36_class_augmented_cnn_model_3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "36 class augmented_cnn model 3.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Rojan116/NepaliHandWrittenAlphabetRecognizer/blob/master/36_class_augmented_cnn_model_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fa0w7bIx81Sq",
        "colab_type": "code",
        "outputId": "5928980d-71f2-411c-a931-48f3ee325075",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!git clone https://github.com/Rojan116/NepaliHandWrittenAlphabetRecognizer.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'NepaliHandWrittenAlphabetRecognizer' already exists and is not an empty directory.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jHtN-fzK9KOB",
        "colab_type": "code",
        "outputId": "f8de161e-a4c3-41f7-ac63-b8253b26b1a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ls"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;34mNepaliHandWrittenAlphabetRecognizer\u001b[0m/  \u001b[01;34msample_data\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6-Y4SAWO9Q1G",
        "colab_type": "code",
        "outputId": "0b759b7a-a28a-4f27-bb1c-91f48232ce7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd NepaliHandWrittenAlphabetRecognizer"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/NepaliHandWrittenAlphabetRecognizer\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AK0RS0g19VaY",
        "colab_type": "code",
        "outputId": "4dbec1da-7a8c-41c7-d26d-21bf7bbce7cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import tensorflow as tf\n",
        "from tqdm import tqdm\n",
        "import random\n",
        "from keras.utils import to_categorical\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.callbacks import ReduceLROnPlateau\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import *\n",
        "from keras.optimizers import *\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUB5C0nB9YOg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "PATH = os.getcwd()\n",
        "DATADIR = PATH + '/nepalibarnasnew/Train'   \n",
        "train_data_path = PATH + '/nepalibarnasnew/Train'\n",
        "test_data_path = PATH + '/nepalibarnasnew/Test'\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOK18pvNNPTZ",
        "colab_type": "code",
        "outputId": "0f1ebbe8-a1fc-4906-c397-7bf0d5e961d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "#counting categories\n",
        "new_cat = []\n",
        "for x in os.listdir(train_data_path):\n",
        "  new_cat.append(x)\n",
        "\n",
        "print(new_cat)  \n",
        "classes = np.unique(new_cat)\n",
        "print(len(classes))\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['character-34-chyan', 'digits_4', 'character-12-tha', 'digits_9', 'character-32-sa', 'digits_6', 'vowel_7_e', 'character-05-n..a', 'vowel_0_a', 'character-22-pha', 'character-28-la', 'character-20-na', 'digits_7', 'character-03-ga', 'vowel_11_am', 'character-24-bha', 'character-29-wa', 'vowel_4_u', 'vowel_2_i', 'character-15-na', 'digits_8', 'character-08-ja', 'character-09-jha', 'character-30-sa', 'character-35-tra', 'character-17-tha', 'digits_5', 'character-10-yaa', 'character-26-ya', 'character-13-da', 'character-01-ka', 'vowel_5_uu', 'vowel_12_ah', 'character-16-ta', 'character-31-sa', 'character-11-ta', 'character-19-dha', 'character-36-gyan', 'character-04-gha', 'digits_1', 'vowel_10_au', 'character-02-kha', 'character-25-ma', 'character-06-cha', 'character-21-pa', 'character-33-ha', 'character-27-ra', 'vowel_8_ei', 'digits_0', 'digits_2', 'character-14-dha', 'vowel_6_re', 'character-18-da', 'vowel_9_o', 'character-07-chha', 'vowel_1_aa', 'vowel_3_ii', 'digits_3', 'character-23-ba']\n",
            "59\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTFBK9wD9aqu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "CATEGORIES =[\n",
        "\n",
        "\"character-01-ka\",\"character-02-kha\",\"character-03-ga\",\"character-04-gha\",\"character-05-n..a\",\n",
        "\"character-06-cha\",\"character-07-chha\",\"character-08-ja\",\"character-09-jha\",\"character-10-yaa\",\n",
        "\"character-11-ta\",\"character-12-tha\",\"character-13-da\",\"character-14-dha\",\"character-15-na\",\n",
        "\"character-16-ta\",\"character-17-tha\",\"character-18-da\",\"character-19-dha\",\"character-20-na\",\"character-21-pa\",\n",
        "\"character-22-pha\",\"character-23-ba\",\"character-24-bha\",\"character-25-ma\",\"character-26-ya\",\"character-27-ra\",\n",
        "\"character-28-la\",\"character-29-wa\",\"character-30-sa\",\"character-31-sa\",\"character-32-sa\",\n",
        "\"character-33-ha\",\"character-34-chyan\",\"character-35-tra\",\"character-36-gyan\"\n",
        "]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "as1xbqAW9d5P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "IMG_SIZE = 32\n",
        "training_data = []\n",
        "testing_data = []\n",
        "\n",
        "def create_training_data():\n",
        "  for catagory in CATEGORIES:\n",
        "    path = os.path.join(train_data_path, catagory)\n",
        "    class_num = CATEGORIES.index(catagory)\n",
        "    for img in os.listdir(path):\n",
        "        try:\n",
        "          img_array = cv2.imread(os.path.join(path,img),cv2.IMREAD_GRAYSCALE)\n",
        "          new_array = cv2.resize(img_array,(IMG_SIZE,IMG_SIZE))\n",
        "          training_data.append([new_array,class_num])\n",
        "        except Exception as e:\n",
        "          pass\n",
        "\n",
        "\n",
        "\n",
        "def create_testing_data():\n",
        "  for catagory in CATEGORIES:\n",
        "    path = os.path.join(test_data_path, catagory)\n",
        "    class_num = CATEGORIES.index(catagory)\n",
        "    for img in os.listdir(path):\n",
        "        try:\n",
        "          img_array = cv2.imread(os.path.join(path,img),cv2.IMREAD_GRAYSCALE)\n",
        "          new_array = cv2.resize(img_array,(IMG_SIZE,IMG_SIZE))\n",
        "          testing_data.append([new_array,class_num])\n",
        "        except Exception as e:\n",
        "          pass\n",
        "\n",
        "\n",
        "\n",
        "create_training_data()\n",
        "create_testing_data()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7a9Hftp9kSg",
        "colab_type": "code",
        "outputId": "dc090b55-589f-4357-fccc-3151b0f76e4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "#shuffle training data for trainning diff image set in every batch\n",
        "random.shuffle(training_data)\n",
        "\n",
        "#hot encode image dataset\n",
        "\n",
        "train_x = []\n",
        "train_y = []\n",
        "test_x = []\n",
        "test_y = []\n",
        "\n",
        "\n",
        "\n",
        "#seperating features and labels from list\n",
        "for features, labels in training_data:\n",
        "\ttrain_x.append(features)\n",
        "\ttrain_y.append(labels)\n",
        "\n",
        "for features, labels in testing_data:\n",
        "\ttest_x.append(features)\n",
        "\ttest_y.append(labels)\n",
        "\n",
        "\n",
        "#converting list to array\n",
        "train_y_array = np.array(train_y)\n",
        "test_y_array = np.array(test_y)\n",
        "\n",
        "#converting image dataset to input 32*32 dataset \n",
        "\n",
        "train_image_array = np.array(train_x).reshape(-1,32,32,1)\n",
        "test_image_array = np.array(test_x).reshape(-1,32,32,1)\n",
        "\n",
        "#counting total number of class in dataset\n",
        "classes = np.unique(test_y_array)\n",
        "print(classes)\n",
        "print(\"total number of class={} \".format(len(classes)))\n",
        "\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
            " 24 25 26 27 28 29 30 31 32 33 34 35]\n",
            "total number of class=36 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJyBFLIrFs_G",
        "colab_type": "code",
        "outputId": "447807a9-1d6d-4434-e4a3-6e51d2bf39ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "# convert from int to float\n",
        "x_train = train_image_array.astype('float32')\n",
        "x_test = test_image_array.astype('float32')\n",
        "# define data preparation\n",
        "datagen = ImageDataGenerator(featurewise_center=True,\n",
        "                             featurewise_std_normalization=True,\n",
        "                             rotation_range=15,\n",
        "                             width_shift_range=0.1,\n",
        "                             height_shift_range=0.1,\n",
        "                              zoom_range=0.2\n",
        "                            )\n",
        "# fit parameters from data\n",
        "datagen.fit(x_train)\n",
        "# configure batch size and retrieve one batch of images\n",
        "for X_batch, y_batch in datagen.flow(x_train, train_y, batch_size=9):\n",
        "\t# create a grid of 3x3 images\n",
        "\tfor i in range(0, 9):\n",
        "\t\tplt.subplot(330 + 1 + i)\n",
        "\t\tplt.imshow(X_batch[i].reshape(32, 32), cmap=plt.get_cmap('gray'))\n",
        "\t# show the plot\n",
        "\tplt.show()\n",
        "\tbreak"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVEAAAD8CAYAAADOg5fGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nL4cbrAEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OcD8KB1rGHpt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "#one hot enoded class \n",
        "train_y = to_categorical(train_y_array)\n",
        "test_y = to_categorical(test_y_array)\n",
        "\n",
        "\n",
        "train_y = np.array(train_y)\n",
        "test_y = np.array(test_y)\n",
        "\n",
        "#all train x, y and test x y are in array form now\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTyQPCKc9AuW",
        "colab_type": "code",
        "outputId": "64b2a8f5-8a5e-44ef-f297-f5246db22fbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "#new model\n",
        "\n",
        "# Set the CNN model \n",
        "# my CNN architechture is In -> [[Conv2D->relu]*2 -> MaxPool2D -> Dropout]*2 -> Flatten -> Dense -> Dropout -> Out\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n",
        "                 activation ='relu', input_shape = (32,32,1)))\n",
        "model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n",
        "                 activation ='relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "\n",
        "model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n",
        "                 activation ='relu'))\n",
        "model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n",
        "                 activation ='relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation = \"relu\"))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(36, activation = \"softmax\"))\n",
        "\n",
        "# Define the optimizer\n",
        "#optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)\n",
        "optimizer = Adam(lr=0.0001)\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer = optimizer , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "\n",
        "#model.summary()\n",
        "\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0731 20:39:57.251045 140303072909184 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "W0731 20:39:57.269196 140303072909184 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0731 20:39:57.272741 140303072909184 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W0731 20:39:57.302094 140303072909184 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "W0731 20:39:57.304583 140303072909184 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "W0731 20:39:57.313711 140303072909184 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "W0731 20:39:57.425114 140303072909184 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "W0731 20:39:57.434215 140303072909184 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ReKcDMub5t6r",
        "colab_type": "code",
        "outputId": "b314f52d-45eb-4a5b-f4db-cf87eca9cb9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "history = model.fit(x=train_image_array,y=train_y,epochs=50,batch_size=100, validation_split=0.3)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 3524 samples, validate on 1511 samples\n",
            "Epoch 1/50\n",
            "3524/3524 [==============================] - 1s 187us/step - loss: 0.0139 - acc: 0.9963 - val_loss: 0.0638 - val_acc: 0.9854\n",
            "Epoch 2/50\n",
            "3524/3524 [==============================] - 1s 156us/step - loss: 0.0094 - acc: 0.9969 - val_loss: 0.0590 - val_acc: 0.9861\n",
            "Epoch 3/50\n",
            "3524/3524 [==============================] - 1s 162us/step - loss: 0.0148 - acc: 0.9949 - val_loss: 0.0659 - val_acc: 0.9828\n",
            "Epoch 4/50\n",
            "3524/3524 [==============================] - 1s 154us/step - loss: 0.0208 - acc: 0.9923 - val_loss: 0.0762 - val_acc: 0.9828\n",
            "Epoch 5/50\n",
            "3524/3524 [==============================] - 1s 156us/step - loss: 0.0120 - acc: 0.9963 - val_loss: 0.0687 - val_acc: 0.9848\n",
            "Epoch 6/50\n",
            "3524/3524 [==============================] - 1s 154us/step - loss: 0.0125 - acc: 0.9969 - val_loss: 0.0574 - val_acc: 0.9861\n",
            "Epoch 7/50\n",
            "3524/3524 [==============================] - 1s 159us/step - loss: 0.0150 - acc: 0.9955 - val_loss: 0.0771 - val_acc: 0.9854\n",
            "Epoch 8/50\n",
            "3524/3524 [==============================] - 1s 156us/step - loss: 0.0190 - acc: 0.9935 - val_loss: 0.0606 - val_acc: 0.9841\n",
            "Epoch 9/50\n",
            "3524/3524 [==============================] - 1s 154us/step - loss: 0.0138 - acc: 0.9952 - val_loss: 0.0652 - val_acc: 0.9828\n",
            "Epoch 10/50\n",
            "3524/3524 [==============================] - 1s 155us/step - loss: 0.0178 - acc: 0.9952 - val_loss: 0.0737 - val_acc: 0.9841\n",
            "Epoch 11/50\n",
            "3524/3524 [==============================] - 1s 155us/step - loss: 0.0151 - acc: 0.9946 - val_loss: 0.0547 - val_acc: 0.9861\n",
            "Epoch 12/50\n",
            "3524/3524 [==============================] - 1s 157us/step - loss: 0.0184 - acc: 0.9943 - val_loss: 0.0622 - val_acc: 0.9848\n",
            "Epoch 13/50\n",
            "3524/3524 [==============================] - 1s 155us/step - loss: 0.0211 - acc: 0.9935 - val_loss: 0.0573 - val_acc: 0.9854\n",
            "Epoch 14/50\n",
            "3524/3524 [==============================] - 1s 159us/step - loss: 0.0178 - acc: 0.9932 - val_loss: 0.0614 - val_acc: 0.9854\n",
            "Epoch 15/50\n",
            "3524/3524 [==============================] - 1s 156us/step - loss: 0.0109 - acc: 0.9963 - val_loss: 0.0599 - val_acc: 0.9828\n",
            "Epoch 16/50\n",
            "3524/3524 [==============================] - 1s 156us/step - loss: 0.0160 - acc: 0.9952 - val_loss: 0.0590 - val_acc: 0.9848\n",
            "Epoch 17/50\n",
            "3524/3524 [==============================] - 1s 156us/step - loss: 0.0105 - acc: 0.9966 - val_loss: 0.0587 - val_acc: 0.9848\n",
            "Epoch 18/50\n",
            "3524/3524 [==============================] - 1s 157us/step - loss: 0.0133 - acc: 0.9955 - val_loss: 0.0571 - val_acc: 0.9835\n",
            "Epoch 19/50\n",
            "3524/3524 [==============================] - 1s 155us/step - loss: 0.0164 - acc: 0.9938 - val_loss: 0.0595 - val_acc: 0.9808\n",
            "Epoch 20/50\n",
            "3524/3524 [==============================] - 1s 155us/step - loss: 0.0148 - acc: 0.9946 - val_loss: 0.0601 - val_acc: 0.9841\n",
            "Epoch 21/50\n",
            "3524/3524 [==============================] - 1s 155us/step - loss: 0.0079 - acc: 0.9986 - val_loss: 0.0627 - val_acc: 0.9835\n",
            "Epoch 22/50\n",
            "3524/3524 [==============================] - 1s 154us/step - loss: 0.0106 - acc: 0.9969 - val_loss: 0.0551 - val_acc: 0.9881\n",
            "Epoch 23/50\n",
            "3524/3524 [==============================] - 1s 158us/step - loss: 0.0144 - acc: 0.9952 - val_loss: 0.0574 - val_acc: 0.9854\n",
            "Epoch 24/50\n",
            "3524/3524 [==============================] - 1s 155us/step - loss: 0.0069 - acc: 0.9977 - val_loss: 0.0607 - val_acc: 0.9854\n",
            "Epoch 25/50\n",
            "3524/3524 [==============================] - 1s 156us/step - loss: 0.0157 - acc: 0.9940 - val_loss: 0.0747 - val_acc: 0.9815\n",
            "Epoch 26/50\n",
            "3524/3524 [==============================] - 1s 155us/step - loss: 0.0133 - acc: 0.9957 - val_loss: 0.0609 - val_acc: 0.9861\n",
            "Epoch 27/50\n",
            "3524/3524 [==============================] - 1s 157us/step - loss: 0.0084 - acc: 0.9977 - val_loss: 0.0658 - val_acc: 0.9848\n",
            "Epoch 28/50\n",
            "3524/3524 [==============================] - 1s 154us/step - loss: 0.0096 - acc: 0.9960 - val_loss: 0.0647 - val_acc: 0.9841\n",
            "Epoch 29/50\n",
            "3524/3524 [==============================] - 1s 158us/step - loss: 0.0159 - acc: 0.9949 - val_loss: 0.0644 - val_acc: 0.9835\n",
            "Epoch 30/50\n",
            "3524/3524 [==============================] - 1s 154us/step - loss: 0.0141 - acc: 0.9949 - val_loss: 0.0598 - val_acc: 0.9848\n",
            "Epoch 31/50\n",
            "3524/3524 [==============================] - 1s 157us/step - loss: 0.0114 - acc: 0.9969 - val_loss: 0.0598 - val_acc: 0.9861\n",
            "Epoch 32/50\n",
            "3524/3524 [==============================] - 1s 156us/step - loss: 0.0132 - acc: 0.9952 - val_loss: 0.0741 - val_acc: 0.9821\n",
            "Epoch 33/50\n",
            "3524/3524 [==============================] - 1s 157us/step - loss: 0.0097 - acc: 0.9972 - val_loss: 0.0554 - val_acc: 0.9841\n",
            "Epoch 34/50\n",
            "3524/3524 [==============================] - 1s 155us/step - loss: 0.0128 - acc: 0.9963 - val_loss: 0.0569 - val_acc: 0.9841\n",
            "Epoch 35/50\n",
            "3524/3524 [==============================] - 1s 159us/step - loss: 0.0063 - acc: 0.9974 - val_loss: 0.0598 - val_acc: 0.9828\n",
            "Epoch 36/50\n",
            "3524/3524 [==============================] - 1s 157us/step - loss: 0.0102 - acc: 0.9972 - val_loss: 0.0718 - val_acc: 0.9815\n",
            "Epoch 37/50\n",
            "3524/3524 [==============================] - 1s 156us/step - loss: 0.0110 - acc: 0.9966 - val_loss: 0.0666 - val_acc: 0.9808\n",
            "Epoch 38/50\n",
            "3524/3524 [==============================] - 1s 158us/step - loss: 0.0117 - acc: 0.9960 - val_loss: 0.0565 - val_acc: 0.9861\n",
            "Epoch 39/50\n",
            "3524/3524 [==============================] - 1s 157us/step - loss: 0.0111 - acc: 0.9957 - val_loss: 0.0549 - val_acc: 0.9861\n",
            "Epoch 40/50\n",
            "3524/3524 [==============================] - 1s 157us/step - loss: 0.0101 - acc: 0.9966 - val_loss: 0.0699 - val_acc: 0.9861\n",
            "Epoch 41/50\n",
            "3524/3524 [==============================] - 1s 155us/step - loss: 0.0161 - acc: 0.9946 - val_loss: 0.0477 - val_acc: 0.9868\n",
            "Epoch 42/50\n",
            "3524/3524 [==============================] - 1s 158us/step - loss: 0.0075 - acc: 0.9969 - val_loss: 0.0670 - val_acc: 0.9815\n",
            "Epoch 43/50\n",
            "3524/3524 [==============================] - 1s 154us/step - loss: 0.0124 - acc: 0.9960 - val_loss: 0.0589 - val_acc: 0.9848\n",
            "Epoch 44/50\n",
            "3524/3524 [==============================] - 1s 158us/step - loss: 0.0156 - acc: 0.9960 - val_loss: 0.0739 - val_acc: 0.9841\n",
            "Epoch 45/50\n",
            "3524/3524 [==============================] - 1s 156us/step - loss: 0.0125 - acc: 0.9963 - val_loss: 0.0528 - val_acc: 0.9881\n",
            "Epoch 46/50\n",
            "3524/3524 [==============================] - 1s 157us/step - loss: 0.0170 - acc: 0.9943 - val_loss: 0.0652 - val_acc: 0.9854\n",
            "Epoch 47/50\n",
            "3524/3524 [==============================] - 1s 155us/step - loss: 0.0086 - acc: 0.9969 - val_loss: 0.0635 - val_acc: 0.9854\n",
            "Epoch 48/50\n",
            "3524/3524 [==============================] - 1s 158us/step - loss: 0.0111 - acc: 0.9969 - val_loss: 0.0745 - val_acc: 0.9835\n",
            "Epoch 49/50\n",
            "3524/3524 [==============================] - 1s 157us/step - loss: 0.0122 - acc: 0.9957 - val_loss: 0.0601 - val_acc: 0.9868\n",
            "Epoch 50/50\n",
            "3524/3524 [==============================] - 1s 155us/step - loss: 0.0102 - acc: 0.9972 - val_loss: 0.0661 - val_acc: 0.9861\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7X2QnE15CTcr",
        "colab_type": "code",
        "outputId": "80a5bebc-6404-4a2c-adfd-945561b17bae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "source": [
        "# summarize history for accuracy\n",
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEWCAYAAABbgYH9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsnXd8VeX9x9/fbAJJgCTMAAl7yt7i\nwoHbKuIerRVtHR3Wlv66ba2tttZRq2K1KipqcWEdKG6QvTeEHQIhCSSMkP38/njuSW4udyb3khC+\n79crr3vvOc8550lyz/k83+c7HjHGoCiKoij+iGrsDiiKoihNHxULRVEUJSAqFoqiKEpAVCwURVGU\ngKhYKIqiKAFRsVAURVEComKhKA1ERF4UkT8F2XaHiJzb0PMoyolGxUJRFEUJiIqFoiiKEhAVC+WU\nwDX9c7+IrBaRoyLyvIi0F5GPROSwiMwVkTZu7S8TkXUiUiQiX4pIP7d9Q0Vkueu4N4AEj2tdIiIr\nXcd+KyKn1bPPt4tItogcEJHZItLJtV1E5B8isl9EDonIGhEZ6Np3kYisd/Vtj4j8rF5/MEXxQMVC\nOZW4CjgP6A1cCnwE/B+Qjr0X7gUQkd7ATODHrn0fAu+LSJyIxAHvAjOAtsB/XefFdexQ4AXgDiAV\neBaYLSLxoXRURM4BHgKmAB2BncDrrt3nA2e4fo8UV5tC177ngTuMMUnAQODzUK6rKL5QsVBOJZ40\nxuQZY/YA3wCLjDErjDGlwDvAUFe7a4APjDGfGmMqgL8BLYBxwBggFnjMGFNhjJkFLHG7xlTgWWPM\nImNMlTHmJaDMdVwo3AC8YIxZbowpA34JjBWRTKACSAL6AmKM2WCM2es6rgLoLyLJxpiDxpjlIV5X\nUbyiYqGcSuS5vT/m5XMr1/tO2JE8AMaYamA30Nm1b4+pW4Fzp9v7bsB9rimoIhEpArq4jgsFzz4c\nwVoPnY0xnwP/BJ4C9ovIdBFJdjW9CrgI2CkiX4nI2BCvqyheUbFQlOPJxT70AesjwD7w9wB7gc6u\nbQ5d3d7vBh40xrR2+0k0xsxsYB9aYqe19gAYY54wxgwH+mOno+53bV9ijLkcaIedLnszxOsqildU\nLBTleN4ELhaRiSISC9yHnUr6FlgAVAL3ikisiFwJjHI79jngThEZ7XJEtxSRi0UkKcQ+zAS+KyJD\nXP6OP2OnzXaIyEjX+WOBo0ApUO3yqdwgIimu6bNDQHUD/g6KUoOKhaJ4YIzZBNwIPAkUYJ3hlxpj\nyo0x5cCVwK3AAax/4223Y5cCt2OniQ4C2a62ofZhLvAb4C2sNdMDuNa1OxkrSgexU1WFwCOufTcB\nO0TkEHAn1vehKA1GdPEjRVEUJRBqWSiKoigBUbFQFEVRAqJioSiKogQkomIhIpNEZJOrZME0L/vj\nReQN1/5FroQjXFEmL7nKGGwQkV9Gsp+KoiiKf2IidWIRicYmDZ0H5ABLRGS2MWa9W7PbgIPGmJ4i\nci3wV2x0ydVAvDFmkIgkAutFZKYxZoev66WlpZnMzMwI/TaKoijNk2XLlhUYY9IDtYuYWGBjz7ON\nMdsAROR14HLAXSwuB37vej8L+Kcr2ckALUUkBltmoRwbM+6TzMxMli5dGtZfQFEUpbkjIjsDt4rs\nNFRnbDarQ45rm9c2xphKoBibpToLm2y0F9gF/M0Yc8DzAiIyVUSWisjS/Pz88P8GiqIoCtB0Hdyj\ngCpsfZwsbK2d7p6NjDHTjTEjjDEj0tMDWlGKoihKPYmkWOzB1tNxyHBt89rGNeWUgs1GvR742FXV\ncz8wHxgRwb4qiqIofoikz2IJ0EtEsrCicC1WBNyZDdyCrbczGfjcGGNEZBdwDjDDVUBtDPBYqB2o\nqKggJyeH0tLSBvwaJwcJCQlkZGQQGxvb2F1RFKUZEjGxMMZUisjdwBwgGlubf52IPAAsNcbMxi7U\nMkNEsrF1dpzaN08B/xGRdYAA/zHGrA61Dzk5OSQlJZGZmUndIqHNC2MMhYWF5OTkkJWV1djdURSl\nGRJJywJjzIfYVcbct/3W7X0pNkzW87gj3raHSmlpabMXCgARITU1FXXyK4oSKZqqgztsNHehcDhV\nfk9FURqHZi8WJy2lxVBZ1ti9UBRFAVQsIk5RURH/+te/QjvIGC66+GKK9myOTKcURVFCRMUiwvgS\ni8rKSt8HVVfy4Ywnad0yIYI9UxRFCR4Vi3BytAAKt4LbglLTpk1j69atDBkyhJEjRzJhwgQuu+wy\n+vfvD8AVV1zB8OHDGTBgANOnT7cHVVWQOfpiCvbnsWPHDvr168ftt9/OgAEDOP/88zl27Fhj/HaK\nopzCRDQaqinxh/fXsT7Xb3mpkOnfKZnfXTqgdsOxg1B+BCpLIbYFAH/5y19Yu3YtK1eu5Msvv+Ti\niy9m7dq1NSGuL7zwAm3btuXYsWOMHDmSq666itTEaHu+6nIw1WzZsoWZM2fy3HPPMWXKFN566y1u\nvPHGsP4uiqIo/lDLIlwYAxUl9n2pb1EaNWpUnVyIJ554gsGDBzNmzBh2797Nli1boKqi9oCqCrKy\nshgyZAgAw4cPZ8eOHZH4DRRFUXxyylgWdSyASFBZCqbavi8rhqT2Xpu1bNmy5v2XX37J3LlzWbBg\nAYmJiZx11lk227yqvPaAqnLi4+NrPkZHR+s0lKIoJxy1LMJF+VH7mtDavq+yDuykpCQOHz7s9ZDi\n4mLatGlDYmIiGzduZOHChXZHHcui3OuxShOm4hi8cSPkazSbUg+aaMi8ikW4qCgBiYZW7eznMjsV\nlZqayvjx4xk4cCD3339/nUMmTZpEZWUl/fr1Y9q0aYwZM8buqCoHiQIEKitQTjL2roIN78PG/zV2\nT5STjdwV8OfOsH9jY/fkOE6ZaaiIU14CcYkQmwhRMTapLrEtAK+99prXQ+Lj4/noo4+O35G3jh2r\n5kHFMdJi4lm7dm3Nrp/97GcR6b4SRvI32deCk8Sy+OphyFsHU15q7J40fdbMgl7nQ0JyZM6//Wuo\nroA9y6Bd38hco56oZREOqqug8hjEtgQRSEiBssO1PoxQMMZaFtFx9qeygdNQe1fBPwbCob0NO48S\nPI5IOKLR1NnyCWz9vLF70fTJ3wxv3QZr3ozcNfYst69NcKChYhEOnCiouET7Gp8MpqrWjxEK1a5p\np+hYiImHqrI6eRshs+4dKN4NeWsDt1XCg7tl0ZD/3YnAGNvPskNwrKixe9O0KdxiX4s9l+UJI7kr\n7GvBlshdo56oWISDcpdYxLoineKTAPEbQusTx7ntWBamGqr9ZHsHYttX9rV4t/92Svgo2GT9V+VH\n4FAEHyzh4Gi+nTIFKM5p3L40dQqz7evhCFnpJQegyLUcdqGKRfOk4qjr4e5yAUVFQ3yr2pswFJzo\nJ8eycN8WKseKYO9K+14fBCeG8hIo2g2Z4+3npj4V5T7dod8R/zhicSg3Mud3rIrOw+HAtrpRkU0A\nFYtwUF4CcS3rbktIsVNIFSGu0ud8QaJclgXUP5RuxzyX30T0QXCiKMwGDPS7zH5ugnPPdagjFmp9\n+qVwq32NlGXhiMWgq+1swsGdkblOPVGxaChV5dbPEJtYd3u8K1qiLMSpqKpyIMpaJ9ENtCy2f2X7\nlTFCxeJE4Tx8u42DFm0gv+mFQNahYIv9jkTHR1YsFj8Hq96I3PlPBDWWRQTFom0P6DzCfm5iAw0V\ni4bi+Cs8LYuYeIhJoChvd2glyqsq7BSUCI898QQlpZX1tyy2fWkfWm2766jxRJG/yebIpPaE9L5N\nPzGvYLPta0pnO30WKb76K3z089r75WSj9BAcyYMWbaH8sI12DDe5K6HTUEjraT83Mb+FikVDqTgK\nSE3hwDokpFBUsC9EsSi3YgE89thjlFRU1c+yOJRrHwRZZ0JKhv1cXRX6eZTQKNgMbTLtYCGtt3V2\nN2UKNtt+pnSJnPV5OM/lSC+CtbMic41Ic8A1BeX4osJtXRzZD4dyrFi0aAMt008ty0JEJonIJhHJ\nFpFpXvbHi8gbrv2LRCTTtf0GEVnp9lMtIkMi2dd6U15ihUK8/Cnjk5n25ydqSpTff//9PPLII4wc\nOZLTTjuN3/3udwAcPXqUiy++mMGDBzNwwiW88d4cnnjiCXJzczn7O7dy9uU3hN6v7V/b1+4usaiu\ntCMjJbI4D1+A9D5QUmhL1zdFHGd8jVhEyLLIW2NfYxNh8fSmH07sDcdfkXmGfT0cZid3risQpdNQ\n+5rWGwqyw3uNBhKxDG4RiQaeAs4DcoAlIjLbGLPerdltwEFjTE8RuRb4K3CNMeZV4FXXeQYB7xpj\nVjaoQx9Ng31rGnSK4+gwEIZcD4mp3vfHteQvv/4JazfvYOXKlXzyySfMmjWLxYsXY4zhsssu4+uv\nvyY/P59OnTrxwf/eh72rKK5KJKVLHx599FG++HAWaXEVUF0NUSFo+7avrMncfpAd2YEdOSZ3avjv\nrXinqtLOa/c8135O62Nf8zdBy7TG65cvDmwFDKT3tq+H99kk0Ji48F7Hue/O/DnM/T3sWgjdxob3\nGpGmMBuQyFkWuSvs+TueZj+n9rQlY5oQkbQsRgHZxphtxphy4HXgco82lwNOjYFZwEQREY8217mO\nbXpUV9poI0/ntoMIxLWyCXrG8Mknn/DJJ58wdOhQhg0bxsaNG9myZQuDBg3i008/5Rc//znfLFpO\nSls38Ymqh5PbGOvczjrDCkxKht2ufovIUrTT/p/SXSLhvDbVqShnmiOtt+s7YiKTF7JvLSRnwKip\nNkpw8fTwXwMge27kptIKs6311ca1vEDYLYsV9v8Qn2Q/p/WGYwfgaGF4r9MAIlkbqjPg/nTKAUb7\namOMqRSRYiAVcLfbr+F4kQFARKYCUwG6du3qvzcX/iX4ngfL0QL7AI7zIRZQ+88vP4oxhl/+8pfc\ncccdxzVbvnw5H773Nr9++F9MXLGN3z7woN3hjPKqyiA2yGVWC7fam767q45UjVhoRFREcXIqHIsi\nJcMmajZVJ3fBFkBsBM7RfLutOAfaZvk9LGTy1kKHQTYIZOhNsOgZOzJP7hi+a1RVwGvXwqjbYdJD\n4TuvQ2E2pPaw93pCSmQsi+5n1X5O6+W67hZo6WPm4gTTpB3cIjIaKDHGeK1VYYyZbowZYYwZkZ6e\nfoJ7h3Vui1uIqxeSUjty+MhRKCvmggsu4IUXXuDIkSMA7Nmzh/3795Obm0tiYiI3XnMl9995M8tX\nrrbHJiVxuMQVCRVKjahtX9jXrDPta0IyxKeoWESampG660YXse+bavhswWZo080OQlK62G3htj4r\njtnrdBhoP4+8zQZaLPtPeK9zcKcNYY+EZWSMHYCluqKUkjqFN9fi0F44sq/WXwG136EmVPYjkpbF\nHqCL2+cM1zZvbXJEJAZIAdztrmuBmRHsY8NwkvGOmzmrJTW9HeNHj2DgqLO48NIruP766xk71s7X\ntmrVildeeYXs7Gzuv/9+oqgmNsrw9PTnAZg6dSqTLr6UTmnJfPHhO8H3a/tX9uZv2712W0qGikWk\nKdgMrdpDi9a129L72OTIpoi7Mz65s30N93dk/wY7VdthkP3ctrut2rr0PzDhZ+Hzj9SU4tgXnvO5\nczTf5ks5YpHcMbxZ3Lmu4oGdh9Vua93NJuU2oYioSIrFEqCXiGRhReFa4HqPNrOBW4AFwGTgc2Ns\nqISIRAFTgAkR7GP9qa6yq+O5Pxh88NpLz9sRT7sBEBPHj370ozr7e/TowQUXXGBv1JJC6DgYgHvu\nuYd77rnH3nDB5lpUV8H2b6DvJXVFLCVDfRaRJn9T7cPXIb0PrH7DxulHqqx1faiuttE2jvUZm2CF\nLtzfEaeAZfuBtdtGT4VXroL178FpV4fnOpEUC+fc7pbF/g3hO3/uCjtD4f43ioq204NNyLKI2DSU\nMaYSuBuYA2wA3jTGrBORB0TEVQuB54FUEckGfgq4h9eeAew2xmyLVB8bRIVH8UB/OAl7FQESkpzS\n5J7ExAfv4N632sazdz+z7na1LCKLU73VcWo7OP6LJnTTA1YUKo/VTneA/Y6EOzFv3xob5NHGzQ/S\n/Rz7IFz8bPiu4y4W4Q7NrRGLHvY1uaMNQ69qQIFPd3JXQLt+x/s+03o1qcS8iPosjDEfGmN6G2N6\nGGMedG37rTFmtut9qTHmamNMT2PMKHdhMMZ8aYwZE8n+NQin/LivSCh3YlwJe0GJRezx26Pjrc8i\nmJvAqTKbdUbd7SkZcOxg/cqmK4E5kmenKrxZFtD0IqIc8XLvbyQS8/athfYD6oZ9R0XZyKicJbXr\nNzQUJ2muqsx+z8NJ4VaIioXWriCapI52au3o/oaf2xgrFp28pJGl9YID2xu+pk2YaNIO7nBgIpUA\nVF5iH+LRQczkRUVZwQgoFhU+LIs4wH+p8prfc/tXkN4PkjrUbVDjwGziJbNPVmoioTzEok2WfdA0\nteqz7mGzDo71Ga57xhg7DeU+veIw5DprlS9+LjzXKtxaa+WHO/m0MNv6WqKi7WcnVykcEVHFu+3U\ns7tz2yGttw27P7ij4dcJA81aLBISEigsLAy/YBhjI6E860H5I7aFjQzx1Rdn3QpflgX49FsYYygs\nLCQhPg52Ljh+Cgo01yLSOA9fz2mo6Bg7192EHJWA7U+LNnUTSlO62KmpkjDF9hfttNZWBy9ikZBi\nBWPtWw3PcC8vsT7Brq7I/GAjlaqroOxI4HbukVBgLQsIT66FU2nWm1ikOhFRTeO706zX4M7IyCAn\nJ4f8/Pzwnri60kZDtDgG+4IsjFZ22JrHhVI7QjnunPshsRLiPFYsq6qAw/shv8qnQCUkJJBhcu3N\nnuVPLNRvERHyN0FcUu2DxJ303uGvHtBQCrbYkat7EERrl/VZtCs8Gef7XM7tDqd53z/ydljyb1j+\nEky4r/7XOeCave423i4PG6yT+9snYOHT8JN13gdpYAXlwDbodV7ttnBaFrkrrOXpzfpqYgUFm7VY\nxMbGkpUV5gQjsEuVzrkVbv8COvcL7phdi+CFKXDtTOh70fH7d8yD/06Bm96FHiPr7qsshz+dAWf+\nAs7+pe9rfP6qrVHllCRwJ6mj3adiERkKNltR8BZGndbHlm6oKA0+sTLSFGyG3ufX3eY+oHAP46wv\neWsBsc5bb7Trawc2yxooFo4DOvN0+xqsWOxba6esdi/2fs+A/VtUldW1LBLT7AM+XJZF+/61C525\nk5BiI9SaSHBEs56Gihg5S+3UkLfRgC86DAQE9q7yvt/xJTjx7u7ExNkbOdDc5bavoNMw+yXzJDrG\nhvypWEQG95wFT9L72GlGxwnb2Bw7aJ2znv0Nd2LevjWurGc/07V9LrLTVQ3xpTli0X6gXUcmWLFw\n7oWtnwU+t7tYREVZn2BDLYsa57aXKSiHtN6BxeL9H8P8xxvWlyBQsagPuxfbXIhQEoriWtp/vC+x\nOOT64qZ4EQuwZa8Pbvd9/tJDsGeZd3+Fg+ZaRIbSYjtP7k8swH8m93t3wZPDYeVr4QvJ9IVTzdSz\nvy3aWCdxuAYU+9bUJuP5IsNlRecsqf91CrfagVB8K/sQD9Zn4WR7b/3c/7mhrliAtdQbalkc3G6/\nO/7EwvF3+fJ1Hi2AFTNOSGVjFYtQOZxnv9hOZdFQ6DjYv2WR0Nr3KKxNpn/LYud8GznR/SzfbTTX\nIjI4Iz9P57ZDak9AfNeIKtwKK161N/y7P4CnRtlV5SK1/oi3SCiwU2itu1ifRUMpLbYWQyDru8Mg\niElomFgc2FqbA9GqfXDRUNVV1u8Ym2jLg/sq2FeYbX1RrdrV3Z7cMTjLorTY97n9Obcd0nrbvClf\nQQdrZll/5+DrAvelgahYhMrG9wED/S8L2PQ4Og2xo5EjXuKzD+2pnTP2RtssexP4ypNY964dFWaM\n8n2OlM72OtXVofVb8Y9nAUFPYlvYGky+ci0W/ss6WO9aBNe8ah9g70yFp0a7HgZhFo2Cza68gW7H\n7wvXgCJvnX0NZFnExEHHIdZary9OkT9wjfiDeIgf3mcHVwOvBExtPTVf5/b0RQVbH+r9H8Fjg7wv\nKZu7wk5np/vxe6YFiIha9ZodhLbvH7gvDUTFIlTWz65dMjNUXGU8vFoXh/Z491c4tMm0r94WcT+4\nA9b8F4bf6t+BmtLFJv4dDXN02KmO8/B1/kfe8LXEaskBa1WcNsVOofS7BO74Gqa8bAXkrdvg8cEw\n83q7FsSq120iWzAhnz77u8U+AL3lCIVrEaSaSKgAYgF2jfi9K+u3fHDJATvqrinF0cFa/4HC5R1B\n7Hupteh9TUUVZh8/BQXWsig/Yqd//bF7iXWQvzMVZt9jw+cdcldaX6a/6Wx/BQXz1ttnyWDPKkqR\nQcUiFEoO2Kilfpf5LR7oE+fG2etlHafiPb79FeAmFl78FvOfsOG44+72f30Nn40MBZt9P3wd0nrb\nEEhPf8SS522481i3/11UFPS/HO6cD5P/Y6cpCrPh2yfhnTvgubPhoc7W8qjPlFHB5rplPtxJybAP\n34aulZ23xi6+5S2U2JMuo+wgpj7hxU7YbFvHsugQXBa3I4htutmp262fHy8wlWX27+tNLJJc4bP+\nrItjB60v8qxfwuk/heUvw7/PtT6j6uraNbf9kdLFWh/eLItVMyEqBgZN9n+OMKFiUXoIvv1ncAuw\nb/rQmq71mYICG6XUtvvxlkV5iV3oxK9l4QoB9vRbHN4HK16xK/YFWgVPE/Mig7cCgp6k97EPxCI3\ny7Ci1NZH6nme9/DSqCg7TXLNDLh7MfxqH9y1GKbMgLN/bUtBfP1IaH2tqrADDl/9dSKiGlrqe99a\nO2oOZlDlTJ3WZyrK0wHtVC4IFBF1yC36sOdE+9D3LA54YDtgfIiF6zr+qs865+s4BM79Hdwwy7af\nfiZ883coPxxYLKKi7fWdqCyHqkpY/aat4HuCVmFUsSjYDJ/8CpbPCNx2/WxbH6ZjA5YD7zjkeLFw\nvnD+fBYt2tg1KTzFYsE/bR3/8T/yelgd1LIIP5Vl9uHry7nt4L7EqsPqN+yU4Lh7grtWdKy9Tv/L\n4Mz7YfgtsHJmaNbFge3WIepLLNwT8+pLVSXsX+87Gc+T5I52Jb2c+ohFts0fcixvx5I5EkAsinPs\n/ZSQDD3Osds8Q2g9CwjW6XMQloXjt2k/wL72Og/u/MZ+/uJPdlsgsQBrBXpaFtu/tL/jCXBsO6hY\nZIyAruOsk7Gqwne70kPWCVbfKSiHjoPtjVhyoHabEzbrz7IQgbaZrtGOi5IDsOQFGHhV3bUrfJHQ\n2lYAVbEIHwe22RwKX85th3TXw9kJn62uhgVP2QeqZ9HHYBn/Y/u9mPdY8Md4LtDkSTgGFAe22vL9\noeQhdRlp85dCpTDbOuqdef9W7e1rIMvCfdo3JcP+/7JDEAtHlPxZFnlrXVNxbnXaUjLg1g/g9J/Y\nhMRA3xuw/6uDO+r6dFbOtPdz7wsCHx8mVCwAxt9rp2bWveu7zeY5dhqh36UNu5Y3J3dNQl6AaSTP\n8NnF022NqtN/Gty1RTTXAuy0X7jqhdVEQvl4+DokpNgHjPOwzv7URkeNu7f+g4+UzjD0RhtnH2xS\nm3P9VB/9TerkyvRvwHfE8T14qwnli4xR9pqhJrq5R0KB2zRUgPMU765ryfecCDu/reuALsyGlu28\nJ7k6y6sGsizaDzj+/xsdC+f+Hm6ZHVwh0rTerqRO10CxtBg2/s/6KrxlfkcIFQuAXhdYhZ//uO+H\nyIbZ0KqD/9DUYPAmFu7zp/5ok2nnvKurrI9l4dPQ5+LQwuZSMiKz9OTJwt5V8Pc+MO/R8Jwv0Ejd\nnbTeteLy7ZP2/z3gioZd//Sf2AdJsBm8BVusIPhaiCkcmf771tjosGBGzQ5dXPdVKFNRxljLzt2n\nENfSlcUdINeiOKeuWPSYaB3jO+fXbvMsIOhJUiff4lZdbaOVQrGufOH0wfmurX/PWm4nKArKQcUC\nrCNx3D02gsNbvHV5CWTPtWGNUQ38kyW2hZSuHpZFjq03E6huUJssa90c3muXpSwtgglBWhUOwcTR\nv34DfBOmh2lT4lAuvHaNrYS65q3wnDN/k/1/BlOBOL2PveH3LIcd38CYH/guYBcsrbvaeetlLwZX\n5sJfJFTNObs0bBGkvLU2VDiUCgcdBtny/KEk5x3Js+Grx2VXB8ji9hZQ0m2cjTrKdguh9bRaPEn2\nk8VdtMNa/eHIf3D+X05BwZUzrWUYjvpdIaBi4XDaFGs5zH/i+H3Zc+1aFP3qGQXlSafBdcNnD+X6\nD5t1cJx4+RutYzvrTOtzCYWUDOtUdTe33SnaZU3cb/4Ox4q8tzkZKTsCr02xFtmQG2H/uvCsClew\nqdYfEYj0PvbhNudXdvQ77JaGXx/sgKG60vt31x1jaqvN+qOhU5VOJFQoxMS7kvNCEAvHp+Dpr0vq\n4F84HcvaifwCO63UbWxtvkVpsa2fVV/LwtO53RDiXdWMC7bYqahd39ry7g3xndYDFQuHmHgYc6e1\nLPaurrtvw2zrqOrmozJlqHQcbM3n0mL7+dAeGw0SiLau8NkvHrKjqjN+Fvq1a0IjfYyINs+xr+VH\n7Gg1GPYsg/fubrqO8+oqm9yWtw6ufrE2cmzLnAae17WOdaCHr4MzLbPrWxh2c/jW5G7bHU67Bpa+\nAEf8JFwe2Q9lxUGIRRdXpn89MseP5NsonWCS8TzpMspmNQe7Mpy3In9gB33+oqGc76nnAK3HOZC/\nwfp/fNWEcie5oxUUb7W88tYB4j87OxTSelmxWP2GPe9p14TnvCGgYuHO8O/aaKFv3UZolWX2Adr3\nouCcUcHghN46jsBACXkOyRk2CWfPUluALXNC6NcOlGuxeY6d7so6AxY9E/jGra6C2fdaJ+vT42x5\niqbGnP+DzR/DhQ/b8MW0XvZ33PxJw85bs451CJYF2P/hmB807NqeTLjPzrkveNJ3m2D9KykZ1lKp\nz4pzea7vdH3m6jNG2N8h2OS8wq126sgz5NyxLHz5H2vEwuO4HhPt69bPgxMLZ3lVb3+nvLWuirtB\nLLscDKkusVg1096b/sLsI0RExUJEJonIJhHJFpFpXvbHi8gbrv2LRCTTbd9pIrJARNaJyBoRifxC\nAC1a25IZa9+ujTPf9pWd4+71o3LAAAAgAElEQVR3efiu4+7kLjtsR3uBnNtgxcqxDCbcVz8z1F9o\nZPlR2P419J4E435k533XBnj4r37T3hgTf2cfmm/dBm9PrbWaGptF063ojfkhjLrdbhOxIYfbv2pY\nprLzUAuUY+HQMt0K/qCrw3+zp/WEgZNh8b99F67zVUDQE+c7Vp9pulDKfHjiBI8E67co3Fp3uVOH\npI7Wt+cri/vQHkBqs7Ad2g+wobdbP3dZLVJrzXvDX65F3vrwTEE5pPW2z4mDO2wCbiMQMbEQkWjg\nKeBCoD9wnYh4entuAw4aY3oC/wD+6jo2BngFuNMYMwA4C/CTBBFGxvzAPkwWPm0/b3jPzi/7K/0d\nKq3a2S9q7srakMdgHx4dBlqx6VXP+OqkToB4F4vtX9uRXe/zbShhu/42asfXCK2iFL540FpK438M\n3/3YljZYMwuePt2GIjYmm+fAx7+wayac/6e6+3qdbyNKdnxTv3NXlMLnf7QiH2ySpghM/RIuCSEv\nIhTO+Jn1rS18yvv+gi222GSgEG0nMa8+fot9a+zfJLFt6MemdLbHBhsR5csBnRQg16J4txUFTwe8\niJ2K2vaF9UW17uo/NNVXrkX5UTvNHI5IKAdn1bzYltD3kvCdNwQiaVmMArKNMduMMeXA64Dn8Pxy\n4CXX+1nARBER4HxgtTFmFYAxptAYE6F6zR6kZNgR2rKXbMnojR/aUWi445mdcuU1CXkBbmCH70yH\nWz+sf1RWTJw10709CDbPsdNw3cbbG2fcPTYT1zNZyWHxdHue8x6w/YmOgbOmwfc+tp9fvBjm/uHE\nWRnGWB/C0hfgv7fCm7fYEe6Vzx0/+sw83d54mz+u37W++qsNNLjsidCmGlqlR261vPQ+NhR30XTY\n+gVs/8Zaxlu/sKPlnCV2CiqQRdqQxLy8tQ17SGaMCM7JXV1lM+f9Jcz5iojyDJt1p8dEa5Fs/sT/\nFBT4tiz2bwRM+C0LsDXD4luF77whEMllVTsD7k+kHGC0rzbGmEoRKQZSgd6AEZE5QDrwujHmYc8L\niMhUYCpA165dw9fz8ffC6tfhre/bELtwRUG503GwfVA51SSDmYaC8MyBJnc+/kFgDGz5xBZVc4Rx\n4GT47AHrw+nlsX7HsYM2YqrnucdbXV1GwZ3z4ONpNp9h4dP2Sz7splohChcVpXaqbPvX9se5cZM6\nwYDv2Jo83m6umHjocbZ9KBgTWp/2LIP5j9mEuPqsaxJJzrjfJpfO8JG/MfSmwOeIT7LZwaFaFvs3\n2FDivheHdpw7GaNsHsHhfXUznz0p3m2nmvzVbfLlcyne4zuktcfZ9rXiaGCxcJZX9bQs8lxTceEU\ni5Qu1jpuaFJwA2iqa3DHAKcDI4ES4DMRWWaMqTPENcZMB6YDjBgxIkwpudh/cs9zbchsbGJkHggd\nBwPGNbKV4C2LcJCSUfuFdshba+dyz3Jb4zsmDkbfCXN/56qQ6Tbd8s2j1mI49w/erxGfBJc/BSNu\ns87vNbOsALfJsg/ZITfYaJKGUFkOr19va/q0TLcO/6wJkHmG9zUIPOl1vg0T3h/C/HJlGbx7l424\nOf/BhvU/ErQfYIX62AFAbDa2OK9Rwf+eKV1CsyzKDsMbN0FiKoz8fr26Drgl5y3x/2CsCZv1Ylm0\n8pPFbYz9vXyVyWiZVmv1BxILZ3lVz+vkrbMWekoYB7COpd+IRFIs9gBugcxkuLZ5a5Pj8lOkAIVY\nK+RrY0wBgIh8CAwD/CyWG2bG3WvFoue54YtocMdxcu+YZ+dPG5qcFQopGVak3EfUTshsr/Prth3x\nXfj6bzav46p/221Fu2HRszD42sDx9J2H2Z/zH7QhyMtn2Ln+Lx60wtEm8/ifdv0C/z2qq+waAVs/\nsz6A4beGbrE4v+vmj4N/iH71sA2vvP6/NiCiKRJqjoM3WnfxvnaKN4yxazUc2Aq3vO/fIghEx8E2\nOW/34gBi4SdaKS7RFgn05rM4dtBGsPnzEfaY6BILPwl5DkkdvVgW66y/r6EJvE2MSP42S4BeIpIl\nInHAtcBsjzazASczaTLwuTHGAHOAQSKS6BKRM4H1Eezr8WSdYctAn3F/ZM6f3MmOhqsrgwubDScp\nXaxz132pxs1zbAVMxznokJBiq5u6R4h98Wf7evavgr9mXKIVl+9+APcshzOnQcfT7Ah43dvw6W/g\nzZvg2Qnwr7G+l58F+3D64Kew7h1rmo/4bv2mtpI7uqYDgwyhzV0J8/5hraLe5wdufzITyop5i6fb\n/8XE31pfUEOIibfFFQNFRBVu9b7cqUNSe+9i4Uyt+Zv2HXydvf87Dw/c32SPlfmMcfltwjgF1USI\nmFgYYyqBu7EP/g3Am8aYdSLygIg4ToDngVQRyQZ+CkxzHXsQeBQrOCuB5caYDyLVV6+I2DLQHYMs\ns1yf8zvWRbD+inDhmWtxtNDenL4irGoixJ6xoZGrZsLoqbVRM6GS2gPO+oVNkJv6JfxiB/xiJ0z9\nCq54xiYE/vtcW5XV2xKwn/3BJgxOuK/hpnnvSTb6xr0KsDcqy+HdH1qBv6AJTj+Fm5QuNlQzUHDC\n7iU2I733hTbcOhw4yXn+qkD7Wu7UwVcWt68cC3fSe1sLKRjL0cnidiIGD+XaMjwqFqFhjPnQGNPb\nGNPDGPOga9tvjTGzXe9LjTFXG2N6GmNGGWO2uR37ijFmgDFmoDHm55HsZ6PhiMWJTrDxjHbJ/hQw\nvudxUzJgwJWw/CX46Bc28zjYSrfB0qK19YkMuc6uENfzXJtM99rVddcsn/eYHd2P+B6c85uGX7fX\nBTaxKnuu/3bf/M2WCLn0Mbu2SHMnmIioo4U26iy5E3zn6fBNu2SMtJavv+S8QHWbkjp6z+IONVQ9\nEMkdrTO8zLW8ak2ZjzCGzTYRmtek2slGo1kWThy960GweY4txewvX2DcPXbEv3OeHdHXJ44+WFqm\nwrWvwcV/tz6dp8fZh/myF62zfeBVcNHfwhNV1WmotRYcn4039q6ykV+nXQt9Lmz4NU8GAiXmVVfB\n29+3dcamvBxeAc0YaV99TUVVllmr2J8DulV771ncxbtt1ndimFaXcxL7nBpR+x2xCEMBwSaGikVj\n0mWMfVCFWgywoSS2hZgWViyqKmweRe/z/Y8MO55mR/spXWHU1Mj3UcRG1dz+hf0bvXIVvP9juwTp\nFc8cnzdRX6KirKM7+1PvNX4Kt8KrU2wfJj0UnmueDARKzPv6bzZ348K/1o2SCwcpGdYy8CUWB3dY\nazBQKQ5vWdyH9lhLKFxWkBPR51SfzVtn7xFva2Cc5DTV0NlTg6T2cH924HbhpmYRpBzYvcjOTQeT\nEX71S/YGjG0R+T46tO8Pt39u8z2Kc+A7z4ZW+joYep0PK1+1votu42q3H9wBL11ql6295X+Rtaaa\nGi3b2agkT7GoqrBW1pd/sY7g4beG/9oi1rrwtSa3v7BZB/e1uN3/b/4S8upDTRa3y7JwFjxqhqhl\ncariiMXmj21ikZOM5I/4Vo3zwIxtYUf118yITBhzj7NtcT/3qaii3VYoyo/Cze81y2kFv0RFHZ+8\nmb8Jnj8PvnzI1re6+NHIlcnuMsou9LXyteP31VSb9bOUsK8V84pz6pYmbyg1Wdy5dnqsYLOKhdLM\nqBGLTyBzvE2iO1VJSLEWhSMWh/ZaoThWBDe9U7+ieM2BlAwrms564c9MsLkXU16Gq56LjHA7DL0J\nup0O7/4A3rurbsHHwmzrc/DnJ3G3LByqKq14hDNUPbaFzXY/tNcKRXVlsx1YqFicqqR0sdEiBZvq\nX5SwOdHrAptsl7MMXr7MOm5vfPuEr0bWpGjd1T6YX77MRqb1OAd+uNCWbok0LVpbi27Cz2DFq/Dv\nibWlcQq3Bc6udrK43SOiDu+1vo5wRx8md7LnbsaRUKBicerifsP4Cpk9lXD+Bi9ebC2uG/4LXUY2\nbp8am5QMmzSZuxIu+ydcN/P4pM1IEh0DE38DN86ydZ6ePRNW/zdw2Cx4z+KuWes+zGLhZHHnrbWR\nVv58KScx6uA+VXHEIrVncGUNmjupPe3aCIdy4bo36jq6T1UGXmUf0qf/FNp0a7x+9DwX7vjGtVaK\nq+5UUKU4POo2BZOQVx+SO1qhyFsH7fqGb5G0Jkbz/K2UwDg3TO9JjduPpoIIXPMqYS8tfTKT3gcu\nfbyxe2FJ6Wwj0j7/I8x/vDYXwx9J7eGwW+VZX8upNpSkTjZxtLqqWVvpKhanKm2729pXQ65r7J40\nHZqpY7LZEB0D5/0Bzvw5xLUM3D6pI+xcUPu5OMcGM4Q7mCO5I2CgpKBZDzRULE5VnNpXinKyEYxQ\ngJ2GOrKvtrpyuMNma67jtrxAMxYLdXAritI8adWhbhb3oZzIlNZxX5elmUZCgYqFoijNFc/EvHBn\nb9dcx2VZtGpvF09qpqhYKIrSPKlZi3ufzcQ/djAya8ckptoqCO2at89LfRaKojRPnJyQw/vcSpNH\nwGcRFWXLnzTzcGsVC0VRmifua3E7BREjtXbMd56OzHmbEDoNpShK8yQu0YbKHslzy94+wWvHNCPU\nslAUpfnSypXF3aINILVVYpWQUbFQFKX5ktTBZnHHp9j30bGN3aOTlohOQ4nIJBHZJCLZIjLNy/54\nEXnDtX+RiGS6tmeKyDERWen6eSaS/VQUpZmS1MHl4N594te6b2ZEzLIQkWjgKeA8IAdYIiKzjTHr\n3ZrdBhw0xvQUkWuBvwLXuPZtNcaEeb1GRVFOKZws7uiY2jXvlXoRSctiFJBtjNlmjCkHXgc8C+Ff\nDrzkej8LmCgSqaW3FEU55XDW4j6wXZ3bDSSSYtEZcF/AN8e1zWsbY0wlUAykuvZlicgKEflKRCZ4\nu4CITBWRpSKyND8/P7y9VxTl5KeVs/6GiUyOxSlEUw2d3Qt0NcYMBX4KvCYiyZ6NjDHTjTEjjDEj\n0tPTT3gnFUVp4iS51W2KRPb2KUQkxWIP4C7lGa5tXtuISAyQAhQaY8qMMYUAxphlwFagdwT7qihK\nc8SpDwXq4G4gkRSLJUAvEckSkTjgWmC2R5vZwC2u95OBz40xRkTSXQ5yRKQ70AvYFsG+KorSHKkj\nFjoN1RAiFg1ljKkUkbuBOUA08IIxZp2IPAAsNcbMBp4HZohINnAAKygAZwAPiEgFUA3caYw5EKm+\nKorSTIltYbO4K8tswT+l3kQ0Kc8Y8yHwoce237q9LwWu9nLcW8BbkeyboiinCEkdoarCLoCk1BvN\n4FYUpXnTeQSYqsbuxUmPioWiKM2bK55q7B40C5pq6KyiKIrShFCxUBRFUQKiYqEoiqIERMVCURRF\nCYiKhaIoihIQFQtFURQlICoWiqIoSkBULBRFUZSAqFgoiqIoAQlKLETkRyKSLJbnRWS5iJwf6c4p\niqIoTYNgLYvvGWMOAecDbYCbgL9ErFeKoihKkyJYsXDKNV4EzDDGrHPbpiiKojRzghWLZSLyCVYs\n5ohIEnadCUVRFOUUINiqs7cBQ4BtxpgSEWkLfDdy3VIURVGaEsFaFmOBTcaYIhG5Efg1UBy5bimK\noihNiWDF4mmgREQGA/cBW4GXI9YrRVEUpUkRrFhUGmMMcDnwT2PMU0BS5LqlKIqiNCWCFYvDIvJL\nbMjsByISBcQGOkhEJonIJhHJFpFpXvbHi8gbrv2LRCTTY39XETkiIj8Lsp+KoihKBAhWLK4ByrD5\nFvuADOARfweISDTwFHAh0B+4TkT6ezS7DThojOkJ/AP4q8f+R4GPguyjoiiKEiGCEguXQLwKpIjI\nJUCpMSaQz2IUkG2M2WaMKQdex05juXM58JLr/SxgoogIgIhcAWwH1gX1myiKoigRI9hyH1OAxcDV\nwBRgkYhMDnBYZ2C32+cc1zavbYwxldgIq1QRaQX8AvhDgH5NFZGlIrI0Pz8/mF9FURRFqQfB5ln8\nChhpjNkPICLpwFysNRAJfg/8wxhzxGVoeMUYMx2YDjBixAgTob4oiqKc8gQrFlGOULgoJLBVsgfo\n4vY5w7XNW5scEYkBUlznHg1MFpGHgdZAtYiUGmP+GWR/FUVRlDASrFh8LCJzgJmuz9cAHwY4ZgnQ\nS0SysKJwLXC9R5vZwC3AAmAy8LkrRHeC00BEfg8cUaFQFEVpPIISC2PM/SJyFTDetWm6MeadAMdU\nisjdwBwgGnjBGLNORB4AlhpjZgPPAzNEJBs4gBUURVEUpYkhdiB/8jNixAizdOnSxu6GoijKSYWI\nLDPGjAjUzq9lISKHAW9qIoAxxiTXs3+KoijKSYRfsTDGaEkPRVEURdfgVhRFUQKjYqEoiqIERMVC\nURRFCYiKhaIoihIQFQtFURQlICoWiqIoSkBULBRFUZSAqFgoiqIoAVGxUBRFUQKiYqEoiqIERMVC\nURRFCYiKhaIoihIQFQtFURQlICoWiqIoSkBULBRFUZSAqFgoiqIoAVGxUBRFUQISUbEQkUkisklE\nskVkmpf98SLyhmv/IhHJdG0fJSIrXT+rROQ7keynoiiK4p+IiYWIRANPARcC/YHrRKS/R7PbgIPG\nmJ7AP4C/uravBUYYY4YAk4BnRcTvErCKoihK5IikZTEKyDbGbDPGlAOvA5d7tLkceMn1fhYwUUTE\nGFNijKl0bU8ATAT7qSiKogQgkmLRGdjt9jnHtc1rG5c4FAOpACIyWkTWAWuAO93EowYRmSoiS0Vk\naX5+fgR+BUVRFAWasIPbGLPIGDMAGAn8UkQSvLSZbowZYYwZkZ6efuI7qSiKcooQSbHYA3Rx+5zh\n2ua1jcsnkQIUujcwxmwAjgADI9ZTRVEUxS+RFIslQC8RyRKROOBaYLZHm9nALa73k4HPjTHGdUwM\ngIh0A/oCOyLYV0VRFMUPEYswMsZUisjdwBwgGnjBGLNORB4AlhpjZgPPAzNEJBs4gBUUgNOBaSJS\nAVQDPzTGFESqr4qiKIp/xJjmEWg0YsQIs3Tp0sbuhqIoykmFiCwzxowI1K7JOrgVRVGUpoOKhaIo\nihIQFQtFURQlICoWiqIoSkBULBRFUZSAqFgoiqIoAVGxUBRFUQKiYqEoiqIERMVCURRFCYiKhaIo\nihIQFQtFURQlICoWiqIoSkBULBRFUZSAqFgoiqIoAVGxUBRFUQKiYqEoiqIERMVCURRFCYiKhRIS\nzWVlRUVRQkPFIoyUVlRRcKSssbsREaqrDffOXME1zy6kvLK6sbujKMoJJqJiISKTRGSTiGSLyDQv\n++NF5A3X/kUikunafp6ILBORNa7XcyLZz3Dxx/+t58LHv6GquvmNvv/+6SZmr8pl8Y4DPPHZlsbu\njqIoJ5iIiYWIRANPARcC/YHrRKS/R7PbgIPGmJ7AP4C/urYXAJcaYwYBtwAzItXPcHGsvIr3VuaS\nf7iMDXsPNXZ3wsp7K/fw1BdbuW5UFyYPz+BfX2azYtfBxu6WoignkEhaFqOAbGPMNmNMOfA6cLlH\nm8uBl1zvZwETRUSMMSuMMbmu7euAFiISH8G+NphP1u/jSFklAAu2FjZyb8LHmpxifj5rNSMz2/CH\nywby20v70zGlBfe9uYpj5VWN3T1FUU4QkRSLzsBut885rm1e2xhjKoFiINWjzVXAcmPMcc4AEZkq\nIktFZGl+fn7YOl4fZi3LoXPrFmSmJrJgW/MQi/2HS5k6YylpreJ5+sbhxMVEkZwQyyOTT2NbwVH+\n+vHGxu6ioigniCbt4BaRAdipqTu87TfGTDfGjDDGjEhPTz+xnXNjX3Ep87MLuHJYZ8b2SGPJ9gNU\nVkXGCVxVbU6IT6Sssoo7ZyyjqKSC6TcPJ61VrWE3rmcat47L5MVvd/BtdkHE+6IoSuMTSbHYA3Rx\n+5zh2ua1jYjEAClAoetzBvAOcLMxZmsE+9lg3l25h2oDVw7LYGyPVA6XVbIuN/x+C2MMt/5nMTe/\nsCjs5/a8zq/fWcvyXUX87erBDOiUclybX0zqS/e0ltw/azWHSisi2h9FURqfSIrFEqCXiGSJSBxw\nLTDbo81srAMbYDLwuTHGiEhr4ANgmjFmfgT72GCMMby1LIdhXVuTldaSMd3bAkRkKuqT9Xl8s6WA\n+dmFrNpdFPbzO/xn/g7+uyyHeyf24uLTOnpt0yIumr9PGcze4mP88f31EeuLoihNg4iJhcsHcTcw\nB9gAvGmMWSciD4jIZa5mzwOpIpIN/BRwwmvvBnoCvxWRla6fdpHqa0NYu+cQW/Yf4arhGQC0S0qg\nR3rLsDu5yyureejDDXRPb0mr+Bj+M397WM/vsHZPMX/+cAPn92/Pjyf28tt2aNc2/PCsnvx3WQ6f\nrs+LSH8URWkaRNRnYYz50BjT2xjTwxjzoGvbb40xs13vS40xVxtjehpjRhljtrm2/8kY09IYM8Tt\nZ38k+1pf3lqeQ1xMFJcM6lSzbWyPVJbuOEBFGP0WMxbuZEdhCb+5pD9Xj8jggzV72X+oNOBxW/IO\n870Xl7C94GjAtqUVVfz0zZW0bRnHw5NPIypKAh5z78Re9O+YzC/fXkNphUZHKUpzpUk7uJs65ZXV\nzF6Vy3n92pOSGFuzfWz3NI6WV7FmT3FYrnPwaDmPz93MGb3TObtPO24dl0llteGVhTv9HmeM4bfv\nrePzjfu57cUlFJf49y08+ulmNucd4eHJp9E6MS6ovsXFRPGLC/tScKSMeVvU2a0ozRUViwbw5ab9\nHDhazlXD60YEj3b8FmGainr8sy0cKavkVxf1A6Bbaksm9m3Hq4t2+R3Nz92wnwXbCpk8PIPdB0u4\n67XlPqO0Fm8/wHPfbOOG0V05q09oM35ju6eSnBDDR2v3hXScojR33lyym+e+3tbY3QgLKhYN4K3l\nOaS1imNCr7phu2mt4undvhULw+Dk3pp/hFcW7uTaUV3p0yGpZvv3xmdReLSc2atyvR5XXlnNnz/c\nQI/0ljx05SD+dMVA5mUX8Mf/He+MPlJWyX3/XUmXNon8n0uQQiEuJopz+7Vn7oa8sE69KcrJzIGj\n5fz+/XX8Y+7mZnFfqFjUk4NHy/l8434uH9KZ2Ojj/4xju6eydMfBBhfde+jDDSTERvPT83rXPX+P\nVPq0T+I/83d4rQT7ysKdbC84yq8v7k9sdBTXjOzK7ROyeGnBTmZ4TF89+MF6cg4e49Epg2kZH1Ov\nfk4a2IHiYxVhEchQ+Da7gBv/vYjlWn5E8UFlVXWj1Gv79zfbKCmvoqS8ipURjF48UahY1JP3V+dS\nUWW4cphnUrplbI9UjlVUsTqn/l+S+dkFzN2wn7vO7lknKQ5ARPju+Ew27D3Eou0H6uwrKinn8c+2\nMKFXGmf1qbV6pl3Yj3P6tuP3s9fV+Be+2LifmYt3c8cZPRiR2bbefT2jdzqJcdF8HMRUVFW14fez\n17Emp2E+nf2HS7ln5grmZRcw+elveWTOxiZTETfSI8nDpRU8+dkW8g83zyrH4eSOGcv4wSvLTug1\nDx4t56Vvd3BG73SixN7LJzsqFvXkreV76NshyWvCGsDoLFu1pL5+i6pqw58+2EBGmxZ8d3ym1zZX\nDO1Mm8RYXphXN4z2ic+yOVxawa8u7odIbURTdJTw+LVD6JHekh++uoxlOw/y87dW07dDEj85z3+Y\nbCASYqM5u0875qzLCziKm7shjxe/3cHfPtlU7+tVVxvue3MVR8oqeesH47hqWAZPfbGVy5+az8Z9\njVvIcV9xKcP/+Cn/+HRzxK4xc/Eu/v7pZi7757wGi25zZveBEj7buJ8vN+c3OFqvqKScu15bzrKd\nga3Y5+dt52h5Fb+6qB+DOqeoWJyqZO8/wqrdRUx25VZ4o03LOPp2SGLh9vqJxaxlu9mw9xDTLuxL\nQmy01zYJsdFcP7orn27IY/eBEgC25R/h5QU7uGZkV/p2SD7umKSEWJ6/ZSQx0VFc/cy3FJWU8/cp\ng4mP8X6NULhgYAcKjpQFnBJ63iVuX2/JZ1dhSb2u9fy87XyzpYDfXtqf4d3a8MjVg3nu5hHkHy7l\nsifn8/SXWxutVPw3W/I5VFrJ459t4cUI5cO8tzKX7uktiRJh8jPf8t5Kz+IICsDby+3fpbyyOqiH\nvD9+8946Pli9l7teXc7Bo+U+2xWXVPDitzu4aFAH+nRIYnzPNFbsKqopNHqyomJRD95enkN0lHDZ\nkE5+29l8i4OUVYY2otlVWMLDH29iWNfWXDzIewa1w01jMokW4aVvdwDw0EcbiY+JOs7H4U6Xtok8\nc+Nw4mOiuf+CPj6to1A5p2874qKj/E5Frd1TzOLtB7jt9CyiRHh1sf/wX2+sySnm4TkbmTSgA9eP\n6lqz/bz+7Znz4zOY2K8df/14I1OeXUBRie+bOlIs3HaANomxnN+/PX/433qfQQj1JXv/YdblHuLG\n0d147+7xDM5ozY9eX8lfPtrYLNdSqS/GGN5ansPgLq2JjhK+3Vr/0f3sVbm8vyqXK4d2pvBoGb94\na7XPVSOfn7+dI2WV3OtKaj29ZxqV1YbF9Rw4NhVULEKkoqqat5bncEavNNolJfhtO7Z7KmWV1azc\nFbzfovBIGbf8ZzGV1YaHJw+uM43kjQ4pCVw0qCNvLNnN3PV5fLo+jx+e3ZP0JP8V3UdltWXl785j\n6hk9gu5bIFrFxzChVxofr93n+0aat52WcdH86NxenNuvHf9dmhOSmB4pq+SemctJaxXPX64adNzf\nJ7VVPP+6YRiPThnMsp0HeWPJbh9nCp69xcdCKpi4cFshY7qn8sR1QxmZ2Zb73lzJN1vCVxV59spc\nogQuOa0jaa3ieeX7o7lxTFee+Wort720hOJjWqsLYMmOg+w6UMItY7sxOCOF+dn1e1jnHSrlN++u\nZUiX1jw8+TR+fkFfPlmfx6uLdh3XtvhYBf+Zv51JAzrUWPbDurUhPiaKeVtULE56QpnLnLs+j7xD\nZVw/ulvAtqOzUhGxI81gKCmv5HsvLSW36Bgv3DqCnu1aBXXcd8dncriskrteW07n1i247fSsoI4L\nx9STJxcM7MCeomOs3cvkhisAABuwSURBVHO832BfcSnvr8rl6hFdSE6I5cYx3ThwtJyP1gSfn/G7\n99ax60AJj10zxGfioIhw5bAMBnZObnDuhzGGH766nFv+s5jDQRRM3H2ghD1FxxjTPZWE2Gieu3kE\nPdJbcceMZWGp52WM4b1VuYzrkUa7ZDtYiYuJ4k9XDOLB7wxk3pYCvvPU/HpP7zUnZi3bTcu4aCYN\n7MD4nmmszikKueilMYb7Z62mrLKKR6cMJiY6ittOz2JCrzT++L/1bM47XKf9i/N3cLi0knsm9qzZ\nlhAbzcjMtg2ybJoCp7xYLNt5kLEPfRZ0aNsri3bSuXULzukbOHEtJTGW/h2TWbAt8Jekoqqau15d\nzpqcIp68bijDuwUfmTS0axuGdGlNWWU1v/Dj4zgRnNevPdFRwkdr9x637+UFO6gypsZhP75HGpmp\niQEz0R3eW7mHt5bncPc5vRjd3XPZk+OZNKADK3cXsbf4WCi/gsc1c1mxq4iKKhNUhroTOjzG1b+U\nFrG8/L1RpLaK47svLmFr/pF69wVgVU4xOwtLuGzw8VOgN4zuxmu3j6HwaDnfe2nJKV0NuKS8kg9W\n7+WiQR1JjIthbI9Uqg0sDnLg5vDqol18vTmf/7uoH93T7eAtKkr4+5TBJCXEcO/MFTWDzUOlFTw/\nbxvn9W9/3NTu+J5pbNx3mP2HA5foaaqc8mLRp0MSBngyiHWlt+UfYX52IdeN6kJ0EHWTwE5FLd9V\n5Nd6Mcbwq3fW8MWmfP54xUDOH9Ah2O7X8JtL+nHnmT241EeV2BNFm5ZxjOne9ripqGPlVby2eBfn\n9WtPt9SWgL3pbhjdjaU7DwaMYNpVWMKv3lnLiG5tuPecnn7bOkwaaP8Wc+ppXZSUV/KXjzYysHMy\nKS1i+Xxj4PJkC7cdoG3LOHq5WYXtkhN4+XujEeDm5xezr7j+D4zZK3OJi47igoHevyOjstry9I3D\n2FFwlB+/vjIoH4YxJ2aNlBPJnHX7OFpeVROEMqyrnQqaH8LofkfBUR78YAMTeqVx05i6MwntkhJ4\n5OrBbNx3mIc+3ADAy9/u4FBpJT/yUoDz9J5pwMm9iuYpLxat4mP4/ulZfLZxP2sD1HJ6ddEuYqKE\nKSO7+G3nztgeqZRXVvuNEHr00828uTSHe8/pyQ1BTG95Y3i3tky7sG9AH8eJYNLAjmwrOMqW/bWj\n6LeW51BUUsH3J3Sv03by8AziYqL8WhfHyqv44WvLEIHHrh1CjJckSG/0bNeKXu1a8fG6+onFM19u\nZd+hUn5/6QDO6J3OF5v2Ux3gobpwWyGjs9oeV4QxK60lL31vFEUl5fx+9rp69aeq2vD+6lzO7ptO\nSotYn+3G9Ujjd5cN4PON+3lkjv/w5P2HS5ny7AIueXJesyoEOWtZDl3atmCkK3coITaaEZltgn5Y\nV1UbfvrmSmKjhYcnn+b1vjq7TztuO90mur63cg//nredc/u1Y2Dn4wNG+ndKpnVi7EldP+2UFwuA\nm8dlkpwQwxN+rItj5VXMWpbDBQM7BHRsuzMyqy1RPvwWxhhmLNzJk59nc82ILvzETwTTycQF/dsj\nQk1UVHW14YX52xnUOYWRmW3qtG3TMo5LTuvIO8v3eA0tNMbwy7dXsy73EI9fO4SMNokh9WXSwA4s\n3n6AwiOhJa/lHCzh2a+3cengTozIbMs5fdMpOFLutziku7/CGwM7p3Dr+EzmrN/HjiCqAHuycFsh\n+YfLuHyI90RQd24a040bRlun97srvIfVrs4p4rIn57M6p5gNew/xry+DW2Nsa/4Rrnl2QZP1i+wp\nOsa3Wwu5alhGHdEe18NOBRUE8V149uutLN9VxB+vGEjHlBY+2/18Uh8GdErmR6+vpKikgh9N9H4P\nR0cJ43qkMj+7wGfwR1NHxQJITojle6dn8cn6PNb7WOHu/dW5FB+r4MYQR/7JCbEM7JzCwq2FVFUb\n1uce4uUFO7j7teWMeegzfvPuWib2bceD3xnYJKyCcNAuOYHhXdvUOJe/2pzPtvyj3HZ6ltff8cYx\n3ThaXuU1V+D5edt5d2Uu953Xm3P6tg+5L5MGdqDa2IWjQuGhjzYiAtMu7AvAmb3bESX4nYry9Fd4\n45axmcRGRdXkmoTCeyv30Co+Jih/GcDvLh3A6Ky2/Pyt1cc512evyuXqZxYQHSW888PxXD6kE09/\nmU32/sM+zmYpq6zintdWsGj7Ad5Yenw0UFPgneU5GANXDaubBzU+yKmgLXmH+cenm7l4UEevviF3\n4mOieeK6obSIjWZi33YMyvAdhj6uRxq5xaVBLRfgjYIjZQEt20iiYuHiu+OySIqP4Z9feLcuXl24\nk57tWtWshBcKY7unsmzXQYY88AkXPfENv31vHUt3HGRUVip/umIgT90wLOiplZOFSQM7sGHvIXYW\nHuX5edtpnxzPRT5yRoZ2aU3/jsm8snBXnVHXN1vy+fOHG7hwYAfuOjs4P4Un/Tsm07VtYlBlSBwW\nbSvkg9V7ufPMHnRubUeVbVvGMbRrmwBicby/wpN2yQl8Z2hn/rtsNwf8JHZ5UlpRxUdr93HBgA5B\nBzDExUTxrxuG0S4pnqkzlpJ3qJTqasMjczZy78wVDM5ozXt3j6d/p2R+c0l/EuNi+L+31/p9ID3y\n8SbW7z1Ep5QE3l+1t8mNko0xzFqWw+istnRpW9cKHdgpmaT4mIBRSU99kU1cdBR/vCK4AVyP9FbM\nve9M/nn9ML/tHL/F/BD9Fmv3FPODV5Yx4k9zuf3lpZSUN05yX/N6QjWAlMRYbh2fyYdr9rFpX93R\n1ZqcYlblFHPD6K71Gv1fPqQzgzNSuOS0TvzjmsF88/OzWfDLc3jyuqHcOKZbo0YvRYoLXE76x+du\nYV52ATePzSQuxvvXTUS4cUw3Nuw9xHJXTsquwhLufm0Fvdol8berA+eb+EJEmDSwA99uLQgq/6Cq\n2vDA/9bTKSWBOzxyUM7p2441e4q9LjpljPHpr/Dk9jOyKK2oZsaC4BMSv9yUz+HSSi4PkAjqSWqr\neJ67eQSHSyuZOmMZU2cs5akvtnLdqK688v3RNTXH0lrF86uL+rF4xwHeWOo9N+Wrzfn8e952bh7b\njR+f15tdB0pYHeZSI/uKS5mxYAc3Pb+IkQ/O5a7XlvP28pyghXXZzoPsKCzxWl0hJjqK0d1T/eZb\n5Bws4f3Ve7l2VFfatgxuTReAzq1b0CLO/33cLTWRzq1bMD9Iv8XK3UXc9uISLnlyng2JHtqZLzbt\n59rpCxslqkrFwo3vjc+iZVw0//wiu872VxbupEVsNFcO813ewx/9OyXz9g/H89CVg/jO0Ay6tE1s\nNlNOvujSNpGBnZN5e8UeEmKjuGF0V7/tLx/SiVbxMby6cCdHyyqZOmMpANNvHl7vSrgOkwZ2oKLK\n8NmGwFNR/9/enUdHVeUJHP/+UllJAmQPkEhCFghBCDsJqwkK2iLqiC2tYNsq2i6jI9KN3ba2S2ur\nM24jnpZuaPddaZaj04IgAiKrBDGAhE0CSBIJAhGy3vmjXmIgSxVJVSpJ/T7ncFJ18/LOvfqqfu/d\n5Xff23iAbw4dZ/YlafU+/BdY+3x8trP+AruCklNNjlfUlRwdSk6faF5du8/pQeXFuYeIDPEnK8nx\n+c+W1q0zT1+dQe6BY/YZd5PTeeyKfvWC95QhcQxPDOfxj7bX+zIqPlnGzHdzSY0J4Q+XpDEhPRY/\nm7R4dboxhvzCE8xZkc/kOWvsXbMLv+FgySmGJ4azfu9R7nk3l8GPLuXKF9cwZ0U+2w8fb/SJ5oPN\nBXTytzX6FDsyOYLvjv5Umx7nbPNX70OA3zi5VulciAijkiP5Yndxk7PPNuw7yrR567h8zho2fVfC\nzAtTWT07m2d+mcHfpw9h15GTXDHnC4ddhq6mwaKOsGB/pmclsGTrIfKtmTw/nqpgYe5BJmd0b3IG\niqpvovV08R+D4hzuvBcc4MsVA3uw5OvD3PnWV3x75AT/O3Vg7TTblsiI60pM5wCHXVHHT1fw35/s\nZEjPsAanIKd1C6Vbl0A+3VE/6Ky1xisynfwyv3lML34oLeeDzQUOjz1xuoJl249waf/uze6unNgv\nlpemDebdWzKZlpnQ4M2KiPDYledzuqKaR5Zsry03xjDrvVyOn67g+akDCfSz0SXIj7Gp0SzZeqhZ\n/eglpeX8c81eLn5uFeOf/rx21tasCb1Zds8Ylt87jhd+NYh19+Ww+I5R3JWTQmW14al/7+Ti51Yx\n6YXVvLfxwBnB9lR5FUtyDzOxX2yjNxhZSY2PW/z4UwVvb/iOSQO613Y/utrIlEiOn65sdObl/NV7\nmfK3tbV54Vb/Pps7c1Jqv3ty0mJ455YRlFVWc+WLX7TqlgBuDRYiMlFEdopIvojMbuD3ASLyjvX7\ndSKSYJVHiMgKETkpIi+4s45nu2lUIoG+Nl60ni4+3FzA6YpqrhvRvCmt3uzKQXFk9org1rHOpRS5\nbkRPyiurWb6jkNkX92FMapTjP3KCj48wMT2Wld8WUdpEMrdnl+7ih9JyHpyU3uiX6QV9olm9q7he\nipIv9/zgcLyiruGJ4QyI68I/Vu11uMbhk2+OUFZZzSQHg62OTEiPZXDPsCaPSYoK4bYLklice4jP\ndtrHZ17+Yh8rdhbxx0vSzkhOeVlGd44cL2PDPucWulVXG1btKuKONzcz/LFPeWhxHv6+Pjw8OZ0v\n78th4e0juf2CZJKjf97ky8dHOD+uC3ePT2XRHaNY/8ccHpmcTllFNbPe30rWX5fz5P/t4NCxU3yS\n9z0nyiqbTPCZGhNCZIh/g+MWr6/bz0/lVcwY06uBv3SNmifD1Q2kj1m45SAPL8ljYnosq36Xza1j\nkwhpIOj1j+vKgtuyiO4cyLR56xqd7eZqbgsWImID5gAXA32BqSLS96zDbgRKjDHJwDPAE1b5aeBP\nwL3uql9jIkICmJbZk39tOcje4lJe/3I/A+K7Njh3WjWte9cg3poxot5AY2N6x4Zy5aAe/DorgZtH\nu/YDO7FfN8oqq1n5bcM5mhZuOcj8NXu5dvh5Tc5oye4dTWl5FRv2/rxuxhjDuj1HGdEr3OnuRRHh\n5jG92FtcyjIH3WMLcw8RFxbEoPO6OnXulvrtuCSSooK5/1/b2LS/hMc/2kF2n2imZ555wzQ+LZog\nP5tTXVHvbypg9JMrmDZvPat2FfOr4efx8V2jWXTHKKZnJhDbxbnp6NGhgUzLTOCT/xrDmzcNZ0jP\nMP62cjejn1zBQ4vz6NE1iBGJjT/diQiZSZGs2f3DGV1Zpyuq+Oca+/4Tad3qZ2t2lciQAPrEhtZL\nWf75t0XMfDeXEb3CefaaDIfjH/Hhnfjg1iwGnRfG3e9sYc5ZXefu4M4ni2FAvjFmjzGmHHgbmHzW\nMZOBV6zX7wM5IiLGmFJjzGrsQaPV3Ty6F342H257YzO7i0q5zkF/u3Kdp6/O4M+XNXxn3xJDE8II\nD/ZvMFfUpv1HmfXeVoYlhvPApelNnicrOQJ/X58zuqLOZbyironpscSHBzG3iT2ai06UsSa/mMkZ\n3VttnCvA18bjV/anoOQU18xdS5dOfjzVwMK0Tv6+5KRF8/G275vc7Gl30Ulmf7CVyBB/np86kHV/\nyOHPl6W36EtZRMhKjmTu9CGsnHUBN422ZzG+YWSCwwkGI5MiKDpRVtvVDLDgq4MUnyzjFjc+VdQY\nlRzJxv0ltV1oWw4c49bXN5ESE8rc6UOcnvDSpZMfr944zLo23FljO3cGix5A3WkVBVZZg8cYYyqB\nHwGnP3EiMkNENorIxqIi12X1jAoN4Nrh9tk5XYL8Wvz4rzzP1+bDRX1jWL79yBn93AeO/sSMVzfR\nvWsgL103uNEZWzU6+fuSlRTBijpTaNc6sb6isTrdODKRTftL2LS/flfOoWOneHDRNqqqjVML8Vxp\nWGI4U4fFU1Fl+J8pA4gIaTiL8WUDunO0tJwvmpgO+uiSPIL8bPzj+qFcNqC7y2f/xYd34r6L09h4\n//h6GQIaUrPeoqbO1dWGv3++h349OjdrAsG5GpkSSXllNRv3lbC76CS/eXkDESH+vHLDUDoHntu4\naICvjWd/mcFvnezqbYl2PcBtjJlrjBlijBkSFeWa/u0at4ztRZCfjWuGxnfIqa3eaEK/WErLq2q7\nAH48VcENL2+gstow/9dDCXNyqmR2n2j2/fATe6ykgOc6XlHXlCHxdAny4++f/7xIr/hkGQ8vzmPc\nU5+xLK+QO7OTSY0JbeIs7vHI5H4snzm2ybGjsb2jCA30ZXEjXVErdhayYmcRd+Y4TpvfWuLDOxEX\nFlR7HSzdfoQ9xaXMGJPUKk9vwxLC8bMJH24uYPq89Qjw2m+G12YRPlci0ir1btmcxKYdBOomUYqz\nyho6pkBEfIEuQJvItBXTOZDPZo0jzMEsHtV+jEyKJDTQl4+3fc+Y1CjueHMz+4pLefXGYbUZRZ1h\nn0L7Dct3FJIYGXzO4xV1BQf4Mm1ET+Z8ls/XBT+yNO975q3ey6mKKqYMjuc/x6e4bWaOI742H4f/\nXQJ8bUxIj+Xf277n0cv7nXFjVVFVzaNL8kiMDObXWa6fitoSI5Mi+XjbYaqqDXM/30NcWBCXNJKc\n0dWCA3wZGB/Gh18dJNjfxtszMkmIbPmsP3dz55PFBiBFRBJFxB+4Blh01jGLgOut11cBy00bWhIa\n0znQYbeEaj/8fX0YnxbD0rwjPLBwG6t2FfPYFefXTqd0Vnx4J1KiQ1i+o7DZ4xV1Tc/qiZ+PD5Ne\nWM3zy/MZ1yeapfeM5Ymr+nssUJyLSQO6c6Ksst7kgdfW7md3USn3/yKtzX2OspIjOH66klfX7mPT\n/hJuGpXYqlkULkqPwd/mw9zpQ5qcUNGWuO3JwhhTKSJ3AP8GbMB8Y8w3IvIwsNEYswiYB7wmIvnA\nUewBBQAR2Qd0BvxF5HLgImNMnrvqq7zDhPRYFnx1kLfWH+CWsb3OKYNwXdlp0cxbtZelVs6plgSL\n6NBA7hqfwtaCY9yZndLuZt6NTIogPNifxbmHalfuHy0t59ll3zI6JdLpXFatqWY9zOMf7aBrJ79m\nXwfNdeOoxNouyPbCnd1QGGM+Aj46q+yBOq9PA1Ma+dsEd9ZNeaexqVFEhgQwNCGM30/o0+zzZPeO\n5qWVe3jxs/xmj1fU1dzcV22Br82HS86P5f1NBZSWVRIc4MvTS3dSWl7FA5f2bZPZCqJDA0mNCeHb\nIye5dUQvOvm79auwHhFpV4EC2vkAt1LnKsjfxspZ43jx2kEOp1g2ZXDPMDoH+lJ8srzZ4xUdyaT+\n3TldUc2y7UfYfvg4b677jmkjepLigYF5Z41NjSLIz8b0rARPV6VdaN1wqlQb0NJcU2C/mx6TGsWS\nrYfJbEEXVEcxNCGc2M6BLM49RGlZFZ2D/Lh7fP0d49qSey7szfTMhNpkiqpp+mShVDP94vxu2HyE\nUSmunbbdHvn4CJf278ay7YWs3fMDMy9MdZgPzNOC/G1OZxdQGiyUaraJ/WL58r4cEtvBtMfWULN4\ntXdMKFOHadaDjka7oZRqJhFpMwvN2oL+cV24MzuZCemxHW4zL6XBQinlIiLCzIt6e7oayk00/Cul\nlHJIg4VSSimHNFgopZRySIOFUkophzRYKKWUckiDhVJKKYc0WCillHJIg4VSSimHpA3tNdQiIlIE\n7G/BKSKBYhdVpz3RdnsXbbd3cabdPY0xDhOcdZhg0VIistEYM8TT9Wht2m7vou32Lq5st3ZDKaWU\nckiDhVJKKYc0WPxsrqcr4CHabu+i7fYuLmu3jlkopZRySJ8slFJKOaTBQimllENeHyxEZKKI7BSR\nfBGZ7en6uIuIzBeRQhHZVqcsXESWisgu62eYJ+voDiISLyIrRCRPRL4Rkbus8g7ddhEJFJH1IpJr\ntfshqzxRRNZZ1/s7ItK2N8puJhGxichXIrLEeu8t7d4nIl+LyBYR2WiVueRa9+pgISI2YA5wMdAX\nmCoifT1bK7d5GZh4Vtls4FNjTArwqfW+o6kEZhpj+gIjgNut/8cdve1lQLYxZgCQAUwUkRHAE8Az\nxphkoAS40YN1dKe7gO113ntLuwEuMMZk1Flf4ZJr3auDBTAMyDfG7DHGlANvA5M9XCe3MMZ8Dhw9\nq3gy8Ir1+hXg8latVCswxhw2xmy2Xp/A/gXSgw7edmN30nrrZ/0zQDbwvlXe4doNICJxwC+Af1jv\nBS9odxNccq17e7DoARyo877AKvMWMcaYw9br74EYT1bG3UQkARgIrMML2m51xWwBCoGlwG7gmDGm\n0jqko17vzwK/A6qt9xF4R7vBfkPwiYhsEpEZVplLrnVfV9ROtX/GGCMiHXYetYiEAB8Adxtjjttv\nNu06atuNMVVAhoh0BRYAfTxcJbcTkUuBQmPMJhEZ5+n6eMAoY8xBEYkGlorIjrq/bMm17u1PFgeB\n+Drv46wyb3FERLoBWD8LPVwftxARP+yB4g1jzIdWsVe0HcAYcwxYAWQCXUWk5iaxI17vI4HLRGQf\n9m7lbOA5On67ATDGHLR+FmK/QRiGi651bw8WG4AUa6aEP3ANsMjDdWpNi4DrrdfXAws9WBe3sPqr\n5wHbjTFP1/lVh267iERZTxSISBBwIfbxmhXAVdZhHa7dxpj7jDFxxpgE7J/n5caYa+ng7QYQkWAR\nCa15DVwEbMNF17rXr+AWkUuw93HagPnGmL94uEpuISJvAeOwpyw+AjwI/At4FzgPe3r3q40xZw+C\nt2siMgpYBXzNz33Yf8A+btFh2y4i/bEPZtqw3xS+a4x5WER6Yb/jDge+Aq4zxpR5rqbuY3VD3WuM\nudQb2m21cYH11hd40xjzFxGJwAXXutcHC6WUUo55ezeUUkopJ2iwUEop5ZAGC6WUUg5psFBKKeWQ\nBgullFIOabBQqg0QkXE1GVKVaos0WCillHJIg4VS50BErrP2idgiIi9ZyfpOisgz1r4Rn4pIlHVs\nhoh8KSJbRWRBzT4CIpIsIsusvSY2i0iSdfoQEXlfRHaIyBtSN4GVUh6mwUIpJ4lIGvBLYKQxJgOo\nAq4FgoGNxph0YCX21fEArwK/N8b0x76CvKb8DWCOtddEFlCTEXQgcDf2vVV6Yc9zpFSboFlnlXJe\nDjAY2GDd9AdhT8pWDbxjHfM68KGIdAG6GmNWWuWvAO9ZuXt6GGMWABhjTgNY51tvjCmw3m8BEoDV\n7m+WUo5psFDKeQK8Yoy574xCkT+ddVxzc+jUzVVUhX4+VRui3VBKOe9T4Cprr4CavY17Yv8c1WQ0\n/RWw2hjzI1AiIqOt8mnASmu3vgIRudw6R4CIdGrVVijVDHrnopSTjDF5InI/9p3IfIAK4HagFBhm\n/a4Q+7gG2NNB/80KBnuAG6zyacBLIvKwdY4prdgMpZpFs84q1UIictIYE+LpeijlTtoNpZRSyiF9\nslBKKeWQPlkopZRySIOFUkophzRYKKWUckiDhVJKKYc0WCillHLo/wHhFLQh0y/2nAAAAABJRU5E\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBNlMFqHKzvT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img = test_x[112].reshape(-1,32,32,1)\n",
        "belongs_to_class = test_y[112]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xtl0LNcwK9Mi",
        "colab_type": "code",
        "outputId": "9636720f-2e3c-4ae8-cef4-b909aa5827d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(img.shape)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 32, 32, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wfkrVqc61jYz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = model.predict(img)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nFRTZVJLSXUq",
        "colab_type": "code",
        "outputId": "719bc7a4-2fac-4cfa-f4ac-4b2cda221f4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "print(x)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[9.94570366e-14 9.48039354e-22 2.44198580e-21 9.06467554e-20\n",
            "  2.05781139e-12 2.22327529e-21 9.56724210e-18 3.08198156e-14\n",
            "  9.32907159e-22 1.19536131e-21 1.46228126e-14 1.00000000e+00\n",
            "  5.80178093e-12 6.76623254e-13 2.46713694e-20 1.76798328e-15\n",
            "  6.88384310e-21 8.44535325e-17 7.47783996e-22 8.74579716e-19\n",
            "  4.10854148e-20 4.86669439e-18 2.46994321e-20 6.20556100e-25\n",
            "  3.05390920e-19 1.07773472e-19 2.16979732e-18 1.34519536e-15\n",
            "  1.59264691e-14 1.39546872e-20 1.14725319e-16 8.73974680e-22\n",
            "  2.43756304e-15 1.04775014e-17 1.87628601e-17 4.26360155e-17]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nK7scl2HR_ow",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "name = CATEGORIES[np.argmax(x)]\n",
        "bc = CATEGORIES[np.argmax(belongs_to_class)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8r1OJfbnVpJt",
        "colab_type": "code",
        "outputId": "a5ba88d0-2c72-4b95-bcc6-7e628a2ca62b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "%matplotlib inline\n",
        "imgplot = plt.imshow(test_x[112])\n",
        "acc=np.amax(history.history['acc'])\n",
        "acc= acc*100\n",
        "acc = np.trunc(acc)\n",
        "\n",
        "print(\"predicted image is of: {} with accuracy {}\".format(name,acc))\n",
        "print(\"Actual class label is: {}\".format(bc))\n",
        "\n"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "predicted image is of: character-12-tha with accuracy 99.0\n",
            "Actual class label is: character-12-tha\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAADkZJREFUeJzt3XGMHOV5x/Hvw2ESFFADxrEc42JC\naSuKiiEXlzaI0kQJLo0KSBUCqRFSUS5CQSpq+geiaUOV/kGqAkJqS3UEK05DAbeAcFMXQ2haFBUZ\nDjDG4KYQZIoPYxtDBFUlwMfTP3Ysna2bvfXu7KzP7/cjWTc77+y+j8b729mdd/edyEwkleeYURcg\naTQMv1Qowy8VyvBLhTL8UqEMv1Qowy8VyvBLhTL8UqGOHeTOEbEGuB0YA76TmTd32/6Uk8dy5YpF\ng3QpqYsdr33Am2/NRC/b9h3+iBgD/gb4ArATeCoiNmTmi3X3WbliEU9uWtFvl5Lmsfri13redpC3\n/auBlzPzlcx8H7gXuHSAx5PUokHCvxyY/TKzs1onaQEY+gm/iJiIiKmImNq7b2bY3Unq0SDhnwZm\nf4A/tVp3kMyczMzxzBxfsnhsgO4kNWmQ8D8FnBkRp0fEccCVwIZmypI0bH2f7c/M/RFxHbCJzlDf\n2sx8obHKJA3VQOP8mbkR2NhQLZJa5Df8pEIZfqlQhl8qlOGXCmX4pUIZfqlQhl8qlOGXCmX4pUIZ\nfqlQhl8q1EDf7VfHxZ9cNeoSVOPY0+aeNu5fnvjnlis58njklwpl+KVCGX6pUIZfKpThlwpl+KVC\nOdSno9r+V3u/gk1pPPJLhTL8UqEMv1Qowy8VyvBLhTL8UqEGGuqLiB3Au8AMsD8zx5soStLwNTHO\n/1uZ+WYDjyOpRb7tlwo1aPgTeCQino6IiSYKktSOQd/2X5CZ0xHxCeDRiPivzHx89gbVi8IEwM8v\n99vE0pFioCN/Zk5Xf/cADwKr59hmMjPHM3N8yeKxQbqT1KC+wx8RH4uIEw8sA18EtjVVmKThGuR9\n+FLgwYg48Dj/kJkPN1LVArPp9S2jLmFe29//v9q261f+Rl+PeeuOJ2rbfuW44/t6TLWn7/Bn5ivA\nOQ3WIqlFDvVJhTL8UqEMv1Qowy8VyvBLhfIrd0cZrxuoXnnklwpl+KVCGX6pUIZfKpThlwrl2f4R\nWuhn5v/o9PofBG2afrbFStQPj/xSoQy/VCjDLxXK8EuFMvxSoQy/VCiH+hpw8amfrm/8cKa9QtqW\nWdtUN4z52p/WDw++eO3fDlySeueRXyqU4ZcKZfilQhl+qVCGXyqU4ZcKNe9QX0SsBb4E7MnMs6t1\nJwP3ASuBHcAVmfn28Mo8MtT/Cu/IGc5r+tJhTf/ycMW3/rO+r2/V97UQLom20PRy5P8usOaQdTcA\nj2XmmcBj1W1JC8i84c/Mx4G3Dll9KbCuWl4HXNZwXZKGrN/P/Eszc1e1/AadK/ZKWkAGPuGXmQnU\nfs8zIiYiYioipvbuO3I+G0ul6zf8uyNiGUD1d0/dhpk5mZnjmTm+ZPFYn91Jalq/4d8AXF0tXw08\n1Ew5ktrSy1DfPcBFwCkRsRP4JnAzsD4irgFeBa4YZpFtanNSzbGzfrG2beMP17dWRzfdhtja3Ff9\n9uUQYb15w5+ZV9U0fb7hWiS1yG/4SYUy/FKhDL9UKMMvFcrwS4UqcgLPmfywtb66DzUt7GGofobR\n2r4+YV1/MX527X0e3vD9YZVzRPHILxXK8EuFMvxSoQy/VCjDLxXK8EuFKnKo75Ll5zX6eBunn+nS\n6uvrbN2GB6+b/rXatpc+816jdeTUtkYfbyHymSkVyvBLhTL8UqEMv1Qowy8Vqsiz/U0bC19Dm/DX\nyzfXN75e3/RLa6+tbVv5jScOu45uPz46muYE9FkrFcrwS4Uy/FKhDL9UKMMvFcrwS4Xq5XJda4Ev\nAXsy8+xq3U3AV4C91WY3ZubGYRUpdfOTP7ijtu3ib7Q7Z+BC0suR/7vAmjnW35aZq6p/Bl9aYOYN\nf2Y+DrzVQi2SWjTIZ/7rImJrRKyNiJMaq0hSK/oN/x3AGcAqYBdwS92GETEREVMRMbV330yf3Ulq\nWl/hz8zdmTmTmR8CdwKru2w7mZnjmTm+ZPFYv3VKalhf4Y+IZbNuXg44J5K0wPQy1HcPcBFwSkTs\nBL4JXBQRq4AEdgBfHWKNR4w41h9B6ugx77M5M6+aY/VdQ6hFUov8hp9UKMMvFcrwS4Uy/FKhDL9U\nKMeuDsMHF55T0zLVah1SEzzyS4Uy/FKhDL9UKMMvFcrwS4Uy/FKhHOo7DMf+29OjLkFqjEd+qVCG\nXyqU4ZcKZfilQhl+qVCGXyqU4ZcKZfilQhl+qVCGXyqU4ZcKZfilQvVyua4VwPeApXQuzzWZmbdH\nxMnAfcBKOpfsuiIz3x5eqUeuNafVXqeUh199ssVKpN71cuTfD3w9M88Czge+FhFnATcAj2XmmcBj\n1W1JC8S84c/MXZn5TLX8LrAdWA5cCqyrNlsHXDasIiU177A+80fESuBcYDOwNDN3VU1v0PlYIGmB\n6Dn8EXECcD9wfWa+M7stM5PO+YC57jcREVMRMbV338xAxUpqTk/hj4hFdIJ/d2Y+UK3eHRHLqvZl\nwJ657puZk5k5npnjSxaPNVGzpAbMG/6ICOAuYHtm3jqraQNwdbV8NfBQ8+VJGpZe5vD7LPBl4PmI\n2FKtuxG4GVgfEdcArwJXDKfE5m16fUtt28WfXHXYj5cfvD9IORpQP/9n3azf+USX1uMb7WuU5g1/\nZv4YiJrmzzdbjqS2+A0/qVCGXyqU4ZcKZfilQhl+qVBerusQ713ymdq2j2x86rAfr9sw1NjST9S2\nbXz2kcPua6H7nU+vqW3bv+uN1ur4uWOOnuG8bjzyS4Uy/FKhDL9UKMMvFcrwS4Uy/FKhHOo7xL9/\n587atqZ/PTaze84pEObvK+p+ZwWbpp8dpKTG9Lev2hvO6/bLzlJ45JcKZfilQhl+qVCGXyqU4ZcK\n5dn+w1B3hvg3JyZq7/PRHwzhcl055yzpQPMjEgudZ/XreeSXCmX4pUIZfqlQhl8qlOGXCmX4pULN\nO9QXESuA79G5BHcCk5l5e0TcBHwF2FttemNmbhxWoUey/5ic7Ot+DssdLI6tfzo+/D9TLVZShl7G\n+fcDX8/MZyLiRODpiHi0arstM/9qeOVJGpZertW3C9hVLb8bEduB5cMuTNJwHdZn/ohYCZwLbK5W\nXRcRWyNibUSc1HBtkoao5/BHxAnA/cD1mfkOcAdwBrCKzjuDW2ruNxERUxExtXffTAMlS2pCT+GP\niEV0gn93Zj4AkJm7M3MmMz8E7gRWz3XfzJzMzPHMHF+yeKypuiUNaN7wR0QAdwHbM/PWWeuXzdrs\ncmBb8+VJGpZezvZ/Fvgy8HxEHPiJ1I3AVRGxis7w3w7gq0Op8Cg2jF+crfnd359zfT79Qv2duvxK\nsJtjzv7l2rZ/feTevh5T7enlbP+PgblmjCxyTF86WvgNP6lQhl8qlOGXCmX4pUIZfqlQTuB5lHl4\nw/db7M3JMRcyj/xSoQy/VCjDLxXK8EuFMvxSoQy/VCjDLxXK8EuFMvxSoQy/VCjDLxXK8EuFMvxS\noQy/VCjDLxXK8EuFMvxSoQy/VCjDLxWql2v1fTQinoyI5yLihYj482r96RGxOSJejoj7IuK44Zcr\nqSm9HPnfAz6XmefQuRz3mog4H/g2cFtm/gLwNnDN8MqU1LR5w58d/1vdXFT9S+BzwD9V69cBlw2l\nQklD0dNn/ogYq67Quwd4FPgp8LPM3F9tshNYPpwSJQ1DT+HPzJnMXAWcCqwG6q/NfIiImIiIqYiY\n2rtvps8yJTXtsM72Z+bPgB8Bvw58PCIOXPTjVGC65j6TmTmemeNLFo8NVKyk5vRytn9JRHy8Wj4e\n+AKwnc6LwO9Vm10NPDSsIiU1r5fLdS0D1kXEGJ0Xi/WZ+YOIeBG4NyL+AngWuGuIdUpq2Lzhz8yt\nwLlzrH+Fzud/SQuQ3/CTCmX4pUIZfqlQhl8qlOGXChWZ2V5nEXuBV6ubpwBvttZ5Pes4mHUcbKHV\ncVpmLunlAVsN/0EdR0xl5vhIOrcO67AO3/ZLpTL8UqFGGf7JEfY9m3UczDoOdtTWMbLP/JJGy7f9\nUqFGEv6IWBMRP6km/7xhFDVUdeyIiOcjYktETLXY79qI2BMR22atOzkiHo2Il6q/J42ojpsiYrra\nJ1si4pIW6lgRET+KiBerSWL/sFrf6j7pUker+6S1SXMzs9V/wBidacA+BRwHPAec1XYdVS07gFNG\n0O+FwHnAtlnr/hK4oVq+Afj2iOq4CfjjlvfHMuC8avlE4L+Bs9reJ13qaHWfAAGcUC0vAjYD5wPr\ngSur9X8HXDtIP6M48q8GXs7MVzLzfeBe4NIR1DEymfk48NYhqy+lMxEqtDQhak0drcvMXZn5TLX8\nLp3JYpbT8j7pUkersmPok+aOIvzLgddm3R7l5J8JPBIRT0fExIhqOGBpZu6qlt8Alo6wlusiYmv1\nsWDoHz9mi4iVdOaP2MwI98khdUDL+6SNSXNLP+F3QWaeB/w28LWIuHDUBUHnlZ/OC9Mo3AGcQeca\nDbuAW9rqOCJOAO4Hrs/Md2a3tblP5qij9X2SA0ya26tRhH8aWDHrdu3kn8OWmdPV3z3Ag4x2ZqLd\nEbEMoPq7ZxRFZObu6on3IXAnLe2TiFhEJ3B3Z+YD1erW98lcdYxqn1R9H/akub0aRfifAs6szlwe\nB1wJbGi7iIj4WESceGAZ+CKwrfu9hmoDnYlQYYQToh4IW+VyWtgnERF05oDcnpm3zmpqdZ/U1dH2\nPmlt0ty2zmAecjbzEjpnUn8K/MmIavgUnZGG54AX2qwDuIfO28cP6Hx2uwZYDDwGvAT8EDh5RHX8\nPfA8sJVO+Ja1UMcFdN7SbwW2VP8uaXufdKmj1X0C/CqdSXG30nmh+bNZz9kngZeBfwQ+Mkg/fsNP\nKlTpJ/ykYhl+qVCGXyqU4ZcKZfilQhl+qVCGXyqU4ZcK9f+/mbg95Q3/4wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8S6af1hIWEPL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_predict = model.predict(test_image_array)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6HADb0G2UkFR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "yPred = model.predict_classes(test_image_array)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "651yPSxWVs33",
        "colab_type": "code",
        "outputId": "620f9acd-4e29-45c1-9934-4640f14131c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "yPred[1]"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RCnzGT4V2OC",
        "colab_type": "code",
        "outputId": "5d183e3f-86e5-48fa-ddba-46469a34819e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "test_y[1]"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0.], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jgGJfEB3WB5Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "newtest_y=np.argmax(test_y, axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b5N-CWJRUxdS",
        "colab_type": "code",
        "outputId": "93c24e20-3e37-469f-f2af-4d2687b6a58d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "print(\"Confusion matrix \\n=======================\")\n",
        "print(confusion_matrix(y_true=newtest_y, y_pred=yPred))\n"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix \n",
            "=======================\n",
            "[[10  0  0 ...  0  0  0]\n",
            " [ 0 10  0 ...  0  0  0]\n",
            " [ 0  0 10 ...  0  0  0]\n",
            " ...\n",
            " [ 0  0  0 ...  9  0  0]\n",
            " [ 0  0  0 ...  0 10  0]\n",
            " [ 0  0  0 ...  0  0 10]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvMNB9Kpqeic",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#datagen.fit(x_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xw1lLQ9QxMt6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cm = confusion_matrix(newtest_y,yPred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IBBFZwOHSfev",
        "colab_type": "code",
        "outputId": "9bd41454-c1dc-40bf-aec3-b03f54153eb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        }
      },
      "source": [
        "plt.figure(figsize= (20, 10))\n",
        "\n",
        "sns.heatmap(cm, annot=True)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f99d745c6d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABAIAAAJDCAYAAACR9LZGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKsVHdCgk0gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FCvckSGgSmsG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}