example-to-load-data.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "example-to-load-data.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "x5Iyaku97THQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Import dataset from Kaggle and read"
      ]
    },
    {
      "metadata": {
        "id": "jrFU8HTn5cFg",
        "colab_type": "code",
        "outputId": "9d6df57c-fe2a-4ff2-efb4-a7546785207c",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-b583711a-0c95-4e12-9eef-070c0679ccf3\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-b583711a-0c95-4e12-9eef-070c0679ccf3\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"akshalaniche\",\"key\":\"2c01262083914425887e3dc862c4aaa0\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "metadata": {
        "id": "6a49QECQ5qfL",
        "colab_type": "code",
        "outputId": "0b9d0ad1-d066-4727-d737-e783f49bd952",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "!ls ~/.kaggle"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "kaggle.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "PUcObYdb5tgI",
        "colab_type": "code",
        "outputId": "04f387ff-7bc6-443b-8e52-af76bd06b159",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "cell_type": "code",
      "source": [
        "!ls -l ~/.kaggle\n",
        "!cat ~/.kaggle/kaggle.json\n",
        "!pip install -q kaggle\n",
        "!pip install -q kaggle-cli"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 4\n",
            "-rw------- 1 root root 68 Mar 16 22:01 kaggle.json\n",
            "\u001b[K    100% |████████████████████████████████| 81kB 3.2MB/s \n",
            "\u001b[K    100% |████████████████████████████████| 5.3MB 6.2MB/s \n",
            "\u001b[K    100% |████████████████████████████████| 92kB 26.0MB/s \n",
            "\u001b[K    100% |████████████████████████████████| 51kB 19.1MB/s \n",
            "\u001b[?25h  Building wheel for kaggle-cli (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h  Building wheel for cliff (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h  Building wheel for pyperclip (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "D1i5_4OY51uj",
        "colab_type": "code",
        "outputId": "29f215fa-027c-451d-eb77-63da4afd99ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "cell_type": "code",
      "source": [
        "!kaggle competitions download -c comp-551-w2019-project-3-modified-mnist"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading train_labels.csv to /content\n",
            "\r  0% 0.00/302k [00:00<?, ?B/s]\n",
            "100% 302k/302k [00:00<00:00, 46.1MB/s]\n",
            "Downloading test_images.pkl.zip to /content\n",
            " 63% 28.0M/44.1M [00:00<00:00, 91.2MB/s]\n",
            "100% 44.1M/44.1M [00:00<00:00, 145MB/s] \n",
            "Downloading train_images.pkl.zip to /content\n",
            " 93% 164M/177M [00:01<00:00, 157MB/s]\n",
            "100% 177M/177M [00:01<00:00, 148MB/s]\n",
            "Downloading sample_submission.csv to /content\n",
            "  0% 0.00/67.3k [00:00<?, ?B/s]\n",
            "100% 67.3k/67.3k [00:00<00:00, 59.2MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3sUGUGzI5_xA",
        "colab_type": "code",
        "outputId": "771a83d9-f0ec-482c-c8d0-9deaa763c135",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "cell_type": "code",
      "source": [
        "!ls -l --block-size=M"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 222M\n",
            "-rw-r--r-- 1 root root   1M Mar 16 22:00 kaggle.json\n",
            "drwxr-xr-x 1 root root   1M Mar  8 17:26 sample_data\n",
            "-rw-r--r-- 1 root root   1M Mar 16 22:01 sample_submission.csv\n",
            "-rw-r--r-- 1 root root  45M Mar 16 22:01 test_images.pkl.zip\n",
            "-rw-r--r-- 1 root root 177M Mar 16 22:01 train_images.pkl.zip\n",
            "-rw-r--r-- 1 root root   1M Mar 16 22:01 train_labels.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "x5s8pxyx59Sx",
        "colab_type": "code",
        "outputId": "5ca25cd0-7c39-458a-c220-9945afb675ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "!unzip train_images.pkl.zip\n",
        "!unzip test_images.pkl.zip"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  train_images.pkl.zip\n",
            "  inflating: train_images.pkl        \n",
            "Archive:  test_images.pkl.zip\n",
            "  inflating: test_images.pkl         \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "id": "Xc3kJo-izmH2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load in \n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the \"../input/\" directory.\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
        "\n",
        "import os\n",
        "#print(os.listdir(\"../input\"))\n",
        "\n",
        "# Any results you write to the current directory are saved as output."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "id": "ywFmuT1izmIC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Files are stored in pickle format.\n",
        "#Load them like how you load any pickle. The data is a numpy array\n",
        "import pandas as pd\n",
        "import pickle\n",
        "\n",
        "infile = open('./train_images.pkl','rb')\n",
        "train_images = pickle.load(infile)\n",
        "\n",
        "infile = open('./test_images.pkl','rb')\n",
        "test_images = pickle.load(infile)\n",
        "\n",
        "\n",
        "#train_images = pd.read_pickle('../input/train_images.pkl')\n",
        "train_labels = pd.read_csv('train_labels.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "b3c7d8acfc830105a5cf04fb957430c65b644a11",
        "id": "e-67B5ffzmIJ",
        "colab_type": "code",
        "outputId": "b82d0943-9e1c-473c-f0c5-a0434734d882",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "train_images.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40000, 64, 64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "e3c1d7b8d0c63e20082273e99aede79da7198f41",
        "id": "OCCRnyU4zmIT",
        "colab_type": "code",
        "outputId": "cc07b82f-065a-4e25-bfac-e248c28078aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Let's show image with id 16\n",
        "img_idx = 200\n",
        "\n",
        "plt.title('Label: {}'.format(train_labels.iloc[img_idx]['Category']))\n",
        "plt.imshow(train_images[img_idx])\n",
        "plt.show()\n",
        "\n",
        "print(train_images[img_idx])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gtjoWb2dthsxm+eGHH7o+a7NkwiTRNwW9e/fGpEmTXKwgO1dt27Z1fdYWaG1u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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[[ 50.  79.   0. ...   0.  88.  62.]\n",
            " [ 74.  98.  46. ...  11.  18.  26.]\n",
            " [ 51.  71.  82. ...  52.  15.   5.]\n",
            " ...\n",
            " [ 12. 136. 195. ... 175. 113.   3.]\n",
            " [ 29.  91. 168. ... 117.  65.  87.]\n",
            " [ 65.   9.  29. ...   6. 104. 164.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "esh-wZfvzmIb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Imports"
      ]
    },
    {
      "metadata": {
        "id": "8zDI2Cs6zmIe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import csv\n",
        "\n",
        "import matplotlib.patches as mpatches\n",
        "\n",
        "from skimage.morphology import label, closing, square\n",
        "from skimage.measure import regionprops\n",
        "from skimage.color import label2rgb\n",
        "from skimage.transform import resize, rotate\n",
        "from skimage.color import rgb2gray\n",
        "from skimage import data\n",
        "from skimage.filters import threshold_otsu, threshold_local\n",
        "from skimage.segmentation import clear_border\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten\n",
        "from keras.optimizers import Adam\n",
        "from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D, GlobalAveragePooling2D, MaxPool2D\n",
        "from keras.layers.advanced_activations import LeakyReLU \n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.utils import np_utils\n",
        "from keras.callbacks import ReduceLROnPlateau\n",
        "from keras import regularizers, initializers\n",
        "\n",
        "import cv2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0QuoEVd2zmIo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Preprocessing"
      ]
    },
    {
      "metadata": {
        "id": "jD4-Q2ztDFIF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Set constants\n",
        "batch_size = 128\n",
        "num_classes = 10\n",
        "epochs = 12\n",
        "seed = 7\n",
        "np.random.seed(seed)\n",
        "img_rows, img_cols = 28, 28\n",
        "number_of_classes = 10\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "0a773eb3e19b2e35292f2a8103bd1ce838aa3dd7",
        "id": "eM2eZHTvzmIr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def preprocess(x, imgDim):\n",
        "    finalData = np.zeros(shape=(x.shape[0], imgDim, imgDim))\n",
        "    finImgs = np.zeros(shape=(x.shape[0] * 4, imgDim, imgDim))\n",
        "    finLabels = []\n",
        "    #Loop through every image in provided data\n",
        "    for n in range(x.shape[0]):\n",
        "\n",
        "        #Binarizing the image\n",
        "        currImg = np.array(x[n])\n",
        "        currImg[currImg != 255] = 0 #Non-black pixel--> threshold as a 0.\n",
        "        currImg[currImg == 255] = 1 #255 intensity --> threshold as a 1.\n",
        "        binarizedImg = currImg # >= thresh\n",
        "        labelledCompImg = label(binarizedImg)  #Labels each connected component in binarized image\n",
        "\n",
        "        biggestRegion = regionprops(labelledCompImg) #initialize current maximum connected component\n",
        "        prevLen = 0\n",
        "        #For every connected component, check if it's the biggest in the image.\n",
        "        for region in regionprops(labelledCompImg):\n",
        "            minRow, minCol, maxRow, maxCol = region.bbox #find bounding box\n",
        "            length = max(abs(minRow-maxRow), abs(minCol-maxCol))\n",
        "            if length > prevLen: #compare length of bounding boxes in img\n",
        "                prevLen = length\n",
        "                biggestRegion = region\n",
        "\n",
        "        #Isolate the largest component by cropping it's bounding box\n",
        "        minRow, minCol, maxRow, maxCol = biggestRegion['BoundingBox']\n",
        "        isolatedNum = binarizedImg[max(0,min(minRow,maxRow)-2):min(max(minRow,maxRow)+2, 64),\n",
        "                                   max(0,min(minCol,maxCol)-2):min(max(minCol,maxCol)+2, 64)]\n",
        "        \n",
        "        #Transform to new dimensions 28x28\n",
        "        finalData[n] = resize(isolatedNum, (imgDim,imgDim))\n",
        "        \n",
        "        #Erosion \n",
        "        kernel = np.ones((3,3),np.uint8)\n",
        "        finalData[n] = cv2.erode(finalData[n], kernel, iterations = 1)\n",
        "\n",
        "        \n",
        "        '''#Rotate and copy the data 4 times, duplicate the labels 4 times\n",
        "        for i in range(4):\n",
        "            idx = 4 * n + i\n",
        "            finImgs[idx] = rotate(finalData[n], 90 * i)\n",
        "            finLabels.append(labels[n])'''\n",
        "\n",
        "        if (n%2000 == 0):\n",
        "            print(\"Currently on example #: \", n)\n",
        "            \n",
        "    return finalData"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vyXUZC1jzmIz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data Splitting & Encoding"
      ]
    },
    {
      "metadata": {
        "id": "RAo7IimezmI0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Read labels and store them\n",
        "yTrain = []\n",
        "with open(\"train_labels.csv\", \"r\") as csv_file: #Because this was quicker than Panda\n",
        "    csv_reader = csv.reader(csv_file, delimiter=',')\n",
        "    for lines in csv_reader:\n",
        "        yTrain.append(lines[1])\n",
        "yTrain.remove('Category')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8Vzg5ztPzmI5",
        "colab_type": "code",
        "outputId": "e9633295-6dd8-41d5-a3e2-08a23498dc2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        }
      },
      "cell_type": "code",
      "source": [
        "#Run the data through the preprocessor \n",
        "xTrain = preprocess(train_images, 28) #[0:1000] is just for quick testing, remove for whole training."
      ],
      "execution_count": 396,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/skimage/transform/_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
            "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Currently on example #:  0\n",
            "Currently on example #:  2000\n",
            "Currently on example #:  4000\n",
            "Currently on example #:  6000\n",
            "Currently on example #:  8000\n",
            "Currently on example #:  10000\n",
            "Currently on example #:  12000\n",
            "Currently on example #:  14000\n",
            "Currently on example #:  16000\n",
            "Currently on example #:  18000\n",
            "Currently on example #:  20000\n",
            "Currently on example #:  22000\n",
            "Currently on example #:  24000\n",
            "Currently on example #:  26000\n",
            "Currently on example #:  28000\n",
            "Currently on example #:  30000\n",
            "Currently on example #:  32000\n",
            "Currently on example #:  34000\n",
            "Currently on example #:  36000\n",
            "Currently on example #:  38000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WddtMqrfzmI_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Validation split \n",
        "x_train, x_valid, y_train, y_valid = train_test_split(xTrain, yTrain, test_size=0.2, random_state=seed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oezTp730zmJC",
        "colab_type": "code",
        "outputId": "54e64fb9-deef-4182-ba1d-e6759c726655",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        }
      },
      "cell_type": "code",
      "source": [
        "img_idx=300\n",
        "#Testing \n",
        "print(xTrain.shape)\n",
        "print(len(yTrain))\n",
        "plt.imshow(xTrain[img_idx])\n",
        "plt.show()\n",
        "print(yTrain[img_idx])\n",
        "plt.imshow(train_images[img_idx])\n",
        "plt.show()\n"
      ],
      "execution_count": 398,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(40000, 28, 28)\n",
            "40000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lABgQlgBgQFgCgAFhCQAGhCUAGBCWAGBAWAKAAWEJAAaEJQAYMIPnJvDrr7+aa6dOnRrC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\nGBCWAGBAWAKAAWEJAAaEJQAYEJYAYPBf7Y9nINO8chIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lW9L3RkdHh3OdR4wY4fRDQVLaWvfjNddc0+WxQ6EQFF0HwbK5IfOHRApCQVYJ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\ngP8HuMpYP+jHJrgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "HJFtt7OgzmJG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Reshape for encoding.\n",
        "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
        "x_valid = x_valid.reshape(x_valid.shape[0], 28, 28, 1)\n",
        "\n",
        "\n",
        "#One-hot encoding\n",
        "y_train = np.array(np_utils.to_categorical(y_train, number_of_classes))\n",
        "y_valid = np.array(np_utils.to_categorical(y_valid, number_of_classes))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ge1XIRuJBWtJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data augmentation"
      ]
    },
    {
      "metadata": {
        "id": "jyfdckCVBmPT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Data augmentation \n",
        "datagen = ImageDataGenerator(\n",
        "        width_shift_range = 0.1,\n",
        "        height_shift_range = 0.1,\n",
        "        rotation_range = 16, # degree range for random rotation\n",
        "        shear_range = 0.075,\n",
        "        zoom_range = 0.05,\n",
        "        fill_mode = 'constant',\n",
        "        cval = 0)\n",
        "\n",
        "datagen.fit(x_train)\n",
        "\n",
        "test_gen = ImageDataGenerator()\n",
        "\n",
        "train_generator = datagen.flow(x_train, y_train, batch_size=batch_size)\n",
        "test_generator = test_gen.flow(x_valid, y_valid, batch_size=batch_size)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "q0fCi3KrzmJJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Example #1 from https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py"
      ]
    },
    {
      "metadata": {
        "id": "F4MtXOkmzmJJ",
        "colab_type": "code",
        "outputId": "7e1236c7-1148-4d7e-e708-b3e51f8c6862",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, kernel_size=(5, 5), activation='relu', input_shape=(28,28,1)))\n",
        "model.add(Conv2D(64, (5, 5), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])\n",
        "\n",
        "history = model.fit_generator(train_generator, epochs=2, steps_per_epoch=x_train.shape[0] // batch_size,\n",
        "                              validation_data=test_generator, validation_steps=x_valid.shape[0]//batch_size)\n",
        "\n",
        "score = model.evaluate(x_valid, y_valid)\n",
        "print()\n",
        "print('Validation accuracy: ', score[1])\n"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/2\n",
            "250/250 [==============================] - 13s 53ms/step - loss: 1.1928 - acc: 0.6151 - val_loss: 0.4512 - val_acc: 0.8863\n",
            "Epoch 2/2\n",
            "250/250 [==============================] - 11s 44ms/step - loss: 0.7747 - acc: 0.7694 - val_loss: 0.3565 - val_acc: 0.9099\n",
            "8000/8000 [==============================] - 1s 103us/step\n",
            "\n",
            "Validation accuracy:  0.908875\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ZY6-DqRnzmJN",
        "colab_type": "code",
        "outputId": "1dd3a169-d6ef-49c0-bc42-a6537ad5179c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "print('Validation loss: ', score[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Validation loss:  0.2850960992276669\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Q59hLLEczmJQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Example 2"
      ]
    },
    {
      "metadata": {
        "id": "gqyoPmtozmJR",
        "colab_type": "code",
        "outputId": "921efc20-c250-4a86-da1b-ccc78f0f2451",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), input_shape=(28,28,1)))\n",
        "model.add(BatchNormalization(axis=-1))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(BatchNormalization(axis=-1))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Conv2D(64,(3, 3)))\n",
        "model.add(BatchNormalization(axis=-1))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(64, (3, 3)))\n",
        "model.add(BatchNormalization(axis=-1))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "# Fully connected layer\n",
        "model.add(Dense(512))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(10))\n",
        "\n",
        "model.add(Activation('softmax'))\n",
        "\n",
        "model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])\n",
        "\n",
        "print(x_train.shape)\n",
        "train_generator = gen.flow(x_train, y_train, batch_size=64)\n",
        "test_generator = test_gen.flow(x_valid, y_valid, batch_size=64)\n",
        "\n",
        "history = model.fit_generator(train_generator, steps_per_epoch=50000//64, epochs=64, \n",
        "                    validation_data=test_generator, validation_steps=10000//64)\n",
        "\n",
        "score = model.evaluate(x_valid, y_valid)\n",
        "print()\n",
        "print('Validation accuracy: ', score[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(800, 28, 28, 1)\n",
            "Epoch 1/2\n",
            "781/781 [==============================] - 28s 36ms/step - loss: 0.1881 - acc: 0.9444 - val_loss: 0.5335 - val_acc: 0.8850\n",
            "Epoch 2/2\n",
            "781/781 [==============================] - 27s 34ms/step - loss: 0.0310 - acc: 0.9916 - val_loss: 0.4128 - val_acc: 0.9150\n",
            "200/200 [==============================] - 0s 159us/step\n",
            "\n",
            "Validation accuracy:  0.915\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SJ9_eTJfxaWE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Model 3 (Parth)"
      ]
    },
    {
      "metadata": {
        "id": "ESNTAPtxxc3M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "outputId": "6090f74c-2d2d-4b82-ba2f-7d7ad03eaf1f"
      },
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(filters = 20, kernel_size = (5,5), padding = 'Same', \n",
        "                 activation ='relu', input_shape = (28,28,1),                  \n",
        "                  ))\n",
        "#model.add(ZeroPadding2D((1,1)))\n",
        "model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n",
        "                 activation ='relu',\n",
        "                 ))\n",
        "model.add(ZeroPadding2D((1,1)))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "\n",
        "model.add(Conv2D(filters = 64, kernel_size = (3,3), padding = 'Same', \n",
        "                 activation ='relu'))\n",
        "#model.add(Dropout(0.2))\n",
        "model.add(Conv2D(filters = 100, kernel_size = (3,3),padding = 'Same', \n",
        "                 activation ='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "\n",
        "model.add(Flatten())\n",
        "#model.add(Dense(300, activation = \"relu\"))\n",
        "#model.add(Dropout(0.5))\n",
        "model.add(Dense(1000, activation = \"relu\"))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "# optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)\n",
        "optimizer='adam'\n",
        "\n",
        "model.compile(optimizer = optimizer , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "\n",
        "learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', \n",
        "                                            patience=3, \n",
        "                                            factor=0.5, \n",
        "                                            verbose=1,\n",
        "                                            min_lr=0.00001)\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 420,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_515 (Conv2D)          (None, 28, 28, 20)        520       \n",
            "_________________________________________________________________\n",
            "conv2d_516 (Conv2D)          (None, 28, 28, 32)        16032     \n",
            "_________________________________________________________________\n",
            "zero_padding2d_75 (ZeroPaddi (None, 30, 30, 32)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_246 (MaxPoolin (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_372 (Dropout)        (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_517 (Conv2D)          (None, 15, 15, 64)        18496     \n",
            "_________________________________________________________________\n",
            "conv2d_518 (Conv2D)          (None, 15, 15, 100)       57700     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_247 (MaxPoolin (None, 7, 7, 100)         0         \n",
            "_________________________________________________________________\n",
            "dropout_373 (Dropout)        (None, 7, 7, 100)         0         \n",
            "_________________________________________________________________\n",
            "flatten_129 (Flatten)        (None, 4900)              0         \n",
            "_________________________________________________________________\n",
            "dense_265 (Dense)            (None, 1000)              4901000   \n",
            "_________________________________________________________________\n",
            "dropout_374 (Dropout)        (None, 1000)              0         \n",
            "_________________________________________________________________\n",
            "dense_266 (Dense)            (None, 10)                10010     \n",
            "=================================================================\n",
            "Total params: 5,003,758\n",
            "Trainable params: 5,003,758\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "isE_6_tFxi5o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3414
        },
        "outputId": "4fb33d95-57a6-4561-995d-cf77561a3fca"
      },
      "cell_type": "code",
      "source": [
        "validation_scores = []\n",
        "training_scores = []\n",
        "predictions = []\n",
        "for i in range (1,21):\n",
        "  print()\n",
        "  print()\n",
        "  print(\"Epoch \", i*10)\n",
        "  history = model.fit_generator(train_generator,\n",
        "                              epochs = 10,\n",
        "                              steps_per_epoch=x_train.shape[0] // batch_size,\n",
        "                              validation_data=test_generator, validation_steps=x_valid.shape[0]//batch_size\n",
        "                             , callbacks=[learning_rate_reduction], verbose=0\n",
        "                             )\n",
        "\n",
        "  score = model.evaluate(x_valid, y_valid)\n",
        "  print('Validation accuracy: ', score[1])\n",
        "  validation_scores.append(score[1])\n",
        "  \n",
        "  print()\n",
        "  score = model.evaluate(x_train, y_train)\n",
        "  print('Training accuracy: ', score[1])\n",
        "  training_scores.append(score[1])\n",
        "\n",
        "  \n",
        "  y_test=model.predict(x_test_r)\n",
        "  predictions.append(y_test)\n",
        "\n",
        "\n"
      ],
      "execution_count": 421,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Epoch  10\n",
            "8000/8000 [==============================] - 2s 265us/step\n",
            "Validation accuracy:  0.950375\n",
            "\n",
            "32000/32000 [==============================] - 9s 290us/step\n",
            "Training accuracy:  0.95565625\n",
            "\n",
            "\n",
            "Epoch  20\n",
            "\n",
            "Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "\n",
            "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "8000/8000 [==============================] - 2s 263us/step\n",
            "Validation accuracy:  0.95575\n",
            "\n",
            "32000/32000 [==============================] - 9s 286us/step\n",
            "Training accuracy:  0.9668125\n",
            "\n",
            "\n",
            "Epoch  30\n",
            "\n",
            "Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
            "8000/8000 [==============================] - 2s 267us/step\n",
            "Validation accuracy:  0.95925\n",
            "\n",
            "32000/32000 [==============================] - 9s 292us/step\n",
            "Training accuracy:  0.97178125\n",
            "\n",
            "\n",
            "Epoch  40\n",
            "\n",
            "Epoch 00010: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
            "8000/8000 [==============================] - 2s 263us/step\n",
            "Validation accuracy:  0.9595\n",
            "\n",
            "32000/32000 [==============================] - 9s 288us/step\n",
            "Training accuracy:  0.974125\n",
            "\n",
            "\n",
            "Epoch  50\n",
            "\n",
            "Epoch 00007: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n",
            "\n",
            "Epoch 00010: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
            "8000/8000 [==============================] - 2s 262us/step\n",
            "Validation accuracy:  0.959625\n",
            "\n",
            "32000/32000 [==============================] - 9s 288us/step\n",
            "Training accuracy:  0.97534375\n",
            "\n",
            "\n",
            "Epoch  60\n",
            "\n",
            "Epoch 00007: ReduceLROnPlateau reducing learning rate to 1e-05.\n",
            "8000/8000 [==============================] - 2s 258us/step\n",
            "Validation accuracy:  0.96\n",
            "\n",
            "32000/32000 [==============================] - 8s 258us/step\n",
            "Training accuracy:  0.9758125\n",
            "\n",
            "\n",
            "Epoch  70\n",
            "8000/8000 [==============================] - 2s 262us/step\n",
            "Validation accuracy:  0.9605\n",
            "\n",
            "32000/32000 [==============================] - 9s 288us/step\n",
            "Training accuracy:  0.97590625\n",
            "\n",
            "\n",
            "Epoch  80\n",
            "8000/8000 [==============================] - 2s 261us/step\n",
            "Validation accuracy:  0.95975\n",
            "\n",
            "32000/32000 [==============================] - 9s 287us/step\n",
            "Training accuracy:  0.97603125\n",
            "\n",
            "\n",
            "Epoch  90\n",
            "8000/8000 [==============================] - 2s 262us/step\n",
            "Validation accuracy:  0.960375\n",
            "\n",
            "32000/32000 [==============================] - 9s 284us/step\n",
            "Training accuracy:  0.976125\n",
            "\n",
            "\n",
            "Epoch  100\n",
            "8000/8000 [==============================] - 2s 263us/step\n",
            "Validation accuracy:  0.96\n",
            "\n",
            "32000/32000 [==============================] - 8s 262us/step\n",
            "Training accuracy:  0.97615625\n",
            "\n",
            "\n",
            "Epoch  110\n",
            "8000/8000 [==============================] - 2s 262us/step\n",
            "Validation accuracy:  0.96025\n",
            "\n",
            "32000/32000 [==============================] - 9s 289us/step\n",
            "Training accuracy:  0.9764375\n",
            "\n",
            "\n",
            "Epoch  120\n",
            "8000/8000 [==============================] - 2s 262us/step\n",
            "Validation accuracy:  0.9605\n",
            "\n",
            "32000/32000 [==============================] - 9s 289us/step\n",
            "Training accuracy:  0.9764375\n",
            "\n",
            "\n",
            "Epoch  130\n",
            "8000/8000 [==============================] - 2s 258us/step\n",
            "Validation accuracy:  0.959875\n",
            "\n",
            "32000/32000 [==============================] - 9s 289us/step\n",
            "Training accuracy:  0.9763125\n",
            "\n",
            "\n",
            "Epoch  140\n",
            "8000/8000 [==============================] - 2s 261us/step\n",
            "Validation accuracy:  0.960625\n",
            "\n",
            "32000/32000 [==============================] - 9s 287us/step\n",
            "Training accuracy:  0.9766875\n",
            "\n",
            "\n",
            "Epoch  150\n",
            "8000/8000 [==============================] - 2s 264us/step\n",
            "Validation accuracy:  0.960375\n",
            "\n",
            "32000/32000 [==============================] - 9s 288us/step\n",
            "Training accuracy:  0.9765625\n",
            "\n",
            "\n",
            "Epoch  160\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-421-b214f4c1223f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m                               \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_generator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx_valid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m                              \u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlearning_rate_reduction\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m                              )\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1416\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m             \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    215\u001b[0m                 outs = model.train_on_batch(x, y,\n\u001b[1;32m    216\u001b[0m                                             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m                                             class_weight=class_weight)\n\u001b[0m\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1215\u001b[0m             \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1216\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1217\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1218\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0munpack_singleton\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1437\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1438\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1440\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "b3-JWDJo83oU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "581b1d65-add9-4e26-98a2-01925e011e8d"
      },
      "cell_type": "code",
      "source": [
        "enum = [x*10 for x in range(1,16)]\n",
        "print(\"Epochs\", enum)\n",
        "print(\"Validation accuracy\",validation_scores)\n",
        "print(\"Training accuracy\",training_scores)\n",
        "\n"
      ],
      "execution_count": 422,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epochs [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150]\n",
            "Validation accuracy [0.950375, 0.95575, 0.95925, 0.9595, 0.959625, 0.96, 0.9605, 0.95975, 0.960375, 0.96, 0.96025, 0.9605, 0.959875, 0.960625, 0.960375]\n",
            "Training accuracy [0.95565625, 0.9668125, 0.97178125, 0.974125, 0.97534375, 0.9758125, 0.97590625, 0.97603125, 0.976125, 0.97615625, 0.9764375, 0.9764375, 0.9763125, 0.9766875, 0.9765625]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "h7m5pPVibxR8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f5757592-ae84-4f29-f05b-e53dd968dc5d"
      },
      "cell_type": "code",
      "source": [
        "y_test=predictions[6]\n",
        "y_pred = np.argmax(y_test, axis=1)\n",
        "y_pred"
      ],
      "execution_count": 425,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7, 1, 6, ..., 7, 7, 9])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 425
        }
      ]
    },
    {
      "metadata": {
        "id": "Bj9ipWVWFbtx",
        "colab_type": "code",
        "outputId": "533cadbc-4272-492e-c8fb-cd082cd755f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(2,1)\n",
        "ax[0].plot(history.history['loss'], color='b', label=\"Training loss\")\n",
        "ax[0].plot(history.history['val_loss'], color='r', label=\"validation loss\",axes =ax[0])\n",
        "legend = ax[0].legend(loc='best', shadow=True)\n",
        "\n",
        "ax[1].plot(history.history['acc'], color='b', label=\"Training accuracy\")\n",
        "ax[1].plot(history.history['val_acc'], color='r',label=\"Validation accuracy\")\n",
        "legend = ax[1].legend(loc='best', shadow=True)\n"
      ],
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lCmzFRT2m6PRnOtmsjqQwGeAH7B4PBrGv6x1z9duDeXzqdCJaLIZt5w7jT+fn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g7g/NMfifrHAmGAqSrOhx/E+v23WFcux7b4IyzLvxPn+/WXJHVNxfTj1rDvm/N2oDqchqXdkZEP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\nWwghhKgi/h9yS6AxPnLgUAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "hW0N2EiEJub4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Model 4\n"
      ]
    },
    {
      "metadata": {
        "id": "i_tH4mHKJv3M",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "initializers.RandomNormal(mean=0.0, stddev=0.05, seed=None)\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(filters = 10, kernel_size = (5,5),\n",
        "                 activation ='relu', input_shape = (28,28,1), \n",
        "                 kernel_initializer='random_normal',\n",
        "                 bias_initializer='zeros',\n",
        "                 kernel_regularizer=regularizers.l2(0.01)\n",
        "                ))\n",
        "model.add(Conv2D(filters = 50, kernel_size = (5,5),\n",
        "                 activation ='relu', \n",
        "                 kernel_initializer='random_normal',\n",
        "                 bias_initializer='zeros',\n",
        "                 kernel_regularizer=regularizers.l2(0.01)\n",
        "                ))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(100, activation = \"relu\"))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(10, activation=\"softmax\"))\n",
        "\n",
        "optimizer='adam'\n",
        "\n",
        "model.compile(optimizer = optimizer , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cbw2K3VaLz8W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "3051e94b-5527-49c5-f2a9-2b6c54c249a7"
      },
      "cell_type": "code",
      "source": [
        "history = model.fit_generator(train_generator,\n",
        "                              epochs = 2,\n",
        "                              steps_per_epoch=x_train.shape[0] // batch_size,\n",
        "                              validation_data=test_generator, \n",
        "                              validation_steps=x_valid.shape[0]//batch_size\n",
        "                              )\n",
        "\n",
        "score = model.evaluate(x_valid, y_valid)\n",
        "print()\n",
        "print('Validation accuracy: ', score[1])"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/2\n",
            "250/250 [==============================] - 15s 59ms/step - loss: 1.4357 - acc: 0.5713 - val_loss: 0.5947 - val_acc: 0.8720\n",
            "Epoch 2/2\n",
            "250/250 [==============================] - 11s 44ms/step - loss: 0.9451 - acc: 0.7356 - val_loss: 0.4912 - val_acc: 0.8899\n",
            "8000/8000 [==============================] - 1s 114us/step\n",
            "\n",
            "Validation accuracy:  0.888875\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5qG4S4qNBHiT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Write test set results"
      ]
    },
    {
      "metadata": {
        "id": "WwLzdoTPBKEA",
        "colab_type": "code",
        "outputId": "08ef6938-ddda-4654-f9bb-565de2ac2679",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "cell_type": "code",
      "source": [
        "x_test = preprocess(test_images, 28)\n",
        "x_test_r = x_test.reshape(x_test.shape[0], 28, 28, 1)\n"
      ],
      "execution_count": 353,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/skimage/transform/_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
            "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Currently on example #:  0\n",
            "Currently on example #:  2000\n",
            "Currently on example #:  4000\n",
            "Currently on example #:  6000\n",
            "Currently on example #:  8000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "gisButeICfEq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_test=model.predict(x_test_r)\n",
        "y_pred = np.argmax(y_test, axis=1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-qV_SB9vJm1e",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "with open(\"submission.csv\", \"w+\") as f:\n",
        "  f.write(\"Id,Category\\n\")\n",
        "  for i in range(y_pred.shape[0]):\n",
        "    s = \"\" + str(i) + ',' + str(int(y_pred[i])) + \"\\n\"\n",
        "    f.write(s)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "i3eEH0uAKXjG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "files.download(\"submission.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BR-arZ-uHxVt",
        "colab_type": "code",
        "outputId": "07d1eb09-1a7c-4f85-8579-dc5587d73e1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 6977
        }
      },
      "cell_type": "code",
      "source": [
        "import random\n",
        "#Checking the test predictions \n",
        "for i in range (10):\n",
        "  idx = random.randint(0, y_test.shape[0])\n",
        "  print (\"Image \", idx)\n",
        "  print (\"Label\", y_pred[idx])\n",
        "\n",
        "  plt.imshow(x_test[idx])\n",
        "  plt.show()\n",
        "  plt.imshow(test_images[idx])\n",
        "  plt.show()\n"
      ],
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Image  8954\n",
            "Label 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LADAgLAHAgLAEAAPCEgAMCEsAMCAsAcCAsAQAA8ISAAwISwAwSMpyRwCwuP3222PalgzM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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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akJmZifr6erqGKpqOiRUALr30Urz00ksJf4Z79r16PsPfeeedoEy8z3DmyLUr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\nYQIPO84ar733Yo/zGvUfe+y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AAbjzzjsxefLkZF06YTZs2IAFCxbgO9/5Dq6//nrU1tZi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\nA02WQgjhQJOlEEI40GQphBAO/g/7kgByVgTaIQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Image  6937\n",
            "Label 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lABgQlgBgQFgCgAFhCQAGhCUAGBCWAGAQkx8s+68lfJEu73NqWaB1u04cE4D4ZQrLsrIy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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1Ojo6sGXLFgCfJCNbs2YNnn322RHPi0ajSSc6EkKIiUDCleXWrVtRVFSE8vJy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\nwlIIIRxIWAohhAMJSyGEcPD/APkH56EBZmVxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Image  3980\n",
            "Label 5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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roo6fOHFCV1555XljS5cuNe3zmWeeMR8fwwMzSwAwICwBwICwBAADwhIADAhLADAgLAHA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\nAAaEJQAY/A/3TlGI6g2lCwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WPux2Xj6GP/roo8qHTcH74osvXGsm3FGwYEFlk4+b8jF8y5Yt1/UxvE2bNsrnn3gM9/l8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\nAUBNTidH45oVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Image  5576\n",
            "Label 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bWytJqqur0/Lly0PbJQBEmM9p1JkzZ9Tb26tt27YNjx04cECvvvqqXC6X0tLS9MILL4S0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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jIi3DS0pK5MSJE6ZlOFrqof6M1zI8KytLGhoaeuUyHAXx9dZJTU01vz+Ln6V1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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Image  6657\n",
            "Label 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GRn3zm98cNhZOd8MfeeQRj+PXrl1TfHy8+3NKSop5nQ/63fD+/n5zbUdHh7l28+bNI8b+\n9Kc/ac2aNcPG6urqzOsMttEe/ltdXa3169cPG/PHw38XL1486rIxL8Nv3rypsrIyVVRUuP9jSU9P\ndx+A+vp6LV261EetAkBoGvPM8sMPP1RPT4/y8/PdY6WlpXr11VfldDo1Y8aMcb3iAQDC0ZhhuXbt\nWq1du3bE+NGjR/3SEACEorCdwTMeo30P54n15V7Tp08fddm2bduGfT5w4IB5+1atra3m2u7ubnPt\n/b7fvXuZv76HtX6/fPXq1Qlvq7S0VEVFRRNejy8NDAyYa//1r3+NaxwTw9xwADAgLAHAgLAEAAPC\nEgAMCEsAMCAsAcCAsAQAA8ISAAwISwAwICwBwMD8PEsMNzQ05HE8IiJixLI7d+74fPu7d+8215aW\nlk54ey6Xa1yPWgsH7FPo2bdvn8fxl156Sb/85S+HjeXl5fl8+5GRo88A58wSAAwISwAwICwBwICw\nBAADwhIADAhLADAgLAHAgLAEAAPCEgAMCEsAMGC6Y5gaz9sNx/N2x9GkpKTo3//+t/vzz3/+c/PP\n/vrXv57w9v0h3KcGejLRfdq0aZO5dvv27V5vZzRJSUkex+Pj43Xt2rURY4HEmSUAGBCWAGBAWAKA\nAWEJAAaEJQAYEJYAYEBYAoABYQkABoQlABiM/nYehLSEhAS/1N5PSkqK+8+FhYXmn/vSl75krn3r\nrbdMdf54CRykxx9/3Fw7b948P3YyUqBn7NyLM0sAMCAsAcCAsAQAA8ISAAwISwAwICwBwICwBAAD\nwhIADAhLADAgLAHAgOmO8MrXvvY1c+20adPMtb29vaa6wcFB8zrv50c/+tGwz9ZplHV1deZttLe3\nj6snq+joaNP4mjVrzOtcuHDhhHqazExhWVZWpjNnzmhwcFAbN27URx99pPPnz7vnam7YsEHLly/3\nZ58AEFRjhuWpU6d04cIFOZ1O9fT0KCsrS4sXL1ZBQYEyMjIC0SMABN2YYblw4ULNnz9fkhQXF6e+\nvj4NDQ35vTEACCVj3uCJiIhQbGysJKm2tlbLli1TRESEampqlJubq+3bt+uzzz7ze6MAEEwOl8vl\nshQeP35cFRUVOnLkiJqbmxUfH6/U1FRVVlbq008/1e7du/3dKwAEjekGz4kTJ3To0CEdPnxYU6dO\nVVpamnvZihUr9Prrr/urP0wCly9fNteWlJSY6nxxN7yqqko//vGPh42F+93wvr4+xcTEDBsbz93w\nLVu2mGtXrlxprp0MxrwMv3nzpsrKylRRUeG++71161a1tbVJkhobGzV37lz/dgkAQTbmmeWHH36o\nnp4e5efnu8defPFF5efnKyYmRrGxseazAQAIV2OG5dq1a7V27doR41lZWX5pCABCEdMdAcDAfDcc\neBBYb/CM5+2WtbW13rZzX1/+8pdHjJ08eVJLliwZNvbee++Z1zmetzs+aDizBAADwhIADAhLADAg\nLAHAgLAEAAPCEgAMCEsAMCAsAcCAsAQAA8ISAAwISwAwICwBwICwBAADwhIADAhLADAgLAHAgLAE\nAAPCEgAMCEsAMCAsAcCAF5YBgAFnlgBgQFgCgAFhCQAGhCUAGBCWAGBAWAKAQWQwNvrmm2/q3Llz\ncjgc2rlzp+bPnx+MNnyqsbFReXl5mjt3riRp3rx52rVrV5C78l5LS4t+8pOf6Ac/+IFycnLU0dGh\nwsJCDQ0NKTExUXv37lVUVFSw2xyXe/epqKhI58+fV3x8vCRpw4YNWr58eXCbHKeysjKdOXNGg4OD\n2rhxo55++umwP07SyP366KOPgn6sAh6Wp0+f1qVLl+R0OtXa2qqdO3fK6XQGug2/WLRokcrLy4Pd\nxoT19vaquLhYaWlp7rHy8nJlZ2crMzNT+/fvV21trbKzs4PY5fh42idJKigoUEZGRpC6mphTp07p\nwoULcjqd6unpUVZWltLS0sL6OEme92vx4sVBP1YBvwxvaGjQqlWrJElz5szR9evXdevWrUC3gfuI\niopSVVWVkpKS3GONjY1auXKlJCkjI0MNDQ3Bas8rnvYp3C1cuFBvv/22JCkuLk59fX1hf5wkz/s1\nNDQU5K6CEJbd3d169NFH3Z+nTZumrq6uQLfhFxcvXtSmTZu0bt06nTx5MtjteC0yMlLR0dHDxvr6\n+tyXcwkJCWF3zDztkyTV1NQoNzdX27dv12effRaEzrwXERGh2NhYSVJtba2WLVsW9sdJ8rxfERER\nQT9WQfnO8m6TZbbl7NmztWXLFmVmZqqtrU25ubmqr68Py++LxjJZjtnzzz+v+Ph4paamqrKyUgcP\nHtTu3buD3da4HT9+XLW1tTpy5IhWr17tHg/343T3fjU3Nwf9WAX8zDIpKUnd3d3uz1euXFFiYmKg\n2/C55ORkPfvss3I4HJo1a5Yee+wxdXZ2Brstn4mNjVV/f78kqbOzc1JczqalpSk1NVWStGLFCrW0\ntAS5o/E7ceKEDh06pKqqKk2dOnXSHKd79ysUjlXAw3LJkiWqq6uTJJ0/f15JSUmaMmVKoNvwuQ8+\n+EDvvPOOJKmrq0tXr15VcnJykLvynfT0dPdxq6+v19KlS4Pc0cRt3bpVbW1tkv7/d7L/95sM4eLm\nzZsqKytTRUWF+y7xZDhOnvYrFI5VUJ46tG/fPv3973+Xw+HQa6+9pieeeCLQLfjcrVu3tGPHDt24\ncUMDAwPasmWLvvWtbwW7La80Nzdrz549am9vV2RkpJKTk7Vv3z4VFRXp9u3bmjFjhkpKSvTwww8H\nu1UzT/uUk5OjyspKxcTEKDY2ViUlJUpISAh2q2ZOp1MHDhzQV77yFfdYaWmpXn311bA9TpLn/Xrx\nxRdVU1MT1GPFI9oAwIAZPABgQFgCgAFhCQAGhCUAGBCWAGBAWAKAAWEJAAaEJQAY/D93GSnPptje\n4gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7jvVR4k//9E/Ttvft2xfUYbMdrHblTZtqy5i+lToWioqK0N3dHYwPpqcxXYxp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\nhBAO/h/i/tNCx2f6cQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Image  7045\n",
            "Label 6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AWAKAAWEJAAaesNtvEocRIr3tViSvv/66ec5Ylgb+5S9/iTierLdoSyaOKfU8/fTTEcff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\nIYpZknezzAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Zo42Niw0SZ9tHPWn7WZ/kbaeLFi3Cu+++G4wfmz8mww033BC0/eQnP4mUazx5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\nQMpSCCEcSFkKIYQDKUshhHDwfwPPpQiQy3TFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Image  6901\n",
            "Label 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jx46pv79f5eXlI2N79+7VM888I5/Pp5kzZ5ovXQFgrAoZlsXFxSouLh41bv3wNACMBxNi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\nwICwBAADwhIADAhLADD4H0/NmZbz2eIvAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HnD7WYcXKGdj7AHBFpuOJDc5ncmWsHEtOFYMcpkyZ4sZYlSog6HApXLgwLr74\nYjfGBoQD3hFrnymAJ2DY+8DKPLBg9jDSbkzlyJY0Odr7qZWlEEKEQJOlEEKEQJOlEEKEQJOlEEKE\nIF8cPJ07d3Zt62AZPXq0O47VubbODWbob9euneuzmSosY4hlQ1hVo9TUVDeGOYuaNm0aaDPnAyup\nYKXvmdPJlhsAvGPGZkhVqVIF48ePd8dZmDoSy76wji/mwGIlD6yyzMqVK/P8vYsvvtip3TD1m44d\nO7o+ez0Br5D097//3Y1hWVn2+rGMmj8S67S77LLL3BiWUWPfCVY3itVij30emzZtGrpmvXXuMUcU\nKzFin22WeceUwKzzzWaK1a5dmzqGbAaPzQbMC60shRAiBJoshRAiBJoshRAiBPlis7T2s/j4+IAC\nOeADkQEetB2mDvNXX33l+mwpVaawzgKYrc3LltQFgOuuu8712YDsDh06uDHM3mptSSyw29pfAa8i\nExu8f/nll2POnDm0bLC197Agf2YjtcHkrFwuU5GxSjbselpF6zVr1rjrwMqmsr+Plf+1qjzs3JmN\n+XjbKC32mWWlk5ldeNKkSYE2u1asdHGOjbR58+ZYsWIFDf5m9nmrdm9V9AFeCcEmA5x99tluDLPJ\n2ufDBsEfKWjd2kSZDTg3tLIUQogQaLIUQogQaLIUQogQaLIUQogQ5IuD58UXX4x+TkpKwosvvuhU\nT2xpXIAbpm3AKwtkZUbvUqVKBdrM+cACmK3jiQWSM0eU/X7mfJg8ebLrsw4JdhwLALdOCyuZX7Jk\nSVpqdOHChYE2SwRg18WeJ1MdmjdvnuuzgcDsvluH4K+//uqM/8yJ179/f9fHyifYgHN2Dq1bt3Z9\nttzF8cY6tVgiBSuNsGjRokCbOQSZolDs+1a1alX67LEkCRuozsobd+3a1fVZBSOmaMQcShs2bAi0\nrXrR3r17qZqVvX7MWcycTDloZSmEECHQZCmEECHQZCmEECHQZCmEECHIFwePdQbs2LHDlSpghurV\nq1e7vqJFiwbaLFqfSeYvWLAg0LY1mQFexzvWOF62bFmqTMRKEFgnDHN2ZGdnuz577kx1hZV+sLL9\ntrTG999/jzZt2rjjrCGclVhgGTy1atUKtOfPn+/GMGeR/ZuZAZ+duzXqn3/++e642PrRObCMpLZt\n2wba7HlhTi3rFGH39Fhp0KCB67OlUJhyj1VQAnx5hl27drkxtu47EFQH27dvHy688EI3hjlP7fez\ncxo7dqzrs6UfZs6c6cbkVf4C8M/sDz/8gBo1arjj7HPMykqw+5CDVpZCCBECTZZCCBECTZZCCBGC\nfLFZWqWZVatWuYBbG1gKeEU2Se34AAAgAElEQVQcwCtt22BzwNs1AaBly5aBNrNvseDyWEWaunXr\n0iBnVo7UqqUwFW9mC7TB7B9//LEbc95557k+a3u0gcjFixentiuruMOCy1mwsC2Fy2w9zB5p7Vms\nbKoNEq9YsaILVGcB9lWqVHF9TLXb2p3DBv4PHz480GYK8sdKSkqK67N2aJakwe6pvVb2/TvSd8UG\nacfFxdHA/23btrk+a3tk94G9k9ZuakvVAsDXX3/t+po0aRJo23emePHi9DibwMLeo9zQylIIIUKg\nyVIIIUKQ5zY8KysLgwcPxo4dO/D777+jb9++qFmzJgYOHIiDBw+iTJkyGD58OM0ZFUKIk4U8J8sv\nvvgCderUwa233opNmzbh5ptvRsOGDZGSkoLk5GQ8/fTTSE1NpTYXIYQ4WchzsuzSpUv085YtW1Cu\nXDmkp6dj6NChAA4H+I4ePTrXydKqe8TFxbkgdCZFz1RBrrrqqkCbGa+ZhP3GjRsD7f3797sxhw4d\ncn0WVoKVyf1bh4sNwge4ZL4tCcDKdTKnhf2unj17ujY7T2v0ZteFOdHsd1lnBADMmTPH9dlxLLjc\nqsMUKVLEGfFZgLYNZge4Q8I+a0yhxgaEA95JMWPGDDfGBlY/8MAD7llnJVRsIDngyycwRyJzjFrn\nFwskZ4HjsUHaLGAb4Mkj1tHF3ltWVtc6VK2jCPAJBIB3JtrvLleuHH2OrcORJXzkRmhveM+ePbF1\n61a8/PLL+Otf/xrddickJNCJQAghTibiIqxq+hFYsWIFBg4ciJ9//jma6rV+/XoMGjQI77///hGP\nW7lyJU0/EkKIgkKeK8vly5cjISEBFSpUQK1atXDw4EEUL14c2dnZKFq0KDIyMuiWJZZOnTpFP69b\ntw5Vq1Z1S3WWh8q2oGG24TYOC/AxXUxIlVVSjM1N7dmzJ5YtW+bGsO2DhW1rWIycrSrIrgGLD7PX\nM/aetGvXDjNmzDjmbTiLlwyzDWfb1DDb8Nhr/Nhjj+Hee+8NtQ1nuehsG96rV69Am23DWZyl3YZb\n0w4Q3IYPHToUDz744HHdhjPHqs3TtxVDj3QOOZoDd9xxB1566SX37wA3VdlY2WPdhrNtMcthz20b\nnpycjE8++YTeG/scs8qivXv3dn055Bk6tGDBAowePRrA4Qdo7969aN68OaZOnQoAmDZt2h8anCuE\nECciea4se/bsifvvvx8pKSnIzs7GAw88gDp16mDQoEEYM2YMEhMT0b1791y/49///rdrf/bZZ4E+\nthpkq7+0tLRAmyn+MMeJ/V+Z/S9t5fgBbwhnNabZKseq1rAsGFYSw668WOYDUz6yRn2bRVGlShWq\nwGOdRWyVzBSF7IqQOXOY6cXat9nOwP6Pf9ppp7lVONsFsJUly1SxWVFJSUluzKeffur6YlV5AL7i\nvuSSS1zbro7YKpytquxqk9VAZ0pZV199daDNSiWwBU7se1O3bl26y2AZbPbZY8exkhwrV64MtNn7\nwHac1slqLYm//fYb3bXZd4k9L7mR52RZtGhRPPXUU67/9ddfP6ofEkKIgowyeIQQIgSaLIUQIgT5\nojo0fvz46OdevXph/PjxzqPLVIBY4Kz1qjFvIFPzsfY6ZldhAb7WHsIUath5Mk9bmPNMTEwMtJlt\nlSmlW3uP/e6SJUvSqAVm37UwZSDrbWTByuwaW0UhZvezqjm7d+9GixYtAn3p6enuOGtTBLjiufUy\ns0QD9ixYrzkrp2yfhXLlyuGnn34K9DHbI7MVW5sai6hggfj2HJjdjx3Xvn376OfzzjuP2qq/++47\n12fHsWjEc8891/V169Yt0Gb3yr4PANz1tM9nnTp1qBfd+g3Y38KeoRy0shRCiBBoshRCiBBoshRC\niBBoshRCiBDki4PHBsleffXVLpDbGnsB0NTChg0bBtphHTWvvvpqoM0Mx8xgP3fu3EB7/fr1bgxT\nTPrmm28CbWuUBrgx2ToyWKAuc4rYwODY8+7evTvmzp1LA/9tUDorhWudMoB38LAAdHatWBqaxZbE\n+Oabb1wpXOYwWLFihetj5VWt04zdB1Zew6Z4slTKWIWf888/H1u2bHHXmCUaMIegdQQxxwk7B+sg\nY0I3zDGac+7ly5fHli1bXNA4wN9Je93tOwqEU4RipUJYiq4tjxubvFKzZk1s3ryZlpm238W+Oze0\nshRCiBBoshRCiBBoshRCiBBoshRCiBDki4OHlZWwfRMmTHDHsUwVa3Rm9ao///xz13fttdcG2swA\nzDJqrObet99+G+o46/C4+OKL3RjrtAC8fiVTecmRx4vFqsjYUhClSpVyjhPAZ3uwrAZmeLdOCltH\nHAC2bt3q+mzGENM5tEpS1apVcxkZ7DjmtGCOPJvNxZwkTEfUZkAx9SDrhIlEIli4cGGgj2Xi1K5d\n2/XZv5HpRLIMLOtQYvW/S5cu7fqaNm0a/ZyZmUmzYFi22j//+c9Am5WxYCpA9u9jzx5zDPXo0SPQ\ntopNO3bsCPwtOdhMLfYu54ZWlkIIEQJNlkIIEQJNlkIIEQJNlkIIEYJ8cfBYw3uRIkWcsZoZ1Fmf\nzZZhzpx27drleQ5nnnmmG8OM0Ndcc02gHStjlcPq1avz7LPlBgBf+gHw2RbMSWJLawDe8G4N/+vX\nrw9VNoM5DGrVquX6bHYJK4vArou9pywTxxr19+zZ47Js2rRp445j8nm2hATgy3Iw5xQrV2Kdi0zC\nLNYh17p1a3z33XfO4cGea5apZUtnMGcHKxViS5+wjBom/bd06VIAh9+fpUuX0vvOMuasc5b9fSw7\nzkrQsd9jzlObwWaLqB06dIgWobPnFUZGMRatLIUQIgSaLIUQIgSaLIUQIgT5YrO0drFChQqhUaNG\ngT4WJJ5jQ4nF2riY7YphA1KZEgtT84lVrWnZsmWgREYOrJSqDXRmpWJZOVJra2G2wEsvvdT12Wts\nbbTVqlWjwcJWyp8FPrPg5C5dugTaw4YNc2OYTcgGQ7Pfq1SpkmvbkrYsWJ/ZcsOUKbY2L4BXL61Z\ns2agzexwtrTvDz/84OyKNmEA4HZTdm0srMyttd2ya8VslpMnTwYA/O1vf8PkyZMxffr0PH//f+H+\n++8PtFlpDfb8f/LJJ4H25ZdfHmhXrFiRPrP2WWA2dTsvxaKVpRBChECTpRBChECTpRBChECTpRBC\nhCBfHDxWZeXHH390RmemgsKCW60BnUntM+O1Naoz4zwzhNtSDGHP0yombd++3Y1hKivWwcNqWrP6\nytahZOuyZ2VloXv37u446xjKMfLH0qxZM9dny2awgP769eu7PntvWCD5RRdd5NpWSYcF2DOHIHOS\nWPUgVm6DlU+wDjKmOmT7Dh486BIgmIOH1RK3TjqmjsRUgFJTUwPt2LILYTnezh0A+M9//pPnmL/8\n5S+uz16r2GD9li1b4vvvv6eOPXsfWOJBbmhlKYQQIdBkKYQQIdBkKYQQIdBkKYQQIcgXB49V16lS\npYozwNqsDQCoUKFCnt/Nsi+YXLyV1meZP1atBfD1vll5CGZktw4dVkObGaGt1D4rlXDWWWe5vlmz\nZgXattzG6aefTh1Y9hyYQ4RlH9na1DbDBvDOOAC48847A23rbAGAxYsXB9rZ2dku24Jle1x44YWu\nj6koWcfatGnT3Bj79wFeASdMuY1TTz3VZdkwRw2rK//++++7vv9r/Pe//81zTMuWLQPtffv20Wwu\nO45lB7H3OwetLIUQIgSaLIUQIgSaLIUQIgT5YrO0ytTly5d3diNmP7BK0ey7mP1n7dq1rs8GoDKF\naWYftLAAdKa6Xrhw4TzP84cffnB9dhyza7IynzYI3V7PTZs2oXr16u44G2jNAqanTJni+qxiEgu6\ntyo9gLeJsuSAc845x7WtvZWVA2bfxdTorSJNmDKtgFeqYjZg+93r1q1zQdRMYUv2yWPHlp2uUaMG\nfUdGjhwZaDOV/tzQylIIIUKgyVIIIUIQarLMzs5Ghw4d8OGHH2LLli248cYbkZKSggEDBlCRTSGE\nONkINVm+9NJLUQGEZ599FikpKXj33XdRpUoVl7QvhBAnI3k6eNauXYs1a9ZEg7jT09MxdOhQAEDb\ntm0xevRopKSk5PodscHJzZs3x6pVq5yqCzOys0Bn6yDIyMhwY1jQtg02XbhwoRvDDPa2hC0LcmbO\nAAtTR2LB7NYxw0qkzpgxw/Vdf/31gbZVKypSpAiV0beB+GynwEqU2vvF7kPbtm1dnw3Ot44wAE5h\nKC4uzpVdsCWRAV6agSkDnX/++YG2DSQHfNlbwCtOtWjRwo2xyQ+lS5d2z6x1Up5s9O/f3/U999xz\nx+33bAJBfHw8TRiwzzYrLZwbea4sn3jiCQwePDjazsrKinqNExISaC0bIYQ42YiLsCXP/8eECROw\nefNm9O3bF8899xwqVqyI4cOHR1OJ1q9fj0GDBuUZ9pCZmUlXe0IIUVDIdRs+c+ZMbNiwATNnzsTW\nrVtRpEgRFCtWDNnZ2ShatCgyMjJobq/lo48+in6+6aab8MYbb7j8X1aljm3D7e9ZEVqAb+k7d+4c\naLNtOPu92FjBatWq0bxzllNuYzaZmCzb6i1YsCDQZttwZgrIbRt+2WWXYdKkSfRe2bhKVgFy9uzZ\nrs9ufSZMmODG9OzZ0/VZk4WNDwWC2/A2bdpg5syZLs+cmTDYNpz9zTYeM+w23Fb/zGsb/txzz6F/\n//5uG87ich9++GHX92cRiURCVZY8Evm9DY+twNq6dWvMmjWL3tOXX3450GZ/4+jRo4/4O7lOls88\n80z0c87KcvHixZg6dSq6deuGadOm0XKuQghxsnHUGTz9+/fHoEGDMGbMGCQmJtJSBRa7BT/rrLNw\n2WWXBfqYQZbJ4VurAdveV6xY0fXZlQJbnbEsFFv7mq1omBPBZm2w/8Vs5gHg1WdYFhPLNPrggw8C\nbevE+Omnn2g2kF2xMan95ORk12czjezKHeBZUvY6MAePXcGdcsop7jwzMzPdcVZpCeAZPFY9iDlc\nbBYR4J1Tc+fOdWOsEyghISGwswL4bii/sc8H4Otov/jii24Me9btKo4pQrFSL4888kie5xmGWAWx\nSCSCNm3a0Dr2djfESsTkRujJMnZpzQrQCyHEyYwyeIQQIgSaLIUQIgT5ojpklXpKlizpVNAnTpzo\njmM2xC5dugTan3/+uRvDSqJaJXZm32L2mFhbWfny5WkAOitXa73mTNGd2Y2s95sp6bDfs33W279w\n4UIaXG6xZYsBbiO132XtjEc6ztqmmfJSrE0xKSkJq1atcrZqZgNjEQfs77G2YqaKziIArFoQuzcj\nRoyIfn7ooYcwYsQIGp2RnwwYMMD1sRKzX3zxRfTzU089RaMLbrnlFte3ZMmSQJt5ojt16uT6/iib\nJYNFrVg/AqvOkBtaWQohRAg0WQohRAg0WQohRAg0WQohRAjyxcETmwrXtm1bTJgwAXfffXdgTKNG\njdxxH3/8seuzQehdu3Z1Y5hsv3WA2LKYAE9Ds0Z8FnBrUzcBYMOGDXl+NzMwW3UklnbHrpUNAGfX\njqkqrVmzJs/zZGVabXA5SxMLk+7IhFhswPmmTZtccgALSt+/f7/rY04meyw7jiVJhCkbbJ05f7Zz\nBwDKlCnj+liZk9iU47PPPpue+2effeb6rPOGpZjmIkHxP8OSXljySO/evQNtllSQG1pZCiFECDRZ\nCiFECDRZCiFECDRZCiFECPLFwVO7dm3XtoZipvLC1G5sVg+rN85Uh6yaji1dAPAsG6uKY9VvAETF\nkGNhNbMtrISDzWxiepbMIWEzWmKVWHLatm4y4MszsKwi5siwjhnmrGKlJqzhnTka7Hdv3rzZaY2W\nKFHCHccycZiyjL1f7LtYjXWrAPXQQw+5MScCNoNt3bp1bgxzuMRm2pUsWdKVVAF4KQabJcUym1id\n9z+Ke++917U7dOjgxq1YsSLQPlrNTq0shRAiBJoshRAiBJoshRAiBPlis2SlKq19kAWSs/os1m7E\ngk+ZIo0NHH/ppZfcGBbAHGv7S05OpjZSFlhtFU5Y+Q2mmG1tnTa4HeA2S6veY+2FS5YsocHsdlx6\nerobU6dOHddn7wML4rYB74C3MTPbrlWEatCggVOEYgpRn376qev77rvvXN+tt94aaDNFfhZY/fe/\n/931/dnUrVvX9W3dujXQrlevnhvDFJpi7Zj79u1ziQAAT2ywNktWppiVb/6j6Natm2vPmTPHjZsy\nZUqgfcEFFxzV72hlKYQQIdBkKYQQIdBkKYQQIdBkKYQQIcgXB491UmzYsMEFW1ujNMADiq1iDDM4\n21KnAFC/fv1AmxmA2Tl88skn0c/Jycl477333BgWDH3uuecG2swxxMrc2uNY8DwL+rWB1jZIfN++\nffT37DVmpYVZ6Qd7/5iDhznarPOGObBiS7L26dMH06dPx969ewNj2DVgDiwbiAwAqampgTYrlWAd\ndCcCrCzI2LFjXZ+9F8x5aoP8AWDPnj3RzwkJCbQkh00wAfy9YI7LDz/80PX9UVgnb6FChah6llUM\nY39fbmhlKYQQIdBkKYQQIdBkKYQQIdBkKYQQIcgXB09WVpZrW8eMVfcBeEkFm2UTK4WfQ5MmTVzf\nrFmzAm3mkGBKLDaTgymVMOUc+10sy4c5U9avXx9o25rrAFf4sc4Ne06nnHIKypcv746zGSBr1651\nY9g52DrhLOOFOd+sahTLxLGZI6eddpq77szZwTKGbI1wwJciYefJnqs/G+YMY45Kq5hUpUoVN2ba\ntGmuL8cp0rx5c6xduxbt27d3Y9g7+fXXX+d5TseTd955J/r5gQcewDvvvEOfdTvHMMdsbmhlKYQQ\nIdBkKYQQIdBkKYQQIcgXm6W1ITZp0sQp2TAl5TBqN7Y8KQAULVrU9V166aWBNlMYmjRpkuuzAe5M\nudyWoQV8yVdmO2PH2cBZZpv79ttvXZ9VgrdKOgcOHKD2Vmt7ZCo9THne3gemULNlyxbXt3DhwkCb\nKeIwG7e1LzFl/aSkJNfHxln7MbN1snvz73//O9B+4IEH3Jjjyc033+z6rC0egLPXMTsjU4KPLRcd\nHx+PBQsWuDEsQcEGgNtn6o/GltFmsKoHNsHjnnvuOarf1cpSCCFCoMlSCCFCoMlSCCFCoMlSCCFC\nkC8Onq+++ir6uUePHvjqq68CxmSAl2CNVUHJoVSpUoE2C5hmAdK2hGfz5s3dmEWLFrk+G0TNjNfM\nuWEdT+ycmBHaXhdWLpcp4tjfY+WHrWIT4NWQmNITC4a2jhPmzGFB/tb5xu6xDXL++uuvnRrTxo0b\n3XENGzZ0fexa2SB0VgqXJQzYe/jXv/7VjXn99ddd3x/Fs88+6/ruvPNO12fvBQvQZskHsc/QmjVr\n6DVmaj75XW7DvjfWAVmxYkX67NkyEj/99JMbw1SVctDKUgghQqDJUgghQpDnNjw9PR0DBgyIbpOr\nV6+OW265BQMHDsTBgwdRpkwZDB8+nMYtCiHEyUIom2WTJk0C9pJ7770XKSkpSE5OxtNPP43U1FSk\npKQct5MUQog/m2Ny8KSnp2Po0KEAgLZt22L06NG5TpZWwr5q1arOWL5r1y53HMvEsdkdzOA8btw4\n19euXbtAm5V5YBlDNjNmwIABbgxz8Ni6zCzzhzkWypQpE2gzQ7V1cgHeYG8l8zdv3kzrsNtMFabY\n9MEHH7g+6+BhzgBW+sE6dFjJg06dOrm2zTi5+OKL3XEsC4w5p3r27BloM2fHjTfe6PpsiYqbbrrJ\njTmeDp62bdu6PvYM2YwdVi6FZSjFXuPy5cvTmu7snRw2bBg/4T8Apphks+FsZlpcXBzNULLOS5b9\nlBuhJss1a9agT58+2L17N/r164esrKzotjshIYHKjwkhxMlEXIQtXWLIyMjAwoULkZycjA0bNqBX\nr17Yu3cv5s+fD+Cw/uKgQYPw/vvvH/E7duzYgYSEhD/2zIUQIh/Jc2VZrlw5dOnSBQBQuXJllC5d\nGsuWLUN2djaKFi2KjIwMGkMYS2xFxH79+uH5559H5cqVA2Ns9T6AL/ntlrdChQpuDFte2204i7+L\nrSqYQ+w2vF27dlTEIr+34Sw+LLdt+COPPIIhQ4aE2oazMWwbbrfrs2fPdmPYNjw9PT3QZo7B2HjJ\nkSNH4rbbbgu1DbcVLoFw2/B58+a5MWG24YzWrVtHP0ciESpecqywbTgzm+zfvz/QZlU2c9uG33rr\nrXj11VepiYu9k5dffvmRT/p/hG3D77vvvkA7VmTl5ptvxujRo2ncdphteJ8+fY54LnlOlhMnTsTP\nP/+M3r174+eff8aOHTvQo0cPTJ06Fd26dcO0adPQqlWrXL+DBRnbi8CCxNn23j58Vt0HAC655BLX\nZ1WSbWlcgCtm2wmNqRyxYG87+TPlbaZEZCdxdg3YJNu0adNA29oQCxcuTAPO7QuxdOlSN4ZN9HYS\nZ/YtNlHZa8xUh6655hrXtueQs7OJJSMjw/Wx6z5y5MhAm+162H/eNqiZPS/WXj5u3DhceeWVbtyx\nYFXtAeDCCy90fbYUrZ08AT6BTpkyBcDhyXLKlCn0P86XXnop9Pn+ETAlMKuoZRNFSpcuTe20VkWJ\nKVLlRp6TZbt27XD33Xfj888/x/79+/HQQw+hVq1aGDRoEMaMGYPExER07979qH5UCCEKGnlOliVK\nlMDLL7/s+o+n108IIU40lMEjhBAh0GQphBAhyBfVIetcKVy4sPP6Tp482R1nA8IBIDExMdBmXm0W\nIG0dAtY7DngDMOCVZZgaDfOmWiM0U35hijvWeWNViABfLpcdZ+3I3bt3p4Z+ez1nzJjhxjCnlvV0\nN2jQwI15++23XZ+9nqwUBDPO2yB7FkjOnEysxLIt1WEdIgAwYsQI13fdddcF2vYZBoAJEyZEP/fo\n0SPQ/l9hqkNhyqqw94E5+2KjWsqWLUuTH2yZlT+Sxx57zPWxSAwbAWMdbdu3b6flL2zkBXMI5oZW\nlkIIEQJNlkIIEQJNlkIIEQJNlkIIEYJ8cfBcdtllrm0dICwLiJVUsMZcln7FIvNtutzBgwfdGJYC\ntm3btujnxMTEQDsHlnLZokUL12dhijvWGG9VlgBu1LcZJ7bO9q5du1xNcsA7U9jfwkp3fPHFF4E2\nS+tjWVnWYfXaa6+5MbH3vUOHDhg/fryrU96sWTN3HEuvtA4sAFi+fHmgHZuimAPLELLOBubUeuut\nt6Kf33zzzUD7eMDeEXsP7bsG8Gc99rrMmTPHZTodb1hpEqZmZtN9bQbdgQMHaHkI+wwdbX1zrSyF\nECIEmiyFECIEmiyFECIE+WKztAHEa9eudaVNWbArsylY+xkL7GaScdZWxgK0mbqO/X6msM7UZ6wt\nif0t1q4I+OvAgoeZHW78+PGB9pw5c6KfW7dujTFjxqB3797uOCs5x+yMzC7MzsvCgvztNWaqOYsX\nLw604+PjnQ2qUKFC7jgmsTdx4kTXZ0vmMtk9G7gO+MB/Jh/Wo0cP1/7www/duD8K9gxZKTl2r954\n4w3Xd+DAgSN+x/+KlWQEgEcffTTQXrdunRvDZNRs6Vvrj6hevTq16y9ZsiTQZupduaGVpRBChECT\npRBChECTpRBChECTpRBChOBPUx367rvvAn0sSJwp/FhHCatRw1R5wqgVsVo61gjMApiZg2D79u2B\nNpPxZ0Zv6wxj58QcUbZ8gnViXHnlle6cALhAdWs8B0AdFBdddFGgzb6bOe2sU4Q5zJiTxAavWwch\nwK8LC6j/6quvAm2WxMDujS1hEusQyaFbt26ufTwdPMxRcyJin0fAO13ZfWBOO+sAjHXCtmnTBitW\nrKCOL/sOtmzZMveTNmhlKYQQIdBkKYQQIdBkKYQQIdBkKYQQIcgXB0+scb5///547bXX0K9fv8AY\nK/kO8CyNTz75JNBm2SxMDt8a+plDgtVltjWlmVoLUxiyDiRWW5xlSVjHBXMi7Nq1y/VZp9bcuXOj\nn9u3b48JEyagS5cu7jirHsScMixLypZ5uPjii90Y5kSzRvwxY8a4MbGG+NatW2PZsmXOAchKAjAV\nIOZAsk475gxYtGiR66tUqVKgbVVsgOBz1atXLyxatMjVDbe1xU82Hn74Yddnrx3g36VTTvFrN+ag\nsyUj7DMViURovXj7DrJyG7mhlaUQQoRAk6UQQoRAk6UQQoQgX2yWjRs3dm2rAM5sDEuXLnV9Vl2c\n2co+//xz12fLlrKAd2uHA4ANGzZEP998880umB4AVq5c6foaNWoUaDPlF6aCnpqaGmgzO855553n\n+jp37hxoW6XvevXq0d+zQf2HDh1yY5jt2AbnM7sfsydbGyy7D9Ymu2vXLmcXTktLc8d99tlnro/Z\nMVetWhVoM/tnvXr1XJ+9Vsx+bc8rLS2Nqq4XVFjguC1vzFS4mO3RJkSsWbPGjWGloO0zxFTN2Lxg\n1bOYnyQ3tLIUQogQaLIUQogQaLIUQogQaLIUQogQ5IuD58wzz3RtK9t/wQUXuOOY08cGSLNyDcy4\na5WPWGB3tWrVXJ9VPWFS9Cw42ZbrZI4hppxjjeNM5Yg5Yazji11zVirWlppgzipWPsEq8DBnBzP0\nW2eKLcUL8KQCG1BsSwkAXHXIlmEGgP/85z+BNitJzJw+dhwL4G/Xrp1rWwfnCy+84I47Ebnnnntc\nH3tHzj///EB79uzZbgxTD7Llm9n1ZE5CW4rElqPIzs52zz/7PVYiODe0shRCiBBoshRCiBBoshRC\niBBoshRCiBDki4PHZs+UKVMGX3/9daCPlU+w2SyAd1wwQ3+NGjVcn1WtYU6Z2FrbOdSvXz/QXrZs\nmRvDSjFs27Yt0GZOJ1ZKw2b62BIIAPDzzz+7PpvpE5v5cPXVV2P69Olo1qyZOy5WnQjwWRUAqGPo\ngQceCLSTk5PdGKa0ZDMy2H23Tp+tW7eie/fugb4JEya442zWFABMnjzZ9V1xxRW5/h7grwvgHV0V\nKlRwY6yaVVZWlsuc+qDxV34AAAsKSURBVOc//+mOs38fALRq1cr1HU8GDRoU+HzVVVe5McwBaN8l\nlq3GMnHs88iedfae7tu3L9C2z2fx4sVpuRnrnGX3PTe0shRCiBCEWllOnDgRo0aNQnx8PO666y7U\nqFEDAwcOxMGDB1GmTBkMHz78qPMshRCiIJHnyjIzMxMvvPAC3n33Xbz88sv4/PPP8eyzzyIlJQXv\nvvsuqlSpQrfLQghxMpHnyjItLQ3NmjVDiRIlUKJECTz88MNo164dhg4dCgBo27YtRo8ejZSUlCN+\nBwuQtirhTD2a2cqsjeubb75xY5jNxNr0bDlNgNtVbHA5s/tNmjTJ9Vm7yqxZs9yYO++80/XZ4HwW\n7M0C3G1gbseOHV2b2RCtHY6potsEAgC4/fbbA20WdMzsW1aBhykoWXXzTZs2ueeF2SeZEtEll1zi\n+mxCQtOmTd0YFuBug9KZjc2O2blzp7NZMjvtbbfd5vpsUDg7p5z3MJapU6cG2uz57Nq1q+srW7Zs\n9HNKSgpVmWdJINYWyJIY2DtZvnz5QHvLli15fjfglaps4sall15K3xv7e0wpKzfyHL1x40ZkZ2ej\nT58+2LNnD/r374+srKzotjshIYE6HIQQ4mQiLsLcRjGMHDkSixYtwvPPP4/NmzejV69eyM7Oxrx5\n8wAcrv0yaNAgvP/++0f8jp07d9IUNiGEKCjkubJMSEhAgwYNEB8fj8qVK6N48eIoVKgQsrOzUbRo\nUWRkZASW74xYm+Ztt92GkSNHum1VXFycO87mHwNeRJQVxerQoYPrs9twG7oE+C03ENwm/uMf/0B6\nerobE2Ybzv5PYttwu/U51m147NbkyiuvxLhx4+g23F53tg23ubiA3x6xbTgzdYTZhsee5/jx43HF\nFVe4nGu2JbWFrIBw23C2bWTX+Gi34W+++SZ69erltuEsPGvGjBmuz26V82sbXq9ePSxdupRuw9lW\n2Rb1Y7C8b2tyYjnzR7sNb926NWbNmhUoepeDFRdmRQv79Onj+nLI08HTsmVLzJs3D4cOHUJmZib2\n7t2L5s2bR2/ItGnT8j0eTAgh8ps8V5blypVDp06dcM011wAAhgwZgrp162LQoEEYM2YMEhMTaUBt\nLHb1t2PHDqfqUrt27TyPA7g8veW9996jf0csbEVjDcCAV1RhK1K7qgOAiy66KNBmKkf/+te/XF/D\nhg0DbVaeNykpyfXZAOnKlSu7tg2UB3yZgF9//dWNYUZ9K+XPrgtzmNWsWTPQZitulsRgg5NZMgJz\nKLFzqFq1aqDNVpFsBWWTCNgz9O233wbacXFxTqkntlRJDuzvsde9ffv2bsxbb73l+uxqzF5zNgYI\nljnJysqiuwWbpAFwB65l//79rs+uptm7bZ8FwO8SWduWkAC8k4mVos6NUO6gnj17omfPnoG+119/\n/ah+SAghCjLK4BFCiBBoshRCiBBoshRCiBDki+qQdT5UqFCBKvVYmGvfGsdZ6QJmFLbhBuy7WUhH\nbPxoixYtaJ1rVkvZhinYWscAaMiVlb5nqissZtUa7GPDdho3bozFixejUqVK7jhbosLW1Ab4NbaO\nIJZRwxSa7N/MHBuLFi0KtM866yyXRcQcG3Xq1HF9zHljHRAsk4M5nmzoEHPa2TIIiYmJTjeBqfmw\nMCR7njYcDeDKTvYestrz1hEF/P/vRNOmTTFz5kxa9oQpetkwHeZUe+mll1yfDdUL64iyz799tw4c\nOODKyADeYcxCBXNDK0shhAiBJkshhAiBJkshhAhBvtgsbdD2unXrqO3DwmwmVt2YBVoze4W1kbLg\nWmbTs3YUFvjMAm5tepdV5wa44rm1QTG7HwvWt2U9c3L3gcMpph9++CENcLf2XRYY3KJFC9dng35Z\n6Vhmb7U2SvYc2PTDK664wt1TG/QP+FRRwAegAz6FdOPGjW4MK8NsbYbs3lj79TnnnOP+RqaQ361b\ntzx/j9ke2T2dNm1aoM1sgSxtN9beGolEaGA+s5va68lSG5nt2NqvrZ0f4O9WoUKFAm37bmVmZtLn\n0b4jechiOLSyFEKIEGiyFEKIEGiyFEKIEGiyFEKIEOQp/iuEEEIrSyGECIUmSyGECIEmSyGECIEm\nSyGECIEmSyGECIEmSyGECEG+5IYDwKOPPoolS5YgLi4O9913H+rVq5dfP33MrFq1Cn379sVNN92E\nG264AVu2bMHAgQNx8OBBlClTBsOHD3dahScCw4YNw8KFC3HgwAHcfvvtqFu3boE476ysLAwePBg7\nduzA77//jr59+6JmzZoF4tyBw3qXXbt2Rd++fdGsWbMCcd7p6ekYMGBAtDBf9erVccsttxSIcweA\niRMnYtSoUYiPj8ddd92FGjVqHL9zj+QD6enpkdtuuy0SiUQia9asiVxzzTX58bP/E7/99lvkhhtu\niAwZMiTy1ltvRSKRSGTw4MGRKVOmRCKRSOSpp56KvPPOO3/mKVLS0tIit9xySyQSiUR27twZad26\ndYE470gkEpk8eXJk5MiRkUgkEtm4cWOkY8eOBebcI5FI5Omnn4706NEjMm7cuAJz3vPmzYv0798/\n0FdQzn3nzp2Rjh07Rn755ZdIRkZGZMiQIcf13PNlG56WloYOHToAOKwktHv3blpy9USiSJEiePXV\nVwPKKOnp6dFypG3btkVaWtqfdXpHpHHjxhgxYgSAwwpCWVlZBeK8AaBLly649dZbARwuRVuuXLkC\nc+5r167FmjVr0KZNGwAF41k5EgXl3NPS0tCsWTOUKFECZcuWxcMPP3xczz1fJsvt27cHyjGUKlWK\nypOdSMTHxzsZsqysrOiSPiEh4YT8GwoVKoRixYoBAFJTU5GUlFQgzjuWnj174u6778Z9991XYM79\niSeewODBg6PtgnLewGFptD59+uC6667D3LlzC8y5b9y4EdnZ2ejTpw9SUlKQlpZ2XM8932yWsURO\nggzLE/1vmD59OlJTUzF69Gh07Ngx2n+inzdwuO7RihUrcM899wTO90Q99wkTJqB+/fpUAxM4cc8b\nOKz32a9fPyQnJ2PDhg3o1atXQG/zRD534HAdpOeffx6bN29Gr169juvzki+TZdmyZQMFwrZt20aL\nip3oFCtWDNnZ2ShatCgyMjJowbETgTlz5uDll1/GqFGjcPrppxeY816+fDkSEhJQoUIF1KpVCwcP\nHkTx4sVP+HOfOXMmNmzYgJkzZ2Lr1q0oUqRIgbnm5cqVQ5cuXQAAlStXRunSpbFs2bICce4JCQlo\n0KAB4uPjUblyZRQvXhyFChU6bueeL9vwFi1aYOrUqQAOV5UrW7Ysrdp2otO8efPo3zFt2jS0atXq\nTz4jzy+//IJhw4bhlVdeiVYHLAjnDQALFizA6NGjARw23ezdu7dAnPszzzyDcePGYezYsbj66qvR\nt2/fAnHewGFv8muvvQbgsHL/jh070KNHjwJx7i1btsS8efNw6NAhZGZmHvfnJd9Uh5588kksWLAA\ncXFxePDBB6nU/YnE8uXL8cQTT2DTpk2Ij49HuXLl8OSTT2Lw4MH4/fffkZiYiMcee4yWsPgzGTNm\nDJ577jlUq1Yt2vf4449jyJAhJ/R5A4dDb+6//35s2bIF2dnZ6NevH+rUqYNBgwad8Oeew3PPPYeK\nFSuiZcuWBeK8f/31V9x9993Ys2cP9u/fj379+qFWrVoF4tyBwyab1NRUAMAdd9yBunXrHrdzl0Sb\nEEKEQBk8QggRAk2WQggRAk2WQggRAk2WQggRAk2WQggRAk2WQggRAk2WQggRAk2WQggRgv8HZx6N\nIYP3Qp4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Image  7007\n",
            "Label 2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oaMBt27dv7/M5MzPTvN9wYGYJAAaEJQAYEJYAYEBYAoABYQkABoQlABgQlgBgQFgCgAFh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\nhCUAGBCWAGBAWAKAAWEJAAb/Bf3Wr/vEwgLzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ubZ9//nlwHMvRPXTo0EiZueixdB6edyuRQTUtLY2683nS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\nFEIIBxKWQgjh4P8Bkd7VhT0VxTgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Image  9115\n",
            "Label 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nZ2eHZCAAiETc7ggABuar4cC/tbS0mNdy0QJTAWeWAGBALAHAgFgCgAGxBAADYgkABsQS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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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//35UVlaivLy834QUQoiBJiEHz7Zt23DHHXcAAObPn4+NGzf2qixn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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Image  3955\n",
            "Label 7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S1+vVokWLtHHjxog2CQBOmzYsy8rKVFZWNmn87bffjkhDABCL4vYLyyoqKsy1Z86cMdcO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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5G2PPX2Zmpps7ywJjKjnWqcVUoxjW+WZVvwDuWAveQ5s3b3YOJoBnH9lzxaIg\nmFd748aNofYNN9zgxjCn1lVXXRVqM5UqFvlhHWusrj2LAMhFb5ZCCBEBLZZCCBEBLZZCCBGBQrFZ\nMpuCtRmyQFZma7GKIyw4mWUQ2KBfFnzNsohs4Corn8lsSXYOffv2dWOKMlFCx5h6urVjMptiUDGp\nR48eWLNmDZKTk0NjbMlggNuAWcC5zeRgdiqWOWVV68eNG+fG2PvxkksucTbK7t27u+NYULq1x7P7\nmpUStiVf27Rp48awMtNBO3uJEiWo8jwLHLelmVlGGwtmt9/PbJ3MtmqTR4L34vjx4zF16lTqk7B/\nzyrfF4TeLIUQIgJaLIUQIgJaLIUQIgJaLIUQIgKF4uCxgcg7duxwRlomH88M2lbphSn+MIWf9u3b\nh9os2NUGkgNhNaSWLVvSObGyrCtXrgy1WUnbc52HHnrI9Q0cODDUjqJQc95557mge1aOYtCgQa7v\nn//8p+uzSQSsJIZ1WgA+eJ3JedmkhV27dqF///6hvuXLl7vjmKPSOqeYw4ypddn7mCV8sJIfwWcw\nMTExsvyhfXZZkD8rSWwTEuw1Brhi0uTJk0Nt+6wtWrTIJaEAYdlCAPjb3/7mxjBnWC56sxRCiAho\nsRRCiAhosRRCiAhosRRCiAgUioMnIyMj73Pv3r2RkZHhSkYwBwhzwthaM0x2nhneg1khAHcs2MwH\nwCuTMCUkZvQuqC7RfyrWOM8cZrbUxeHDh53TgBn+s7OzXZ9VtgG8wg8rIcGcDTYTh9V0b9asWahd\ns2ZNV3aBOTtYrSA7bunSpW4Mu89sTRxWb8pm1QHhEi0HDx6kZV3s8wd4RS323czpY9XBmNOpZMmS\nrs/WT7cqRFdddRV9lq1TkD3L+aE3SyGEiIAWSyGEiIAWSyGEiECh2CytonTz5s3xzjvvhPqYuki3\nbt1cHwuUtRw/ftz1WbUUFtTMVI6syhBTRWc2IasynZOT48YMHz7c9f2nwZRtrDp9p06dnCLU7Nmz\n3XHsHLNa29YeydTvg7XLc7F2UnYvWtvct99+68opM/sdC4KfOHFiqD1y5Eg3htn6bfA1s+++9dZb\nri+ovlS2bFlavpYlgdjzyRJMmG/B2ij//e9/uzGsvrm1idq/l5iYSJXubUliVqs9P/RmKYQQEdBi\nKYQQEdBiKYQQEdBiKYQQESgUB89PfvIT17ZlHVjZTWa8tmUeWElUFohsA4hZEDBTKtm3b1+obQNg\nAV561xqPGzdu7Ma0a9fO9VmnAQucZb/ZYo3Z559/PnVOnY0EHTyxWAydOnXCXXfdFRrTsmXLSN9V\nqVIl12fL1V5++eVuzIUXXugKOlA/AAAgAElEQVT6bJIEc+zZchS7d+9294ItyQoAs2bNcn3WKWlL\n+AJcOceWmnj99dfdGFZ6N3iuKlWqRM8Bc3xZ5xtzAjHseWFldlkJaevAsn8/Pj4eLVq0cMdZpzJT\nC8sPvVkKIUQEtFgKIUQEtFgKIUQEtFgKIUQECsXBY+sBf/PNN6hfv36ozyqzAL7GNOCzNFgmh3XK\nAD5DiCnNsKwea/z/9NNPC5wT4B1RH374oRtz9913uz6r6sLqH7NSAtbZYGs3v/POO9So36NHj1A7\nMzPTjTkbsI41Vi+bOdpeeukl12edFKxeNVPAsZkjTJnIKuJ8/vnnzhllVYFO9V2LFy8OtZlD6dVX\nX3V9lStXDrWtAwTgvy/oONm5cyfNNLr11ltdH1N7stjzAniHDrvX33zzTddnVb7seWnfvj3N4LEK\nTUyZKD/0ZimEEBHQYimEEBHQYimEEBEoFJultQ0cOnTI2dRYKVCmlG4VhYJKKbmwoF9rE2U2GxYY\nb+duy5MCXDnHBsFbBW3AB44DcCWCmc2G2R6tLbVr166hdkJCArXNjRs3LtRmges33HCD6ytsgmr7\nAA98zsrKcn3MLmXPO/vN7NpY9R6meG7vq27dujnlI3YPsaQFa0P/+c9/7sYw7D3DbP8ffPCB6wva\nUmvVqoUOHTq4MbZ8LRBNCYwp4tu+1157zY1hgfG2tK+10R46dIgqH9kkkPzK3jL0ZimEEBHQYimE\nEBEocBuenZ2NkSNHYu/evTh69CgGDx6MOnXqYMSIETh+/DjKly+PsWPHulAZIYQ4lyhwsZw/fz7q\n16+PgQMH4ssvv8Q999yDJk2aICUlBcnJyRg/fjxmzJiBlJSUwpivEEL8KBS4WAaN+zt37kTFihWR\nkZGBhx9+GMDJANC0tLR8F0tbUrNJkyZOyv/99993xzGniA3uZsohzDFkA4OXL1/uxkyaNMn19e7d\nO9Rmwa5MGejaa68NtVngOpu7dXzZNgDUqFHD9VllJ1sS4LzzzqNGfWscZ6VOu3fv7vqYks2ZZP78\n+aG2dWAB3GnHHAQ2QNqeOwAoXbq067OOGbabsgHT9evXd4415uCx5UsAoE+fPqE2U6liClRW0WvE\niBEFjgHCKkdVqlShJR2Ys8g6HFlSCCttsWXLllCbnU/m+LLB7MESxT179sTq1aup49cG2a9cudKN\nueOOO1xfLpG94X369MFXX32FSZMm4e677877YYmJiU6WSgghzjXiYiwW4BSsW7cOI0aMwO7du/MK\nvm/duhWpqamYNm3aKY87cuSIC4kRQoiiRIFvlmvWrEFiYiIqV66MunXr4vjx4yhZsiRycnKQkJCA\nrKwsl3NpCb7uJiUlYfHixS4Ocd26de44tg23W8kfchtutwVAeBs+YMAATJgwwY1ZtWqV64uyDWfb\nMQuLs2RbE5ufG4yRa968OT766CMsWbLEHRdlG/7EE0+4vsLahsdiMcTFxTmhWLYNZ+eYbQltXCDb\nhtvtNOCv1yWXXOLGBGN1k5OT8dZbb7mc8po1a7rjtm/f7vrsNWXi1FGEoKNuw3O3rh07dsS7775L\n87nZNtyafE53G84EuZnYtt2GB6tCPvTQQ3jooYdOexv++OOPu75cCgwdWr58OdLS0gCctNcdOXIE\nSUlJmDNnDgBg7ty5VM1YCCHOJQp8s+zTpw9+97vfISUlBTk5OfjDH/6A+vXrIzU1Fenp6ahSpQpu\nueWWfL9j6tSpeZ+TkpIwdepUZxRmb1ns7c9G3X/yySduDMvgsf+L2Tc/AE4JCfBvDyVLlnRjrBMI\n8G+EBw4ccGNYyQObcdKgQQM3hmHfXiZPnpz3uXnz5pg8eTJ1FtWrVy/UZueusJ05jP79+4faLGuK\nmXrY77FvY0zthjkb7Fs4q0/PSqjYMggsO4g5p2yGEFObYrXu7W7LluQAgKefftr15f7mjh07YvPm\nzfRNnd3/NluGZeuwTKrq1auH2h07dnRjcl/UggQdOoB/jjZs2EB3SPaN/vuqDhW4WCYkJNBX0+AC\nKIQQ5zrK4BFCiAhosRRCiAgUiuqQVQnZunWrUy5nHsKDBw+6vkWLFoXazPPGlJvt97MgYGu/A7wN\nitlImX3ktttuC7WtNxfgaje5jrNcmHK5tZ0B3rtpFWtKlChBFbPt9zOb1NkAK3dqsV5ZgCuq29/I\nbIGsz9o/meK5tR3Hx8e76AWW2MDu9aCXF/C2ulPN03p92TVt1KiR6wveC5mZmbSaAHu2bCQLi7tm\nXnubMMBs6qyagPUHWLWpcuXK0fP5+eefh9r2GS0IvVkKIUQEtFgKIUQEtFgKIUQEtFgKIUQECsXB\n07dv33zbAC9pe/3117s+W+6Uycez9MuNGzeG2sxxYsvCAmGDdt26damEPjOyWwcS+31MRebEiROh\ndpcuXdwYlqZlg+ytMbt8+fLU0G8dIL/97W/dmMKmc+fOrm2D0FnpWJsGB/CSBzZomjmGbDou4O8r\nFpRu57l161ZXioE5M1m6r02kYGVBmDrS6NGjQ212Xhi5TtennnoKL730EnXKDBs2zPXZdFhbPhoA\n+vXr5/rsvc5SMFlgvHU82etQrlw56gyzqZNRUkWD6M1SCCEioMVSCCEioMVSCCEioMVSCCEiUCgO\nHutYKFmypMtCYUbvt99+2/VZhSPmqGFqIjbzgGXdMGOy1cZkajSsrITNRmAZJ8xJ0bNnz1Cb/RZm\n6B8+fHiobTOGdu7cSTNOckWcc2FOi8LG3gs1a9Z01++iiy5yxzFHBsuWsVk2wXIKubAsKZsZw/RP\n7TwPHDjgdETZ32P3nnXUsLroPyRBh8epnB9M29TC7uuHHnrI9Y0cOTLUZuecaWra+8M63tq2bUsV\njGzGECuzkh96sxRCiAhosRRCiAhosRRCiAhosRRCiAgUioPn1Vdfzfvcs2dPvPrqq6F65ACXjGJ9\npUqVCrWZA+SLL75wfVZ+nxWkYg4lm43ASgKwAmlW2s06BwBeeMwa8VkxLTZPm5ViSwskJCTQ7Iuz\nEZvZceLECVd2gZ0XlrnFCmXZY+09darvsk4Ylj1mv6tOnTrOyZSenu6Oy8jIcH1n2qHzY2MdXeyZ\nZAX7rAPpxhtvDLVLlSpFy47Y8iFMkjE/9GYphBAR0GIphBAR0GIphBARKBSbpS1f26ZNG2dXtGUx\ngXDB+lysjXLt2rVuDFM9seo6LEjclmIAvFIJs28x+5m1mbRo0cKNWbNmjeuziiqsHDCzw9lg6Chl\nNM4G/vznP7s+qz7Ts2dPV26D2aRq167t+piikLWVMfWnxYsXuz57bZjNkqkc2cDq3//+927MfyJW\nXcqW0QB4yWq7ntjkgIMHD9L1xJZKZqVe8kNvlkIIEQEtlkIIEQEtlkIIEQEtlkIIEYFCcfBYyfx1\n69a5IGMWoM3qD9uA5VtvvdWNYbW9rZGd1ey2QauAdyTYeucAXNkAwCucRAmUB06q1ARhpSCYao1V\niRkxYkTe5+HDh4faZxPMURMM/E9OTsa6deuc0lK1atXccUyNyQbnAz6omTm/mjVr5vpsqQmmcmT/\nXnZ2Nj7++GM3Tpws0xKEPVvjxo1zfQMGDAi1H3vssbzPHTp0QEZGBlWlss439kz+5S9/OeV89WYp\nhBAR0GIphBAR0GIphBAR0GIphBARKBQHjy278Nlnn+Hqq68O9bFMHGZkt44aVubBGo7ZHFjWjVVC\nAnyUP8vEYU4K64hi2TpMVck6A1hGCCv9MG3aNNd3NmKN86yUgFV2Ov/889GtW7dQH3Ou7Nu3z/V1\n6NDB9X366aehNnMszJ071/VZVRzmgLT3R2JiIp555hk3TnhYKQt7vzCCKk6//e1vkZ6ejkqVKrlx\nNvuP3Xv5oTdLIYSIgBZLIYSIgBZLIYSIQKHYLK3tsVmzZk5NhAUZv/jii66vbNmyoTZTD7LqIgDQ\noEGDUNuWQwW4XXHRokV5n9u1a4eFCxe6MVYlBwBKly4darOyqXZOgLfBrly50o1h5UGLCrZE6a5d\nu9wYa2+qVKmSU4Riij9MjZ7ZtK1CNrM93nbbba7P2jqZ3XTq1Kl5n5OSkkLtswlme1+2bFmo/fDD\nD7sxDz744Bmb0+liy2FnZmZSZS57ndmY/NCbpRBCRECLpRBCRCDSYpmTk4NOnTrhlVdewc6dO9Gv\nXz+kpKRg2LBhdOsjhBDnGpEWy2eeeSbPBjdhwgSkpKTgpZdeQo0aNTBjxowzOkEhhDgbKNDBs3nz\nZmzatAnXXXcdgJMlO3MNv+3bt0daWhpSUlK+9x+2cv+2zOipWL16daidnJzsxrDSD7b8KDPudurU\nyfXVqlUr3zbAHQs2mPzgwYNuzKRJk1yfLWnLSv0yB9bZyC233OL6rGPNlvsAgE2bNoXa27dvR9u2\nbUN9TOmJlSGx5TYAOMUrFsBslYly5xGElVK1fb/4xS8wZcoUN64w6dOnj+u75pprXN/AgQPzPk+e\nPPmMzulMM2/ePNeXlJQUarN7Lz8KfLMcPXo0Ro4cmdfOzs7O8zAmJiZST6IQQpxrxMXYf9H/y2uv\nvYYdO3Zg8ODBmDhxIqpWrYqxY8diyZIlAICtW7ciNTW1wFS7rKyss7ZglhBCRCHfbfiCBQuwbds2\nLFiwAF999RWKFy+OEiVKICcnBwkJCcjKykKFChUK/CMTJ07M+/zoo49i1KhRaN++fWgMi5ubOXOm\n67N52Gd6Gx4UlO3VqxdefvllN4ZVELTio2xbYLd1gN+GszhEtg3PL/84FovRuNIzDduG2y0h+y3B\nbfivfvUr/M///E+kbbjNxwe4icRuw9kc2Dbc6gvYao9AeBvepEkTrFixAk2bNnXjCpOo2/Dc52bA\ngAGnNB0Et+pnI/nd63Ybzs7B+PHjT/nd+S6WQdXg3DfLTz75BHPmzEH37t0xd+5cdxMLIcS5yPfO\n4Bk6dChSU1ORnp6OKlWq0LcHi629fdVVV+Gdd94J9fXo0aPA4wCfDbRnzx43hr2l2r5y5cq5McyZ\nYtWJLrjgAjeGKQNZhw5TxLHZSGxeLMuHZRp179491H799dfdmDOJLR1yKmz2EZP/t9lPTZs2zTP9\n5MLe6li5DVY+xF7Tzz//3I1hilD2WrCSFR999FHe5yZNmmD9+vVuzA8J2w3ZebGdls1GAsKZaNnZ\n2bjvvvt+gBmePdjnjdWLz4/Ii+XQoUPzPp+tKVxCCHGmUAaPEEJEQIulEEJEoFBUh55++um8zykp\nKXj66add6VvraQS4vc7aFW3JWYDbs6wtMEqwMuBtasz2yJSBrH2ElUNlc58+fXqofc8997gx9evX\nd3033nhjqB20nQEn7XlWuef/QvXq1UNt9vuYErW1bVolesBHBMRiMadSxdSEmIfcqq4D3kPOwtqy\nsrJcn517jRo13BjriY2Li3Nmq9atW7vjcpM+ggwfPjzUZp59ZkO3dlL2+/JTDxo6dGiRt1f+7ne/\nc302Jvz7Pg96sxRCiAhosRRCiAhosRRCiAhosRRCiAgUioPHqntceumlzoDOgsu3bt3q+oJlHgCe\nqsYCsq2hnynNsNTCYMBy586dqXoQC3y2dOnSxfWxgGUbQMzSK5nB3jpcgk613PbOnTvdcYsXLw61\nWZC4dR4B3tnAnA8s8L9Nmzahtg02t9917bXXYsuWLS4Qn5VNtWURAO7ss6WSWWkS5pyywd4bN250\nY+z1O3TokHNeNmnSxB3HHHl27v/4xz/cmH/961+u78iRI67vXMKWx7VO1x49elAHXc2aNUNtOXiE\nEOIMoMVSCCEioMVSCCEioMVSCCEiUCgOnmLFirn2xRdfHOpj2o4sI8MqxjAnEHP6BHUpAV43nDlq\nbBbR3r173RibcQJ4p9aBAwfcGKb8YvVBWW1q5ji56aabQm177mKxGM2IsvXMrV4owNWYrHakvS4A\n1/m0hnfmMLP3wqZNm9x1ZopNTI2JqfJYhxUz9LN7wTpT2BxuvfXWULtz587Ytm1bqI9pm6alpbm+\nRx991PUJ7+CxdcAuu+wyt+YAwNy5c0Ntm0VYEHqzFEKICGixFEKICGixFEKICBSKzdLaEEuUKOFs\nekzNhymQW1tStWrV3BimSGMVhawaN+BLsAI+UN3anwAf7Ap4m15mZqYbs2rVKtd3ySWXhNqsfgr7\nzTYQ+fLLLw+1K1eujH//+9/uuNq1a4faNmD7VPO0Nlg2Jxbkb7/fJhkA3ga8f/9+1KlTJ9THShIz\n1X5m833//fdDbWazZHbhu+++O9Rmv++LL77I+1yzZk188cUXLricJWD8kIpQRRX2TLJgfWujtOpM\nw4cPx9ixY91x9h5688033ZgnnnjilPPTm6UQQkRAi6UQQkRAi6UQQkRAi6UQQkSgUBw8ffv2dW1r\nxJ84caI7jgU6W0l+5rRgwcnWqN6hQwc3hgWyWpWcfv36uTEsyNg6eGyBd4AHNVs1JOZcYSVfbTC7\ndWgdO3aMBs9bRRzm2GBqPlaBhznHWKlR63ApX768G9O/f3/XtmVAmINn6dKlro8lO1jnlL3GgC9B\nAPj7g6kVPfnkk3mf27VrhyeffJIqR/2nwZxvtixHr1693BgbSA54R82KFSvyPl9//fVYsWIFXTts\nGWaWpJEferMUQogIaLEUQogIaLEUQogIaLEUQogIFIqDJ+ikSEpKwqpVq5CYmBga895777njHn/8\ncddno/ytYwPgNZ8tTD3I1qYGgM2bN+d9btu2LZ0nc97YcgklS5Z0Y6yTBPDOhuPHj7sxTM3HOovs\nOcjKyqKOL+soYQpD9erVc31WBYhl8LDr0KhRo1D7Zz/7mRsTzOpp0qQJFi1ahGuuuSY0Jicnxx3H\nYCUcrPMrIyPDjenevbvrs5lorFa6deb8Jzh3bAbNBx984MawZ8s6BZmz1pZLAcKlXgBg0KBBoXbz\n5s2p8phVjVq9erUbkx96sxRCiAhosRRCiAhosRRCiAgUis3S2pdycnJcudr69eu744L2wlxssDAL\nLrdq1YAPKJ41a5Ybw+yYl112WahtS+oCwLvvvuv67G9myuzMnmaDcK19DQDat2/v+qxqTVDlqEeP\nHsjMzKR2RauIwwKtX3jhBddnA9yDajunGgN41SZmk7rgggtc2wbGd+7c2R135ZVXur61a9cW2Meu\nKQuWZ7/nXCKoBHbppZfSSgXsetkSyyy5g5UutgpbK1eudGOY0r31dwRtn4mJidi0aRNatWrljrNr\nB1M1yw+9WQohRAS0WAohRAS0WAohRAS0WAohRAQKxcHTtGlT17bGeGZMZkG/NtjbGokBH/gM+EBu\nq4QEABs3bnR9VqGGOQNYsLd1wrDf99FHH7k+6whiAeHWwA34AHfr8ClfvjzKlCnjjrNqTEwJadiw\nYa7PBnJ/++23bgxz+thAdVuiGPDByWvXrsVVV10V6mPOuPXr17s+lgxgDf3sfJ7rzhx2TYPP0uDB\ngzF79mw3ZtSoUa5vzJgxoTZLFGHYe7Ry5cpujC0hAfgyLrZUycUXX+xUv4BoZSXyQ2+WQggRAS2W\nQggRgQK34RkZGRg2bFjetrlWrVoYMGAARowYgePHj6N8+fIYO3YsragohBDnCpFsli1atMCECRPy\n2r/97W+RkpKC5ORkjB8/HjNmzEBKSsoZm6QQQvzYnJaDJyMjAw8//DCAk46MtLS0fBfL+fPn531u\n27Yt5s+f74y0zNjKjOw2O4BF4bMMAms83rBhgxvD+ho3bhxq27rlAC+fYLNQWGkGNndrrF63bp0b\nYw3VgHdk2Df94sWLu1rigM++2LJlixtjfwvgHUoss4NlGlmDPStHYb+rSpUqLpOKlX1gTp9PPvnE\n9XXt2jXUvuiii9yYosLQoUNd38GDB0PtqA6X4LXfsmWLc8wC4RIOudhr8+WXX7oxpUqVcn3WuchU\nqtq2bev6rOPQPkdHjx51qliAz8r6vhk8kRbLTZs2YdCgQThw4ACGDBmC7OzsvIcxMTGR3rhCCHEu\nERdjMS0BsrKy8PHHHyM5ORnbtm3DnXfeiSNHjuS9KW3duhWpqamYNm3aKb9j165dkf93E0KIs5EC\n3ywrVqyIG264AcDJ7VG5cuWwevVq5OTkICEhAVlZWQUuhJMmTcr7/Ic//AF//OMfcffdd4fGRN2G\nWwHPFi1auDEsiT7KNpzFdQa34a1bt8af/vQnN4aJ8dptx+luw5kABxMKsWKnQdPHvffei+eee46e\nK7sNZ3GPNsYR8LGQdusHgG777TacnYPgNvz+++/HuHHjnOgI+3vsmrJYzyjbcCZW+32JxWL0+v2Q\n/JDb8Fzh56eeegq//OUvqYBuxYoVXZ8V42XbcLadtvcQE3FhYjP2mt588815nxs2bIhVq1ZRYe2L\nL7441P7Vr37lxrz44ouuL29+p/yX/+WNN97A7t270b9/f+zevRt79+5Fjx49MGfOHHTv3h1z586l\nJyKIDew+cOAARowYEeqz6jcAP3lWnYiVOmX2EbsosHKyttwq4O11LOCdKaVbBZWg+ncu7KXeXmQW\nMG1tPYAvaWuPK126ND1XtowvC0pnC5O1NTJ1JKZAXrt27VCb/UdjF68qVargyJEjBR7H/rNjQel2\nwWaRHLbkMsCvYWHy3HPPuT4b2A34a8ieI6boFbw21apVw759+9wYds/ec889oTb7T4uVprX3LPuP\nk9kxrY8g+Gw3bNgQO3fupIu6LfP8m9/8xo3JjwIXyw4dOuD+++/Hu+++i++++w4PPfQQ6tati9TU\nVKSnp6NKlSq0JrAQQpxLFLhYlipVKrSNzmXq1KlnZEJCCHE2ogweIYSIgBZLIYSIQKGoDlmvdtWq\nVZ2hmBlymSfRBpbee++9bszhw4ddny3xap1OADdCWweBdVCc6rus950FrjPDe40aNUJtJscfLC2c\ni1VDCpYIyG1bJxebA1N+YQ6eq6++OtRmXmdWxtcGCzdr1syN+eqrr0LtvXv3OqcWc3zZSALgZNia\nxZ7Thg0bujGTJ092fWeSpUuXur7FixeH2ux8svIX1vlm7wWAO8iCTrpjx45RjzIL9p45c2aozaJY\nOnbsmO/fA7xXHeDrgo2ICUZPdO3aFRs2bKAJChZWhiQ/9GYphBAR0GIphBAR0GIphBAR0GIphBAR\nKBQHj63t3aFDB5feWKtWLXccU8CxxvgLL7zQjbFpVGwcS8liaWFBx0Lt2rWpE4hlr1hVJeZ0YupB\nttQEc+YwZ5FNz7NZKaVLl6YZGb169Qq1bdkOgJ8Xa+ivVKmSG8PqlL/33nuhNjt31rGQkJDgsnpY\nKl737t1dH3OKMKePhZWoGDJkSKhts59OxYABA0Lt22+/3Y1h95V1ijDBGna9SpcuHWqze485XIJq\nXVdeeSVmzZrlxlgHJOCz4WxteIA7a61aEUurZWnC1jlsnbDlypWjTi17j7J7Iz/0ZimEEBHQYimE\nEBHQYimEEBEoFJulLcFapkwZF4zMVFCspBLglYFsADPAlXNskCpTo2G2FmujYbYyZndLT08Pta1a\nEsDVkaxdhdmImA2RKYLnUrt2bWRkZLhAcsAr/LB5sjmkpaWF2iyAmUm02fOXmZnpxgSv1cCBA7Fs\n2TJcc801oTFWQQbwdmIA1E5rA/Hff/99N4YpNAVLqwCgmgl/+9vfQu3SpUs7ezwLJGfX1M6B2VqZ\nvJy9Fiz4miUfBL+/cuXK1O7HJPxycnJCbfb72DNpv4slYLAgeGtvtceVKFGC2vrtOmTnDfA1Jxe9\nWQohRAS0WAohRAS0WAohRAS0WAohRAQKxcFjnTLZ2dnO8M6MwkwlxwZb79+/341hwa3WIcC+mwVW\n22Dh1atXuzELFiwocJ6sVCwz2FsHCDPOs7ISNhDZlgMuW7YsdaJZJ5OV+gd4MLQtt8ECfD/88EPX\nZ6/DJZdc4sZYR9SwYcOcM2DNmjXuOObMWbt2retr2bJlqG2dRwC/H+15Z0HwvXv3DrVnz57tkitY\n+Qvm7Fu+fHmozRI32H1lHWusxAIroRJU+MnKyqLngDlF7H3MHImsxMgrr7wSatt72M4pF1uXx567\nkiVLUgUqq4xlS5UA3EmYi94shRAiAloshRAiAloshRAiAloshRAiAoXi4Hn77bfzPtepUwdvv/22\nM0yzDIbvvvvO9dlyAjYqH+AZNVay3jooAG68DmYCVK9ePfRbcmHZEHaejRs3dmNWrFjh+qwRmsn/\nM2eKVXU5ceKEazOlJTt3dl42btzo+qzyCzt3rGyGdTawa2UVoi688EKXqcUUqVh5D5apZR0n7Dim\nCNW1a9dQm2WzPPvss3mfk5KSMH36dPddzAHC6nHb62VLowC85IfN4GHnipXzCDpKSpUqRet/M0eU\nfU4/+OADN+amm25yffZ8stIk7B6ya4V1ipYrV45mYFmHFTt3+aE3SyGEiIAWSyGEiIAWSyGEiECh\n2CytUvPhw4edTY+Vwbziiitcn7XhMZsXC1i2Qbg7duxwY15//XXX17Rp03z/PsCVX+w4pgrElHPs\nubLK6QBXRrHnytowq1at6uyYADB9+vRQmykF2QB3wAeAM1WZzp07uz4LK1kaLK3661//2pVaBXig\nPOOWW25xfValhikfWfVvwCdAsOtgA6abNGnibHEssYGdv2+//TbUZopXVnke8DZZFtjNfAS592xy\ncjL+8Y9/0BLBbA72/LH7xVZGALx9PCkpyY3p16+f67M2bZtsERcXRxM3rP2a2bPzQ2+WQggRAS2W\nQggRAS2WQggRAS2WQggRgUJx8LRt29a1WcCrhQVRW6n7Tz/91I1hiiM2AJWVy7XzBLyxnDmUWNB2\n1apVQ21mwGeGd6v6wxxK7PfZeQbl+Js2bYoZM2bQAH5rLGcqTswQbhVimLODlQSw54odZ4PEDxw4\n4AKKWdA4+y6mLHP++eeH2rVr13Zj2LmySQTMSWKdIg0bNnQOHlY+wapUAdHUg+x9BgB/+tOfQm3m\nKO3WrZvrCz5bv/zlL3G26VgAAAwuSURBVGnyw9y5cwucJ3sm2TztdWBqTMxRY5WqgqpHjRo1QmZm\npnOOse+3JboLQm+WQggRAS2WQggRAS2WQggRAS2WQggRgUJx8Cxbtizvc7t27bBs2TJnVGfGeVvS\nAfDR+sHvzoVlGdgMHlaCgNUgtwouzMh+9OhR12cdC2yerEyAzQZiGS6sJIY1hFsVm1gsRp0dzZs3\nD7WZA4spuFgDOjsvTKHGZsYwtSLrAPn666/d77v++uvdcUxJh513e/6YahQ7V9aRwbKygg6yxo0b\nY926dc4pyUqFzJ492/VZBwTLOrvxxhtdn3VOMYcLu69yHY5JSUlYunQprQXPzpU9n0wpiCk02eeG\nrQE2Ew3wc7cOtC+++ILO0zqZmLPKZmAF0ZulEEJEINKb5RtvvIEpU6YgPj4e9913H2rXro0RI0bg\n+PHjKF++PMaOHUtDH4QQ4lyhwDfL/fv346mnnsJLL72ESZMm4d1338WECROQkpKCl156CTVq1MCM\nGTMKY65CCPGjUeCb5ZIlS9CqVSuUKlUKpUqVwiOPPIIOHTrg4YcfBgC0b98eaWlpSElJOeV3tGjR\nwrWtTcgGqAI8MHj+/PmhNrM9Llq0yPVZlSNWFpbZOqPMidl2rPIKU4Hetm2b67N2U1aelwXvWjVs\nWw64ePHi2LRpkzvO2khZOVJme7T2H1aSlQW4W8WdPn36uDG2bOqhQ4dQo0aNUB9TUGLnipUbfuqp\np0JtplzO7Mk2CP26665zY+x1b9y4sbtHWcA0U92ytsZ7773XjWG2Tvu82YQFgCc7BO3HR44cobZO\nVrrY/mZ2HS677DLXF6xCAHiFL4CrvFv7p70O1113Hb3u1j4e5XkPUuBiuX37duTk5GDQoEH45ptv\nMHToUGRnZ+dtuxMTEyPLZQkhRFElLsaKfwR47rnnsGLFCjz55JPYsWMH7rzzTuTk5GDp0qUATqa0\npaamYtq0aaf8jsOHD39v7TghhDibKPDNMjExEVdffTXi4+NRvXp1lCxZEsWKFUNOTg4SEhKQlZVF\nc2SDBLeN7dq1wwcffOC24SxswG6dAeCVV14JtdetW+fGsDAW+11M8LWg1/LJkyfj+eefd/2nuw1n\nRNmGszf5/Lbhf/zjH/GHP/yBbvVsqATbhrO/Z7fhbJvFtuHvv/9+qM224U8//XTe5+effx79+/d3\n2/BGjRq5426++WbXN2XKFNc3YcKEUPt0t+FMrDZ43evWrYt169a5bTh7P3nuuedcnw13Y9twZoay\n2/CXX37ZjclvGz5q1Cg8+uijdAvMtuE2vIeFg0XZhnfq1MmNYXOwBMPf2rRpgw8//DDSNpzlnd93\n332n/DsFOnjatGmDpUuX4sSJE9i/fz+OHDmCpKQkzJkzB8DJWCUmQCGEEOcSBb5ZVqxYEV27dsXt\nt98O4OT/Og0aNEBqairS09NRpUoVKt0fxAaIVq5c2andsDe99evXuz7rbGCKMSzg1qoMMSUWFpBq\njcksSJa9ya5cuTLUZipL9evXd332DZiVeahWrZrrs29etjxFyZIlcdttt7nj7Plk55y9hdjA6oUL\nF7ox7dq1c322lMBbb73lxtSrV8+1rWoUc9CxN1n2BmrfZplzit1X1hnGypAE32gef/xxTJkyBUOG\nDAmNmTdvnjuO7U5s2ZHcF5QgrPyFnRdTD2KKUMHfd/jwYXp/slIo9hqygHBW6sU6S1npa+ZQtbua\noEO3TZs2WLRoEXV82WvKznl+RIqz7NOnj7vBpk6d+r3+kBBCFGWUwSOEEBHQYimEEBHQYimEEBEo\nFNUhq0ISHx/vMjmYcZ6p3VhlGRbuY0MSAF+iwpZvAHw5A+Bk9kgu/fr1o9kXb7/9tuuzYR/su22Z\nAsBnMrGc+wYNGrg+G8dqszYOHz5MQzpsiEXXrl3dmFmzZrm+Dz74INRu06aNG8OcWjaLKDdeN4gN\n7alVq5YLOWIOGObIYKFXLVu2DLWZ044p0thzZZ1OgM+cqlSpEv7617+G+pjT6aabbnJ99l5gWW5M\niciG6bCMNuaUDDp4KlasSB0uzBFrM2jYs8wcStbJy0q9sPBBe18988wzeZ9TU1PxzDPP0OtuHTrM\n4ZkferMUQogIaLEUQogIaLEUQogIFIrNMhiEO3DgQMybNy9SGczOnTu7PmujYarTVv0b8AGvNjga\n4MrXNmib2SzZd9lSrVFtpI0bNw61WWocUw+yc7d24qysLBpQbAPAWVA6uza25CsLHmZB/tauyEoE\nB2253bp1w4oVK1yWGLMzsnkuXrzY9dlrymxzTAHH2mmZPc3eCxdccIFTn2f2tLS0NNd31113hdrs\nHmLlom3aJ1MPYt8VPC8nTpygivzs/rdJC8yezM6xDV5n6bjW9g8ANWvWDLWtDbhRo0Y0KH348OGh\nNrOj5ofeLIUQIgJaLIUQIgJaLIUQIgJaLIUQIgIFiv8KIYTQm6UQQkRCi6UQQkRAi6UQQkRAi6UQ\nQkRAi6UQQkRAi6UQQkSgUHLDAeCxxx5DZmYm4uLi8MADD7jc4rORDRs2YPDgwfjZz36Gvn37YufO\nnRgxYgSOHz+O8uXLY+zYsVRv8sdmzJgx+Pjjj3Hs2DH8/Oc/R4MGDYrEvLOzszFy5Ejs3bsXR48e\nxeDBg1GnTp0iMXfgZKnVbt26YfDgwWjVqlWRmHdGRgaGDRuWp7FQq1YtDBgwoEjMHQDeeOMNTJky\nBfHx8bjvvvtQu3btMzf3WCGQkZERu/fee2OxWCy2adOm2O23314Yf/b/xOHDh2N9+/aNjRo1KvbC\nCy/EYrFYbOTIkbHZs2fHYrFY7PHHH4+9+OKLP+YUKUuWLIkNGDAgFovFYvv27Ytde+21RWLesVgs\n9uabb8aee+65WCwWi23fvj3WpUuXIjP3WCwWGz9+fKxHjx6xmTNnFpl5L126NDZ06NBQX1GZ+759\n+2JdunSJHTx4MJaVlRUbNWrUGZ17oWzDlyxZkldA/fLLL8eBAwdCCuRnI8WLF8fkyZNRoUKFvL6M\njAx07NgRANC+fXssWbLkx5reKWnevDmeeOIJACeVr7Ozs4vEvAHghhtuwMCBAwEAO3fuRMWKFYvM\n3Ddv3oxNmzblqYYXlXkzisrclyxZglatWqFUqVKoUKECHnnkkTM690JZLPfs2ROS8Cpbtix2795d\nGH/6tImPj3cy9NnZ2Xmv9ImJiWflbyhWrFheqY0ZM2agXbt2RWLeQfr06YP7778fDzzwQJGZ++jR\nozFy5Mi8dlGZN3BS8m/QoEG44447sGjRoiIz9+3btyMnJweDBg1CSkoKlixZckbnXmg2yyCxcyDD\n8mz/DfPmzcOMGTOQlpaGLl265PWf7fMGgGnTpmHdunUYPnx4aL5n69xfe+01NG7cGNWqVaP/frbO\nGzipDTlkyBAkJydj27ZtuPPOO0Oao2fz3AHg66+/xpNPPokdO3bgzjvvPKP3S6EslhUqVMCePXvy\n2rt27UL58uUL40//oJQoUQI5OTlISEhAVlZWaIt+NrFw4UJMmjQJU6ZMwYUXXlhk5r1mzRokJiai\ncuXKqFu3Lo4fP46SJUue9XNfsGABtm3bhgULFuCrr75C8eLFi8w5r1ixIm644QYAQPXq1VGuXDms\nXr26SMw9MTERV199NeLj41G9enWULFkSxYoVO2NzL5RteOvWrTFnzhwAJ1WbK1So4NSqiwJJSUl5\nv2Pu3LlOvfts4ODBgxgzZgyeffZZXHzxxQCKxryBkxUpcxXD9+zZgyNHjhSJuf/lL3/BzJkzMX36\ndPTq1QuDBw8uEvMGTnqTn3/+eQDA7t27sXfvXvTo0aNIzL1NmzZYunQpTpw4gf3795/x+6XQVIfG\njRuH5cuXIy4uDg8++KAru3C2sWbNGowePRpffvkl4uPjUbFiRYwbNw4jR47E0aNHUaVKFfz3f/83\nLU/6Y5Keno6JEyfi0ksvzev785//jFGjRp3V8wZOht787ne/w86dO5GTk4MhQ4agfv36SE1NPevn\nnsvEiRNRtWpVtGnTpkjM+9ChQ7j//vvxzTff4LvvvsOQIUNQt27dIjF34KTJZsaMGQCAX/ziF2jQ\noMEZm7sk2oQQIgLK4BFCiAhosRRCiAhosRRCiAhosRRCiAhosRRCiAhosRRCiAhosRRCiAhosRRC\niAj8P1e+BSV2qyCmAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}