{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Chapter8.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "vlgfrYr1hu5R",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "# Multi Layer Perceptron\n"
      ]
    },
    {
      "metadata": {
        "id": "O-vGzvRrhs1j",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aA7w6V7_Xlld",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# set seed for reproducibility\n",
        "seed_val = 9000\n",
        "np.random.seed(seed_val)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4htRAMpvzwpJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "63b3f7fd-37bf-4751-8195-2f02b9e17813"
      },
      "cell_type": "code",
      "source": [
        "from keras.datasets import mnist"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "kj4A1dlbVWBI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "77e2e336-54a2-4725-9dca-68f40456e2b7"
      },
      "cell_type": "code",
      "source": [
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 4s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "383HE9kGVZwB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "26b9db3a-4253-4301-c4dc-439d3f27a62b"
      },
      "cell_type": "code",
      "source": [
        "print('Size of the training_set: ', X_train.shape)\n",
        "print('Size of the test_set: ', X_test.shape)\n",
        "print('Shape of each image: ', X_train[0].shape)\n",
        "print('Total number of classes: ', len(np.unique(y_train)))\n",
        "print('Unique class labels: ', np.unique(y_train))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Size of the training_set:  (60000, 28, 28)\n",
            "Size of the test_set:  (10000, 28, 28)\n",
            "Shape of each image:  (28, 28)\n",
            "Total number of classes:  10\n",
            "Unique class labels:  [0 1 2 3 4 5 6 7 8 9]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "IaQgGn0eVZzs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "3c7ee66c-6c95-4cc8-b2d0-2493c242bace"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# Plot of 9 random images\n",
        "for i in range(0, 9):\n",
        "    plt.subplot(331+i) # plot of 3 rows and 3 columns\n",
        "    plt.axis('off') # turn off axis\n",
        "    plt.imshow(X_train[i], cmap='gray') # gray scale"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+TW8jvnbt2kqPX7hwoet1fn5+tIeEBDVz5kzJurV44cIFyZs2bXK9R09/PnfunOd59Z54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\n76Fl5HIC0xUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f81738c6320>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "u84baf1yoruv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "f988d321-1e62-4aef-e872-c344c6db491b"
      },
      "cell_type": "code",
      "source": [
        "print('Maximum pixel value in the training_set: ', np.max(X_train))\n",
        "print('Minimum pixel value in the training_set: ', np.min(X_train))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Maximum pixel value in the training_set:  255\n",
            "Minimum pixel value in the training_set:  0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "TBmhrLEEhO80",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "3c746b09-b5c8-427e-d4d3-fd599312a379"
      },
      "cell_type": "code",
      "source": [
        "!pip install keras"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: keras in /usr/local/lib/python3.6/dist-packages (2.1.6)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras) (3.13)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras) (2.8.0)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras) (1.11.0)\n",
            "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras) (0.19.1)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras) (1.14.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "62APeEgHXuxd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Number of epochs\n",
        "epochs = 20\n",
        "\n",
        "# Batchsize\n",
        "batch_size = 128\n",
        "\n",
        "# Optimizer for the generator\n",
        "from keras.optimizers import Adam\n",
        "optimizer = Adam(lr=0.0001)\n",
        "\n",
        "# Shape of the input image\n",
        "input_shape = (28,28,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aRabej2hzT_C",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "model = Sequential()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yXgvv7_VzUFI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.layers import Dense"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_qEhdg-dzULQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.add(Dense(300, input_shape=(784,), activation = 'relu'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dXQ61rtdzUJA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.add(Dense(300,  activation='relu'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "n_oEzLiEzUC5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.add(Dense(10,  activation='softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "G-SC1hIXzeqC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.compile(loss = 'sparse_categorical_crossentropy', optimizer=optimizer , metrics = ['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0I8FTXg1zg8n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "4747d853-519a-45d7-8c4b-ca04f5b44b1b"
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_1 (Dense)              (None, 300)               235500    \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 300)               90300     \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                3010      \n",
            "=================================================================\n",
            "Total params: 328,810\n",
            "Trainable params: 328,810\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "a8uovO1E3LbQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "4ad23f26-5322-4dcd-94e4-c5704d7a59d6"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, stratify = y_train, test_size = 0.08333, random_state=42)\n",
        "\n",
        "X_train = X_train.reshape(-1, 784)\n",
        "X_val = X_val.reshape(-1, 784)\n",
        "X_test = X_test.reshape(-1, 784)\n",
        "\n",
        "print('Training Examples', X_train.shape[0])\n",
        "print('Validation Examples', X_val.shape[0])\n",
        "print('Test Examples', X_test.shape[0])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Examples 55000\n",
            "Validation Examples 5000\n",
            "Test Examples 10000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "f1w9C3tH3mMw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "outputId": "2889253d-eaac-4f00-8581-48afae97f2b3"
      },
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, epochs = epochs, batch_size=batch_size, validation_data=(X_val, y_val)) "
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 55000 samples, validate on 5000 samples\n",
            "Epoch 1/20\n",
            "55000/55000 [==============================] - 3s 64us/step - loss: 7.1672 - acc: 0.5436 - val_loss: 5.5122 - val_acc: 0.6494\n",
            "Epoch 2/20\n",
            "55000/55000 [==============================] - 2s 45us/step - loss: 4.4734 - acc: 0.7121 - val_loss: 4.0397 - val_acc: 0.7408\n",
            "Epoch 3/20\n",
            "55000/55000 [==============================] - 2s 45us/step - loss: 3.8726 - acc: 0.7528 - val_loss: 3.9086 - val_acc: 0.7508\n",
            "Epoch 4/20\n",
            "55000/55000 [==============================] - 2s 44us/step - loss: 3.7606 - acc: 0.7606 - val_loss: 3.9568 - val_acc: 0.7488\n",
            "Epoch 5/20\n",
            "55000/55000 [==============================] - 2s 44us/step - loss: 3.6868 - acc: 0.7661 - val_loss: 3.7129 - val_acc: 0.7626\n",
            "Epoch 6/20\n",
            "55000/55000 [==============================] - 2s 44us/step - loss: 3.6235 - acc: 0.7706 - val_loss: 3.7490 - val_acc: 0.7612\n",
            "Epoch 7/20\n",
            "55000/55000 [==============================] - 2s 45us/step - loss: 3.5857 - acc: 0.7729 - val_loss: 3.6783 - val_acc: 0.7652\n",
            "Epoch 8/20\n",
            "55000/55000 [==============================] - 2s 44us/step - loss: 3.5463 - acc: 0.7758 - val_loss: 3.6492 - val_acc: 0.7696\n",
            "Epoch 9/20\n",
            "55000/55000 [==============================] - 2s 44us/step - loss: 3.5238 - acc: 0.7777 - val_loss: 3.6053 - val_acc: 0.7718\n",
            "Epoch 10/20\n",
            "55000/55000 [==============================] - 2s 44us/step - loss: 3.4849 - acc: 0.7809 - val_loss: 3.6315 - val_acc: 0.7688\n",
            "Epoch 11/20\n",
            "55000/55000 [==============================] - 2s 45us/step - loss: 3.4761 - acc: 0.7809 - val_loss: 3.6378 - val_acc: 0.7696\n",
            "Epoch 12/20\n",
            "55000/55000 [==============================] - 2s 44us/step - loss: 3.4597 - acc: 0.7821 - val_loss: 3.5786 - val_acc: 0.7720\n",
            "Epoch 13/20\n",
            "55000/55000 [==============================] - 2s 44us/step - loss: 3.4357 - acc: 0.7842 - val_loss: 3.5871 - val_acc: 0.7716\n",
            "Epoch 14/20\n",
            "55000/55000 [==============================] - 2s 44us/step - loss: 3.0561 - acc: 0.8063 - val_loss: 2.2018 - val_acc: 0.8560\n",
            "Epoch 15/20\n",
            "55000/55000 [==============================] - 2s 45us/step - loss: 2.0143 - acc: 0.8695 - val_loss: 2.1566 - val_acc: 0.8582\n",
            "Epoch 16/20\n",
            "55000/55000 [==============================] - 2s 45us/step - loss: 1.9224 - acc: 0.8766 - val_loss: 2.0603 - val_acc: 0.8646\n",
            "Epoch 17/20\n",
            "55000/55000 [==============================] - 2s 45us/step - loss: 1.8755 - acc: 0.8797 - val_loss: 2.0580 - val_acc: 0.8666\n",
            "Epoch 18/20\n",
            "55000/55000 [==============================] - 2s 44us/step - loss: 1.8449 - acc: 0.8822 - val_loss: 2.0403 - val_acc: 0.8670\n",
            "Epoch 19/20\n",
            "55000/55000 [==============================] - 2s 44us/step - loss: 1.8198 - acc: 0.8838 - val_loss: 1.9892 - val_acc: 0.8698\n",
            "Epoch 20/20\n",
            "55000/55000 [==============================] - 2s 44us/step - loss: 0.6319 - acc: 0.9553 - val_loss: 0.5265 - val_acc: 0.9574\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "cDUsdS0K4Q5x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "989daffe-a089-4ba8-fb78-647249c10d8f"
      },
      "cell_type": "code",
      "source": [
        "loss,acc = model.evaluate(X_test, y_test)\n",
        "print('Test loss:', loss)\n",
        "print('Accuracy:', acc)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 63us/step\n",
            "Test loss: 0.5244417602744154\n",
            "Accuracy: 0.9609\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-1MQcvrH4eRy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "outputId": "ddc6619a-0132-41e4-d0ce-82f87c9cd8d2"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def loss_plot(history):\n",
        "    train_acc = history.history['acc']\n",
        "    val_acc = history.history['val_acc']\n",
        "\n",
        "    plt.figure(figsize=(9,5))\n",
        "    plt.plot(np.arange(1,len(train_acc)+1),train_acc,  marker = 'D', label = 'Training Accuracy')\n",
        "    plt.plot(np.arange(1,len(train_acc)+1),val_acc,  marker = 'o', label = 'Validation Accuracy')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.title('Train/Validation Accuracy')\n",
        "    plt.legend()\n",
        "    plt.margins(0.02)\n",
        "    plt.show()\n",
        "\n",
        "    train_loss = history.history['loss']\n",
        "    val_loss = history.history['val_loss']\n",
        "\n",
        "    plt.figure(figsize=(9,5))\n",
        "    plt.plot(np.arange(1,len(train_acc)+1),train_loss, marker = 'D', label = 'Training Loss')\n",
        "    plt.plot(np.arange(1,len(train_acc)+1),val_loss, marker = 'o', label = 'Validation Loss')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.title('Train/Validation Loss')\n",
        "    plt.legend()\n",
        "    plt.margins(0.02)\n",
        "    plt.show()\n",
        "    \n",
        "loss_plot(history)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wc+cPzJz5ISqViqSksfTp05OFCxfzzDNlHa5fe+1Fhg8fwXXXjbzk774lCAsLbPHLUDRW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\nS5pbPPjgg/z7v/87t99+O08++STvvvsujY2NPPfcc26q/uq09T2Xlpb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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f57525ae048>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6u/L3sS/7OPGmuFb3T4838/2xYjbvzmXskNhOnTs2NpSioqpO1dFXBXPfIbj7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\nEBEREZ9S+BARERGfUvgQERERn/o/NwhPKPprdGwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f57273ffe10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "hV1yskYHhjpm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zxX3wVhthlUn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Convolution\n"
      ]
    },
    {
      "metadata": {
        "id": "HgpHRJkg3NJe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "275d094b-a2da-44e8-905f-80a4677bc904"
      },
      "cell_type": "code",
      "source": [
        "array = np.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1])\n",
        "kernel = np.array([-1, 1, 0])\n",
        "\n",
        "# empty feature map\n",
        "conv_result = np.zeros(array.shape[0] - kernel.shape[0] +1).astype(int)\n",
        "\n",
        "for i in range(array.shape[0] - kernel.shape[0] +1):\n",
        "    # convolving\n",
        "    conv_result[i] = (kernel * array[i:i+3]).sum()\n",
        "    print(kernel, '*', array[i:i+3], '=', conv_result[i])\n",
        "    \n",
        "print('Feature Map :', conv_result)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-1  1  0] * [0 1 0] = 1\n",
            "[-1  1  0] * [1 0 1] = -1\n",
            "[-1  1  0] * [0 1 0] = 1\n",
            "[-1  1  0] * [1 0 1] = -1\n",
            "[-1  1  0] * [0 1 0] = 1\n",
            "[-1  1  0] * [1 0 1] = -1\n",
            "[-1  1  0] * [0 1 0] = 1\n",
            "[-1  1  0] * [1 0 1] = -1\n",
            "Feature Map : [ 1 -1  1 -1  1 -1  1 -1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "M8HXH3q88W6n",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.layers import Conv2D"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2VUDY4DZ-FnF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "fe420faa-d130-4c03-9b30-b22d0760ba29"
      },
      "cell_type": "code",
      "source": [
        "X_train = X_train.reshape(-1,28,28,1)\n",
        "X_val = X_val.reshape(-1,28,28,1)\n",
        "X_test = X_test.reshape(-1,28,28,1)\n",
        "\n",
        "print('Train data shape:', X_train.shape)\n",
        "print('Val data shape:', X_val.shape)\n",
        "print('Test data shape:', X_test.shape)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train data shape: (55000, 28, 28, 1)\n",
            "Val data shape: (5000, 28, 28, 1)\n",
            "Test data shape: (10000, 28, 28, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zOn0kJr28mZr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = Sequential()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SWCzx9vC8ox3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.add(Conv2D(32, kernel_size=(3,3), input_shape=input_shape, activation = 'relu'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "iy9TYs1P8sZ0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.layers import Flatten\n",
        "model.add(Flatten())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Xo2YGv0G8shL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.add(Dense(128, activation = 'relu'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uGVjoCKF9NAr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.add(Dense(10, activation = 'softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "80dw1wPS9PJX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "8d877b7f-d41c-4924-f190-150740b586cb"
      },
      "cell_type": "code",
      "source": [
        "model.compile(loss = 'sparse_categorical_crossentropy', optimizer= optimizer, metrics = ['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 21632)             0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 128)               2769024   \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 2,770,634\n",
            "Trainable params: 2,770,634\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "drBYss2V9RIb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "outputId": "28a7bba4-96d8-4098-89e9-a8da1d46b643"
      },
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, epochs = epochs, batch_size=batch_size, validation_data=(X_val, y_val))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 55000 samples, validate on 5000 samples\n",
            "Epoch 1/20\n",
            "55000/55000 [==============================] - 6s 115us/step - loss: 8.4453 - acc: 0.4725 - val_loss: 6.0399 - val_acc: 0.6208\n",
            "Epoch 2/20\n",
            "55000/55000 [==============================] - 5s 98us/step - loss: 5.4658 - acc: 0.6578 - val_loss: 5.3724 - val_acc: 0.6616\n",
            "Epoch 3/20\n",
            "55000/55000 [==============================] - 5s 97us/step - loss: 5.2454 - acc: 0.6719 - val_loss: 5.2497 - val_acc: 0.6704\n",
            "Epoch 4/20\n",
            "55000/55000 [==============================] - 5s 97us/step - loss: 5.1453 - acc: 0.6781 - val_loss: 5.1577 - val_acc: 0.6776\n",
            "Epoch 5/20\n",
            "55000/55000 [==============================] - 5s 96us/step - loss: 5.0740 - acc: 0.6828 - val_loss: 5.1027 - val_acc: 0.6810\n",
            "Epoch 6/20\n",
            "55000/55000 [==============================] - 5s 98us/step - loss: 5.0082 - acc: 0.6871 - val_loss: 5.0538 - val_acc: 0.6826\n",
            "Epoch 7/20\n",
            "55000/55000 [==============================] - 5s 98us/step - loss: 4.9755 - acc: 0.6896 - val_loss: 5.0741 - val_acc: 0.6820\n",
            "Epoch 8/20\n",
            "55000/55000 [==============================] - 5s 98us/step - loss: 4.2943 - acc: 0.7298 - val_loss: 3.4664 - val_acc: 0.7800\n",
            "Epoch 9/20\n",
            "55000/55000 [==============================] - 5s 98us/step - loss: 3.3766 - acc: 0.7864 - val_loss: 3.4378 - val_acc: 0.7810\n",
            "Epoch 10/20\n",
            "55000/55000 [==============================] - 5s 97us/step - loss: 3.3141 - acc: 0.7913 - val_loss: 3.3936 - val_acc: 0.7834\n",
            "Epoch 11/20\n",
            "55000/55000 [==============================] - 5s 97us/step - loss: 3.2866 - acc: 0.7938 - val_loss: 3.3858 - val_acc: 0.7832\n",
            "Epoch 12/20\n",
            "55000/55000 [==============================] - 5s 98us/step - loss: 3.2671 - acc: 0.7957 - val_loss: 3.3507 - val_acc: 0.7860\n",
            "Epoch 13/20\n",
            "55000/55000 [==============================] - 5s 98us/step - loss: 3.2559 - acc: 0.7970 - val_loss: 3.3436 - val_acc: 0.7856\n",
            "Epoch 14/20\n",
            "55000/55000 [==============================] - 5s 97us/step - loss: 3.2495 - acc: 0.7973 - val_loss: 3.3118 - val_acc: 0.7900\n",
            "Epoch 15/20\n",
            "55000/55000 [==============================] - 5s 97us/step - loss: 2.0415 - acc: 0.8667 - val_loss: 1.8521 - val_acc: 0.8716\n",
            "Epoch 16/20\n",
            "55000/55000 [==============================] - 5s 97us/step - loss: 1.6792 - acc: 0.8913 - val_loss: 1.7773 - val_acc: 0.8790\n",
            "Epoch 17/20\n",
            "55000/55000 [==============================] - 5s 97us/step - loss: 1.6506 - acc: 0.8953 - val_loss: 1.7557 - val_acc: 0.8818\n",
            "Epoch 18/20\n",
            "55000/55000 [==============================] - 5s 98us/step - loss: 1.6395 - acc: 0.8967 - val_loss: 1.7487 - val_acc: 0.8822\n",
            "Epoch 19/20\n",
            "55000/55000 [==============================] - 5s 98us/step - loss: 1.6332 - acc: 0.8978 - val_loss: 1.7535 - val_acc: 0.8810\n",
            "Epoch 20/20\n",
            "55000/55000 [==============================] - 5s 97us/step - loss: 1.6295 - acc: 0.8982 - val_loss: 1.7252 - val_acc: 0.8830\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zO9DUDDdEbYy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "967bdb46-c561-4174-9d96-ca03750ac921"
      },
      "cell_type": "code",
      "source": [
        "loss,acc = model.evaluate(X_test, y_test)\n",
        "print('Test loss:', loss)\n",
        "print('Accuracy:', acc)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 2s 167us/step\n",
            "Test loss: 1.7270047664460713\n",
            "Accuracy: 0.8844\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "f69zV_s9GmVn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "outputId": "c8a10da3-7e37-443c-eb14-e0dc2fd0f528"
      },
      "cell_type": "code",
      "source": [
        "loss_plot(history)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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88QlZWdl07dqdxMTTtGnTDqPRB6PRx32brFWrNhw7dpSzZxMZPnxEtZ+5EKJx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\nRETEK/x/w8AiOW6iOnUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5726b6c4a8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xM4l/7/4vTpez3nO12ZiIiEjb6NLhA6oCyF3Db6V3cE++y9zaYABxr/vQZmMi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\nQgivkuBDCCGEEF71/8BsNFHfX/2JAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5717934400>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "E45UqAByiAWx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Z4ONmO_7iC7l",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Pooling\n"
      ]
    },
    {
      "metadata": {
        "id": "qLunwvrkyRAx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "26c024ec-c28b-4127-d79b-b150e0782fd7"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "array = np.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1])\n",
        "result = np.zeros(len(array)//2)\n",
        "for i in range(len(array)//2):\n",
        "    result[i] = np.max(array[2*i:2*i+2])\n",
        "result"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 1., 1., 1., 1.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "metadata": {
        "id": "qTFIc5m30WhB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7bb142bd-7fb8-43f8-b5e2-1a5f5eae73c8"
      },
      "cell_type": "code",
      "source": [
        "array = np.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1])\n",
        "result = np.zeros(len(array)//2)\n",
        "for i in range(len(array)//2):\n",
        "    result[i] = np.mean(array[2*i:2*i+2])\n",
        "result"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.5, 0.5, 0.5, 0.5, 0.5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "yzRSnXHTFLm5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "3beb90ce-3c6d-4a36-d82d-3b2d416b2cac"
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(X_train[0].reshape(28,28), cmap='gray')"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f57176e07b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f57176b3ef0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "OTeOyMaJFLtc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def square_max_pool(image, pool_size=2):\n",
        "    result = np.zeros((14,14))\n",
        "    for i in range(result.shape[0]):\n",
        "        for j in range(result.shape[1]):\n",
        "            result[i,j] = np.max(image[i*pool_size : i*pool_size+pool_size, j*pool_size : j*pool_size+pool_size])\n",
        "            \n",
        "    return result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "baKCoLPgFOMM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "ce2217a8-cc5c-4586-fa81-e60cc5bdd48b"
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(square_max_pool(X_train[0].reshape(28,28)), cmap='gray')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f5726599da0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUsAAAFKCAYAAACU6307AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYEBZAoDBfwB24vt5OHL6AwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f572655b470>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "3Fu9K4ilihnY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.layers import MaxPool2D"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mUKgJRCGGtD9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "c4eca7c4-9aec-428c-b807-30a5525950ca"
      },
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32, kernel_size=(3,3), input_shape=input_shape, activation = 'relu'))\n",
        "model.add(MaxPool2D(2,2))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = 'relu'))\n",
        "model.add(Dense(10, activation = 'softmax'))\n",
        "\n",
        "model.compile(loss = 'sparse_categorical_crossentropy', optimizer= optimizer, metrics = ['accuracy'])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 5408)              0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 128)               692352    \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 693,962\n",
            "Trainable params: 693,962\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "JCz-wVuFUeVo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "outputId": "a5ea2208-0579-4b53-8b30-b3d39d0fa9ff"
      },
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, epochs = epochs, batch_size=batch_size, validation_data=(X_val, y_val))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 55000 samples, validate on 5000 samples\n",
            "Epoch 1/20\n",
            "55000/55000 [==============================] - 4s 81us/step - loss: 4.2527 - acc: 0.7252 - val_loss: 3.6207 - val_acc: 0.7662\n",
            "Epoch 2/20\n",
            "55000/55000 [==============================] - 4s 74us/step - loss: 2.7122 - acc: 0.8228 - val_loss: 2.0632 - val_acc: 0.8620\n",
            "Epoch 3/20\n",
            "55000/55000 [==============================] - 4s 75us/step - loss: 1.8784 - acc: 0.8750 - val_loss: 1.8170 - val_acc: 0.8778\n",
            "Epoch 4/20\n",
            "55000/55000 [==============================] - 4s 75us/step - loss: 1.7310 - acc: 0.8846 - val_loss: 1.7118 - val_acc: 0.8864\n",
            "Epoch 5/20\n",
            "55000/55000 [==============================] - 4s 76us/step - loss: 1.6446 - acc: 0.8909 - val_loss: 1.6465 - val_acc: 0.8882\n",
            "Epoch 6/20\n",
            "55000/55000 [==============================] - 4s 75us/step - loss: 1.5861 - acc: 0.8943 - val_loss: 1.6268 - val_acc: 0.8882\n",
            "Epoch 7/20\n",
            "55000/55000 [==============================] - 4s 75us/step - loss: 1.5515 - acc: 0.8981 - val_loss: 1.6269 - val_acc: 0.8874\n",
            "Epoch 8/20\n",
            "55000/55000 [==============================] - 4s 75us/step - loss: 1.5256 - acc: 0.9003 - val_loss: 1.6034 - val_acc: 0.8914\n",
            "Epoch 9/20\n",
            "55000/55000 [==============================] - 4s 75us/step - loss: 1.5135 - acc: 0.9026 - val_loss: 1.5775 - val_acc: 0.8924\n",
            "Epoch 10/20\n",
            "55000/55000 [==============================] - 4s 75us/step - loss: 1.5040 - acc: 0.9035 - val_loss: 1.5851 - val_acc: 0.8922\n",
            "Epoch 11/20\n",
            "55000/55000 [==============================] - 4s 75us/step - loss: 1.4973 - acc: 0.9046 - val_loss: 1.5884 - val_acc: 0.8922\n",
            "Epoch 12/20\n",
            "55000/55000 [==============================] - 4s 75us/step - loss: 1.4928 - acc: 0.9046 - val_loss: 1.5737 - val_acc: 0.8944\n",
            "Epoch 13/20\n",
            "55000/55000 [==============================] - 4s 75us/step - loss: 1.4870 - acc: 0.9060 - val_loss: 1.5785 - val_acc: 0.8926\n",
            "Epoch 14/20\n",
            "55000/55000 [==============================] - 4s 74us/step - loss: 1.4843 - acc: 0.9060 - val_loss: 1.5708 - val_acc: 0.8946\n",
            "Epoch 15/20\n",
            "55000/55000 [==============================] - 4s 74us/step - loss: 1.4822 - acc: 0.9065 - val_loss: 1.5692 - val_acc: 0.8940\n",
            "Epoch 16/20\n",
            "55000/55000 [==============================] - 4s 75us/step - loss: 1.4789 - acc: 0.9073 - val_loss: 1.5700 - val_acc: 0.8928\n",
            "Epoch 17/20\n",
            "55000/55000 [==============================] - 4s 75us/step - loss: 1.4800 - acc: 0.9069 - val_loss: 1.5691 - val_acc: 0.8952\n",
            "Epoch 18/20\n",
            "55000/55000 [==============================] - 4s 75us/step - loss: 1.4834 - acc: 0.9055 - val_loss: 1.5781 - val_acc: 0.8916\n",
            "Epoch 19/20\n",
            "55000/55000 [==============================] - 4s 75us/step - loss: 1.4808 - acc: 0.9057 - val_loss: 1.5735 - val_acc: 0.8948\n",
            "Epoch 20/20\n",
            "55000/55000 [==============================] - 4s 75us/step - loss: 1.4770 - acc: 0.9067 - val_loss: 1.5731 - val_acc: 0.8948\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Q14ObrEqG5Ns",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "f42ea24d-c6bd-4fd6-e85d-c6c968cfce90"
      },
      "cell_type": "code",
      "source": [
        "loss,acc = model.evaluate(X_test, y_test)\n",
        "print('Test loss:', loss)\n",
        "print('Accuracy:', acc)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 96us/step\n",
            "Test loss: 1.5539939710008075\n",
            "Accuracy: 0.8933\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "DZVLtn0YGtBR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "outputId": "6b1b407f-e64e-4561-e7b2-17bf3b97116f"
      },
      "cell_type": "code",
      "source": [
        "loss_plot(history)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uSEfiLJ6Y221txuUTFobsnqHU9n6F6o+40fh+KRT+PjoJunhmmWcA/v8Lbn/7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\nGSGEEEIYNElmhBBCCGHQ/h/kltFUaukPggAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5726cc3550>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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upJNwEhluISk2Qi0nIiIifSzow0nTMvadPWMHvINiK6obKKvqeNE2ERERCZyg\nDyfJNgcmw+TnoFiNOxEREelrQR9OrCYLyZFJ5FTm4/F4Ojy2aTqxxp2IiIj0naAPJ+AdFFvrqqW0\nrqzD4zIcmk4sIiLS10IjnPiWse/4CcUp8ZGYTYaesSMiItKHQiOc+Jax7/gBgBazibREO9mFVbg7\n6QISERGRwAiJcJLm5wMAwTsotr7BTUFpTaCLJSIiIm0IiXCSFJmA1WTt9AGAcGxQ7JGjGhQrIiLS\nF0IinJgME2n2FPKqj+Jyuzo8dpCjaaVYjTsRERHpCyERTsA7KNbpdlJQU9ThcYM0Y0dERKRPhUw4\nSYvybxn7+OhwIsMtZBeqW0dERKQvhEw4ybD7t4y9YRhkOOzkF9fQ4Oy4C0hERER6XsiEE39bTsDb\nteP2eMgprA50sURERORbQiacxIbFYLNE+hlO9IwdERGRvhIy4cQwDNKjUimoLqLe1dDhsXrGjoiI\nSN8JmXAC3hk7HjzkVXe8UmyGWk5ERET6TGiFk6Zl7Cs7Dif2CCvx0eEKJyIiIn0gpMKJbxn7qo4f\nAAje1pPSynoqazruAhIREZGeFVLhJN3unbHTWcsJNB93otYTERGR3hRS4cRmtREXHtvFGTsaFCsi\nItKbQiqcgHdQbGldGdUNHa9hopYTERGRvhGwcFJTU8OPfvQjrr32Wq688krWrl3bYv/GjRu54oor\nWLRoEc8880ygitHKscXYOu7aSUu0YzIMtZyIiIj0soCFk7Vr1zJ+/Hhee+01nnzySR555JEW+5cu\nXcrTTz/NG2+8wYYNG9i/f3+gitKCbxn7Trp2rBYTKQmRZBdW4vF4eqNoIiIiAlgCdeHzzz/f9zo3\nN5eUlBTf+6ysLGJjY0lL8waFGTNmsGnTJkaOHBmo4vj4Wk46ecYOeLt2couqKSqvJSk2MtBFExER\nEQIYTposXryYvLw8nn/+ed+2goICEhISfO8TEhLIysoKdFEASLWlYGD4NSg2w2Hn893elWIVTkRE\nRHpHwMPJm2++ya5du7j33ntZuXIlhmF06zrx8TYsFnOPlCk12kFudT5JSVEdluekEQ5WrD9ISXUD\nDkf0cd+3J64xkIVy/UO57hDa9Q/lukNo1z+U6w7HV/+AhZMdO3aQmJhIWloaY8eOxeVyUVxcTGJi\nIsnJyRQWFvqOzc/PJzk5ucPrlZT03BOCUyKSya04yv7sbOLCY9s9LibcOyRn76FiCgoqjuueDkf0\ncV9jIAvl+ody3SG06x/KdYfQrn8o1x38q39H4SVgA2IzMzN5+eWXASgsLKS6upr4+HgABg0aRGVl\nJUeOHMHpdLJ27VrOOuusQBVewIMCAAAgAElEQVSllXS7f8vYJ8VFEm41axl7ERGRXhSwlpPFixfz\ns5/9jGuuuYba2lp+/vOfs2LFCqKjo5kzZw4PPvgg99xzD+AdPDts2LBAFaWVtKhjy9iPTRzd7nEm\nwyA9yc43+RU4XW4s5pBbFkZERKTX+RVOduzYQUFBATNnzuS3v/0tW7du5Yc//CGTJ09u95yIiAge\nf/zxdvdPmTKFZcuWdb3EPcDflhPwrhR7MLecvOJq38JsIiIiEjh+NQUsXbqUYcOGkZmZyfbt21my\nZAlPPfVUoMsWMI7IRCwmi5/L2HsDibp2REREeodf4SQ8PJyhQ4fy8ccfc9VVVzFy5EhMpoHbxWE2\nmUm1JZNblY/b4+7w2IzGZ+xka6VYERGRXuFXwqipqWHVqlV89NFHnH322ZSWllJeXh7osgVUmj2V\nBncDhTXFHR537Bk7CiciIiK9wa9wcvfdd/Puu+/y4x//mKioKP7yl7/w3e9+N8BFC6yMxkGxnXXt\nxNjDiLFZ1a0jIiLSS/waEHvGGWcwfvx4oqKiKCwsZNq0aUycODHQZQuoNLt3GfvcyjwmOMZ3eGyG\nI4pdh0uoqXMSGR7wdetERERCml8tJ7/61a9YtWoVpaWlLF68mNdee40HH3wwwEULrIwo73N9ujIo\nNrtQXTsiIiKB5lc4+eqrr7jyyitZtWoVl112GU8++SSHDx8OdNkCKi48lghzhJ8PAPQOilXXjoiI\nSOD5FU48Hg8A69atY9asWQDU19cHrlS9wDAM0qNSOFpTSIPb2eGxg5IbW06OquVEREQk0PwKJ8OG\nDeP888+nqqqKsWPHsmLFCmJj238mzUCRbk/F7XFztLqg4+MS7RhAdqFaTkRERALNr9GdS5cuZe/e\nvYwYMQKAkSNH8uijjwa0YL3Bt4x9Za5vDEpbwsPMOOIiOVJQhcfj6faTlUVERKRzfoWT2tpa1qxZ\nw+9+9zsMw2DChAmMHDky0GULON8y9lWdL2Of4bDzxb5CyqrqiYsKD3TRREREQpZf3TpLliyhsrKS\nxYsXc9VVV1FYWMj9998f6LIFXFM48W9QrJaxFxER6Q1+tZwUFhbyxBNP+N7PnDmT6667LmCF6i1R\nYXZiwqL9m07cOCj2yNEqxg9LDHTRREREQpbfy9fX1NT43ldXV1NXVxewQvWmdHsqxbUl1DhrOzxu\nkO8ZO2o5ERERCSS/Wk4WLVrEggULGD/eu5Lqzp07+dGPfhTQgvWW9KhUdpfsI7cqn+GxQ9o9Ljk+\nEovZxBE9Y0dERCSg/AonV1xxBWeddRY7d+7EMAyWLFnCX/7yl0CXrVekNQ2KrczrMJyYTSbSE23k\nFFXhdnswmTRjR0REJBD8flBMWloaaWnHptt++eWXASlQb/P3AYDgfcbON0crOVpaQ2qCLdBFExER\nCUl+jTlpS9OqsQNdauMDAP2asZPsHXey/F9fB7RMIiIioazb4SRYFiILN4eRFJHgV8tJVr53MGzm\nngJWrD8Q6KKJiIiEpA67dWbMmNFmCPF4PJSUlASsUL0tPSqNLwt3UlFfSXRYVJvHrFh/gE++OrZY\n28oNhwC4dPrw3iiiiIhIyOgwnLz++uu9VY4+lW5P4cvCnWRX5nJiwqhW+1esP+ALI80poIiIiPS8\nDsNJRkZGb5WjTzU9Yye3Kr9VOGkvmDRRQBEREelZ3R5zEkyOLWOf28clEREREYUTIMXmwGyYyWnj\nAYCXTh/OxWcNbffci88aqlYTERGRHqRwAphNZlJsDnKr8nB73K32txdQLjpTwURERKSnKZw0So9K\npc5VT3FtaZv72woo44Yl9ELJREREQovCSSPfMvYdrHfSFFDOOMm7cNvmPQW9UjYREZFQonDSyLeM\nfScrxV46fTg3XTCWyHALW/YeDZqVckVERPoLhZNGTS0n/qwUazGbmDAykaLyOg7lVQS6aCIiIiFF\n4aRRQkQcYeYwv56xAzBpTDIAmXuOBrJYIiIiIUfhpJHJMJFuTyW/ugCX29Xp8eOHJRBuNbN5T4G6\ndkRERHqQwkkz6fYUXB4X+dWdD3QNs5o5eUQiR0tqOFJQ1QulExERCQ0KJ82kR6UBHc/YaW7yGAcA\nm9W1IyIi0mMUTppJs3unCPs77uTk4YlYzCZNKRYREelBCifNpDdNJ25jGfu2RIZbGD8sgezCKnKL\n1LUjIiLSExROmokJiybKavdrOnGTSY1dO1v2qvVERESkJyicfEu6PZWimmLqXPV+HT9hVBJmk0Gm\nunZERER6hMLJt6RFpeLBQ56fXTv2CCsnDonncF4FhaU1AS6diIhI8LME8uKPPvoomzdvxul08v3v\nf5+5c+f69s2aNYvU1FTMZjMAjz32GCkpKYEsjl8y7MeWsR8SM9ivcyaNcbDzYDGb9xYwb+oJgSye\niIhI0AtYOPnkk0/Yt28fy5Yto6SkhMsuu6xFOAF48cUXsdvtgSpCt6RF+b+MfZOJoxz8ZfUeNu9R\nOBERETleAQsnU6ZM4ZRTTgEgJiaGmpoaXC6Xr6Wkv+rqdGKAGHsYowfFsSerlJKKOuKjwwNVPBER\nkaAXsDEnZrMZm80GwFtvvcU555zTKpg88MADXH311Tz22GP9Zgn4SEsECRHxXWo5Ac3aERER6SkB\nHXMC8NFHH/HWW2/x8ssvt9h+5513Mn36dGJjY7n99ttZvXo18+fPb/c68fE2LJbeaXUZGp/Bltwd\nhMcYxIRH+XXO3DOH8fpH+9h+sJjF88e2eYzDEd2TxRxwQrn+oVx3CO36h3LdIbTrH8p1h+Orf0DD\nyfr163n++ef54x//SHR0y0JeeumlvtfnnHMOe/fu7TCclJRUB6yc35YU5m0F2XF4P6PiR/h93oj0\nGLZ/XciBw0VE28Ja7HM4oikoqOjRcg4koVz/UK47hHb9Q7nuENr1D+W6g3/17yi8BKxbp6Kigkcf\nfZQXXniBuLi4Vvtuvvlm6uu9a4l8/vnnjBo1KlBF6bKmcSfZXe7aScbjgS/2FQaiWCIiIiEhYC0n\n77//PiUlJdx1112+baeffjpjxoxhzpw5nHPOOSxatIjw8HBOOumkDltNelt643Ti3C4MigWYOMbB\nX9fuZ/OeAs45NT0QRRMREQl6AQsnixYtYtGiRe3uv+GGG7jhhhsCdfvjkmJPxmSY/H7GTpPkuEhO\nSI7iq0PFVNc2YIuwBqiEIiIiwUsrxLbBarKQHJlEblVel2cRTRrjwOX2sG1/UYBKJyIiEtwUTtqR\nFpVKjbOW0rqyLp03aUwyAJl7jgaiWCIiIkFP4aQdvmXsuzgoNj3JTlqijR0Hi6mtdwaiaCIiIkFN\n4aQdvmXsuzgoFrytJw1ON9sPFPd0sURERIKewkk70puWse9iywnA5MbVYjera0dERKTLFE7akRSZ\niNVk7VbLyeDkKBxxEWz7uogGpysApRMREQleCiftMBkm0uzJ5FUfxeXuWsAwDINJo5Opq3ex82BJ\ngEooIiISnBROOpBuT8PpdlJY0/VpwZPUtSMiItItCicdSIvq3jL2AMPSY4iPDmfr/kKcLndPF01E\nRCRoKZx0oLvL2AOYDIOJox1U1TrZ/Y26dkRERPylcNKB9KbpxF1cxr7JsVk7BT1WJhERkWCncNKB\n2LAYbJZIcqpyu3X+qEFxxNisfLG3AJe7a8vgi4iIhCqFkw4YhkGaPZWC6iLqXQ1dPt9kMjhttIPy\n6ga+Oqhn7YiIiPhD4aQTGVGpePCQV929rp2mWTubtnev9UVERCTUKJx0Is03KLZ74eTEE+KxhVvY\n9GUO7i4+4VhERCQUKZx04tig2K7P2AGwmE1MGJVEYVktB3PLe7JoIiIiQUnhpBO+Z+x0Yzpxk0ma\ntSMiIuI3hZNO2Kw24sJju91yAjB+WAKR4WY27zmKR107IiIiHVI48UO6PZXSujKqG2q6db7VYmby\n2FQKSmvJOlrZw6UTEREJLgonfmhaxv54Wk/OPCUNgEx17YiIiHRI4cQPvmXsjyOcTDoxBavFpAcB\nioiIdELhxA++GTvdnE4MEBluYfywBHKLqskprOqpoomIiAQdhRM/pNpSMDC6vYx9E9+snb3q2hER\nEWmPwokfwsxWHJGJ5FbmH9dsmwkjkzCbDHXtiIiIdEDhxE/pUalUOaspq+/+Qmq2CCtjh8bzTX4l\nR0u7N/NHREQk2Cmc+Ol4l7FvMnlMMgBbNGtHRESkTQonfjreZeybTBiVhGGgrh0REZF2KJz4qWk6\n8fEsYw8QYwtjzOA4vs4pp7i8tieKJiIiElQUTvzkiEzEYrIcd8sJwKSmrh3N2hEREWlF4cRPZpOZ\nVFsyuVX5uD3u47rWxNF6EKCIiEh7FE66IM2eSoO7gcKa4uO6Tnx0OCMzYtl7pJTyqvoeKp2IiEhw\nUDjpgvTGZ+wczzL2TSaOduDxwBf71HoiIiLSnMJJF/TUoFhotlqsunZERERaUDjpgsJab3fOPw5+\nyEOfPkFm/tZuX8sRF8mQlGh2HS6hqrahp4ooIiIy4Cmc+Ckzfyt/2/uO731OVR6v7Hz9uALKpDEO\nXG4PW/cV9kQRRUREgoLCiZ9WH1rT5vYPD6/t9jXVtSMiItKawomf8qrbXtE1t6r7y9mnJdrJSLKz\n42AxNXXObl9HREQkmCic+CnVltzm9hSb47iuO2mMA6fLzfYDRcd1HRERkWChcOKneUNntbm91llH\nWV1Ft6/btFpsprp2REREgACHk0cffZRFixZx+eWX8+GHH7bYt3HjRq644goWLVrEM888E8hi9IjJ\nKRO4cdw1ZESlYTJMpNtTGRM/kpK6Up7Y/AwF1d1r+RjksJMcF8n2r4uob3D1cKlFREQGHkugLvzJ\nJ5+wb98+li1bRklJCZdddhlz58717V+6dCkvvfQSKSkpXHvttcybN4+RI0cGqjg9YnLKBCanTPC9\n93g8vH/wn7x/6CMe3/IMt596M4OjM7p0TcMwmDTGwapPv2HnwWJOG3183UQiIiIDXcBaTqZMmcLv\nfvc7AGJiYqipqcHl8rYMZGVlERsbS1paGiaTiRkzZrBp06ZAFSVgDMPgguFzuWr0pVTWV/HklhfY\nW/J1l6+jrh0REZFjAtZyYjabsdlsALz11lucc845mM1mAAoKCkhISPAdm5CQQFZWVofXi4+3YbGY\nA1Xc43KFYx7piUk8/ekrPLvtJe6cdhOnDzqt1XEOR3Sb5yclRZEUF8mXXxcSF2/HagnOoUDt1T8U\nhHLdIbTrH8p1h9CufyjXHY6v/gELJ00++ugj3nrrLV5++eXjuk5JSXUPlSgwRkWO5rZTbuKF7X/i\niQ0vcvWYhZyVcbpvv8MRTUFB+wNnJ4xM5KPMI6zf/A0nD0/sjSL3qs7qH8xCue4Q2vUP5bpDaNc/\nlOsO/tW/o/AS0F/R169fz/PPP8+LL75IdPSxQiQnJ1NYeGxV1Pz8fJKT256qO5CcmDCKu077Pnar\njdf3vM0Hhz7G4/H4de7kxq6dzXvaXk9FREQkVAQsnFRUVPDoo4/ywgsvEBcX12LfoEGDqKys5MiR\nIzidTtauXctZZ50VqKL0qiExg7l70m0kRMTz7oHV/G3fStwed6fnjcyIJcYexpa9hbjcnR8vIiIS\nrALWrfP+++9TUlLCXXfd5dt2+umnM2bMGObMmcODDz7IPffcA8D555/PsGHDAlWUXpdic3DPpNt4\nZutL/OvIBqoaqrg78eYOzzGZDCaOdrDui2z2ZpUxdkh8L5VWRESkfzE8/vY79LGB2HdX3VDNc1++\nyoGyQ5yaOpbrR19DhCW83eN3Hizm8WVbmTUxg2vnjunFkgZeKPe/hnLdIbTrH8p1h9CufyjXHfr5\nmJNQZ7Pa+OGE/2J84li25e3iqa1/oLK+qt3jx5wQhz3Cwpa9BbgHRmYUERHpcQonARZmDuOWk6/n\n3KHTOFyexRNbnqW4tqTNYy1mExNGJVFaWc+rq3azYv2BXi6tiIhI31M46QVmk5lbp17HnBPOJb+6\ngMc3P0tOZV6bxzYtyPafL3NZueGQAoqIiIQchZNeYhgGl448n8tGXkBpXRm/3fIcB8oOtTruQHZZ\ni/cKKCIiEmoUTnrZ7BNmcP3YRdS66njqixfZUbjLt2/F+gP8Y9PhVucooIiISChROOkDp6dN4vsn\n3wDAC9v/xKe5m1mx/gArNxxq9xwFFBERCRUKJ31kfNJY7jzte4Sbw/nzrmUccm3r6yKJiIj0Cwon\nfWh47FDunngrsWEx7HNvYuwZeUD7U4jLq+opq6zrvQKKiIj0AYWTPpYelco9k24nxebgkHsrI04/\nBLRcvn7qicmkJthYtzWH+174hBXrD1BT5+yT8oqIiARawJ9KLJ1LjIzn7om38ey2lzlcsYfkqUWU\nVddiRFYRbcQzedx8vueYyvovc3ln/UFWbjjEuq05XHL2MKafkobFrIwpIiLBQ59q/URUmJ07T7uF\ndHsqFRRislViGB4qKeaVna/zRcGXnDshg4e/fwaXnj2MunoXf1m9hyUvfcbmPQV+P/1YRESkv1M4\n6UciLOHtjjhZfWiN95gwCxefPYxHfjCNmadlUFBSwzN/387Dr21h/5Gyds4WEREZOBRO+pn86qNt\nbs+pyuMfB1ZTVFMMQKw9jOvmjeFX/zWVSaMd7M8u439f28wzy7eTV1zdm0UWERHpURpz0s+k2pLJ\nqWq9tL2BwapDH/PBoTWMiR/JmelTOcUxjrREO7cvPJn9R8r469r9bN5bwBf7CpkxIZ2Lzx5GrD2s\nD2ohIiLSfQon/cy8obN4ZefrrbZfO/ZKPMCmnM/YXbKP3SX7sFttTE2dyJlpUxk5KJWfXDuRLXsL\neetfX7P2i2w27sxjwdQTmDt1MBFh+qsWEZGBQZ9Y/czklAkAfHh4LblV+aTZU5g7ZKZv+7S0yeRV\nHWVj7md8mruZtVn/YW3WfxgWcwLT0qcwacSpnDpyKuu35fDOfw6y4j8HWftFNpdM987sMZuO9eQ1\nrTh76fThvV9RERGRdhieATLNo6Cgoq+LcFwcjuger4PT7WR74S425n7GrqK9ePAQZg5jcvKpnJk+\nlZTwdD78PIsPPvuG+gY3aYk2rpgxggmjknjnPwd9y+VffNbQgAeUQNR/oAjlukNo1z+U6w6hXf9Q\nrjv4V3+HI7rdfWo5GcAsJgunJZ/MacknU1xbwie5mWzKzWRj7udszP2cNHsKZw6dypLxp/Dxp0f5\n97Zcnl6+nYTocIorjq002xRS1IIiIiL9gcJJkEiIiOf8YXOYP/Q89pTsZ2POZ2wr2Mnb+97FYrzP\nKYPG8d0xp/DePyvJL67FnJCLJf1rjMgqPDV23tuVCyigiIhI31M4CTImw8TYhNGMTRhNZX0Vn+Vt\nZkPu52w5+iVb+BL34Ais8bFYEvN95xi2SsJGbuO9Xd73CigiItKXFE6CWFSYnVknnMPMwdM5WP4N\nb2z5mGzLvhbBpDlL+gE+3jyYBqebiWMcDEuLwWQYvVxqEREJdQonIcAwDIbHDuFnM2/irX/vZk3D\ny7SVOUyRFThjs/jgi3JWfXqY+OgIJo5yMHGMg9GDY1vM9BEREQkUhZMQc8U5J/L5mgQqKW690wDT\nkG1EDAGr20ZtWRzrsuJY81UCNiOO00Y5mDTawUlDE7BaFFRERCQwFE5C0JXj5re50NvFwxcQZrby\ndelB9pcepCE+h7D4HADczjA+K49n0/p4rP9M4uT0oUwek8rJwxM6XeBtxfoD2O3hzJmYEZD6iIhI\ncFE4CUFNC7r9bedqqighParlQm8zB5+Nx+PhaHUB+0sPsq/0IPtLv6bEko85wTteZbvzE7Z9FQef\nJTIsZihnDBvDpNEpREVaW9xrxfoDvqnKVVV1GmwrIiKd0iJsvSQYFuQpqilhf+kB9pceYHfRAYrr\ni3z7PG4Tnso44k3pnJIyipljxrNpewHv7drUYsryqdFn8IMZc/uwFr0vGP7uj0co1z+U6w6hXf9Q\nrjtoETbpRYmR8SRGTuL0tEkAlNdXsL/0IF/m7WVP0deURxdRZhSzvmoH/85cgac+grCRNb7zDVsl\n210f8dw6D7eeO6/HypWZv5XVh9aQV32UVFsy84bO8rUCDaR7iIiIl8KJdFtMWDQTk09hYvIpAFQ3\nVLMtdx//+HILxe4cDHtZm+dtd33Mj9dsIsEejd1iI8pqw2a1YW/6Y/F+/fY2q9na6lqZ+VtbjJ/J\nqcrzve+p8NAb9xARkWMUTqTH2Kw2pp1wKvmHo1m54RARU1YDbfca1tZCvqsEjykf/FxKJcxkxW61\nY7NGYrfasVtt7Cne1+axy/f9g+qG6hbbOuu/9LRzxIeH1ra5/f2D/+SkhNFEWiIxtB6MiEiPUTiR\nHtc06HV1mR3DVtlqv7UhlpSi+RzOr6DB6QJzA4algQibm1SHhcQEMzExEGl346SOqoZq7x9nNdUN\n1RTVFJNdmdthGcrqy1m2d0VA6tckv7qAe9c/iNkwEx0WRbTVTlRYFDFh0USF2Ym2el8Pcjpw1Zi8\n2612LKb2f+zUfSQionAiAXLp9OHk/esMtrs+arXvutMuYHLKBJwuN1lHKzmQU974p4yDe2s42OzY\n5Lh4hmfEMDYthhGDYhmcHIXFbMLpdlLtrOFX/3mKalp3H8WHx3HZyAtabe9OC8fyff+gpK601Xab\nJZIRccOorK+kvL6S/JpCsipzWl9gV8u3kZZIYsKiiLJGeUNN45+SmlI25X3uO26gdx9pLJCIdJdm\n6/SSUB25/fy/PmRbxacYEZVEmxK4cty8Dj88KmsafEGlKbRU1zl9+y1mE0NSoxieFktxRS1bC74k\nbOS2Vtc52Ty7x2YFfXvMSZMbx13Tqi51rnoq6iupqK+ksqGS8voK3NYG8kuKKa+voKKhikrf/qp2\nu5KaMxtmhsYMJiY8htiwaGLCookJjyEmLNr7PtzbImMy/FsYr7dCg7/fs/58j6b7dPf71ZWf+2AM\nWqH6/z3of3/3vf3v63hn6yic9JJQ/iE9nkXY3B4P+cXVzVpXysk6Wom72T9b7xOWD2BEVOKpjcKZ\nMxxXcRoXnTmEy84Z0SN1yMzf2u66MJ1p7+/e7XFT1VBNRb03xPx+6x/bDSsGRodBxmSYiLbaW4aW\n5iEm3Pt+f+kh/rJrWavz2/pAd3vcON1OnG4nDW4XTneD973H1bjN6dt/7DgnTo+TVQc/oqy+dZ2j\nrHampk7E4/Hgxo3L48bjceP2eHA3fvXgPvba4252XNMx3n1ZFUeodze0uofNEskZaZOJMIcTYYkg\nwhJOhDmCCEsEkb7Xx752FOqONwD5+3M/EIJWf7tPf79Hf/q7761/X0338vd7pnDSD4RyOIGerX9d\ng4vXPtzDhu15nR6bFBvR+CeSpLgIHLGRJMZG4IiLJDYqzO8HGzZfTO7is4Z2aTE5f+v+0KdPkFPV\nuk4ZUWn8z+Q7qWyopry+nPL6CsrqKiivr/C+r6ugrL6C8rpyyuoraGjjA7szZsNMlNWG0+2iweMN\nGm6Pu8vXGYjCzGFENgWZpuBiiSDCHM6Ool1UfWtgNXhnqp2TcSYujwu3x43L4zr22t20zY01zERV\nbR1utwtX43Hu5l/d3td5VUdxepyty2ayMix2CBaTBavJgsVkwWJYsJjMWE1W73uTGYvJ2vjVgtVo\nPK7ZH6vJwv7Sg7x38MNW97hy1MWc4hiHgYFhGBgYgOH72TAMA1PjNu9+MAzfFt85TV2mm/O38epX\nb7S6z43jrmFS8qnAscHnzT9+PE1bPc1juPe197jGc4Avjn7J/+1+q9U9vnPilUxMPrlFWcE4Nua+\nqfwcq1vTEd/u8m3vA/3qMZczLnEMDe4GGtxO71eXs+V7t5MIm4mSskrq3U6c7gbqG49reu10O6l3\nN7C/5CD17vpW9wkzWRkSMxizYcZkMmE2TN7XhglT42tz42uTYcZsame7YWJN1nrK2/hlIS48lguG\nzWn65vi2t/hOGG1vN9qYyXCo/Bv+nb2p1fb2QpDCST+gcNLz9W8eGL4tw2EnMtxCYWkNpZWtf/DB\n20WUGBuBoynAxEWS1BhcEmMjiI60YhhGm/fpSkDpzd+gPB4Pta46b3Cpawwy9RWUN4aZT/M2t1/O\nyMQWH2aWFh9yTR+G5mYfkM0+MJt9SFoNM+98vYriNsbpOCITuXHcNb7/wZoMEyYMDN97A5NhwsD7\n2myYju2jcV/jMe2FuRSbg++edDW1rlpqnHXUueqoddZS66yjxuX9Wtv01Vnre920rzvhrju8HyTe\nD5NaV12v3FM61/Sh60+Xq/gnIyqNn079cavtWoRNglJTOOgsODQ4XRSW1VJUVktBWS2FpTUUlNVS\nVFZDQWkt+cWtfzMGCA8zE2YxUVHd+sNq5YZDuN0eFs7omW4j8A56zdx9tEtjdL7NMAwiLRFEWiJI\nsTla7c+qyG63daat/3l0m2G0GbQuHD6PITGDe+QW84bOavMe5w+bwwkxg7p9XZfbRW1joHl66x8p\nqClsdUxiRDzXnHjFsd9UW/3W6v2anBRLaUmNL4iYmv1W2/w39XZbzexp/M+UO33dZa260Hx/XDS4\nG5p1uTXgdLtaHPvBoY/a/Lg1gCmNXW0ePMe+cqzFwtsNd+x1077Wx7vZV3qg3e/tqLhjP5cGRquW\nDGjZmtFUQONYWwcAO4q+Ncq8mfGJJzaVFO9/zdphPE2tL81baZpt8x3q4euyQ+3eY3LKBMJMVm8Y\nN1uwmqxYTVbCTJbGbVYS46KoqXRhNTXu9x3X9N57zmOZv2/3Z/K+KT/ydWU2b3lraqFz+fY12+5x\n4XK33L5s7wqKa0ta3SM+PJaLhs9vsa1lu1XzHe1sb/b9e2P38jZDXW5Vfrvfy/YonMiA9u2A0laL\nhtViJi3RTlqivc1r1NQ5KSqrpbCsloKyGgpLayksq+Hr7DLK2wgmTf6x6TCrP/sGR7yNWHsYsVFh\n3q/2cGKjwoizhxEbFSovxxsAABL4SURBVE6kPRyPx9PpTKEV6w/w2SYLcBYAtcARawyTU/z7Xvij\nvQ/0uUNm9txN6Pz5TT15jw8PryW3Kp80e8/cw2wyYzd5F/+7cPjcth+SOWIBJyaM6vRaCbZoXFXm\nTo9r9+9l6EzMJjNmkxkI96v87dlWsKPND8H0qDRuOGnxcV27uY66J++a+IOA3+PWU28K+D1uHHdN\np+f722La0c9kU+vi8ap11bV5j0tHXtCjP5Prsja0+T1Ls3f9f2IKJzLgNQ8j3XmwYGS4hUHJUQxK\njmqxvaNuoyYRYRbKKuvIKazq8DiL2USsPYy4KG9gaRFmosLZuq+Af29rvXZL0/176oGJR/bGUL//\n1BYDiE+NPj0ggyKP7I2h4LMpAJx01lAmp/T8Qx8np0zgyN4YTrLApVMDc30IbMhqfp+eDlrN9VYw\n7Y37BMs9oHf+7nvjHtCz37OAhpO9e/dy22238d3vfpdrr722xb5Zs2aRmpqK2ez9reKxxx4jJaUH\nf0WUkBKIpx23123UpHkrTYPTTXlVPaVVdZRX1lNaVU9ZZR1lVfXU1LspKKmitLKeQ3kVuNzlXSrH\nyg2H+Hz3UYalxRAeZibcaibCaibMaiai8X3T9mOvTYSHWQi3mgizmjG1GDuThqs4zXf9z4BU04Ee\n/R5+O9j1dMhq7z6B+HfQGyELgi9o9Uar2UC/R/N7BfLvvjfvAd4QlFeVT+pxhKCAhZPq6mp+9atf\nMW3atHaPefHFF7Hb225qF+kP/B3XYrV4B9cmxka0ukbz5l23x0NVTQNllfWUVdVTWlnHxh157Drc\nuj+4udyianKL2h4b4w+TCdwdTL5ZueEQ2/YXMnpwPGFWE1aLiTCLufGrCavV+z7M0rjP2vi68at3\nuxmL2eCd/xxsM9D1dEDpjQDUWyGrrXsN5KDVG/cJlntA7/zd98Y94FgImhLbveUjmgQsnISFhfHi\niy/y4osvBuoWIr3Cn3Et/jIZBtG2MKJtYTQN2Tzr5LQOu5AumDaEOVMGU1/vorbBRV2Dq8XrunoX\ndQ1u6uqdjV8btzfbn1dcTVlV27OWmhzOr+RwfuvHDfSklRsOsWlHHulJdixmE2azgdVswmw2YTEb\nWMymxj/HXh87xrvNajaRuecon+062ub1K6obmDtlMGaTgclkNPtqarWtI+39nQzUENRXrVkDtS76\nfh3/vaqq6rp9j4BPJX766aeJj49vs1tn4sSJZGdnM2nSJO65554OBww6nS4sls4HlokEyuurdwNw\nzbwTA3b9Nz7c02Lb1XPH9Nj92rp+k4unD2f+tKHe4OP7427xvq7B3ey1q9k+t2/bkaMVFJbW9kh5\nA80waAwp3tBiMXsDjMlkUFvvpLq29bojzaUl2TkhJRqLxYTFZMJiaR6ujgWsdoNX43kbt+fwn21t\nPPYAmHv6CSyYNgzDAFNjoDIZ3q+GQePU62bvW+w3MDUe87eP9/LXj9t+SGZv/RvrqfsEyz166z69\nVZf27tXde/RZOFmxYgXTp08nNjaW22+/ncsuu4z58+e3cxWtczLQhXL9u1L341nsravXb9LT9+mo\nFeiis4Zy4bShOF3uxj8eXC43DS43LpcHp9uN0+nx7mvrtdvNF3sL+P/t3X9QVFX/wPH33d1AV0FF\nQVS+5I+U1EfzR5I/QSlL7bFsrJQZshqcyt8aidiI0DQpqDlpNaZmNeGvyurJzAYnyxlN3BzkwdTm\ni8bTqNgXEVTA7MHdvd8/ll13YQF/7N1dls9rxlnuPbt7ztl7r/dzzzn3nl+LKxotQ3REW/4noi0W\nVcViUbFaVSxWFatqy89irV3nnK7WvlpUqv6q4XqNxWO/ib/TKba72hQFR1CjKIpj2R4IKfbbep2X\na1+r/7pB1fXGnxHTqV0rOndojWIPohR7wHUzsFIUe8tWbR5O7y2+cJXfLzQ+Zqt3VDtiots77nxV\nVefbinHcXtxQenHJVf7zf40fr906taFbeBvHfqWqti5ba+0+ZrWq6A16/vtfs22fs6qo9jQVrFaV\nyms1LlNzuNM6WE9I66Da3+Dmb27/newBqfM6+7bT6RQuXv6LsiYuFqI7t6Vnl1CX4Fdf52/F3tqo\nuLY82rebXqdwrKiMY0X1b7uHhv+P8cvnnEyZMsXxd1xcHEVFRY0GJ0K0BHd759Gtfr+WAdCtjNO5\nx3Dnt0eOHdSt0QDIU3VqLI9/jryXx4d3twVNtQGW2Vob+FhsJyOzxUpIaGsulVc7lu0BmP09+f97\nsclAq2fXUHp2DUW1ghX7SY7ax/urWK22k6vV6SSp1p4AVVXlz/K/uHjleqN5hNbeNabWftb+am1g\n2X7CdU7/+0bTwdyl2lv2tXT6/FVOn68/GagnlVy6RkkTd+gBN4MvnWsrl06nYGlsEFgtVYUas6X+\ndsW2D9j3BdvUDs7Pabl1Z0urOeuF7ly4vf/TfBKcVFVVsXDhQjZs2EBQUBBHjx7lscce80VRhPA7\nWg1Uc/f9WuWldRB0qwOVtc4jmMa7msPDQygLCWowPe6Brj4PtDz5mzWVz+RR3bFab7YyqE4tCXVb\nHuqut5+gDxSUcPB4/dvuAUb9I5LRA7vcfJCbYn/YW+3j25xaflzSwWmdwk/HznPg3+672h55MIoJ\nsdEuXWi2V1yCj84RoU22mHp6u9gfkOf8e33783/Ye+Ss+7oMjeLhoVGOQNO5hdFqvfnP4limXrrF\nqpJfdJHCM+W3VdamaBacnDhxguzsbEpKSjAYDOTm5pKQkEBUVBTjx48nLi6OadOmERwcTL9+/aTV\nRAgv0joAcs7jTid9vNXv92YrkK9amppDHreaj/7OG80A6NEllA4hwZrWZcaE+wltE+QXv9ftsM8n\npNPfHL/59Nj7MOh1mtZl9MDGB/XfSV4yt46XtOQxF9Cy69+S6w7a1/9fB22PS9cy4LrTPPxpvJG3\n8vBWPv6eR0vc9rc7ps0vx5wIIYQneLMVyFt5aN3dpmUezt+tVauZcx51/25ueXgrn+bWnSstJ14i\nV88tt/4tue7QsuvfkusOLbv+Lbnu/zpYfEuBaWMtJ80mOBFCCCFEy3CXQ5OEEEIIITxLghMhhBBC\n+BUJToQQQgjhVyQ4EUIIIYRfkeBECCGEEH5FghMhhBBC+BV5CJsGVq1aRX5+PmazmZdffplHH33U\nkZaQkEBkZCR6vW1OjjVr1tC5c2dfFdWjTCYTCxYsoHfv3gD06dOH9PR0R/rhw4dZu3Yter2euLg4\n5syZ46uiauKLL75g9+7djuUTJ05QUFDgWO7fvz9DhgxxLH/yySeO/aA5KyoqYvbs2bzwwgskJSXx\n559/kpqaisViITw8nNWrVxMU5Dq/zIoVKygsLERRFF5//XUGDhzoo9LfHXd1X7p0KWazGYPBwOrV\nqwkPD3e8v6ljpLmpW/+0tDROnjxJ+/btAUhOTmbs2LEunwnUbT9//nwuX74MwJUrVxg0aBBvvvmm\n4/1fffUV69atIzo6GoCRI0cya9Ysn5T9btU9xw0YMMDzx7wqPCovL0+dOXOmqqqqWlFRocbHx7uk\njxs3Tq2urvZBybR35MgRdd68eQ2mT5w4Ub1w4YJqsVjUxMRE9fTp014snXeZTCY1MzPTZV1sbKyP\nSqOda9euqUlJSeqyZcvUnJwcVVVVNS0tTd27d6+qqqr69ttvq9u2bXP5jMlkUl966SVVVVX1zJkz\n6rPPPuvdQnuIu7qnpqaq3333naqqqrp161Y1Ozvb5TNNHSPNibv6L1myRP3xxx8b/Ewgb3tnaWlp\namFhocu6L7/8Us3KyvJWETXj7hynxTEv3ToeNmzYMNatWwdAaGgo169fx2JpeirxQHfu3DnatWtH\nly5d0Ol0xMfHk5eX5+tiaeb9999n9uzZvi6G5oKCgti8eTMRERGOdSaTiYcffhiAcePG1dvOeXl5\nPPLIIwD06tWLq1evUl2t7ZTtWnBX94yMDMcM6x06dODKlSu+Kp7m3NW/KYG87e2Ki4upqqpqti1C\nTXF3jtPimJfgxMP0ej1GoxGAXbt2ERcXV6/pPiMjg8TERNasWYMaYA/oPXPmDK+88gqJiYn8/PPP\njvVlZWWEhYU5lsPCwigrK/NFETV3/PhxunTp4tKcD1BTU0NKSgrTp0/n448/9lHpPMtgMNCqVSuX\nddevX3c06Xbs2LHedr506RIdOnRwLDfXfcFd3Y1GI3q9HovFwvbt25k8eXK9zzV0jDQ37uoPsHXr\nVmbMmMGiRYuoqKhwSQvkbW/36aefkpSU5Dbtl19+ITk5meeff55Tp05pWUTNuDvHaXHMy5gTjfzw\nww/s2rWLjz76yGX9/PnzGTNmDO3atWPOnDnk5uYyYcIEH5XSs7p3787cuXOZOHEi586dY8aMGezb\nt69e32Og27VrF0899VS99ampqTzxxBMoikJSUhIPPvggAwYM8EEJvedWgu9AC9AtFgupqakMHz6c\nESNGuKQF+jHy5JNP0r59e/r27cumTZt47733WL58eYPvD7RtX1NTQ35+PpmZmfXSHnjgAcLCwhg7\ndiwFBQUsWbKEb7/91vuF9BDnc5zzuEpPHfPScqKBgwcP8sEHH7B582ZCQlwnNpoyZQodO3bEYDAQ\nFxdHUVGRj0rpeZ07d2bSpEkoikJ0dDSdOnWitLQUgIiICC5duuR4b2lp6W01BzcnJpOJwYMH11uf\nmJhImzZtMBqNDB8+PKC2vTOj0cjff/8NuN/OdfeFixcv1mtlas6WLl3Kvffey9y5c+ulNXaMBIIR\nI0bQt29fwDb4v+4+Hujb/ujRow125/Tq1csxOHjw4MFUVFQ02y7/uuc4LY55CU48rKqqilWrVrFx\n40bHiHXntOTkZGpqagDbjmwftR8Idu/ezZYtWwBbN055ebnjTqSoqCiqq6s5f/48ZrOZn376iVGj\nRvmyuJooLS2lTZs29a6Ei4uLSUlJQVVVzGYzx44dC6ht72zkyJHk5uYCsG/fPsaMGeOSPmrUKEf6\nyZMniYiIoG3btl4vpxZ2797NPffcw/z58xtMb+gYCQTz5s3j3LlzgC1Ir7uPB/K2B/j111+5//77\n3aZt3ryZPXv2ALY7fcLCwprl3XruznFaHPPSreNhe/fu5fLlyyxcuNCx7qGHHiImJobx48cTFxfH\ntGnTCA4Opl+/fgHTpQO2K6XXXnuN/fv3c+PGDTIzM9mzZw8hISGMHz+ezMxMUlJSAJg0aRI9evTw\ncYk9r+7Ymk2bNjFs2DAGDx5MZGQkTz/9NDqdjoSEhIAYMHfixAmys7MpKSnBYDCQm5vLmjVrSEtL\n47PPPqNr165MmTIFgEWLFrFy5UqGDBlC//79mT59OoqikJGR4eNa3Bl3dS8vLyc4OJjnnnsOsF0t\nZ2ZmOuru7hhprl067uqflJTEwoULad26NUajkZUrVwItY9u/++67lJWVOW4Vtps1axYbNmxg8uTJ\nLF68mJ07d2I2m3nrrbd8VPq74+4cl5WVxbJlyzx6zCtqoHX6CSGEEKJZk24dIYQQQvgVCU6EEEII\n4VckOBFCCCGEX5HgRAghhBB+RYITIYQQQvgVuZVYCOEx58+fZ8KECfUeQhcfH8/MmTPv+vtNJhPv\nvPMOO3bsuOvvEkL4LwlOhBAeFRYWRk5Ojq+LIYRoxiQ4EUJ4Rb9+/Zg9ezYmk4lr166RlZVFnz59\nKCwsJCsrC4PBgKIoLF++nPvuu48//viD9PR0rFYrwcHBjgd6Wa1WMjIy+O233wgKCmLjxo0ApKSk\nUFlZidlsZty4ccyaNcuX1RVC3AUZcyKE8AqLxULv3r3JyckhMTGR9evXA7YJEZcuXUpOTg4vvvgi\nb7zxBmCbvTs5OZlt27YxdepUvv/+ewB+//135s2bx+eff47BYODQoUMcPnwYs9nM9u3b2blzJ0aj\nEavV6rO6CiHujrScCCE8qqKiwvH4drvFixcDMHr0aACGDBnCli1bqKyspLy83PEo/9jYWF599VUA\njh8/TmxsLACPP/44YBtz0rNnTzp16gRAZGQklZWVJCQksH79ehYsWEB8fDzPPPMMOp1cewnRXElw\nIoTwqMbGnDjPlqEoCoqiNJgOuG39cDdZWseOHfnmm28oKChg//79TJ06la+//ppWrVrdSRWEED4m\nlxZCCK85cuQIAPn5+cTExBASEkJ4eDiFhYUA5OXlMWjQIMDWunLw4EHANtnY2rVrG/zeQ4cOceDA\nAYYOHUpqaipGo5Hy8nKNayOE0Iq0nAghPMpdt05UVBQAp06dYseOHVy9epXs7GwAsrOzycrKQq/X\no9PpyMzMBCA9PZ309HS2b9+OwWBgxYoVnD171m2ePXr0IC0tjQ8//BC9Xs/o0aPp1q2bdpUUQmhK\nZiUWQnhFTEwMJ0+exGCQayIhROOkW0cIIYQQfkVaToQQQgjhV6TlRAghhBB+RYITIYQQQvgVCU6E\nEEII4VckOBFCCCGEX5HgRAghhBB+RYITIYQQQviV/weTaz+4IIzMpAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5726571e10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "zn28S5WPiMha",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "V1W8U5QeiNW_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Dropout"
      ]
    },
    {
      "metadata": {
        "id": "ALycknYhi7bq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.layers import Dropout"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "h303HDzm30Eo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "9b1c81d2-e1be-4f91-a8d2-2aa14ce08fe1"
      },
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32, kernel_size=(3,3), input_shape=input_shape, activation = 'relu'))\n",
        "model.add(MaxPool2D(2,2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = 'relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(10, activation = 'softmax'))\n",
        "\n",
        "model.compile(loss = 'sparse_categorical_crossentropy', optimizer= optimizer, metrics = ['accuracy'])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_4 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 5408)              0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 128)               692352    \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 693,962\n",
            "Trainable params: 693,962\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "oYDUZQ68Uj3d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "outputId": "f9df0463-2f1e-4872-9fb1-982221415863"
      },
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, epochs = epochs, batch_size=batch_size, validation_data=(X_val, y_val))"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 55000 samples, validate on 5000 samples\n",
            "Epoch 1/20\n",
            "55000/55000 [==============================] - 5s 90us/step - loss: 4.0026 - acc: 0.7301 - val_loss: 0.8546 - val_acc: 0.9174\n",
            "Epoch 2/20\n",
            "55000/55000 [==============================] - 5s 84us/step - loss: 0.6120 - acc: 0.9097 - val_loss: 0.2460 - val_acc: 0.9432\n",
            "Epoch 3/20\n",
            "55000/55000 [==============================] - 5s 83us/step - loss: 0.3025 - acc: 0.9259 - val_loss: 0.1773 - val_acc: 0.9528\n",
            "Epoch 4/20\n",
            "55000/55000 [==============================] - 5s 82us/step - loss: 0.2163 - acc: 0.9417 - val_loss: 0.1420 - val_acc: 0.9650\n",
            "Epoch 5/20\n",
            "55000/55000 [==============================] - 5s 82us/step - loss: 0.1668 - acc: 0.9548 - val_loss: 0.1175 - val_acc: 0.9690\n",
            "Epoch 6/20\n",
            "55000/55000 [==============================] - 5s 82us/step - loss: 0.1418 - acc: 0.9599 - val_loss: 0.0992 - val_acc: 0.9732\n",
            "Epoch 7/20\n",
            "55000/55000 [==============================] - 5s 82us/step - loss: 0.1171 - acc: 0.9658 - val_loss: 0.0877 - val_acc: 0.9772\n",
            "Epoch 8/20\n",
            "55000/55000 [==============================] - 5s 83us/step - loss: 0.1016 - acc: 0.9688 - val_loss: 0.0847 - val_acc: 0.9780\n",
            "Epoch 9/20\n",
            "55000/55000 [==============================] - 5s 83us/step - loss: 0.0864 - acc: 0.9734 - val_loss: 0.0788 - val_acc: 0.9794\n",
            "Epoch 10/20\n",
            "55000/55000 [==============================] - 4s 82us/step - loss: 0.0741 - acc: 0.9770 - val_loss: 0.0794 - val_acc: 0.9798\n",
            "Epoch 11/20\n",
            "55000/55000 [==============================] - 5s 83us/step - loss: 0.0667 - acc: 0.9785 - val_loss: 0.0789 - val_acc: 0.9796\n",
            "Epoch 12/20\n",
            "55000/55000 [==============================] - 5s 82us/step - loss: 0.0583 - acc: 0.9811 - val_loss: 0.0809 - val_acc: 0.9804\n",
            "Epoch 13/20\n",
            "55000/55000 [==============================] - 5s 82us/step - loss: 0.0525 - acc: 0.9839 - val_loss: 0.0692 - val_acc: 0.9832\n",
            "Epoch 14/20\n",
            "55000/55000 [==============================] - 5s 83us/step - loss: 0.0499 - acc: 0.9840 - val_loss: 0.0705 - val_acc: 0.9826\n",
            "Epoch 15/20\n",
            "55000/55000 [==============================] - 4s 82us/step - loss: 0.0455 - acc: 0.9850 - val_loss: 0.0704 - val_acc: 0.9832\n",
            "Epoch 16/20\n",
            "55000/55000 [==============================] - 5s 82us/step - loss: 0.0419 - acc: 0.9863 - val_loss: 0.0694 - val_acc: 0.9834\n",
            "Epoch 17/20\n",
            "55000/55000 [==============================] - 4s 81us/step - loss: 0.0378 - acc: 0.9877 - val_loss: 0.0700 - val_acc: 0.9830\n",
            "Epoch 18/20\n",
            "55000/55000 [==============================] - 4s 81us/step - loss: 0.0337 - acc: 0.9887 - val_loss: 0.0748 - val_acc: 0.9836\n",
            "Epoch 19/20\n",
            "55000/55000 [==============================] - 4s 81us/step - loss: 0.0308 - acc: 0.9896 - val_loss: 0.0684 - val_acc: 0.9830\n",
            "Epoch 20/20\n",
            "55000/55000 [==============================] - 4s 81us/step - loss: 0.0282 - acc: 0.9908 - val_loss: 0.0655 - val_acc: 0.9852\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-BYo_wsUSOX-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "b127a0bb-e157-46b4-d66d-e61a76567e30"
      },
      "cell_type": "code",
      "source": [
        "loss,acc = model.evaluate(X_test, y_test)\n",
        "print('Test loss:', loss)\n",
        "print('Accuracy:', acc)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 96us/step\n",
            "Test loss: 0.05984082971687239\n",
            "Accuracy: 0.9843\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "CYxZaUs230Bt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "outputId": "9bd2c23b-06b9-4cfd-fab8-e401861871d6"
      },
      "cell_type": "code",
      "source": [
        "loss_plot(history)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SiZVUlrL26Hq+OL4Bp7uccGs4U9ImMK7HGKJske1atqXrD7bKQ/MqKj0cySnm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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5717128828>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zAgDfffcd1qxZg5UrV9ptnzt3LkaNGoXg4GDMmTMHW7duRWJiYqPXCQ31h1rt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\naSsofRQEAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f57b47e4d68>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "jKc8GOWr3z-F",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "whGwMCQQ3fi2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CV2NYFUQiSgc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Deeper Convolution"
      ]
    },
    {
      "metadata": {
        "id": "1M2tzc4VGs-Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "outputId": "ae2b0efd-1eb0-4cdc-ddc9-98300f127652"
      },
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32, kernel_size=(3,3), input_shape=input_shape, activation = 'relu'))\n",
        "model.add(MaxPool2D(2,2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Conv2D(64, kernel_size=(3,3), activation = 'relu'))\n",
        "model.add(MaxPool2D(2,2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Conv2D(128, kernel_size=(3,3), activation = 'relu'))\n",
        "model.add(MaxPool2D(2,2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = 'relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(10, activation = 'softmax'))\n",
        "\n",
        "model.compile(loss = 'sparse_categorical_crossentropy', optimizer= optimizer, metrics = ['accuracy'])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_5 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 11, 11, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 3, 3, 128)         73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 1, 1, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 1, 1, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_4 (Flatten)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 110,474\n",
            "Trainable params: 110,474\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8VfyFEnuUzWh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "outputId": "47d855d9-63b2-43ce-e29f-87a72881f750"
      },
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, epochs = 20, batch_size=128, validation_data=(X_val, y_val))"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 55000 samples, validate on 5000 samples\n",
            "Epoch 1/20\n",
            "55000/55000 [==============================] - 8s 140us/step - loss: 1.9736 - acc: 0.6047 - val_loss: 0.3445 - val_acc: 0.9056\n",
            "Epoch 2/20\n",
            "55000/55000 [==============================] - 7s 125us/step - loss: 0.5228 - acc: 0.8365 - val_loss: 0.2066 - val_acc: 0.9420\n",
            "Epoch 3/20\n",
            "55000/55000 [==============================] - 7s 125us/step - loss: 0.3724 - acc: 0.8879 - val_loss: 0.1623 - val_acc: 0.9540\n",
            "Epoch 4/20\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.3021 - acc: 0.9072 - val_loss: 0.1362 - val_acc: 0.9590\n",
            "Epoch 5/20\n",
            "55000/55000 [==============================] - 7s 123us/step - loss: 0.2515 - acc: 0.9247 - val_loss: 0.1205 - val_acc: 0.9626\n",
            "Epoch 6/20\n",
            "55000/55000 [==============================] - 7s 123us/step - loss: 0.2238 - acc: 0.9333 - val_loss: 0.1072 - val_acc: 0.9672\n",
            "Epoch 7/20\n",
            "55000/55000 [==============================] - 7s 123us/step - loss: 0.2024 - acc: 0.9393 - val_loss: 0.0993 - val_acc: 0.9690\n",
            "Epoch 8/20\n",
            "55000/55000 [==============================] - 7s 123us/step - loss: 0.1801 - acc: 0.9461 - val_loss: 0.0928 - val_acc: 0.9726\n",
            "Epoch 9/20\n",
            "55000/55000 [==============================] - 7s 122us/step - loss: 0.1692 - acc: 0.9491 - val_loss: 0.0842 - val_acc: 0.9732\n",
            "Epoch 10/20\n",
            "55000/55000 [==============================] - 7s 123us/step - loss: 0.1515 - acc: 0.9544 - val_loss: 0.0771 - val_acc: 0.9760\n",
            "Epoch 11/20\n",
            "55000/55000 [==============================] - 7s 122us/step - loss: 0.1419 - acc: 0.9580 - val_loss: 0.0751 - val_acc: 0.9768\n",
            "Epoch 12/20\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.1303 - acc: 0.9613 - val_loss: 0.0717 - val_acc: 0.9792\n",
            "Epoch 13/20\n",
            "55000/55000 [==============================] - 7s 122us/step - loss: 0.1257 - acc: 0.9619 - val_loss: 0.0681 - val_acc: 0.9808\n",
            "Epoch 14/20\n",
            "55000/55000 [==============================] - 7s 122us/step - loss: 0.1141 - acc: 0.9660 - val_loss: 0.0630 - val_acc: 0.9820\n",
            "Epoch 15/20\n",
            "55000/55000 [==============================] - 7s 122us/step - loss: 0.1114 - acc: 0.9665 - val_loss: 0.0621 - val_acc: 0.9804\n",
            "Epoch 16/20\n",
            "55000/55000 [==============================] - 7s 123us/step - loss: 0.1016 - acc: 0.9697 - val_loss: 0.0605 - val_acc: 0.9816\n",
            "Epoch 17/20\n",
            "55000/55000 [==============================] - 7s 123us/step - loss: 0.0953 - acc: 0.9713 - val_loss: 0.0589 - val_acc: 0.9822\n",
            "Epoch 18/20\n",
            "55000/55000 [==============================] - 7s 122us/step - loss: 0.0956 - acc: 0.9715 - val_loss: 0.0574 - val_acc: 0.9822\n",
            "Epoch 19/20\n",
            "55000/55000 [==============================] - 7s 122us/step - loss: 0.0906 - acc: 0.9730 - val_loss: 0.0543 - val_acc: 0.9836\n",
            "Epoch 20/20\n",
            "55000/55000 [==============================] - 7s 123us/step - loss: 0.0841 - acc: 0.9747 - val_loss: 0.0521 - val_acc: 0.9856\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0GsdP62EUwSL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "bb5cb3df-92d1-4673-f77e-c153a0f251e3"
      },
      "cell_type": "code",
      "source": [
        "loss,acc = model.evaluate(X_test, y_test)\n",
        "print('Test loss:', loss)\n",
        "print('Accuracy:', acc)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 107us/step\n",
            "Test loss: 0.0439609499128419\n",
            "Accuracy: 0.9864\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "UeTHHCTHU9n5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "outputId": "f5b293cf-5f15-417d-c7a9-399d09adfc29"
      },
      "cell_type": "code",
      "source": [
        "loss_plot(history)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YWIhZs2Zhy5YtUCgUHq8bHR0GmYf+7WDkcDpwuvwc9lw4iD0XDqLM6HqcukIq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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f57273fa978>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PeHTeRRcNxnvv/QdWqxWA87bU7bffiQ8+eAdz596M666bi6KiQmRmnsT27T802de/f1SH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\nVWSSLCQ0AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5727407198>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "zWLqJAr5U9lK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CjfT18YtR9Wa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "outputId": "b7fdbe5a-e3c9-4b8d-a549-30201cf406e2"
      },
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32, kernel_size=(3,3), input_shape=input_shape, activation = 'relu'))\n",
        "model.add(MaxPool2D(2,2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Conv2D(64, kernel_size=(3,3), activation = 'relu'))\n",
        "model.add(MaxPool2D(2,2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Conv2D(128, kernel_size=(3,3), activation = 'relu'))\n",
        "model.add(MaxPool2D(2,2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = 'relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(10, activation = 'softmax'))\n",
        "\n",
        "model.compile(loss = 'sparse_categorical_crossentropy', optimizer= optimizer, metrics = ['accuracy'])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_8 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 11, 11, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 3, 3, 128)         73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 1, 1, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_9 (Dropout)          (None, 1, 1, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dropout_10 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 110,474\n",
            "Trainable params: 110,474\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "X_53f3a4U5P-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1394
        },
        "outputId": "17290018-b159-4fe7-df7c-fc0fb38c197c"
      },
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, epochs = 40, batch_size=128, validation_data=(X_val, y_val))"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 55000 samples, validate on 5000 samples\n",
            "Epoch 1/40\n",
            "55000/55000 [==============================] - 8s 140us/step - loss: 1.8529 - acc: 0.6259 - val_loss: 0.3431 - val_acc: 0.8994\n",
            "Epoch 2/40\n",
            "55000/55000 [==============================] - 7s 128us/step - loss: 0.5133 - acc: 0.8410 - val_loss: 0.2216 - val_acc: 0.9322\n",
            "Epoch 3/40\n",
            "55000/55000 [==============================] - 7s 126us/step - loss: 0.3743 - acc: 0.8860 - val_loss: 0.1750 - val_acc: 0.9460\n",
            "Epoch 4/40\n",
            "55000/55000 [==============================] - 7s 125us/step - loss: 0.3031 - acc: 0.9092 - val_loss: 0.1463 - val_acc: 0.9544\n",
            "Epoch 5/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.2584 - acc: 0.9223 - val_loss: 0.1258 - val_acc: 0.9614\n",
            "Epoch 6/40\n",
            "55000/55000 [==============================] - 7s 125us/step - loss: 0.2299 - acc: 0.9305 - val_loss: 0.1113 - val_acc: 0.9660\n",
            "Epoch 7/40\n",
            "55000/55000 [==============================] - 7s 125us/step - loss: 0.2062 - acc: 0.9371 - val_loss: 0.1008 - val_acc: 0.9708\n",
            "Epoch 8/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.1812 - acc: 0.9446 - val_loss: 0.0915 - val_acc: 0.9730\n",
            "Epoch 9/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.1729 - acc: 0.9477 - val_loss: 0.0870 - val_acc: 0.9728\n",
            "Epoch 10/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.1620 - acc: 0.9517 - val_loss: 0.0826 - val_acc: 0.9766\n",
            "Epoch 11/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.1451 - acc: 0.9554 - val_loss: 0.0754 - val_acc: 0.9776\n",
            "Epoch 12/40\n",
            "55000/55000 [==============================] - 7s 125us/step - loss: 0.1377 - acc: 0.9577 - val_loss: 0.0742 - val_acc: 0.9778\n",
            "Epoch 13/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.1320 - acc: 0.9603 - val_loss: 0.0704 - val_acc: 0.9800\n",
            "Epoch 14/40\n",
            "55000/55000 [==============================] - 7s 125us/step - loss: 0.1209 - acc: 0.9636 - val_loss: 0.0647 - val_acc: 0.9812\n",
            "Epoch 15/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.1115 - acc: 0.9667 - val_loss: 0.0624 - val_acc: 0.9824\n",
            "Epoch 16/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.1070 - acc: 0.9663 - val_loss: 0.0614 - val_acc: 0.9836\n",
            "Epoch 17/40\n",
            "55000/55000 [==============================] - 7s 125us/step - loss: 0.1027 - acc: 0.9690 - val_loss: 0.0596 - val_acc: 0.9840\n",
            "Epoch 18/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.0989 - acc: 0.9696 - val_loss: 0.0560 - val_acc: 0.9846\n",
            "Epoch 19/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.0950 - acc: 0.9708 - val_loss: 0.0528 - val_acc: 0.9856\n",
            "Epoch 20/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.0902 - acc: 0.9724 - val_loss: 0.0520 - val_acc: 0.9860\n",
            "Epoch 21/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.0856 - acc: 0.9739 - val_loss: 0.0513 - val_acc: 0.9864\n",
            "Epoch 22/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.0788 - acc: 0.9753 - val_loss: 0.0496 - val_acc: 0.9864\n",
            "Epoch 23/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.0806 - acc: 0.9756 - val_loss: 0.0483 - val_acc: 0.9870\n",
            "Epoch 24/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.0775 - acc: 0.9765 - val_loss: 0.0479 - val_acc: 0.9868\n",
            "Epoch 25/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.0723 - acc: 0.9781 - val_loss: 0.0469 - val_acc: 0.9866\n",
            "Epoch 26/40\n",
            "55000/55000 [==============================] - 7s 125us/step - loss: 0.0714 - acc: 0.9775 - val_loss: 0.0460 - val_acc: 0.9872\n",
            "Epoch 27/40\n",
            "55000/55000 [==============================] - 7s 125us/step - loss: 0.0695 - acc: 0.9784 - val_loss: 0.0466 - val_acc: 0.9866\n",
            "Epoch 28/40\n",
            "55000/55000 [==============================] - 7s 123us/step - loss: 0.0675 - acc: 0.9796 - val_loss: 0.0457 - val_acc: 0.9872\n",
            "Epoch 29/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.0666 - acc: 0.9794 - val_loss: 0.0450 - val_acc: 0.9872\n",
            "Epoch 30/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.0606 - acc: 0.9814 - val_loss: 0.0422 - val_acc: 0.9884\n",
            "Epoch 31/40\n",
            "55000/55000 [==============================] - 7s 125us/step - loss: 0.0615 - acc: 0.9816 - val_loss: 0.0442 - val_acc: 0.9878\n",
            "Epoch 32/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.0584 - acc: 0.9813 - val_loss: 0.0436 - val_acc: 0.9892\n",
            "Epoch 33/40\n",
            "55000/55000 [==============================] - 7s 125us/step - loss: 0.0580 - acc: 0.9820 - val_loss: 0.0419 - val_acc: 0.9886\n",
            "Epoch 34/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.0567 - acc: 0.9821 - val_loss: 0.0420 - val_acc: 0.9896\n",
            "Epoch 35/40\n",
            "55000/55000 [==============================] - 7s 125us/step - loss: 0.0529 - acc: 0.9832 - val_loss: 0.0405 - val_acc: 0.9898\n",
            "Epoch 36/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.0517 - acc: 0.9838 - val_loss: 0.0393 - val_acc: 0.9894\n",
            "Epoch 37/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.0507 - acc: 0.9841 - val_loss: 0.0416 - val_acc: 0.9884\n",
            "Epoch 38/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.0493 - acc: 0.9845 - val_loss: 0.0396 - val_acc: 0.9894\n",
            "Epoch 39/40\n",
            "55000/55000 [==============================] - 7s 125us/step - loss: 0.0483 - acc: 0.9847 - val_loss: 0.0392 - val_acc: 0.9880\n",
            "Epoch 40/40\n",
            "55000/55000 [==============================] - 7s 124us/step - loss: 0.0455 - acc: 0.9855 - val_loss: 0.0389 - val_acc: 0.9894\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "gpm0y4mRYO30",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "f761c56d-524f-4737-c0cc-0438028ad96e"
      },
      "cell_type": "code",
      "source": [
        "loss,acc = model.evaluate(X_test, y_test)\n",
        "print('Test loss:', loss)\n",
        "print('Accuracy:', acc)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 103us/step\n",
            "Test loss: 0.03201477632472088\n",
            "Accuracy: 0.9893\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "kS7yd8eOGs70",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "outputId": "dd244b97-0340-4eec-94bc-35e8739ec1a9"
      },
      "cell_type": "code",
      "source": [
        "loss_plot(history)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6FlTWXcW3Z9KtjtPSU12lrD16TnS1RvSNCUFucTVyi6tx3s4qpE2Nu607Hh7f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\nahoQAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5715f9b438>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hhYiIyFeIO7RwV1wiIiKfIe7QoqzbFZcbzBEREXk7cYcWzmkhIiLyGaIOLXzT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\nP0x5bCToAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5715f8dcf8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "kv2mVnB3CLEP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "##Data Augmentation"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QeYx53e4LbJJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2mTR1QtbgQ8q",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_datagen = ImageDataGenerator(horizontal_flip=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EUT_yiqTgWS7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# fit the augmentor\n",
        "train_datagen.fit(X_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "l8Ea9IRWNJiz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "39e58972-b4ac-4275-889a-54a0a2c0a956"
      },
      "cell_type": "code",
      "source": [
        "# transform the data\n",
        "for img, label in train_datagen.flow(X_train, y_train, batch_size=6):\n",
        "    for i in range(0, 6):\n",
        "        plt.subplot(2,3,i+1)\n",
        "        plt.title('Label {}'.format(label[i]))\n",
        "        plt.imshow(img[i].reshape(28, 28), cmap='gray')\n",
        "    break\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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P1+LFi1VYWKh69eqpVatWWrBggfbs2aPx48f73LewsND4RGIgHoWS6xL5jsg5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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5714b38c88>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "EpEEcNr6T0cl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "7de40e33-9a6b-4755-b977-7122f4fdf27a"
      },
      "cell_type": "code",
      "source": [
        "train_datagen = ImageDataGenerator(zoom_range=0.3)\n",
        "#fit\n",
        "train_datagen.fit(X_train)\n",
        "\n",
        "#transform\n",
        "for img, label in train_datagen.flow(X_train, y_train, batch_size=6):\n",
        "    for i in range(0, 6):\n",
        "        plt.subplot(2,3,i+1)\n",
        "        plt.title('Label {}'.format(label[i]))\n",
        "        plt.imshow(img[i].reshape(28, 28), cmap='gray')\n",
        "    break\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YbbGflJSkxHJzc5VY6dnupXG1kON4Qq5TxaRbZxQWFrqhJ/pjvles9ArR0o/3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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f57144dc898>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "rlSpRCDkXH-m",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_datagen = ImageDataGenerator(\n",
        "        rescale=1./255,\n",
        "        zoom_range=0.2,\n",
        "        horizontal_flip=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bfDFaQhuXIEy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_datagen.fit(X_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pVdT9N_jXICn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32, kernel_size=(3,3), input_shape=input_shape, activation = 'relu'))\n",
        "model.add(MaxPool2D(2,2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Conv2D(64, kernel_size=(3,3), activation = 'relu'))\n",
        "model.add(MaxPool2D(2,2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Conv2D(128, kernel_size=(3,3), activation = 'relu'))\n",
        "model.add(MaxPool2D(2,2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation = 'relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(10, activation = 'softmax'))\n",
        "\n",
        "model.compile(loss = 'sparse_categorical_crossentropy', optimizer= Adam(lr=0.001), metrics = ['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aOsZP0cec6Gu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "9b3fe1d6-cab3-43fc-ee15-893c927a5195"
      },
      "cell_type": "code",
      "source": [
        "# fits the model on batches with real-time data augmentation:\n",
        "history = model.fit_generator(train_datagen.flow(X_train, y_train, batch_size=128),\n",
        "                    steps_per_epoch=len(X_train) / 128, epochs=10,\n",
        "                    validation_data=(train_datagen.flow(X_val, y_val)))"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "430/429 [==============================] - 18s 43ms/step - loss: 0.7970 - acc: 0.7317 - val_loss: 0.3087 - val_acc: 0.9038\n",
            "Epoch 2/10\n",
            "430/429 [==============================] - 17s 41ms/step - loss: 0.3466 - acc: 0.8902 - val_loss: 0.1981 - val_acc: 0.9412\n",
            "Epoch 3/10\n",
            "430/429 [==============================] - 18s 41ms/step - loss: 0.2568 - acc: 0.9203 - val_loss: 0.1654 - val_acc: 0.9486\n",
            "Epoch 4/10\n",
            "430/429 [==============================] - 18s 41ms/step - loss: 0.2198 - acc: 0.9313 - val_loss: 0.1332 - val_acc: 0.9600\n",
            "Epoch 5/10\n",
            "430/429 [==============================] - 17s 40ms/step - loss: 0.1857 - acc: 0.9418 - val_loss: 0.1192 - val_acc: 0.9632\n",
            "Epoch 6/10\n",
            "430/429 [==============================] - 17s 41ms/step - loss: 0.1711 - acc: 0.9469 - val_loss: 0.1026 - val_acc: 0.9690\n",
            "Epoch 7/10\n",
            "430/429 [==============================] - 17s 41ms/step - loss: 0.1561 - acc: 0.9517 - val_loss: 0.0977 - val_acc: 0.9712\n",
            "Epoch 8/10\n",
            "430/429 [==============================] - 18s 41ms/step - loss: 0.1456 - acc: 0.9543 - val_loss: 0.0935 - val_acc: 0.9694\n",
            "Epoch 9/10\n",
            "430/429 [==============================] - 18s 42ms/step - loss: 0.1367 - acc: 0.9578 - val_loss: 0.0953 - val_acc: 0.9720\n",
            "Epoch 10/10\n",
            "430/429 [==============================] - 17s 41ms/step - loss: 0.1289 - acc: 0.9602 - val_loss: 0.0916 - val_acc: 0.9718\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "phG_3o8_lmW0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "429bd985-8685-4898-a767-13a7e5ff252f"
      },
      "cell_type": "code",
      "source": [
        "for test_img, test_lab in train_datagen.flow(X_test, y_test, batch_size = X_test.shape[0]):\n",
        "    break\n",
        "\n",
        "loss,acc = model.evaluate(test_img, test_lab)\n",
        "print('Test loss:', loss)\n",
        "print('Accuracy:', acc)"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 107us/step\n",
            "Test loss: 0.07422855299189687\n",
            "Accuracy: 0.9764\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "C7IlEoFoy0wZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "outputId": "a79ae713-ed92-42b6-cb93-502a97bad342"
      },
      "cell_type": "code",
      "source": [
        "loss_plot(history)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hoLhc72pByS2uQVF5HRoPtw9UyTA+MQzDogMQPygIcdEB8FfKuvQ+XBqeiDyB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\nduF/AXxVmDxfMa2TAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f56f37b4898>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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K6xhOiIiIXIQrxJ6HuLAAaNUKHDhugK3L7u5yiIiIvBLDyXmQCALSknQwt9tQ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\nrkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f56f37a8ef0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "_FD6qNQqVSDv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Autoencoder\n"
      ]
    },
    {
      "metadata": {
        "id": "Rad_c1lgnP0r",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "from keras.datasets import mnist\n",
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "\n",
        "from keras.layers import Conv2D, MaxPooling2D, UpSampling2D\n",
        "from keras.models import Model, Sequential\n",
        "from keras.optimizers import Adam\n",
        "from keras import backend as k\n",
        "\n",
        "# for resizing images\n",
        "from scipy.misc import imresize"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RjJ5f-0qgezF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "a75fe775-e2e7-4868-a6ab-5d1e15842387"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "def reshape(x):\n",
        "    \"\"\"Reshape images to 14*14\"\"\"\n",
        "    img = imresize(x.reshape(28,28), (14, 14))\n",
        "    return img\n",
        "\n",
        "# create 14*14 low resolution train and test images\n",
        "XX_train = np.array([*map(reshape, X_train.astype(float))])\n",
        "XX_test = np.array([*map(reshape, X_test.astype(float))])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/scipy/misc/pilutil.py:482: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if issubdtype(ts, int):\n",
            "/usr/local/lib/python3.6/dist-packages/scipy/misc/pilutil.py:485: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
            "  elif issubdtype(type(size), float):\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "83r3iEMNjVza",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# scale images to range between 0 and 1\n",
        "#14*14 train images\n",
        "XX_train = XX_train/255\n",
        "#28*28 train label images\n",
        "X_train = X_train/255\n",
        "\n",
        "#14*14 test images\n",
        "XX_test = XX_test/255\n",
        "#28*28 test label images\n",
        "X_test = X_test/255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4qeKtbXBn4_X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "cdf51f51-be10-466a-c6c2-d2fef3c9311b"
      },
      "cell_type": "code",
      "source": [
        "batch_size = 128\n",
        "epochs = 40\n",
        "input_shape = (14,14,1)\n",
        "\n",
        "def make_autoencoder(input_shape):\n",
        "    \n",
        "    generator = Sequential()\n",
        "    generator.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=input_shape))\n",
        "    generator.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "    \n",
        "    generator.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
        "    \n",
        "    generator.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
        "    generator.add(UpSampling2D((2, 2)))\n",
        "    \n",
        "    generator.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
        "    generator.add(UpSampling2D((2, 2)))\n",
        "    \n",
        "    generator.add(Conv2D(1, (3, 3), activation='sigmoid', padding='same'))\n",
        "\n",
        "    return generator\n",
        "\n",
        "\n",
        "autoencoder = make_autoencoder(input_shape)\n",
        "autoencoder.compile(loss='mean_squared_error', optimizer = Adam(lr=0.0002, beta_1=0.5))\n",
        "\n",
        "autoencoder.summary()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_16 (Conv2D)           (None, 14, 14, 64)        640       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_17 (Conv2D)           (None, 7, 7, 128)         73856     \n",
            "_________________________________________________________________\n",
            "conv2d_18 (Conv2D)           (None, 7, 7, 128)         147584    \n",
            "_________________________________________________________________\n",
            "up_sampling2d_7 (UpSampling2 (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_19 (Conv2D)           (None, 14, 14, 64)        73792     \n",
            "_________________________________________________________________\n",
            "up_sampling2d_8 (UpSampling2 (None, 28, 28, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, 28, 28, 1)         577       \n",
            "=================================================================\n",
            "Total params: 296,449\n",
            "Trainable params: 296,449\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Qcn1El52kwuO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1394
        },
        "outputId": "d4de5ed0-88fd-4918-f757-875127b48d33"
      },
      "cell_type": "code",
      "source": [
        "autoencoder_train = autoencoder.fit(XX_train.reshape(-1,14,14,1), X_train.reshape(-1,28,28,1), batch_size=batch_size,\n",
        "                                    epochs=epochs,verbose=1,\n",
        "                                    validation_split = 0.2)\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/40\n",
            "48000/48000 [==============================] - 11s 236us/step - loss: 0.0282 - val_loss: 0.0127\n",
            "Epoch 2/40\n",
            "48000/48000 [==============================] - 11s 225us/step - loss: 0.0082 - val_loss: 0.0068\n",
            "Epoch 3/40\n",
            "48000/48000 [==============================] - 11s 226us/step - loss: 0.0060 - val_loss: 0.0054\n",
            "Epoch 4/40\n",
            "48000/48000 [==============================] - 11s 225us/step - loss: 0.0050 - val_loss: 0.0048\n",
            "Epoch 5/40\n",
            "48000/48000 [==============================] - 11s 227us/step - loss: 0.0044 - val_loss: 0.0044\n",
            "Epoch 6/40\n",
            "48000/48000 [==============================] - 11s 223us/step - loss: 0.0040 - val_loss: 0.0040\n",
            "Epoch 7/40\n",
            "48000/48000 [==============================] - 11s 221us/step - loss: 0.0037 - val_loss: 0.0036\n",
            "Epoch 8/40\n",
            "48000/48000 [==============================] - 11s 222us/step - loss: 0.0035 - val_loss: 0.0034\n",
            "Epoch 9/40\n",
            "48000/48000 [==============================] - 11s 221us/step - loss: 0.0033 - val_loss: 0.0034\n",
            "Epoch 10/40\n",
            "48000/48000 [==============================] - 11s 220us/step - loss: 0.0031 - val_loss: 0.0030\n",
            "Epoch 11/40\n",
            "48000/48000 [==============================] - 11s 219us/step - loss: 0.0029 - val_loss: 0.0036\n",
            "Epoch 12/40\n",
            "48000/48000 [==============================] - 11s 221us/step - loss: 0.0028 - val_loss: 0.0027\n",
            "Epoch 13/40\n",
            "48000/48000 [==============================] - 11s 220us/step - loss: 0.0027 - val_loss: 0.0027\n",
            "Epoch 14/40\n",
            "48000/48000 [==============================] - 11s 222us/step - loss: 0.0026 - val_loss: 0.0025\n",
            "Epoch 15/40\n",
            "48000/48000 [==============================] - 11s 221us/step - loss: 0.0025 - val_loss: 0.0025\n",
            "Epoch 16/40\n",
            "48000/48000 [==============================] - 11s 221us/step - loss: 0.0024 - val_loss: 0.0024\n",
            "Epoch 17/40\n",
            "48000/48000 [==============================] - 11s 222us/step - loss: 0.0023 - val_loss: 0.0024\n",
            "Epoch 18/40\n",
            "48000/48000 [==============================] - 11s 221us/step - loss: 0.0023 - val_loss: 0.0023\n",
            "Epoch 19/40\n",
            "48000/48000 [==============================] - 11s 222us/step - loss: 0.0022 - val_loss: 0.0023\n",
            "Epoch 20/40\n",
            "48000/48000 [==============================] - 11s 222us/step - loss: 0.0022 - val_loss: 0.0022\n",
            "Epoch 21/40\n",
            "48000/48000 [==============================] - 11s 222us/step - loss: 0.0021 - val_loss: 0.0021\n",
            "Epoch 22/40\n",
            "48000/48000 [==============================] - 11s 223us/step - loss: 0.0021 - val_loss: 0.0020\n",
            "Epoch 23/40\n",
            "48000/48000 [==============================] - 11s 222us/step - loss: 0.0020 - val_loss: 0.0022\n",
            "Epoch 24/40\n",
            "48000/48000 [==============================] - 11s 223us/step - loss: 0.0020 - val_loss: 0.0024\n",
            "Epoch 25/40\n",
            "48000/48000 [==============================] - 11s 221us/step - loss: 0.0020 - val_loss: 0.0020\n",
            "Epoch 26/40\n",
            "48000/48000 [==============================] - 11s 223us/step - loss: 0.0019 - val_loss: 0.0019\n",
            "Epoch 27/40\n",
            "48000/48000 [==============================] - 11s 221us/step - loss: 0.0019 - val_loss: 0.0020\n",
            "Epoch 28/40\n",
            "48000/48000 [==============================] - 11s 221us/step - loss: 0.0019 - val_loss: 0.0019\n",
            "Epoch 29/40\n",
            "48000/48000 [==============================] - 11s 221us/step - loss: 0.0018 - val_loss: 0.0019\n",
            "Epoch 30/40\n",
            "48000/48000 [==============================] - 11s 221us/step - loss: 0.0018 - val_loss: 0.0018\n",
            "Epoch 31/40\n",
            "48000/48000 [==============================] - 11s 222us/step - loss: 0.0018 - val_loss: 0.0018\n",
            "Epoch 32/40\n",
            "48000/48000 [==============================] - 11s 221us/step - loss: 0.0018 - val_loss: 0.0018\n",
            "Epoch 33/40\n",
            "48000/48000 [==============================] - 11s 222us/step - loss: 0.0017 - val_loss: 0.0018\n",
            "Epoch 34/40\n",
            "48000/48000 [==============================] - 11s 221us/step - loss: 0.0017 - val_loss: 0.0018\n",
            "Epoch 35/40\n",
            "48000/48000 [==============================] - 11s 220us/step - loss: 0.0017 - val_loss: 0.0020\n",
            "Epoch 36/40\n",
            "48000/48000 [==============================] - 11s 221us/step - loss: 0.0017 - val_loss: 0.0017\n",
            "Epoch 37/40\n",
            "48000/48000 [==============================] - 11s 222us/step - loss: 0.0017 - val_loss: 0.0017\n",
            "Epoch 38/40\n",
            "48000/48000 [==============================] - 11s 227us/step - loss: 0.0016 - val_loss: 0.0017\n",
            "Epoch 39/40\n",
            "48000/48000 [==============================] - 11s 219us/step - loss: 0.0016 - val_loss: 0.0016\n",
            "Epoch 40/40\n",
            "48000/48000 [==============================] - 11s 220us/step - loss: 0.0016 - val_loss: 0.0016\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "buWQMOaCltz-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 923
        },
        "outputId": "5ea34851-1f78-4f15-d191-90a84b38fff2"
      },
      "cell_type": "code",
      "source": [
        "loss = autoencoder_train.history['loss']\n",
        "val_loss = autoencoder_train.history['val_loss']\n",
        "epochs_ = [x for x in range(epochs)]\n",
        "plt.figure()\n",
        "plt.plot(epochs_, loss, label='Training loss', marker = 'D')\n",
        "plt.plot(epochs_, val_loss, label='Validation loss', marker = 'o')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "print('Input')\n",
        "plt.figure(figsize=(5,5))\n",
        "for i in range(9):\n",
        "    plt.subplot(331 + i)\n",
        "    plt.imshow(np.squeeze(XX_test.reshape(-1,14,14)[i]), cmap='gray')\n",
        "plt.show()\n",
        "\n",
        "# Test set results\n",
        "print('GENERATED')\n",
        "plt.figure(figsize=(5,5))\n",
        "for i in range(9):\n",
        "    pred = autoencoder.predict(XX_test.reshape(-1,14,14,1)[i:i+1], verbose=0)\n",
        "    plt.subplot(331 + i)\n",
        "    plt.imshow(pred[0].reshape(28,28), cmap='gray')\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ulK3p+7Smr1TC+YP2//6itcdq+vQzefPN1wFYvvwdpk49A4C+ffvy29/+htmzr2P16lVU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\nYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f30e4526e48>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Input\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TM4u/+c1vxLj0Gre77rpLbHvq1KnocpgzEqQqnbkwjdI9/PDDnlhRUZHYtnJy\nzXj33eedRLZ58+Zi26lTp0aXT5w4gWbNmgEwj6ynIqhcpJKHvn37inHT6xel554BefYO03fi3Llz\nYvzs2bOe2LPPPiu2jZ255vTp09Fndc+cOSO2T0WiPPBIjYicwqJGRE5hUSMip7CoEZFTatVAQZcu\nXcS4NFU0AMyaNcsTe/HFF8W2sT/H2jpQYPqsJk2aeGLnz58X254+fVqMS4+9Pf7442LbRx99NLr8\n1ltv4brrrgMAvP3222L7VL4DmRgoMP23Fi1aiPG8vDwxfuutt3pi0rT3gHlC1Y4dO3pipgk/hw8f\nHl0+cOBAdODt4MGDYvtUcKCAiGoNFjUicgqLGhE5hUWNiJyS1HxqSqkCACsBLNBaP62U6ghgGYC6\nAPYDGKe1lifZosAwD/ZgLuxVbVFTSuUCWAhgfUx4NoBFWuuXlVLzAEwAID8rYZEhQ4aI8caNG4vx\n3bt3e2KmF8seOXLEf8eSkA15MI0iBvF40o4dOzwx06j1HXfcIa5v3bpVbJ/qK/XCzoXp52qaaNMU\nlx59qnzpdrxOnTqJcWnUeuPGjWLb2EcHpfV0Seb0swTATQBiH9YbjAuvBAOAVQCuD7ZbJGAe7MFc\nWKzaIzWt9TkA5+JeJpsbc2h9EED7NPSNYjAP9mAu7BbEOwqqvRmxsLAQBQUF0fVsmSv+gw8+8P3/\nZmAfk7opNFtzUROVp5/xp6Vp5Ox3QnL//fcnFS8uLg6jO76L2imlVGOt9RkAeah6GO4R+4KMTD5R\ncM8994jxRx55RIxLd2CbXjIRe00tDU8UmP5TSnkA7MlFqurXr++JmV7Gc/nll0eX77jjDrzwwgsA\ngAcffFBsn8o1taBykYk8dO/e3RP729/+JrY1XVOT+rlkyRKxbez3qri4GLm5uQDMT42kItEfAb+3\ndKwDMKpieRSAN3x+DtUM82AP5sISyYx+9gbwBIDOAMqUUqMBjAXwvFJqIoC9AF5IZyeD0qZNGzFu\nekXe5MmTPbFFixaJbUMY/XQmD36UlZV5YqbncGOP1GL169dPjK9evdoTS3QkkK25kM4yHnroIbHt\nD3/4QzH+/vvve2LLly8X28YfkQVxhJaMZAYKNuPCyE68GwLvDRkxD/ZgLuzGJwqIyCksakTkFBY1\nInIKixoROaVWzXzbtWtXMd6/f38xLt18a7pPLfa5uto6823YTH2/4Yb/v16/Zs0aDBs2DIB8nxYA\nLF682BMrLS0V22Zi5tt0qlNHPq6pvKcsnjSCaZrFOFaY3wkeqRGRU1jUiMgpLGpE5BQWNSJySq0a\nKAgLBwoyK/bh99LSUjRo0ABZp0DkAAAB8ElEQVSA+eL38ePHPTHT98K1gYKwcKCAiMgnFjUicgqL\nGhE5hUWNiJzCokZETgll9JOIKCw8UiMip7CoEZFTWNSIyCksakTkFBY1InIKixoROcXvG9p9UUot\nAHANgAiAn2itPwpz++mklCoAsBLAAq3100qpjgCWAagLYD+AcVrrkkz2sRLzYEceAHdzkck8hHak\nppQaBKCb1vpaAHcBeCqsbaebUioXwEIA62PCswEs0loPALALwIRM9C0e82BHHgB3c5HpPIR5+jkE\nwKsAoLXeCaClUqpZiNtPpxIANwEoiokNBvBaxfIqANeH3CcT5sEeruYio3kIs6i1A3AoZv1QRSzr\naa3Paa3PxIVzYw6vDwJoH3K3TJgHeziZi0znIZMDBW7PileVzftqc9+CZvu+2t6/oKR1P8MsakWo\n+leoAy5cMHTVKaVU44rlPFQ9FM8k5sEetSkXoeUhzKK2FsBoAFBKXQWgSGt9MsTth20dgFEVy6MA\nvJHBvsRiHuxRm3IRWh5CnaVDKfUYgIEAygFM0VpvDW3jaaSU6g3gCQCdAZQB2AdgLIDnATQCsBfA\neK11WYa6WAXzYEceADdzkek8cOohInIKnyggIqewqBGRU1jUiMgpLGpE5BQWNSJyCosaETmFRY2I\nnMKiRkRO+T+rACbvw+49gQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f30e450d470>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "GENERATED\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RxUazK9kWer8PnXg+YsQI0f379xetl6PpBN3wGS5YsEDaQuRZvx43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\nSBU2qBmGkSr+H3tmA2jgXlZcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f30e4582d30>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "_SQRqmdnsNVo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}