch 1/10\n",
            "20/20 - 1s - loss: 1.4523 - accuracy: 0.5458 - val_loss: 2.0393 - val_accuracy: 0.6548\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.3958 - accuracy: 0.8781 - val_loss: 2.0794 - val_accuracy: 0.2940\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.2418 - accuracy: 0.9269 - val_loss: 2.3909 - val_accuracy: 0.1657\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.1795 - accuracy: 0.9471 - val_loss: 2.7999 - val_accuracy: 0.1160\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.1424 - accuracy: 0.9582 - val_loss: 3.1173 - val_accuracy: 0.1073\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.1187 - accuracy: 0.9650 - val_loss: 3.3434 - val_accuracy: 0.1059\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.1013 - accuracy: 0.9702 - val_loss: 3.4955 - val_accuracy: 0.1041\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.0889 - accuracy: 0.9737 - val_loss: 3.5007 - val_accuracy: 0.1047\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.0786 - accuracy: 0.9775 - val_loss: 3.4629 - val_accuracy: 0.1054\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.0694 - accuracy: 0.9798 - val_loss: 3.3784 - val_accuracy: 0.1063\n",
            "Score for fold 0: loss of 3.3784303665161133; accuracy of 10.625000298023224%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 1 ...\n",
            "Epoch 1/10\n",
            "20/20 - 1s - loss: 1.3096 - accuracy: 0.5983 - val_loss: 2.0384 - val_accuracy: 0.5454\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.3473 - accuracy: 0.8930 - val_loss: 1.8877 - val_accuracy: 0.7646\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.2235 - accuracy: 0.9322 - val_loss: 1.9359 - val_accuracy: 0.4696\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.1687 - accuracy: 0.9496 - val_loss: 1.9897 - val_accuracy: 0.2191\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.1368 - accuracy: 0.9598 - val_loss: 1.9864 - val_accuracy: 0.1833\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.1156 - accuracy: 0.9651 - val_loss: 1.9957 - val_accuracy: 0.1596\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.0990 - accuracy: 0.9705 - val_loss: 1.9641 - val_accuracy: 0.1759\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.0867 - accuracy: 0.9739 - val_loss: 1.9096 - val_accuracy: 0.2131\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.0769 - accuracy: 0.9771 - val_loss: 1.8086 - val_accuracy: 0.2686\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.0689 - accuracy: 0.9801 - val_loss: 1.6864 - val_accuracy: 0.3392\n",
            "Score for fold 1: loss of 1.6863505840301514; accuracy of 33.924999833106995%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 2 ...\n",
            "Epoch 1/10\n",
            "20/20 - 2s - loss: 1.3044 - accuracy: 0.5952 - val_loss: 2.0668 - val_accuracy: 0.5333\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.3723 - accuracy: 0.8855 - val_loss: 2.1055 - val_accuracy: 0.3236\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.2374 - accuracy: 0.9290 - val_loss: 2.2935 - val_accuracy: 0.1878\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.1767 - accuracy: 0.9470 - val_loss: 2.4969 - val_accuracy: 0.1785\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.1409 - accuracy: 0.9575 - val_loss: 2.6872 - val_accuracy: 0.1772\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.1171 - accuracy: 0.9651 - val_loss: 2.8248 - val_accuracy: 0.1786\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.0993 - accuracy: 0.9711 - val_loss: 2.9103 - val_accuracy: 0.1806\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.0863 - accuracy: 0.9748 - val_loss: 2.9729 - val_accuracy: 0.1811\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.0758 - accuracy: 0.9781 - val_loss: 2.9664 - val_accuracy: 0.1818\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.0678 - accuracy: 0.9805 - val_loss: 2.9251 - val_accuracy: 0.1826\n",
            "Score for fold 2: loss of 2.92510724067688; accuracy of 18.264999985694885%\n",
            "Model: \"sequential_20\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_40 (Conv2D)           (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "batch_normalization_40 (Batc (None, 26, 26, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_40 (MaxPooling (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_41 (Conv2D)           (None, 11, 11, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_41 (Batc (None, 11, 11, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_41 (MaxPooling (None, 5, 5, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_20 (Flatten)         (None, 800)               0         \n",
            "_________________________________________________________________\n",
            "dense_40 (Dense)             (None, 64)                51264     \n",
            "_________________________________________________________________\n",
            "dense_41 (Dense)             (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 61,738\n",
            "Trainable params: 61,610\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.9570 - accuracy: 0.1860\n",
            "Test loss : 2.9569616317749023\n",
            "Test accuracy: 18.60000044107437\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
            "  if sys.path[0] == '':\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mzZvH+o/HOpxHcKd3NHnyZJJIJDRu3DgyGAwkl8vZ9Z999tkQvvzkk0+YZcx7YryFPGHCBNauUqlkAsQdDqlUSnFxcW7jnYPXG3Q6He3YsYMaGhqosbGRWltbqbm5merr61l/DJ7LFy9epNjYWDKbzUwm6HQ6kkgkwyokp9NJUqmUgoOD6eLFizR58mQmsBQKBclkMhKLxfT6669Tc3MzdXZ20vr165myzM7OJpFIRAEBATR58mT2TGKxmHJycui5555jz+ppXGbOnMmePSYmhgwGA+Xm5pJeryeNRkNxcXFkNpuZwhEKhWwtgPeYhEIh+fj4UFZWFq1du5bq6+uJiKi1tZUSEhIoLS2NNmzY4HHt5jcLYI7jaMaMGSSVSlng3tfXl/2/b98+Kisro9bWVmpoaKCOjo4hTDUcNTU1uQSxPQk+juMoJSWFIiIiKCwsjFQqFe3YsYOys7NJqVSS0+mkhQsXUlJSEo0ZM4YiIiLolVdeoebmZrf37evro/r6evrll19IIBCQxWJhguDtt98mHx8ftzhMJpMLQ1dXV1N5eTl5e3uTxWKhxx9/nB5//HGKjY2lKVOm0MqVK2ns2LHMqouNjaW//OUv9PTTT5O3tzetW7eOYaqurmaW49ixY0mhUHhkKI7jaM6cOTR+/HgSCAS0du1a0uv1FB8fTyaTiQQCAW3cuJG5kk6nk2w2G3l7e1NcXBwlJibSihUrqLa21qVfysvLyWq1kkajIafTycb51wQwALp69SrFxcUxV5p3b8ePH09JSUkUFxdHFouFxGIxJSYmUlJSEkkkElKpVGQ0GiktLY1WrFhBWVlZpFQqSalUUm5uLplMJjIajfTKK6945A9eWM2YMYPy8/Np6dKlNGnSJJLL5SQWi5mF53A4SKlUUkJCAm3fvp3q6urc8uzJkyfJYDDQkiVLSCaT0Zw5cxiP5uTkkJ+fn1scgxemlEolKRQKmjp1Kun1+iGKefv27UPue+rUKWbZDhYEHMcxgaXRaCg3N5ctGLnDMWLECLJYLJSXl0e+vr7EcRwlJCTQpEmTSKlUkt1up3379tGtW7eos7PT7RxxR/39/XTu3DkSi8V0//33k0wmoz179njkDz48KBKJaNq0aRQcHEw2m40mT57ssuC2atUqSk1NpS1btrB7tbS00Pr165l3EhMTwxZJ+et411+r1bLFSk98emeCwCOPPEJJSUkUFBREMpmMGZNWq5WCgoJIq9WS2WymsLAwCggIIKPRSGazmQQCASUkJLgYdq2traxvN27c6BEH/VYBDIDFVIHbK6i5ubkUGxtLhw8fpq6urn978O6kuro6Cg4OJrFYTGvXriW+kzzh4F2dJUuWkFQqpYCAANJqtbR06VJ6/PHHyeFwUHx8PO3atYtu3LjhUfgOptLSUhKLxZSWlkaFhYVkNptpxYoVHjWYUChkK+EA2D38/PxIKBSSwWBgFoJarabY2FiaOXMmRUdHU3p6Oi1YsIACAwPJ6XRSYmIiNTY2MizV1dWkVqspNzeXvvnmGzb53OGQyWSk1Wrpww8/pOzsbIqMjKQlS5bQ8uXLmfC2Wq2Unp5O8+bNo/Xr11NaWhrpdDpKSkqiKVOm0P/8z/8M6Y+qqiomtHlFM5ylFRYWRqGhoTR58mQKCQkhmUxGdrud/Pz8SKPRkNVqJR8fH4qKiiIvLy9av349FRUVUVlZGZWXl9OVK1fo6tWrVFxcTOXl5VRbW0s//vgjiUQicjqdbO1gypQptHTpUo84QkJCiOM4WrVqFX399dfk7+9P7733HkVGRpJIJCK5XE4ZGRkUHx9P7733HpWVlVF7e7tHvujq6qJDhw4xr89isTCh6OvrS2Kx2C0OXsheuHCBCQqtVkvh4eEklUopPT2dzGYzyeVyys3NpcuXL7vc95dffqEZM2Ywqzc+Pp7ef/99iouLI4lEQlOnTqW9e/eSj48PCYVCcjqdbnGIxWKaN28eWSwWNn9zcnLIYrGw1fujR4/+6vxwR01NTZSZmUk6nY6ys7Np48aNHucL3x82m43y8vJYBgMfmgFAEydOpKioKFIqlfT++++73OuTTz4hX19f8vLyIqPRSBERERQTE0NyuZyioqIoPDycjY3JZPI4LgqFgtauXUsPPvggCYVCslgs9P3335Ofnx/5+vrShx9+SMnJySSVStm6BB8C40OJU6dOpXfffZdkMhlNnTqVYeTlmEqlIofDwcKr7nDQfyKAB7+EQiEFBQWRWq2mK1eu/NuDlp+fT1FRUZSWlkYvvfQSxcbGUnp6OonFYpozZw75+fmx9CxPHQjcToHz8vIijuPotddeo+joaMrNzWWxHb1eT19//fW/jau0tJT4+HJ0dDRNnz6dJkyY4FHw8czDD3hLSwsR3RbAiYmJZDabKSAggCZNmkSZmZmUnp5OTqeTlEol6XQ6ioyMpKSkJAoPD6esrCyGo76+npYtW0avvfYaWa1WGjt2LKWkpAwba3z++ecpOTmZcnJyaN++feRwOOj06dO0fPlymjZtGiUlJZG/vz+ZzWYmEAUCAcXGxtKmTZvo1q1bQ/qjtbWVXnnlFTbWs2bNYhPFHQ65XE4ymYx0Oh2tWbOGpFIpSaVSio6OZh6Nr68v6fV6euWVV5jLNhxVVVXRgw8+SDqdjqWTGY1G+vvf/+4RBx8qy83NpeTkZJowYQJb4MnLy6Pw8HD617/+RUVFRWzMfo0uXbpEOp2OHn74YXrzzTdp4cKFLIVsOIvPZrNRTk4OLVq0iHJyckgmk5HT6SSJREJJSUks/vruu+8Osb6PHj1KKSkpZLPZaOnSpfT3v/+dFixYQCqViq1T8G39msEyOA0TuB0Tnzt3Lm3atIkCAgL+YwHc3t7OwgM+Pj4UGhrq0TOxWCz09NNPk0wmYxbspk2bKDc3l6WxhYeHk16vpw0bNgzhjw8++IBiYmJo9uzZJBAISKFQkFKpZAqKD/mNHTt22NirQCAgPz8/2rhxI82aNYvkcjlt2LCBIiMjKSEhgSIiIkij0TDltGbNGjp9+jSbTyEhIZSRkUF2u51iY2OppKSEYayqqiKO48hms9GKFSuY1+gOB9F/kIY2ZswYxMbG4qOPPoJEIkFpaSlaW1tRXV3tMU0KAD777DO89NJLwG00aGlpwfHjx/HFF1/g1KlT+PHHH5GUlISffvoJN2/eRHJyMmw2m9u2uru7MWHCBDQ1NaG6uhpEhH/84x9YtWoVUlNT0dXVBavVinXr1mH06NH/9rMZDAb8+c9/Zmlwx44dw88//+wx3cnPzw/t7e0AgJaWFp7ZAQCXLl1CWFgYAgICcOXKFfzwww/4+eefUVlZicTERIwdO5alYlVVVUEikbBrJRIJbDYb3n//fYwePRonTpzA2bNnPabVmM1mbN26FZWVldi/fz+ee+45lJSU4OGHH8aOHTvQ2tqKsrIyPProowgJCUF5eTnEYjFmzpyJlStX4v7774ePjw9rr7S0FPPnz8f8+fPxj3/8AwAgl8vx448/4pFHHvHYf52dnejq6kJTUxM+/vhjzJs3DxzHoa2tDRMnToROp8Pq1auxe/duzJs3DwaD4VfHZGBgADKZDC+88AKam5vR09ODtLQ0vPHGGx6vaW1tBQAUFxejubkZZ8+eRUlJCdrb23H+/HnU1NRAr9fD4XBArVYPuf7rr7/GCy+84LbdEydOYPz48fDx8UF/fz+ioqKG7Y++vj7s2rUL+/fvx8DAAMaPH4/a2lr09vYiOjoaGo0GAwMD0Gq1kMvlLtdHRERg8uTJUCgUAID9+/fDarWira0NRITGxkZcvnwZUqkUer3eJTVzMPn4+KCurg5KpRIPPPAAxGIxTp48ie+++w5vvPEG5HL5kHtv3LgRv//97zFx4kTMmjULGzdudNt2V1cXduzYgbi4OFRWVuLKlSsu82AwtbS04IsvvkBUVBRUKhVCQ0OxadMmFBcXo6+vDwqFAtevX4evry9UKtUQedLX14crV66gqqoKDz30EEwmE55++mlYLBZ0dHRAKBRCIBAgPj4e/f39HnEMDAygpqYGhYWFKCkpQXd3Nz7//HOUlpbi0qVLMBqNEIlEiImJgdFohK+vL0aNGoVRo0YhIiIC/f39yM/PR1lZGcxmM0aMGAEAqK6uxty5c/G3v/2N9fl3333nFgNPomG/vYN8fHxYZ9XX14OIMGbMGMTExODPf/4zkpKSsHDhQpbzydPHH3+Mjz76CAMDA7h69SpUKhVqa2vR09ODiooKEBHa2tpw6dIl1NbWAgCam5tZDuSdJBQKkZWVhe+//559dvHiRWzatAnNzc3w8fFBbm4upkyZwvIr3VFPTw9OnDiBL774Aq+99hqUSiXmzp2LxsZG7Nq1i2EZnEd5J2m1Wtx3332477772EQJDg5GS0sLOI5DWVkZTCYTbty4gZaWFggEAly4cAFCoZDlTlosFqSmprI2W1tbsXnzZly4cAGtra1YuHAhZDIZ/vnPf7rF0NLSgpqaGqhUKrS1taGmpgYAcPLkSQBAbGwsvL29sXv3biiVSvj6+sJms6G9vR3Xr1/H9OnTWVuFhYV49NFHUVBQAKlUiqSkJEgkEiQkJODdd9/F4cOHPfaFSqXChAkT8NVXX+HKlSsYGBhAf38/ysvLIZfL0d3dDafTicTERJfrqqurcfDgQYhEIsyePdvlu7a2NuzZswenTp0CcFt5l5SUMF50RwMDA9BoNGhvb0d2djZWrlwJPz8/9PT04IcffkBLS4vHZyguLsa+ffvQ1NTk8rlYLIa3tzeuXr2K119/Hd9//z0eeugh9PT0eGyLFwKtra1obW3FoUOHIBAI0N3dDYlEgpKSEnR0dCAsLAwff/wxTCYTxo8fz67XaDRISEjAwYMH8eWXX6KxsRHNzc147LHH8Oabb7LfdXZ2ejQSAKC9vR2vv/46VqxYgePHjyMlJQXfffcdWltbUVFRgbi4OOzYsQOvvvoqIiMj0d7eji+++IIp6vb2diiVyiHt1tTU4IEHHkBhYSHUajWICAEBAUP6jieFQoHMzEx88MEH6O/vR2pqKnbu3ImSkhLIZDKMGjUKpaWlqK6uxvHjxzF69GiWmw/cHnuenyorK2GxWJCdnY2ysjK89957EAqFWLt2LaxWKwAM2yd+fn7417/+BYFAACLChQsX0NXVBSLCxYsXMXLkSJb3O5ja2tpw69YtdHZ2Ij4+HmvXrmXfdXV14ccff0RQUBCuXr2KsrIyVFVVecQA/MatyPymifr6epSWlmLKlCmoqalBUlIS6urqUFhYiGeeeQaLFy/G6dOn2XVRUVGYOHEiRowYgdraWnz99ddsMs6aNQsmkwl6vR6NjY148sknMX78eFy6dAn19fVucfT392Pz5s0wm82QSqUYP348wsPDIRKJcO7cOSxevBjTp08fIny3b9+OrKws/OlPf8LWrVuxYcMGvP766zh79uztzhAIoNVqcf36ddy8eRPA7c0JQqHQY390dXXhvvvuwx//+EeIRCL2eUVFBa5fv47q6mqUlZVBrVYjJCQEMTExEIvFKC8vh0ajQUREBOLi4jBp0iTWbk9PD4qLizF79mxcv34dP/30E44ePTrsxgPgNoNqNJoh3586dQo3btzATz/9hP7+fnh7e6O6uhr+/v5ITk52+W1DQwN++OEHtLW1obm5GRcuXEBVVRVOnDgB4LZ17Im6u7tx/vx5zJs3D0KhEA888ABCQkLQ29uLoqIirF69Gk6n0+WazZs3Y+HChVi/fj27B09lZWX461//isDAQJw8eRIcx0Gn06GwsBDXr1/32B9isRhdXV0ICgrCV199hb6+Puh0OvT19aGxsRH9/f1DrsnPz8dLL72EtWvXIj8/H15eXi7f9/f3o7m5mVl8NTU1iIuLQ0FBgcf+AG4bEgEBAUhKSkJtbS0qKiqYpXr58mWEhoYCAI4cOYJnnnkGe/fuHXJfoVAItVqNtrY2nD17FhcvXgTwfwJmYGAAeXl5HjG0trbiX//6F6RSKUpKSnD58mVkZGQwvhGJRPj222/xzTff4K233sKePXtQX1+Prq4uGI1GLF682K3n09PTg6KiIjzwwAOoq6uDUCjEmjVr2IaEO6mjo23ZaV0AACAASURBVANHjhxBdXU16urqcOXKFfj5+UGj0aC7uxtqtRr33HMP5s6di4qKiiEKVqPRQK/Xo7a2Ft3d3QgODsaLL74Iu90OkUgEm82GQ4cOMa/N07wVi8Wora1FdnY2mpubMXr0aDz00ENMyVRXV+PWrVswm814/vnnXeZId3c3Ojs7MWbMGLz88suIiYlxabu7uxu7du1Ca2sr7r//fuTk5HgcF+A3WsBdXV3o7+9HbGwsvv32W4hEIpSUlGDTpk2QSqVISEjAP//5Tzb5eAoPD0d1dTV+/vlnXLx4Ec3NzfjjH/+InJwcEBHa29tx8OBBNDc348yZM2hpaRnWSpFKpVCpVGhvb0d/fz9u3ryJzs5OSCQSvPDCC5g2bZpb1/L8+fPYu3cvQkNDUVBQwITgYPdfKBQyaykxMRGPPPKIx909/O6dd955BxMnTmQToqurC7GxsQgODsZXX32F5uZmtLa2oqOjA21tbcxFNpvNsNlsGBgYgNlsZu0qFApMnToVM2bMQGFhIc6ePQuDweD2mYD/25HX29vLdvgUFBSgr68Pc+fOxYEDBxAREYGSkhKmWNLS0vDwww8jMjKStVNVVYWjR48yDGPGjIFWq0VJSQlTqO6E12AcxcXFmDFjBgDgl19+wT333IOSkhI8++yzWLhwoUvY4cCBA9ixYwf0ej3uu+8+l52PRUVFeOaZZ3Dy5ElMnz4dP/zwA8RiMWbNmoVNmzZBIpEM62L29/fj1KlTuHTpEogIkZGRSE9Px+bNmzF//nzs2LED77//Pux2OwstlJaWsl1OvLHBE79b8vz587Barairq8PHH3+MoqIij/0B3OaF1tZWVFZWgogglUrR3d2NgYEBVFRUsLaam5tx/vx5lJeXu1wfGBiIxMREnDx5Emq1GjExMejt7UVsbCzMZjP27dsHLy8vnD171qO3JxAI8O2330IqlQK4rUR1Oh3sdjuUSiUqKirQ0NCArq4uNDQ0wGQyQa1WIy4uDgsWLMA999wDu90+pF1+R11RURFsNhsqKipQUVHhUfD19PTg559/Zu8jIiJw4MABqNVq1NTU4NSpU1i9ejUKCgrQ29uL0tJS3Lx5k4Ui+fnV1tYGkUiEgoIC3Lx5EzExMbDb7QgICMCxY8eg1WoBeOZVXpkWFBTgySefxLfffguHwwEvLy8IBAK0tbWhsrISTz31FHJycqDX6wEAX331FU6cOIHw8HAEBwcjKSkJAHDjxg2sW7cOarUaQqEQ4eHhiIiIwLVr13Dt2rVhLfHfJIDVajWLder1ehw/fhxpaWlobGxEVVUVampq0NXVBZ1Ohy1btkChULDtjA6HA5mZmRg1ahQuX76MS5cu4YcffoBMJkNZWRm0Wi36+vrwww8/ICgoaFgc/f39sNlsUCqVCAgIwPnz5zEwMAC9Xo+ZM2e6TPJdu3ahsbERdXV1+Omnn6DRaHDr1i3cvHkTLS0t8PHxcZn4/f39qKiowEMPPYTr16/j9OnTHrcSchyHrq4uNuA8dXZ2IiYmBhUVFejs7ISvry8qKytd3JHx48fjkUcegVardbEYampqsGbNGrS0tODzzz9HW1sb4uPj4e/vj/z8fLc4eItKIBBAJpOhtrbWJWyiVqvR1NQEi8UCh8OBixcvIjIyckj8sri4GJ988glEIhG0Wi1aW1sRERGBc+fOwW63QyKR4PLly8OOjUQiwTfffAOj0YhTp07B29sbzz77LB599FEmII4cOYL8/HwcOnQIoaGhyM3NRUREhEs7VVVV2LVrF+RyORu3jo4OJvB6e3uHFcBKpZJtl9+4cSMKCwshkUjQ29uLq1ev4siRI6isrERwcDCam5sxZswYZGRkQCqVsq2kg0koFMJsNrPQUUtLC/Lz8+Ht7Y3KykqP/TFu3DicPn2abUnmlW1DQwM6Ojpw8eJFtLe3w8vLiymOwSQQCNDf34/Ozk7IZDIEBATg+PHjLjzZ1dWF3bt3MwF7J8nlcrS2trpsmS4oKIDT6URraysMBgOys7OZu+/r64uuri44HA42f++kmpoa/OUvf4HFYsGnn37K3H4+puqOiAgvv/wydu/eje+//x7Xrl1DV1cXoqKiEBkZiebmZjgcDpw8eRINDQ04ceIE7r33XiaATSYTxo4di/LychQWFkKhUKCnpwdnzpzB73//exQVFaG3t5eFMtx5g4Pp8OHDuOeeeyAQCLBz504olUoEBwezeTJt2jQmfHft2oVNmzahtLQUo0aNwty5c1k7dXV12LJlC6Kjo8FxHAQCAfNcDQaDRz4FfqMA7unpgUAgQFFREXMlu7q60NPTAx8fH3zzzTeora2FQCDA8ePH4efnBy8vLwQGBsLPz49ZloWFhXjvvffw5ZdfsoU8Ly8vyOVyqNVqeHt74/z588ylv5O6u7tx8+ZNqNVq9PT0YOTIkTCZTKivr3dhzI8//hibNm1CY2Mj6uvr0dnZCY7j0NvbC6lUira2NpSWliIgIMCl7UOHDmHy5MloamoaVuDwbnpbW5vL552dnThy5AguXbqEadOmYfTo0cwK5gdj9OjRLnFfnhoaGvDWW29BrVZDLpdDJBKho6MDVVVVHmNrUqkUvb29WLBgATQaDV599VX09fXB6XQyV/LMmTMICgpCU1MTfv/73w9xnWpra3Hs2DHU1NRgzJgxOH/+PFM+Pj4+0Ov1aGpqQlRUFL788ku3OIRCIYhu10gwGAzgOA7nzp3D119/7WKdyWQy6HQ6pKSkYOLEiUOE75UrV/Dhhx9CIpGgs7MTZ86cgdVqRWtrK86cOQMAbPHTHUkkErZAKpVKQUTMGmlsbMS2bdsAAHPnzsWoUaPQ29uL8ePHexQ2FRUVePnll3Hjxg3Ex8cjPz8fHMfB19eX8ZInstvtOHXqFCZMmIDDhw+jt7cXCoUCWq0W5eXlaGhoAACEhoaivLx8SEz56tWr2LdvHwYGBiAWi6HX61FcXAyO4zBixAhMnz4dX331FQB4XATna0+0tbWhoqKC8dHNmzfh4+ODRYsWISsr699aFG1oaMDhw4dx6tQpbNmyBWPHjmXPxtOd3gNPRITe3l5YLBYEBgaioaEBfX19GBgYgEKhgNlsxunTp9HU1ITe3l4UFBTgxo0b7PrOzk4QEZKTkxEZGYlr165Bo9GgubkZlZWVGDlyJEpKSpgXMZzh5HQ6cfnyZXzyyScwGo24du0ajEYjnnrqKURGRiI4ONhlYVKhUGDSpEkQCASw2+2YMGECgNvexAcffAAfHx+0tbUxz6uhoQFKpRJ2u31Y/vhNAri7uxtRUVFQKpU4fPgwBgYGWMfHx8ejuLgY7e3tGDlyJEQiEb777jvU1tYiJyeHZSNIJBLExMTA29sbRqMR77zzDiQSCZKSkrBr1y50d3cPccPuJKlUisuXL6OlpQUNDQ0ICQnBmDFjmOXD09atW3Hx4kWEhoaip6cH1dXV8Pb2hre3N6qqqtDZ2Qmj0ehiLUqlUkyYMAEFBQVoaWlBUlISjh075hZHV1cXwsPDcfjwYfT397MiNQ8//DCampoQGxuLRx99lK2S/hpVV1fjs88+Q1xcHM6dOweHw4GqqioUFBRAJpN5jK3JZDIkJCRAIBCwxVEigtPphEQiYaGaW7duISwsDLNnz0ZcXBy7vrCwEFu2bMHhw4dhsVgwadIk/Pjjj+jr60NhYSHmz5+Py5cv4/r16yxDxBPxGBsaGuDv74+4uLghK+wJCQlISEhwe/3ly5fx6quv4vPPPwdwW6h7eXkhNTUV27ZtQ0pKCr766ivExcXh+PHjbtsQiUTo6enB0aNHYbFY0N/fzyZdQUEBMjIyYLFYsGjRoiExaXc0MDDAlO2oUaMAAEqlEgKBADU1NR6zU1QqFU6dOoW2tjaIxWLWb3zRJeD2vOnu7maC/M7FrtraWpw5cwadnZ0QiUQoKiqCRCJBcHAwU7L5+fmsGJQ76urqgkwmQ0dHh4s73NbWhoCAAEyaNOnfEr58XzQ3N6OkpASRkZG4cOECxowZw9Zj+AVgdyQUCrFp0yYAgMVigV6vR1FREcrKynD+/HkYjUYcP34cVVVV8PPzw61bt5iCAm7HsgsLC9He3g6bzYabN29Cp9MhIyMDH330EQICAiCXy9nis6f5MjAwgFGjRkGj0eCXX35ha018XN9dEbC0tDSkpaW5fFZdXY2tW7fi3XffRXBwMJvn586dQ2hoKKqqqvDjjz8OO19+kwCWSqUwGo3QaDSs0alTp+Lq1as4ffo01Go1DAYDbDYbiouLUVdXhx07duDWrVv405/+5GJh+Pj44JFHHkF5eTn27NmD8ePHY9u2bRCJRMzq9KTRpVKpy2JQRUUFpFIpnnrqqSFxUpFIBJPJhPb2dvj6+iI1NRXe3t44cOAANBoNS2fhSSgUwt/fn7k5t27d8miJC4VCyOVyiMVi7NixA7Nnz4ZIJMLKlSvd/j4/Px9FRUXQ6XTQaDTM/fvDH/4AAGyBMiUlBefOnUNLSwtMJhNu3rzpMa4G3BYGXl5eeOuttzB69Gim+Xnr3mQyISAgAM3NzViwYIGL8AWAEydOYOPGjZDJZAgMDIS3tzdEIhGcTieKi4vR1NSEU6dOob29HcXFxR7Tnfr7+1kmBnDbarBardi+fTvmzJnzqy4hcNvi++abb2CxWODr64vTp0/DbrdjxIgRkEgkWLhwIQCwtCV3xC82JSQkQCQSQSgUoq6uDnq9Hg888ACee+65ISmOBQUFkEgkbFFsMNlsNqxbtw5KpRIHDhwAcHvsy8vLodfrPWbriMViNhb79u1DYmIi2tvbUVlZieLiYgwMDLC0stOnT0OpVA5RViKRCN7e3sxT5GPaCoUCBQUFOHToEFP8w010qVSKCxcuQKFQYOLEifjxxx/R3t4OiUSC3bt3IysryyXjwBOZTCZMmjQJFy9eRGNjI0pKStDV1QWlUompU6fi4MGDHq8VCoUICAhAfn4+uru7IRAIoFKpIBKJUFNTg5CQEJY10tfXN0SAKpVKEBHL5uEXVkNDQzFq1Ch8+eWX0Gq10Gq1aG5u9jhniAidnZ3Q6/UQi8UIDQ1Fa2srEhMTh4QTh6Ouri6IxWKkp6fju+++wz333AOtVssymyZMmIAjR44MmynzmwSwQCBAb28v8vPz2WJcXFwcS7cyGAywWq0oKCjA1atXYbfbodVqsXv3bigUCixevBg6nQ7R0dEAwEpEfvrpp3jvvffAcRzkcjn6+voQERGBwsJCtzjEYjGEQiFUKhWam5sRFhaG4uJilwH79ttvmbu1Z88eyOVypKWlISUlBU1NTbDb7bj33nvx1FNPsWs6Ojqwbds2bN++naWf1NfXe7QuOI5Dd3c3zGYzXnjhBQQFBaG6upqVNKyurnbB9P777+PIkSNwOBz43e9+h9LSUjgcDiaA5XI5/Pz8cOHCBXR2duLGjRtYvnw5CgoK0N7e7lGAcRyH7du3g4jgcDhw/fp1ZjmrVCrce++9AG7H9u6Mr7e0tKC+vh5yuRxGoxFNTU24dOkSYmNjYbfbceXKFVRUVLB4cFRU1LCTLCYmBvn5+TCbzSgqKsK1a9ewa9cuFlfmy/QNDAzAaDTCbre7KEB/f3/Ex8fj0qVLSElJwcmTJ9HR0YETJ06gr68PFRUVyMzMxJw5czwuSvJWXmdnJ4qLi1nYRyAQYPXq1UPSJM+ePYsjR44gLCzMrQAGbi+Yrlq1Ct3d3Rg1ahT6+/vxww8/IDIyEvv373d7TWNjI+bMmQOJRIIzZ87gD3/4A7q6uvDdd9+hqakJgYGBKC0tZfHgvr6+Ie67w+FAVlYWLl++DJ1OB51Oh9LSUpw6dQoCgQCXL19mCnE4ARwcHIyioiJ0dnayBaLS0lLU19dj/fr14DgOPj4+rLyjRCJBQ0MDZDIZQkNDXRRWQ0MDDh06hBEjRsDPzw83btyARqOBUCjE+fPnPWLo6+uDQCAAx3GorKyEVquFVCpl6V/h4eEYGBhAX18fCgoKYDabXfLT+Zzbmzdvwt/fH1VVVairq8PatWuRl5eHs2fPIjAwEPfeey/effddj7Fo4LbXd+3aNSQnJ8PHxwfXr1/Hyy+/zJTQt99+y+L94eHhbhchAwICsGDBAkilUpw7dw47d+5EaGgo+vv7ce7cOcyYMQOVlZW4dOmSRxy/aSec2WymtWvXUlxcHHEcR8uWLSO5XE5xcXGkUqlY4RSn00kajYZV0kpOTqaHH36Y8vLy6J133nHZ3VJUVORSzJnjOAoMDKTNmzeTyWRyu4PEbDaTRCKhgIAA4jiOUlNTSSwWU1lZGWv3wQcfpMDAQFaIx8/Pj3Jzc+nNN9+kpUuX0oYNG9hvm5qaaO/evbRz506SSCQ0adIk8vLyIgDDbiUEQO+99x499thjZLPZaM6cOWQwGCghIYFmzZpFer2ezGYzq6ZksVjI19eXxo8fT7NmzaKEhAR6+OGHGY7CwkIKCgoikUhEJpOJbb/l+0er1brFoVKpWA0MvsReQEAA24WzfPlyCgoKon379tGddObMGVZDg39mq9VKTzzxBIWFhblUv9q6dSvb2uwOB19ti+M4Cg8PZzuUoqOjyc/Pj1QqFaWkpFBwcDAFBARQRkYGHT582AXPhQsXaN68eWS1WikzM5NsNhvpdDq2U9LpdFJ2dvaw5Sj5La8qlYr8/f0Zj9xZ64Lv83Xr1tHBgwddPr916xb9/PPPQ37PU1dXF8XGxjIsnvgjNTWVFi5cSEKhkBISEmj69OmUnJzMqgJmZmZSWloaeXl5kVKppLfeemvIvfbv308Oh4Oys7MpOTmZ9Ho9mUwmtq1YJpORUqkcdscmAEpMTGS75lJTU0kmk5FQKGRlSzUaDQUGBlJKSgrbgRcZGUmffPKJC56TJ09SYGAgHThwgMaPH08SiYR8fX3ZrtDhcPDPyWPiC+0DYIcF8KVIs7Oz6cyZMy73/uyzz8jPz4+Sk5PZwQIqlYrmzp1LZrOZ8vLy6O2332bV54brDwC0YcMGtpuWlx/8Tt3k5GTy8/Oj9957zyMfnD17ltLS0ig1NZXUajUtWbKEBAIBOxCAL0nqDgcR/TYLuLa2Fm+88QYrjLx7924IhUJYLBaUlZUxs99gMECn00Gv1+P3v/89Zs6ciZEjRw5pr62tDWfOnIHFYmFpZ/ziwtWrVz2movE7ovgA/cGDB+FwOFwKhcfFxUGr1WLPnj1oaGhAY2Mjix9FRETgySefZG198cUX+NOf/gSr1YqRI0eioKAA1dXV4DgOiYmJHhP+AbCFP51Oh+PHj6OpqQknTpxAZGQkvLy8YLFYcOnSJdjtdlRVVcFiscDb2xuXLl1CVVUVUlJSWFvd3d24du0ai+mOGDECb7zxBoKCgoZNzeNd/qSkJAwMDOB3v/sdOI5DVVUV+vv7UVBQgJEjR7oUGweApqYmnD17FpcuXUJ/fz+zGJqamnDw4EHU19czN47jOHh5eaGnp8djuhPHcTh69CjLnKD/TTHkY31SqRQnT55kixUcx+H48eMICgqCn58fgNshiN27d0Mul8NkMiEsLAw///wzy80dPXo0Tp06BbVa7RIfHEwymQzBwcFoa2tjFuXWrVtdLG2eDh06hNGjR2PcuHHss8rKSrzzzjs4evQoXn/9dSgUCtTX17ssXA4MDODkyZO4cOGCR0uc4zgcPHiQ7Zq7evUqfvrpJ8yaNQsKhQKHDx8GEcHb25tlFrnb8ADczgr59ttvkZ6ejsrKSvT09LCsFD7n+datW26v5Rfvjh07BqVSiZ6eHsTHxzNrlN9hplKpkJycjLKyMty4cQPd3d0Qi8UuaZrA7XCGTCbDunXr8PPPP0Mmk6GzsxMGg4GFNdyRQCCAl5cXiAhqtRq1tbWw2+1obm5mRdJv3rwJvV6Pmzdv4uzZszh48CD0ej1bKBcKhWhqakJ+fj5kMhnbBbdt2za2qzA3NxcymWzYhVrgdmhp69ataG1thZ+fHyoqKnDjxg08+uijqKioAACMGDHCxWO6efMmC3/29/ejtbUVoaGh+Pvf/w6O47Blyxa2+9NsNoPjOI8hTAC/vRbEggULWF1X3prhi53rdDqaNm0aZWVl0bPPPkuFhYUeNUdHRwd98cUXLrUl8L/aky/SbrVa3WoOvuAG/1KpVPTiiy+6VCRatGgRrV27lrKzs2n27Nk0depUiomJoVdffdUFx5EjR0ij0dCYMWPIYDDQunXrmCXOF57xtKecL8azZcsW2rBhA/n7+7PCHRkZGTRx4kSyWq3MEhWLxeRwOMjpdJLJZCKNRkM5OTkMy8mTJ1lVp+nTp1NCQoJL7dbhqsPx9+DrEA+uEgXA7V7/r7/+2qWwtVqtpqCgIBKLxeTv70+BgYFkNBrJ6XSywi18NTdP/TF9+nTavn07ORwOMhqNJJfLWZvA/9XetVqtJJfLKTg4mD766COGaevWrczzWLFiBes3u91OUVFRJBQK6cEHH2RF9d3hkMvltHHjRlq6dCk7Isud9euOamtradWqVSSXy8lut1N6ejo9+eSTFB4eTqWlpXTq1Cm6du0aHTt2jJxOJ/n5+Xn01PgjoPgDC8aOHctKhDqdTvLx8SGZTEZSqZQdr7Ny5cohWI8ePUpOp5N8fX1p/vz5JBQKWZEYjuPIz8+PvLy8PFriZrOZpk+fzqqgCYVCeuutt1wKEK1atYqysrJYURu+at2d84WIqKysjBVR5/mHt8b5Y4Pc4RhcOXDq1KkuxxEBYJUN+fcSiYREIhG98MIL7N579+6lwMBAEovF5OXlRcHBwaymRlhYGG3bts1FlrjDwX+fm5tLFouFrFYrLViwgBYvXkxarZasViuZTCYSiURDqtS9/PLLJJPJSK/Xs5Kz/LMLhULmRQqFQpo9ezarpOYOB9F/UIzn2WefZa6KWq0moVBIycnJpFQqKTs7m9LT0+nEiRPDMnlrayt9+eWXFBkZSXq9njiOY8Va1Go1mc1mkkqlHs9S4iezWq1mhT2cTidVVlYOe9/B1NLSQleuXKFPPvmEnTrAn9eG/y2Z5+/vTxaLxaPLrVar6cknn2QFSDIyMmj27Nnk4+NDSqWSncPGFwbhJ2xYWBjFx8eTv78/5eXlMUwFBQVksVjIYDAQx3Gk0+koLCyMFYcfztWNiIhgrh1f9UsgEJBer6fg4GA6efLkkD7461//SsHBwaxAUGJiIr311ltkt9vpiSeeILPZTAsWLKBXX32VlWA0Go0e+4MvgpOZmUkbNmygpUuXUlhYmEut12nTplFubi4tWbKEoqKihrjcn376KVMmvHs8WJFwHEchISEUHBzsse7s4LKZfPGhf6fwT21tLa1fv56sVquLi5yenk5Go5FmzpzJqspJpVLav38/O73C07jMmDGDOI6jiRMnsoL8MpmMHWDAF8VPSEigyMhIcjqd9Pbbbw/BduzYMQoODqYZM2aw2rU8f61evZq8vb3Jy8vLLQ6LxcJOFFm0aBHp9Xqqq6tzaX/btm2UmppKTqeTIiIihoQdeOrq6qK9e/eSyWSixx9/nF555RUymUwUHx9PAoGAlixZ4lHw8Yre29ubZDIZOydy8Okg7l6DBXBLSwv985//ZIKbL0ofEhJCIpGIJk+ezK77teJEcXFxNG3aNDZHecEO3K7zHRUVRd98840Lfzz99NNM9oSHh1NycjIlJyezzxYsWMDufe7cuWGrw9F/IoDffPNNSk1NJYFAQOPHjyez2Uyvv/46RUdHD2vxEhH19PRQfX09bdmyhfhKUQsWLCC5XE7p6ekkk8koPT2dXnjhBXI4HB4Hkmf6KVOmUGRkJLMiysvLh70/EVFzczOVlpbSO++8w0rb8Ydk+vv7s6pR/OCuXr3aowbjf/fpp5+SUqmkadOmUUJCAs2bN480Gg2r7MZPukWLFtHkyZNJKBRSTEwMrVq1ig4cOMCw3bp1i/Ly8pgmnjp1KjsYlD/00x0OgUBAW7ZsoaSkJBdmViqVtGDBgiH90tTUROXl5bRx40Z6+OGHac2aNTRt2jSSy+UUGhpK48aNo+DgYEpLS6MXX3yRdu7cSTqdjoxGI+Xm5nrEwXtCkZGRxHEc7dmzh7RaLTsBIj4+np588klKT0+ngIAASklJof3797tg27NnDztlwmq10ty5c5nFxyvGnJwcNkbucAw+eSEjI4PMZvMQgcNTbW0tlZaWUmlpKS1btoysVis5HA52yCwfS5RKpaRSqWj+/PmkUCiYhZOYmDisZ8IfdMmXMXziiScoKiqKJBIJO4IqMDCQ1baVy+W0du3aITjz8/MpMDCQli9fToGBgaRUKunpp58mgUBASUlJ9MEHHwy7VsF7DFFRUZSSkkJNTU2/MlOGUnd3Nx0+fJjGjh1LycnJtGvXLuax8aUa+eOl3OHgreNPPvmEpk6dSk6nk+RyOZlMJhab5pUsH9/VarX0t7/9jWG4ePEiPf744+wwYJVKRQqFgjIyMljBfb6d4QyWZ599lhksWq2WjEYjZWVlsaPWANDevXvZfRsaGmj58uUuBePj4uJo1apV7HgmoVDIeHfwwbHe3t5ucdBvFcD8uWX8BOfNd3elDAdTa2srq+06Z84ccjgclJmZSQ6Hgx0zsnDhQlaMe968eYT/Ddi7wxEREUGpqamMkWNiYig2NpYKCwupr6/PI4729nZau3Yt03YpKSlkNpspOTmZLBYLqVQqSk9PdxHC/FEpngaSH6ywsDD2XPwE5TiOnYwqFotJJpNRUFAQO39txYoVLkWn+RCEVCqlxMREF0Xw8ssve5zofNiDnwx8uUKeKe5cyFi1ahWZTCYKDAykkJAQmjBhAi1ZsoRiY2Np8+bNlJmZSWq1mnx9fentt98mqVRKK1eupLVr1w67+KVQKOiRRx6hnJwcksvllJmZOeS0XLlcTnl5ebR582Zas2YN7d2716UM44kTJyglJYUkFXxiAwAADXVJREFUEgnZ7XZKTEwkPz8/On36NDsIgD/3bjiBM9iimjlzJlVUVFBdXR1VV1dTTU0NVVdXU0NDw//X3tWGtHW2/3PMqyYxiW+JrSZZdJrFTEMb3KGRmmDSKp1oUImhL3qwlUhrG4m2irJpyyp0L52Uroy9iBW3sjnoEIWyD906GGyj7b7sU8fGn7HC2Bhuhdg5tt//Q3dfnLMl7unDoDxwfnA+qG1y3fd93dfbfZ37h+7ubqKwYs7L4/Hg/PnzWFpaoqtJWdRbVlaGjo4Oiq63up6zoKCAIqldu3YRhxtb0+HhYRnLSFtbG9rb27NGwLdu3UIwGEQoFEIgEEBJSQkmJiagVqsxPj6Ot956K2dpiI1LpVJBq9WirKzsP8oIGH799Vf8+OOPWF1dpTt3WZmQcai53W6Ew2GEQqGc+4VdGv/+++/jm2++QSAQQCAQwKVLl2RZR2lpKbq7uyEIAqanp/H999+TLCsrK4jH47hy5QqqqqrAcQ+YPFKpFFpbW4lmipUwculHZ2cn7ZOJiQnY7XY0NDRgfn6e6Kmk0W8qlYLD4UBnZydaWlrg9/tx8OBByrCke1Wr1WJoaIhYTHIdBuJhDTDbTMwL/NNdqplMBuvr6zh58iQqKytlk8Pu2iwuLsbu3buh1WpRVFSE+fl5GXV8LoXiuAcdCIFAAP39/ZTS3Lx5E+vr61mf5557Dj6fDx6PB01NTXj22Wfh9/sRCASQn5+PUCiEn376CV988YVMzieeeCLnQnq9XphMJkr5W1paiEHCYrGgpKQEZ8+eRTAYxP79+xEOh6FWq1FVVYXW1la88MILNF83b96kSCAbdUyuWiNzHnq9HqIowul0EnWOy+XCl19+KVuXwcFBKk+wNMzv96OyshL79u2Dw+HAuXPn0NnZCa/XC47jSFkFQci50VlE4fV6iSoqGo1SZJeXl4fBwUEq0/A8j23btuHVV18l2ZaXl2EwGGA2mxEOh9Ha2kon/qyO6fP5IAhCzhSTOXWmq0eOHEE6nabzCovFApPJhFAohJ6eHoiiiFAoBKvVirKyMrjdbpjNZlRUVCAYDMLj8WBtbY0iHUarxKKbXI5Rr9fj7t27MBgM8Hq9cLvdRG2j0WjoMnBWfguHwxAEAWfOnMHPP/+Me/fuIZPJ4JdffsHq6iqKi4uxb98+KvcxXVGpVETplGvfslo+uyv3n7LF3377jb773XffRV1dHQoKCrB9+3aUl5ejpaWFHFZRURGam5sRjUbB8zwef/zxrHI8+eSTGBwclAUHUh3X6XR0fuL1enHgwAFcuHABn3/+Ocl1+/ZtzM3N4c0334RGo4FOp6O6M/8n3x5zqLlSfxYds1KB0+mEXq9HRUUFKioq6K7mjz76COvr69jY2MCJEycoaPH5fGhsbERxcTHpMRuP3W6HWq2G3W7H+Pg4OI7bkpb+oQywdMGnp6exvr6OTCaDjY0NbGxsYHNzExsbG8hkMshkMhBFkQyp0+mkkJzVYPbv34/p6WlUVVWhs7OTlJsNMpcBZgzMbOBqtRpqtZo4naQGXlorevrppzE7O4t0Oo1kMknRWklJCfx+P0W7UqXgeT4nOzOLKkRRRDgchtPpxJEjRzA0NISOjg6q5c7OzqK5uTlrnSsej2NzcxObm5v4+OOPKX0ZGxuTybBVTWvbtm344IMPMDU1BbVajUQiQZeh8zyPW7du4f79+8hkMtjc3MQbb7xBpYZIJILy8nJ4PB4ZI7IgCDCbzUgmkzAajbhx4wYRazqdzpyKzeaPOQGmgE6nU5Zm1tfXw+FwQKVS4fTp07LNv7a2BpPJRJmI0WikiCs/Px9Wq5Wi0GxysHKNSqXC5cuXUV1djWPHjsFqtWJ5eRm1tbV46aWXEAgEsLS0hL6+PnDcA6qmaDQKt9tNHHEc9+BAcGFhAbW1tTCbzTh06BDNz8DAQM4Uk33moUOHYDQaIQgCbDYbUqkUioqKUF9fj6tXr8pKRzU1NWhoaIDZbIbf75e1UzocDjpczcvLI+Pv9/vB8zzq6+uzyqFWqzE2NkZknmz//vDDD7RXpfs2k8lgeXkZoijSPmStnCz6drlcWamVGJVPrvmQPoIgyDjpCgsLoVarYbFY0N7eDq/Xi46ODty4cQP379/HxsYGFhYWiNPQaDQSU7W07HDx4kUKyLLJYTAY0NvbS7o6PT2Nvr4+OlMqLS0lp8hxHEZHR5FIJHD48GE6QJ2amsKlS5fg9XqJXZs5fpaZS2X7VwwwW7yuri64XC6UlZVR1MBu7Xc4HDCZTNDpdBTBsJNBaRoq7aFkdTaNRoN4PI4XX3wRp06d2tKDmc1m6vEcHR3F5OQkvF4veTaHwwGr1UqTLAgCduzYAbvdDpfLhXg8juvXr4PR1xQXFyOdTqO7u1tmKNkhUDY5GhoacPjwYYo22fdotVqMjIxgenoaLpcLgiDA7XbD5XJR2sqifIfDAVEUkU6nZakTWzye56mXMJccNpsNfr+fNkMymZQRPn722WcIBoPIy8vDxMQEYrGYTMGcTicCgQAEQcDbb7+NRCIBo9FIDpOl/cwg5ZLDZDJhYGCACE3ZQdnx48cxMjIiI5YMhUJIp9MQRRGLi4v4/fffZUZ4c3MT165dkzkhs9mMe/fuobCwcMt1YdF6LBYjo8/zPFKpFEUtrLZut9uJvbuiooJq7VIdYOvlcrmIHkqr1eLKlSt47bXXcspRWlqK3t5e+qw7d+7A7XbLPputkd/vlzktaYBRUFBAUR57DAYDxsfHoVKpaF5zycFxHN555x0sLi5Co9EgFosR3Q7TW3aozpxsJBLByZMn0dzcDI7jUFlZiXQ6jfPnz9N36fV6Yhtn+jwyMoLHHntsSwPM6H2k45H+/PLLLyMSiRDDzdDQECKRCPEYDg0N4fXXX0dFRQX13ErXSqPR4KuvvsoZeXIchz179tDBIYu+u7q68O2338Lv92NmZob2VK4DwpKSEuzcuVO2juyzhoeHaVy5atH/lQGWPtFoVNbYHQgEUFBQgOrqavT29mLnzp3Q6XSwWCyk2NKHsQBPTExkHWCu02Wz2YxXXnkFeXl55DVNJhN1UUgXs6amhhqty8vLEYvF6KSU/5PWJRQKoaqqCiqVCg6Hg/7OiDm3YnkVBAGRSAR37tyRKcL8/DwGBgZw+vRpCIKA48ePIxgMorq6Gv39/YhGowiHwzh69ChtKJfLJaNSlz7/9ELIxYsXIYqijOl1ZGRENu+jo6NElCmKImUdHPegbMBeyJB+r9lsxuLiIkRRxCeffLLlRq+srITP58Pk5CRCoRDi8ThWV1f/Ruh55swZSufb2toQCoUwMjKCP/74g57r16+TofN6vfQiRnt7O8mYa4OVlZURV9+OHTvA2uOY0d2+fTvOnTv3t+hJOhfRaJS+g9Hk9Pf3U1Su1+tx+/ZtOvTJpR/vvfeeLFplj8/nQ0FBAQYHB1FTU4OWlhZZia65uRmJRII6SNipu1ROnU6HsbExWedINjny8/PB8zyamprIULBODOnnMcPT1dUFs9lMNWN29sNaBysrK2EwGNDW1oZYLAaNRkPzZbVatwyceJ7H2bNnsbKyQuvBcQ8IfZnD53ke8Xgczc3NMr642tpaKm05HA7qPpAa8cbGRuzZs2dLPZWOW0rOyV56Ymc4HMfh+eefJ3b0vzpk9rPb7SaKLCZHfX09VCrVliSlD22AHQ4HeXSVSoWBgQHYbDbYbDZ8+umn6OnpgcFgQGNjI8LhMGZmZtDW1oa9e/dS3x+LQNjgPR4PHA4H6urqZAMMBoNbGpxdu3bJCvds43R0dIDRks/OzmJhYQHhcBharRaJRAKTk5NoamrC5OQkFhcXSblbW1shiiLcbjf1WNpsNhw9ejQnG7GU/vzq1aukUKIoIpVKUWTS09NDb4YVFRXBYrGQ0zCZTPB4PEgmk3SKK32D5q9PNjmkSrS2tkYMyna7HXv37pW1ciUSCaRSKSQSCVlJSKpMqVSK1sFoNFLXBmuxySWHxWLBysoKLBYLfD4f6urq/hbpsBSW4x7wkjU0NJAxkW5I5pR2796N3t5eIq68du0alpaWtpSD1X85jkNfXx/m5uZgNBoRi8Vw8OBBVFVVweVywe12kzG3WCyUNWWbd4/Hg7t378Lv9yMSicBqtVKfci45CgsL8fXXX8vm4MMPPyTiRr1ej7m5OYRCIcrcpqamcOLECQpkpGvEeMayybeVHBzH4dixY2Q0dDodTp06hbq6uqzOfnh4GLFYDGq1Gv39/ZiZmQHHcdS3LZWJ8S76fD7wPL8ld6E0Uk8mk9BqtXjqqadkpalEIoHLly/T/j9w4IDMALIe3Gxj53keNpuNmLm3mg9W9pH+f5/PR73B7Hc2mw3JZBKhUIj2x4ULF/Ddd9/hmWeeySqHVqtFX18feJ5HKBTasguC/1Og/wg8z//Acdz//eM//PfgBFD6118qcihyKHIocvyvy8Fx3MMZYAUKFChQ8O/hoVmRFShQoEDBvwPFACtQoEDBI4JigBUoUKDgEUExwAoUKFDwiKAYYAUKFCh4RFAMsAIFChQ8IigGWIECBQoeERQDrECBAgWPCIoBVqBAgYJHhP8HwUSObhznBHkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "azr6ZIbi5KlQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f267ca8c-319e-4e7e-bf18-3a1923629814"
      },
      "source": [
        "main_noisy(noise_factor_train = 0.7, noise_factor_test = 0.4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for fold 0 ...\n",
            "Epoch 1/10\n",
            "20/20 - 2s - loss: 1.5988 - accuracy: 0.5011 - val_loss: 2.0868 - val_accuracy: 0.4678\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.5016 - accuracy: 0.8424 - val_loss: 1.9849 - val_accuracy: 0.4218\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.3324 - accuracy: 0.8963 - val_loss: 2.1510 - val_accuracy: 0.2359\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.2550 - accuracy: 0.9219 - val_loss: 2.3627 - val_accuracy: 0.2160\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.2053 - accuracy: 0.9384 - val_loss: 2.5281 - val_accuracy: 0.2060\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.1708 - accuracy: 0.9494 - val_loss: 2.6209 - val_accuracy: 0.2028\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.1462 - accuracy: 0.9566 - val_loss: 2.6727 - val_accuracy: 0.2035\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.1289 - accuracy: 0.9613 - val_loss: 2.6743 - val_accuracy: 0.2033\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.1131 - accuracy: 0.9661 - val_loss: 2.6512 - val_accuracy: 0.2025\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.1020 - accuracy: 0.9698 - val_loss: 2.5860 - val_accuracy: 0.2044\n",
            "Score for fold 0: loss of 2.5859901905059814; accuracy of 20.444999635219574%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 1 ...\n",
            "Epoch 1/10\n",
            "20/20 - 2s - loss: 1.4616 - accuracy: 0.5279 - val_loss: 2.1247 - val_accuracy: 0.3736\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.4705 - accuracy: 0.8553 - val_loss: 2.1132 - val_accuracy: 0.1681\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.3069 - accuracy: 0.9070 - val_loss: 2.3111 - val_accuracy: 0.0987\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.2312 - accuracy: 0.9299 - val_loss: 2.5474 - val_accuracy: 0.0984\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.1856 - accuracy: 0.9439 - val_loss: 2.7288 - val_accuracy: 0.0984\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.1539 - accuracy: 0.9534 - val_loss: 2.8818 - val_accuracy: 0.0984\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.1303 - accuracy: 0.9603 - val_loss: 2.9572 - val_accuracy: 0.0984\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.1130 - accuracy: 0.9653 - val_loss: 3.0241 - val_accuracy: 0.0984\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.0990 - accuracy: 0.9701 - val_loss: 2.9869 - val_accuracy: 0.0984\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.0874 - accuracy: 0.9735 - val_loss: 2.9762 - val_accuracy: 0.0984\n",
            "Score for fold 1: loss of 2.976152181625366; accuracy of 9.839999675750732%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 2 ...\n",
            "Epoch 1/10\n",
            "20/20 - 1s - loss: 1.4670 - accuracy: 0.5332 - val_loss: 2.0937 - val_accuracy: 0.4194\n",
            "Epoch 2/10\n",
            "20/20 - 1s - loss: 0.4109 - accuracy: 0.8730 - val_loss: 2.0468 - val_accuracy: 0.2618\n",
            "Epoch 3/10\n",
            "20/20 - 1s - loss: 0.2683 - accuracy: 0.9186 - val_loss: 2.3240 - val_accuracy: 0.2419\n",
            "Epoch 4/10\n",
            "20/20 - 1s - loss: 0.2047 - accuracy: 0.9383 - val_loss: 2.6523 - val_accuracy: 0.2121\n",
            "Epoch 5/10\n",
            "20/20 - 1s - loss: 0.1658 - accuracy: 0.9503 - val_loss: 2.8996 - val_accuracy: 0.2037\n",
            "Epoch 6/10\n",
            "20/20 - 1s - loss: 0.1400 - accuracy: 0.9582 - val_loss: 3.1103 - val_accuracy: 0.2016\n",
            "Epoch 7/10\n",
            "20/20 - 1s - loss: 0.1210 - accuracy: 0.9637 - val_loss: 3.2359 - val_accuracy: 0.1985\n",
            "Epoch 8/10\n",
            "20/20 - 1s - loss: 0.1053 - accuracy: 0.9683 - val_loss: 3.2885 - val_accuracy: 0.1854\n",
            "Epoch 9/10\n",
            "20/20 - 1s - loss: 0.0934 - accuracy: 0.9727 - val_loss: 3.2969 - val_accuracy: 0.1919\n",
            "Epoch 10/10\n",
            "20/20 - 1s - loss: 0.0840 - accuracy: 0.9754 - val_loss: 3.2406 - val_accuracy: 0.1918\n",
            "Score for fold 2: loss of 3.2406129837036133; accuracy of 19.175000488758087%\n",
            "Model: \"sequential_23\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_46 (Conv2D)           (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "batch_normalization_46 (Batc (None, 26, 26, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_46 (MaxPooling (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_47 (Conv2D)           (None, 11, 11, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_47 (Batc (None, 11, 11, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_47 (MaxPooling (None, 5, 5, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_23 (Flatten)         (None, 800)               0         \n",
            "_________________________________________________________________\n",
            "dense_46 (Dense)             (None, 64)                51264     \n",
            "_________________________________________________________________\n",
            "dense_47 (Dense)             (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 61,738\n",
            "Trainable params: 61,610\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 3.1047 - accuracy: 0.2111\n",
            "Test loss : 3.1046996116638184\n",
            "Test accuracy: 21.10999971628189\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
            "  if sys.path[0] == '':\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9_c7EWwvkB4"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}