MNIST_CNN_LeNet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MNIST_CNN-LeNet.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPcVrQSy7CieOm0VOezNjBQ",
      "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/prateekdesai04/MNIST-Classification/blob/main/MNIST_CNN_LeNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zoo6QK7xmp5H"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import keras\n",
        "from keras.datasets import mnist\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.optimizers import Adam\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from keras.models import Model\n",
        "from keras.layers import Flatten \n",
        "from keras.layers.convolutional import Convolution2D\n",
        "from keras.layers.convolutional import MaxPooling2D\n",
        "from keras.layers import Dropout\n",
        "import random\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OlMY6Baxm1_z"
      },
      "source": [
        "np.random.seed(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oMB1o58Sm7gL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "f67c8132-78fb-44cc-f7eb-965d03bebee2"
      },
      "source": [
        "(X_train, y_train), (X_test, y_test)= mnist.load_data()\n",
        " \n",
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "assert(X_train.shape[0] == y_train.shape[0]), \"The number of images is not equal to the number of labels.\"\n",
        "assert(X_train.shape[1:] == (28,28)), \"The dimensions of the images are not 28 x 28.\"\n",
        "assert(X_test.shape[0] == y_test.shape[0]), \"The number of images is not equal to the number of labels.\"\n",
        "assert(X_test.shape[1:] == (28,28)), \"The dimensions of the images are not 28 x 28.\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "(60000, 28, 28)\n",
            "(10000, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJUAl7CEnAj7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "543d2eae-de52-4406-b5dc-baacce2c3bfa"
      },
      "source": [
        "num_of_samples=[]\n",
        " \n",
        "cols = 5\n",
        "num_classes = 10\n",
        " \n",
        "fig, axs = plt.subplots(nrows=num_classes, ncols=cols, figsize=(5,10))\n",
        "fig.tight_layout()\n",
        " \n",
        "for i in range(cols):\n",
        "    for j in range(num_classes):\n",
        "      x_selected = X_train[y_train == j]\n",
        "      axs[j][i].imshow(x_selected[random.randint(0,(len(x_selected) - 1)), :, :], cmap=plt.get_cmap('gray'))\n",
        "      axs[j][i].axis(\"off\")\n",
        "      if i == 2:\n",
        "        axs[j][i].set_title(str(j))\n",
        "        num_of_samples.append(len(x_selected))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x720 with 50 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09rjXhOMnMYP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "55c95763-b3f7-4447-b081-270b97a6e647"
      },
      "source": [
        "print(num_of_samples)\n",
        "plt.figure(figsize=(12, 4))\n",
        "plt.bar(range(0, num_classes), num_of_samples)\n",
        "plt.title(\"Distribution of the train dataset\")\n",
        "plt.xlabel(\"Class number\")\n",
        "plt.ylabel(\"Number of images\")\n",
        "plt.show()\n",
        " \n",
        "\n",
        "\n",
        "X_train = X_train.reshape(60000, 28, 28, 1) # not passing each pixel like previous neural network\n",
        "X_test = X_test.reshape(10000, 28, 28, 1)\n",
        "\n",
        "\n",
        "y_train = to_categorical(y_train, 10)\n",
        "y_test = to_categorical(y_test, 10)\n",
        " \n",
        "X_train = X_train/255\n",
        "X_test = X_test/255"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[5923, 6742, 5958, 6131, 5842, 5421, 5918, 6265, 5851, 5949]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kSdKsNUzQfrA9Hw6cWVWfBbad7KQk2w+MhG8PHARcBSwHxlYOWQp8qm0vB45pq488C7irTTG5ADgoyU7tS5AHtTZJkiRp1lowxDFrkvwD8CLg7UkexXABfTfgX5KMXecjVfW5JJcA5yY5DrgROKodfz5wGLAKuBc4FqCq1iU5DbikHXdqVa0b6qeTJEmSRmSYoH0U3Soff1NVd7bpHv9zspOq6nrgGeO0fx84cJz2Ak6YoK+zgbOHqFWSJEmaFSYdma6qe+nmUT+nNT0AXNdnUZIkSdJcN8yqI6cAbwBOak3bAB/qsyhJkiRprhtmrvVvAEcA9wBU1c3AY/ssSpIkSZrrhgnaPxxc77qtICJJkiRpI4YJ2ue2VUcWJvlD4N+Af+y3LEmSJGlum3TVkar6myQvAu4Gnga8uapW9F6ZJEmSNIcNs7wfLVgbriVJkqQhTRq0k6ynzc8ecBewEnh9Wy9bkiRJ0oBhRrT/FlgNfAQIcDTwFOAyupvIPK+v4iRJkqS5apgvQx5RVf9QVeur6u6qOhM4uKo+BuzUc32SJEnSnDRM0L43yVFJtmqPo4D72r4Np5RIkiRJYrig/XLgFXS3Yb+tbf9ekkcDr+6xNkmSJGnOGmZ5v+uBl0yw+0vTW44kSZI0Pwyz6sh2wHHAzwPbjbVX1e/3WJckSZI0pw0zdeSDwE8DBwNfBPYA1vdZlCRJkjTXDRO0n1pVbwLuqaplwOHAAf2WJUmSJM1twwTtH7XnO5M8HdgR+Kn+SpIkSZLmvmFuWHNmkp2ANwHLgR2AN/dalSRJkjTHDbPqyPva5heBJ/dbjiRJkjQ/DLPqyELgGGDx4PFV9cf9lSVJkiTNbcNMHTkf+ApwJfBQv+VIkiRJ88MwQXu7qvrT3iuRJEmS5pGh1tFO8odJdk+y89hj2Ask2TrJ5Uk+017vleSrSVYl+ViSbVv7o9rrVW3/4oE+Tmrt30py8BR/RkmSJGnGDRO0fwj8NfBl4NL2WDmFa7wWuHbg9duBd1XVU4E76O46SXu+o7W/qx1Hkn2Ao+nuTHkI8HdJtp7C9SVJkqQZN0zQfj3dTWsWV9Ve7THU6iNJ9qC7wc372usALwDOa4csA17ato9sr2n7D2zHHwmcU1X3V9V3gFXA/sNcX5IkSRqVYYL2KuDeTez/b4E/4ydfonw8cGdVPdBerwYWte1FwE0Abf9d7fgft49zzo8lOT7JyiQr165du4nlSpIkSdNjmC9D3gNckeQi4P6xxsmW90vyYuD2qro0yfM2q8ohVNWZwJkAS5Ysqb6vJ0mSJG3MMEH7k+0xVc8GjkhyGLAd8Djg3cDCJAvaqPUewJp2/BpgT2B1kgV0t3r//kD7mMFzJEmSpFlpmDtDLpvsmAnOOwk4CaCNaP+Pqnp5ko8DvwWcAywFPtVOWd5ef7nt/0JVVZLlwEeSvBN4ArA38LVNqUmSJEmaKRMG7STnVtVRSa4EHjEVo6p+cROv+QbgnCRvBS4HzmrtZ9EtJbgKWEe30ghVdXWSc4FrgAeAE6rqwU28tiRJkjQjNjai/dr2/OLNvUhVXQxc3LavZ5xVQ6rqPuBlE5z/NuBtm1uHJEmSNFMmDNpVdUt7vnHmypEkSZLmh2GW95MkSZI0RQZtSZIkqQcTBu0kF7bnt89cOZIkSdL8sLEvQ+6e5Ffp1sI+B8jgzqq6rNfKJEmSpDlsY0H7zcCb6G4Q884N9hXwgr6KkiRJkua6ja06ch5wXpI3VdVpM1iTJEmSNOcNc2fI05IcATy3NV1cVZ/ptyxJkiRpbpt01ZEkf0l385pr2uO1Sf6i78IkSZKkuWzSEW3gcGDfqnoIIMkyulunv7HPwiRJkqS5bNh1tBcObO/YRyGSJEnSfDLMiPZfApcnuYhuib/nAif2WpUkSZI0xw3zZciPJrkY+OXW9IaqurXXqiRJkqQ5bpgRbarqFmB5z7VIkiRJ88awc7QlSZIkTYFBW5IkSerBRoN2kq2TfHOmipEkSZLmi40G7ap6EPhWkifOUD2SJEnSvDDMlyF3Aq5O8jXgnrHGqjqit6okSZKkOW6YoP2m3quQJEmS5plh1tH+YpInAXtX1b8leQywdf+lSZIkSXPXpKuOJPlD4DzgH1rTIuCTQ5y3XZKvJfl6kquTvKW175Xkq0lWJflYkm1b+6Pa61Vt/+KBvk5q7d9KcvDUf0xJkiRpZg2zvN8JwLOBuwGq6jrgp4Y4737gBVX1DGBf4JAkzwLeDryrqp4K3AEc144/Drijtb+rHUeSfYCjgZ8HDgH+Lokj6pIkSZrVhgna91fVD8deJFkA1GQnVecH7eU27VHAC+hGyAGWAS9t20e217T9ByZJaz+nqu6vqu8Aq4D9h6hbkiRJGplhgvYXk7wReHSSFwEfBz49TOdtHe4rgNuBFcC3gTur6oF2yGq6qSi055sA2v67gMcPto9zjiRJkjQrDRO0TwTWAlcC/w04Hzh5mM6r6sGq2hfYg24U+mc3sc5JJTk+ycokK9euXdvXZSRJkqShDLPqyENJlgFfpZv68a2qmnTqyAZ93JnkIuBXgIVJFrRR6z2ANe2wNcCewOo2PWVH4PsD7WMGzxm8xpnAmQBLliyZUn2SJEnSdBtm1ZHD6aZ8nAG8B1iV5NAhzts1ycK2/WjgRcC1wEXAb7XDlgKfatvL22va/i+0QL8cOLqtSrIXsDfwteF+PEmSJGk0hrlhzTuA51fVKoAkTwE+C/zrJOftDixrK4RsBZxbVZ9Jcg1wTpK3ApcDZ7XjzwI+mGQVsI5upRGq6uok5wLXAA8AJ7Rbw0uSJEmz1jBBe/1YyG6uB9ZPdlJVfQN45jjt1zPOqiFVdR/wsgn6ehvwtiFqlSRJkmaFCYN2kt9smyuTnA+cSzdH+2XAJTNQmyRJkjRnbWxE+yUD27cBv9621wKP7q0iSZIkaR6YMGhX1bEzWYgkSZI0n0w6R7ut9PEaYPHg8VV1RH9lSZIkSXPbMF+G/CTdiiCfBh7qtxxJkiRpfhgmaN9XVWf0XokkSZI0jwwTtN+d5BTg88D9Y41VdVlvVUmSJElz3DBB+xeAVwAv4CdTR6q9liRJkjSOYYL2y4AnV9UP+y5GkiRJmi+2GuKYq4CFfRciSZIkzSfDjGgvBL6Z5BIePkfb5f0kSZKkCQwTtE/pvQpJkiRpnpk0aFfVF2eiEEmSJGk+GebOkOvpVhkB2BbYBrinqh7XZ2GSJEnSXDbMiPZjx7aTBDgSeFafRUmSJElz3TCrjvxYdT4JHNxTPZIkSdK8MMzUkd8ceLkVsAS4r7eKJEmSpHlgmFVHXjKw/QBwA930EUmSJEkTGGaO9rEzUYgkSZI0n0wYtJO8eSPnVVWd1kM9kiRJ0rywsRHte8Zp2x44Dng8YNCWJEmSJjBh0K6qd4xtJ3ks8FrgWOAc4B0TnSdJkiRpkuX9kuyc5K3AN+hC+X5V9Yaqun2yjpPsmeSiJNckuTrJawf6XJHkuva8U2tPkjOSrEryjST7DfS1tB1/XZKlm/UTS5IkSTNgwqCd5K+BS4D1wC9U1Z9X1R1T6PsB4PVVtQ/dDW5OSLIPcCJwYVXtDVzYXgMcCuzdHscD72117AycAhwA7A+cMhbOJUmSpNlqYyParweeAJwM3Jzk7vZYn+TuyTquqluq6rK2vR64FlhEtzTgsnbYMuClbftI4APtpjhfARYm2Z3u5jgrqmpdC/orgEOm/JNKkiRJM2hjc7SndNfIjUmyGHgm8FVgt6q6pe26FditbS8Cbho4bXVrm6h9w2scTzcSzhOf+MTpKl2SJEnaJNMWpieSZAfgE8DrquphI+FVVUBNx3Wq6syqWlJVS3bdddfp6FKSJEnaZL0G7STb0IXsD1fVP7fm29qUENrz2Bcr1wB7Dpy+R2ubqF2SJEmatXoL2kkCnAVcW1XvHNi1HBhbOWQp8KmB9mPa6iPPAu5qU0wuAA5KslP7EuRBrU2SJEmatSa9BftmeDbwCuDKJFe0tjcCpwPnJjkOuBE4qu07HzgMWAXcS7dmN1W1LslpdCugAJxaVet6rFuSJEnabL0F7ar6EpAJdh84zvEFnDBBX2cDZ09fdZIkSVK/ev8ypCRJkrQlMmhLkiRJPTBoS5IkST0waEuSJEk9MGhLkiRJPTBoS5IkST0waEuSJEk9MGhLkiRJPTBoS5IkST0waEuSJEk9MGhLkiRJPTBoS5IkST0waEuSJEk9MGhLkiRJPTBoS5IkST0waEuSJEk9MGhLkiRJPTBoS5IkST0waEuSJEk9MGhLkiRJPTBoS5IkST0waEuSJEk96C1oJzk7ye1Jrhpo2znJiiTXteedWnuSnJFkVZJvJNlv4Jyl7fjrkiztq15JkiRpOvU5ov1+4JAN2k4ELqyqvYEL22uAQ4G92+N44L3QBXPgFOAAYH/glLFwLkmSJM1mvQXtqvp3YN0GzUcCy9r2MuClA+0fqM5XgIVJdgcOBlZU1bqqugNYwSPDuyRJkjTrzPQc7d2q6pa2fSuwW9teBNw0cNzq1jZR+yMkOT7JyiQr165dO71VS5IkSVM0si9DVlUBNY39nVlVS6pqya677jpd3UqSJEmbZKaD9m1tSgjt+fbWvgbYc+C4PVrbRO2SJEnSrDbTQXs5MLZyyFLgUwPtx7TVR54F3NWmmFwAHJRkp/YlyINamyRJkjSrLeir4yQfBZ4H7JJkNd3qIacD5yY5DrgROKodfj5wGLAKuBc4FqCq1iU5DbikHXdqVW34BUtJkiRp1uktaFfV70yw68Bxji3ghAn6ORs4expLkyRJknrnnSElSZKkHhi0JUmSpB4YtCVJkqQeGLQlSZKkHhi0JUmSpB4YtCVJkqQeGLQlSZKkHhi0JUmSpB4YtCVJkqQeGLQlSZKkHhi0JUmSpB4YtCVJkqQeGLQlSZKkHhi0JUmSpB4YtCVJkqQeGLQlSZKkHhi0JUmSpB4YtCVJkqQeGLQlSZKkHhi0JUmSpB4YtCVJkqQeGLQlSZKkHsyZoJ3kkCTfSrIqyYmjrkeSJEnamDkRtJNsDfwf4FBgH+B3kuwz2qokSZKkic2JoA3sD6yqquur6ofAOcCRI65JkiRJmlCqatQ1TCrJbwGHVNUftNevAA6oqlcPHHM8cHx7+TTgWzNe6MzZBfjeqIvQjPHz3rL4eW9Z/Ly3PH7m88+TqmrX8XYsmOlK+lJVZwJnjrqOmZBkZVUtGXUdmhl+3lsWP+8ti5/3lsfPfMsyV6aOrAH2HHi9R2uTJEmSZqW5ErQvAfZOsleSbYGjgeUjrkmSJEma0JyYOlJVDyR5NXABsDVwdlVdPeKyRmmLmCKjH/Pz3rL4eW9Z/Ly3PH7mW5A58WVISZIkaa6ZK1NHJEmSpDnFoC1JkiT1wKA9h3gb+i1Hkj2TXJTkmiRXJ3ntqGtS/5JsneTyJJ8ZdS3qX5KFSc5L8s0k1yb5lVHXpP4k+ZP25/lVST6aZLtR16T+GbTnCG9Dv8V5AHh9Ve0DPAs4wc97i/Ba4NpRF6EZ827gc1X1s8Az8LOft5IsAv4YWFJVT6db2OHo0ValmWDQnju8Df0WpKpuqarL2vZ6uv8BLxptVepTkj2Aw4H3jboW9S/JjsBzgbMAquqHVXXnaKtSzxYAj06yAHgMcPOI69EMMGjPHYuAmwZer8bgtUVIshh4JvDV0Vainv0t8GfAQ6MuRDNiL2At8E9tutD7kmw/6qLUj6paA/wN8F3gFuCuqvr8aKvSTDBoS7NYkh2ATwCvq6q7R12P+pHkxcDtVXXpqGvRjFkA7Ae8t6qeCdwD+N2beSrJTnT/Cr0X8ARg+yS/N9qqNBMM2nOHt6HfwiTZhi5kf7iq/nnU9ahXzwaOSHID3bSwFyT50GhLUs9WA6urauxfqs6jC96an14IfKeq1lbVj4B/Bn51xDVpBhi05w5vQ78FSRK6uZvXVtU7R12P+lVVJ1XVHlW1mO6/7S9UlaNd81hV3QrclORprelA4JoRlqR+fRd4VpLHtD/fD8Qvv24R5sQt2OVt6LdAzwZeAVyZ5IrW9saqOn+ENUmaXq8BPtwGT64Hjh1xPepJVX01yXnAZXSrSl2Ot2LfIngLdkmSJKkHTh2RJGFIwh8AAAMxSURBVEmSemDQliRJknpg0JYkSZJ6YNCWJEmSemDQliRJknpg0JakWSDJTyc5J8m3k1ya5PwkP5NkcZKrRl3fVCR5XpLPjLoOSRo119GWpBFrN7D4F2BZVR3d2p4B7AbcNMraRiHJ1lX14KjrkKTN5Yi2JI3e84EfVdXfjzVU1der6j8GD2qj2/+R5LL2+NXWvnuSf09yRZKrkvxakq2TvL+9vjLJn2x40bb/jCT/N8n1SX6rtT9sRDrJe5K8sm3fkOQv27VWJtkvyQVtJP5VA90/Lslnk3wryd8n2aqdf1CSL7f6P55kh4F+357kMuBl0/XGStIoOaItSaP3dODSIY67HXhRVd2XZG/go8AS4HeBC6rqbUm2Bh4D7AssqqqnAyRZOEGfuwPPAX4WWA6cN0Qd362qfZO8C3g/3Z1MtwOuAsb+srA/sA9wI/A54DeTXAycDLywqu5J8gbgT4FT2znfr6r9hri+JM0JBm1Jmju2Ad6TZF/gQeBnWvslwNlJtgE+WVVXJLkeeHKS/w18Fvj8BH1+sqoeAq5JstuQdSxvz1cCO1TVemB9kvsHAv3Xqup6gCQfpQvz99GF7//sZsuwLfDlgX4/NuT1JWlOcOqIJI3e1cAvDXHcnwC3Ac+gG8neFqCq/h14LrAGeH+SY6rqjnbcxcCrgPdN0Of9A9tpzw/w8P8/bDfBOQ9tcP5D/GQApzY4p1r/K6pq3/bYp6qOGzjmnglqlKQ5yaAtSaP3BeBRSY4fa0jyi0l+bYPjdgRuaSPQrwC2bsc+Cbitqv6RLlDvl2QXYKuq+gTddI2pTMm4EdgnyaPaCPWBm/Az7Z9krzY3+7eBLwFfAZ6d5Kmt7u2T/MzGOpGkucypI5I0YlVVSX4D+Ns2b/k+4AbgdRsc+nfAJ5IcQzfveWwE+HnA/0zyI+AHwDHAIuCfxr6ECJw0hXpuSnIu3Zzr7wCXb8KPdQnwHuCpwEXAv1TVQ+1LlR9N8qh23MnA/9uE/iVp1kvVhv+6J0mSJGlzOXVEkiRJ6oFBW5IkSeqBQVuSJEnqgUFbkiRJ6oFBW5IkSeqBQVuSJEnqgUFbkiRJ6sH/B6Q9iFeLyVJIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ALhgXg7Aq49j"
      },
      "source": [
        "#define LeNet model function\n",
        "def leNet_model():\n",
        "  model = Sequential()\n",
        "  model.add(Convolution2D(30, (5, 5), input_shape = (28, 28, 1), activation = \"relu\")) # 5,5 is the filter tuple size and is a hyperparameter stride def = 1 and padding def = valid\n",
        "  model.add(MaxPooling2D(pool_size = (2, 2)))\n",
        "  model.add(Convolution2D(15, (3, 3), activation = \"relu\")) # because more dense input - less filters but higher parameters \n",
        "  model.add(MaxPooling2D(pool_size = (2, 2)))\n",
        "  model.add(Flatten())\n",
        "  model.add(Dense(500, activation = \"relu\"))\n",
        "  model.add(Dropout(0.5)) # 50% nodes are dropped\n",
        "  model.add(Dense(num_classes, activation = \"softmax\"))\n",
        "  model.compile(Adam(lr = 0.01), loss = \"categorical_crossentropy\", metrics = [\"accuracy\"])\n",
        "  return model\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2mQlhKYD36GE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "f25e69e0-917e-4892-ed0c-7ceb257b0776"
      },
      "source": [
        "model = leNet_model()\n",
        "print(model.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 24, 24, 30)        780       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 12, 12, 30)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 10, 10, 15)        4065      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 15)          0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 375)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 500)               188000    \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 500)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                5010      \n",
            "=================================================================\n",
            "Total params: 197,855\n",
            "Trainable params: 197,855\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nuDx3zjP4Ub6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "outputId": "56f3f1b7-bbe0-48aa-908c-2eaf245fa28e"
      },
      "source": [
        "history = model.fit(x = X_train, y = y_train, epochs = 10, validation_split = 0.1, batch_size = 400, verbose = 1, shuffle = 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "135/135 [==============================] - 1s 9ms/step - loss: 0.2319 - accuracy: 0.9253 - val_loss: 0.0499 - val_accuracy: 0.9843\n",
            "Epoch 2/10\n",
            "135/135 [==============================] - 1s 8ms/step - loss: 0.0730 - accuracy: 0.9780 - val_loss: 0.0406 - val_accuracy: 0.9882\n",
            "Epoch 3/10\n",
            "135/135 [==============================] - 1s 8ms/step - loss: 0.0555 - accuracy: 0.9831 - val_loss: 0.0434 - val_accuracy: 0.9880\n",
            "Epoch 4/10\n",
            "135/135 [==============================] - 1s 8ms/step - loss: 0.0498 - accuracy: 0.9840 - val_loss: 0.0441 - val_accuracy: 0.9890\n",
            "Epoch 5/10\n",
            "135/135 [==============================] - 1s 8ms/step - loss: 0.0476 - accuracy: 0.9855 - val_loss: 0.0379 - val_accuracy: 0.9888\n",
            "Epoch 6/10\n",
            "135/135 [==============================] - 1s 8ms/step - loss: 0.0454 - accuracy: 0.9858 - val_loss: 0.0380 - val_accuracy: 0.9905\n",
            "Epoch 7/10\n",
            "135/135 [==============================] - 1s 8ms/step - loss: 0.0383 - accuracy: 0.9885 - val_loss: 0.0440 - val_accuracy: 0.9898\n",
            "Epoch 8/10\n",
            "135/135 [==============================] - 1s 8ms/step - loss: 0.0419 - accuracy: 0.9874 - val_loss: 0.0372 - val_accuracy: 0.9917\n",
            "Epoch 9/10\n",
            "135/135 [==============================] - 1s 8ms/step - loss: 0.0347 - accuracy: 0.9892 - val_loss: 0.0470 - val_accuracy: 0.9898\n",
            "Epoch 10/10\n",
            "135/135 [==============================] - 1s 8ms/step - loss: 0.0395 - accuracy: 0.9881 - val_loss: 0.0345 - val_accuracy: 0.9903\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ziQpwBE15VrP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "bcf1b5dd-7071-4fdb-9e47-0f8c43e06c45"
      },
      "source": [
        "plt.plot(history.history[\"accuracy\"])\n",
        "plt.plot(history.history[\"val_accuracy\"])\n",
        "plt.legend([\"accuracy\", \"val_accuracy\"])\n",
        "plt.title(\"accuracy\")\n",
        "plt.xlabel(\"epoch\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'epoch')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8UkrOMZ5pqh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "b2e1be0a-94ef-4304-c5cd-16eabccedb50"
      },
      "source": [
        "plt.plot(history.history[\"loss\"])\n",
        "plt.plot(history.history[\"val_loss\"])\n",
        "plt.legend([\"loss\", \"val_loss\"])\n",
        "plt.title(\"loss\")\n",
        "plt.xlabel(\"epoch\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'epoch')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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39TJ/iZmtNrM2M7survzS8Alhna9mM3tfOO9BM3sjbl7lwP2ss1dRFtwQVqWOYBEZ5foMADNLB+4DrgbmATea2bwei+0CbgUejS909xXuXunulcBlQCPwTNwid3bOd/eqs/8ZA2dOaQE5mWlU7VIAiMjolsgjIRcDW919O4CZPQZcC2zoXMDdd4TzOs6wneuA37l741nXdghkpqexYLJGBhWR0S+RJqApQE3c51hY1l83AL/sUfZNM1trZt83s+zeVjKz28xslZmtqqurO4uv7b/KaIR1tQ20tp8pz0RERrYh6QQ2s0lAObA8rvjLwFzgImAc8KXe1nX3+919kbsvKikpGfS6QjA09Im2DjbtPTok3ycikgyJBEAtEI37XBaW9ccHgd+4e2tngbvv8cAJ4GcETU3DQufIoBoYTkRGs0QCYCUw28xmmFkWQVPOsn5+z430aP4JzwowMwPeB6zr5zYHTdnYXIrHZCkARGRU6zMA3L0NuJ2g+WYj8IS7rzeze8zsGgAzu8jMYsD1wI/NbH3n+mY2neAM4s89Nv2ImVUD1cB44N5z/zkDw8yoiEY0MqiIjGqJXAWEuz8NPN2j7Otx0ysJmoZ6W3cHvXQau/tl/anoUKuMRlixaT9Hm1spyMlMdnVERAac7gQ+jYpoBHeojmlgOBEZnRQAp1FRFgwNrTuCRWS0UgCcRiQvixnjx+iOYBEZtRQAZ1BRpjuCRWT0UgCcQWU0wr4jJ9jToJFBRWT0UQCcQUV4Q5guBxWR0UgBcAbzJheSmW68pgAQkVFIAXAG2RnpzJtUqDMAERmVFAB9qIxGqI410N6hR0SKyOiiAOhDRTTC8ZZ2tu4/luyqiIgMKAVAH9QRLCKjlQKgDzOKx1CYk6GOYBEZdRQAfUhL08igIjI6KQASUBmNsGnfUZpa2pNdFRGRAaMASEBFWYT2Dmfdbo0MKiKjhwIgAeoIFpHRKKEAMLOrzGyTmW01s7t6mb/EzFabWZuZXddjXruZVYWvZXHlM8zslXCbj4ePmxyWSgqymRLJVUewiIwqfQaAmaUD9wFXA/OAG81sXo/FdgG3Ao/2sokmd68MX9fElX8b+L67zwIOAx87i/oPmcqp6ggWkdElkTOAxcBWd9/u7i3AY8C18Qu4+w53Xwt0JPKl4YPgLwOeDIseIngw/LBVWRYhdriJA8dOJLsqIiIDIpEAmALUxH2O0cszfs8gx8xWmdnLZta5ky8G6sMHzp9xm2Z2W7j+qrq6un587cBSP4CIjDZD0Qk8zd0XAR8CfmBmM/uzsrvf7+6L3H1RSUnJ4NQwAQumFJKeZlQpAERklEgkAGqBaNznsrAsIe5eG75vB/4ELAQOAhEzyzibbSZDXlYG508sUACIyKiRSACsBGaHV+1kATcAy/pYBwAzG2tm2eH0eOBtwAZ3d2AF0HnF0EeA3/a38kOtMrwjOKi+iMjI1mcAhO30twPLgY3AE+6+3szuMbNrAMzsIjOLAdcDPzaz9eHqFwCrzGwNwQ7/W+6+IZz3JeALZraVoE/gpwP5wwZDZbSII81tvHHgeLKrIiJyzjL6XgTc/Wng6R5lX4+bXknQjNNzvb8A5afZ5naCK4xGjMroWADWxOo5ryQ/ybURETk3uhO4H2ZNyCcvK52qXeoHEJGRTwHQD+lpRvmUIqpiGhNIREY+BUA/VU6NsHH3EU60aWRQERnZFAD9VFkWoaW9g417jia7KiIi50QB0E+VU3VHsIiMDgqAfiotzGFCQbZuCBOREU8B0E9mdvKGMBGRkUwBcBYqohG2HzhOQ2NrsqsiInLWFABnobJzZNCYzgJEZORSAJyF8rIizFA/gIiMaAqAs1CYk8nMknz1A4jIiKYAOEuV0QhrYhoZVERGLgXAWaqIRjhwrIXY4aZkV0VE5KwoAM7SQnUEi8gIpwA4S3NKC8jOSNPIoCIyYikAzlJmehoLphTpDEBERqyEAsDMrjKzTWa21czu6mX+EjNbbWZtZnZdXHmlmb1kZuvNbK2ZLY2b96CZvWFmVeGrcmB+0tCpKItQXdtAa3tHsqsiItJvfQaAmaUD9wFXA/OAG81sXo/FdgG3Ao/2KG8EbnH3+cBVwA/MLBI3/053rwxfVWf5G5KmIlpEc2sHm/dpZFARGXkSOQNYDGx19+3u3gI8Blwbv4C773D3tUBHj/LN7r4lnN4N7AdKBqTmw8DCzkdE1ugBMSIy8iQSAFOAmrjPsbCsX8xsMZAFbIsr/mbYNPR9M8s+zXq3mdkqM1tVV1fX368dVNFxuYwbk0VVzeFkV0VEpN+GpBPYzCYBPwc+6u6dZwlfBuYCFwHjgC/1tq673+/ui9x9UUnJ8Dp5MDMqyop0BiAiI1IiAVALROM+l4VlCTGzQuAp4Cvu/nJnubvv8cAJ4GcETU0jTkU0wub9Rzl2oi3ZVRER6ZdEAmAlMNvMZphZFnADsCyRjYfL/wZ42N2f7DFvUvhuwPuAdf2p+HBRGY3gDtV6ULyIjDB9BoC7twG3A8uBjcAT7r7ezO4xs2sAzOwiM4sB1wM/NrP14eofBJYAt/ZyuecjZlYNVAPjgXsH9JcNkYqy4KImjQwqIiNNRiILufvTwNM9yr4eN72SoGmo53q/AH5xmm1e1q+aDlNjx2QxrThPI4OKyIijO4EHQOfIoCIiI4kCYABUlEXY09DMviPNya6KiEjCFAADoHKq+gFEZORRAAyAeZMKyUw3BYCIjCgKgAGQk5nOBZMK1REsIiOKAmCAVJRFWBtroL1Dj4gUkZFBATBAKqIRjp1oY3vdsWRXRUQkIQqAAVIZVUewiIwsCoABct74MRTkZCgARGTEUAAMkLQ0o6JMN4SJyMihABhAFdEiXt9zlObW9mRXRUSkTwqAAVQZHUtbh7N+t0YGFZHhTwEwgCqiRQC8tkvNQCIy/CkABtCEghymRHJZo2cDiMgIoAAYYBXRIt0RLCIjggJggFWURdh1qJGDx04kuyoiImeUUACY2VVmtsnMtprZXb3MX2Jmq82szcyu6zHvI2a2JXx9JK78TWZWHW7zX8JHQ454nTeErVUzkIgMc30GgJmlA/cBVwPzgBvNbF6PxXYBtwKP9lh3HPAN4M0ED33/hpmNDWf/EPgEMDt8XXXWv2IYWTCliDSD19QMJCLDXCJnAIuBre6+3d1bgMeAa+MXcPcd7r4W6Oix7ruAP7j7IXc/DPwBuCp8IHyhu7/s7g48TPBg+BFvTHYG508sUD+AiAx7iQTAFKAm7nMsLEvE6dadEk73uU0zu83MVpnZqrq6ugS/Nrk6HxEZZJuIyPA07DuB3f1+d1/k7otKSkqSXZ2EVEYj1De2svNgY7KrIiJyWokEQC0QjftcFpYl4nTr1obTZ7PNYa8i7AjWuEAiMpwlEgArgdlmNsPMsoAbgGUJbn85cKWZjQ07f68Elrv7HuCImV0cXv1zC/Dbs6j/sDR7Qj65mem6I1hEhrU+A8Dd24DbCXbmG4En3H29md1jZtcAmNlFZhYDrgd+bGbrw3UPAf9IECIrgXvCMoBPAT8BtgLbgN8N6C9Looz0NMrLinQGICLDWkYiC7n708DTPcq+Hje9ku5NOvHLPQA80Ev5KmBBfyo7klRGIzz4lx20tHWQlTHsu1pEJAVpzzRIKqMRWto6eH3vkWRXRUSkVwqAQVKhR0SKyDCnABgkk4tyKCnIVgCIyLClABgkZuEjIhUAIjJMKQAGUWW0iG11x2loak12VURETqEAGESV0WDcu2qNDCoiw5ACYBCVlwWPiKyqOZzkmoiInEoBMIiKcjOZWTKGqhqdAYjI8KMAGGQV0QhVNRoZVESGHwXAIFsYjXDg2Al2NzQnuyoiIt0oAAbZyZFBdTmoiAwzCoBBNre0kKyMNN0QJiLDjgJgkGVlpDF/cqECQESGHQXAEKgoi1Ada6Ctvecjk0VEkkcBMAQWTo3Q1NrOlv3Hkl0VEZGTFABDoKJMI4OKyPCTUACY2VVmtsnMtprZXb3Mzzazx8P5r5jZ9LD8JjOrint1mFllOO9P4TY7500YyB82nEwrziOSl6krgURkWOkzAMwsHbgPuBqYB9xoZvN6LPYx4LC7zwK+D3wbwN0fcfdKd68EbgbecPequPVu6pzv7vsH4PcMS50jg+oMQESGk0TOABYDW919u7u3AI8B1/ZY5lrgoXD6SeDy8GHv8W4M101JldEIm/cd5fiJtmRXRUQESCwApgA1cZ9jYVmvy4QPkW8AinsssxT4ZY+yn4XNP1/rJTAAMLPbzGyVma2qq6tLoLrDU2U0QofDulqNCyQiw8OQdAKb2ZuBRndfF1d8k7uXA5eEr5t7W9fd73f3Re6+qKSkZAhqOzguDEcGfWn7wSTXREQkkEgA1ALRuM9lYVmvy5hZBlAExO/pbqDH0b+714bvR4FHCZqaRq3i/GwqoxF+8MctfOzBlazfrTMBEUmuRAJgJTDbzGaYWRbBznxZj2WWAR8Jp68DnvNw+EszSwM+SFz7v5llmNn4cDoTeA+wjlHukY+/mTvfNYeVOw7xt//yIp9+dDVbdW+AiCRJRl8LuHubmd0OLAfSgQfcfb2Z3QOscvdlwE+Bn5vZVuAQQUh0WgLUuPv2uLJsYHm4808H/gj8+4D8omFsTHYGn750Fh++eBo/eWE7P33xDX5XvYf3/00Zn718NtFxecmuooikEBtJ49QvWrTIV61alexqDJiDx07wwz9t4+GXd+Lu3HDRVG6/bBYTC3OSXTURGUXM7FV3X3RKuQIg+fY2NPOvz23h8ZU1pKcZt7xlGp98xyzGjclKdtVEZBRQAIwAuw428oNnN/Ofr9WSm5nOxy45j49fMoPCnMxkV01ERjAFwAiyZd9Rvv/HzTxdvZei3Ez+/u0z+chbp5GX1WeXjYjIKRQAI9C62gb+v2c2sWJTHePzs7n90pnc+OapZGekJ7tqIjKCKABGsFU7DvHd5Zt45Y1DTC7K4Y7LZ3Pdm8rISNdgriLSt9MFgPYgI8Ci6eN47LaL+fnHFlNSmMNdv67miu/9md9W1dLRMXICXESGFwXACGFmXDK7hP/81Fv591sWkZOZzmcfq+Lqf36BZ9bvZSSdyYnI8KAAGGHMjHfOm8jTd1zCv9y4kJb2Dm77+au8777/4fnNdQoCEUmYAmCESkszrqmYzB8+v4TvfOBCDhxr4ZYH/srS+19m5Y5Dya6eiIwA6gQeJU60tfPYX2v41+e2cuDYCd5+fgn/95VzKA9HIRWR1KWrgFJEU0s7D720gx/9eRv1ja1cNb+UL1x5PudPLEh21UQkSRQAKeZIcys/feENfvLCdhpb23lf5RQ+d8VsphWPSXbVRGSIKQBS1KHjLfzoz9t46C87aO9wrl8U5Y7LZzGpKDfZVRORIaIASHH7jjRz34qt/PKvu8KH1Bdx/sQCzp9YwOyJ+cyZWEBxfnayqykig0ABIADUHGrkgf95g+pYA5v3HeVIc9dD6ovHZIWhkM/siQXMKS3g/AkFFOVpMDqRkex0AaDRxVJMdFwe33jvfADcnX1HTrB539G41zGefDXG8Zb2k+tMKMhmTmkBsycUMKc0CIfZE/Ip0CilIiNaQgFgZlcB/0zw9K6fuPu3eszPBh4G3kTwLOCl7r7DzKYDG4FN4aIvu/vfh+u8CXgQyAWeBj7rI+l0ZBQwM0qLcigtymHJ+SUny92d2vomtuw7xqYwGLbsO8ajf91Jc2vHyeWmRHKZPTH/ZFPS+RPzmTUhX6OWiowQff5LNbN04D7gnUAMWGlmy9x9Q9xiHwMOu/ssM7sB+DawNJy3zd0re9n0D4FPAK8QBMBVwO/O+pfIgDEzysbmUTY2j0vnTjhZ3t7hxA43smnvUbbsP8bmfUfZtPcof9l6kJb2jnBdiI7N4/y4YJg9MZ+ZJfnkZGoUU5HhJJFDtcXA1s5n+prZY8C1QHwAXAvcHU4/CfybmdnpNmhmk4BCd385/Pww8D4GKwB2vQzpmTBpIaTp5uezlZ5mTCsew7TiMVw5v6u8rb2DnYca2bw3aELqbE7606Y62sLB6tIMphePOdnhPHtiAbMm5FM8JovC3EyFg0gSJBIAU4CauM8x4M2nWyZ8iHwDUBzOmxkCyt8AABJSSURBVGFmrwFHgK+6+wvh8rEe25zS25eb2W3AbQBTp05NoLq9ePYfYeeLkDsOZl4KMy+HmZdB4aSz2550k5GexsyS4Cj/6vKu8pa2Dt44cLxbH8OWfcf4w4Z99BzENDsjjaLcTIpyMykM309+zsnoVtZzfl5WOmc43hCR0xjsxto9wFR3Pxi2+f+nmc3va6V47n4/cD8EVwGdVS2ufxC2/wm2PQvbnoN1vwrKJ8wLgmDmZTDtrZCpa+MHUlZGGnNKg6uJ4jW3trOt7hjb645T39TKkaZWGppaaWhs5UhzML3vSHNwlVJTK0dPtHGm3qGMNDsZCoVhYBSdITAKc7qmC3IySEtTeEhqSiQAaoFo3OeysKy3ZWJmlgEUAQfDTt0TAO7+qpltA84Ply/rY5sDJ78ELrw+eLnDvnVBEGx9Fv56P7z0b5CRA9PeFoTBrMuhZG7QoC0DLicznfmTi5g/ObFxito7nGPNbTQ0dQVE5+tI/HS4TENTK7HDTSfntZ3hmQlmkJ/dPTB6hkfPAIk/M9FDeUaxxkNQuxomL4QxxX0vPwL1eR9AuEPfDFxOsJNeCXzI3dfHLfNpoNzd/z7sBH6/u3/QzEqAQ+7ebmbnAS+Eyx0ys78Cd9DVCfyv7v70meoyKPcBtByHnX8JwmDbs3Bgc1BeOCVsLroMzrsU8sYN7PfKkHB3Glvau8Kj8dTAOHKaQGloauVEW8cZtz8mK/2Us4zTBkZuJkW5Xc1ZerTnMNPRAXvXwpY/wJZnoHYVeAekZcL574KKG2D2uyAjK9k17bdzuhHMzN4N/IDgMtAH3P2bZnYPsMrdl5lZDvBzYCFwCLjB3beb2QeAe4BWoAP4hrv/V7jNRXRdBvo74DN9XQY6JDeC1dcEZwfbng2ajZobAIMpfxM2F10OZYuCTmUZ9Zpb20+GQrezj8ZWGpra4sLk1ABpjLuXojc5mWndAqK0KJcLpxRREY2wYEqhLqcdCk31sH0FbPkjbP0DHNsXlE9eCLOvhLLFwfy1T8Dx/UE/Yvl1QRhM/psR00qgO4HPRnsb7H4tCIOtz3YdEWQXwowlXc1FY6cPXZ2SqaMDOlqhvRXaW6CjLZg+WdY53RbO7ywLP5+cDtcHGD8bJs6H3LHJ/W2DoKWtgyPNp55VHIk/A4k7I9l1qJHa+iYguGrq/IkFXFgWBEJFWYQ5pQVkDlaTU0c7HHoD9m+A9CyYXAkFpYPzXcnkHvzGLc8ER/q7XgZvh5yi4OBu9pXBv+n8Cd3Xa28LgqDqUXj9KWg/AePnQOWNcOFSKJycnN+TIAXAQGg6DG88HzYXPQcN4cVR42Z2hcH0SyA7P3l1dIcTR+D4AWg82PXeeCBu+iC0NZ9hp90Wvrd0n/YzN4eck8IyKF0AExeE7+UwbgakpVYzyYFjJ1gbq2dNTQNrYvWsjTVw6HgQltkZacybXEhFWYSKaBEVZRGmF4/pfyf28QNBP9i+DbBvPexfD/tfh7am7ssVTIJJlcHR8OTwveeOcSQ4cRS2/zk4wt/yBzgSdjeWloc7/HdC2UWQnuAZV1M9bPhPqPol1LwMGJz3Dqj8EMz9W8gafiPuKgAGmjsc3NrVd7DjRWhtDNoLo2+GWWFzUemF53bvQUdHEDwnd+AHeuzcO6cPdk13Hl33lJELY8YH/RkZuUEzVnpmUOf0TEjLCI7+Tk5nBp87pzuXO930adfPDP5xpYWfO6e9Heo2BTujveuC9wNbgnKAzDyYcEEYCuXB+8R5wdFainB3YoebWBOrZ01NPWtiDVTHGmhqDf5GBTkZVJRFup0plBblBCu3NkPd6+FOfkPXTv/4/q4vyBsfnIF1vibMg7YTwZnvnqrg/cAWINxPFEzuHgiTKoOLLAbotx5pbmNvQzP7jzZTWpjDzJL8/gece1DnLc8Er51/CQ5isgpg5jvCnf4VA3PUfnAbrH0c1vwS6ndBVj7Me19wZjD1rcPmviMFwGBrOxGcTm57FrY+B/uqg/K88d3vPcgd23VEHn+Efrqde9Ph0x95ZxdCXnG4Ux8fXKmQN773sjHjh+WRySlam6FuY7DT6gyFvdXQXN+1TGRqcIYQf8YQmT5s/rENtvYOZ+v+Y6ypqacqVk91zSGO7d3OLHYx13ZRkV3L/PRaJrbGSCP8fycjB0rmBH+vCfO6dviJHNGfOAp71nYFwu4qOLila35hWRgInaFw6lUzHR3OweMt7G1oZk9DE/uONLOnoZm9Dc3sPdL13rPfJD87gwvLiqiMRqiIRlgYjTChMOfUOrY0BgdhnTv9+p1BeckFMPuKYKcfvXjwOnA7OmDXX4IgWP9baDka/H964Q1Bf0HxzMH53gQpAIba0X1Bm2Fnc1HjgT5WsCAcettxn3wvjtu5F0NGigzf7B6ctu9bH4RB5xnDoW1d4ZiVH+zYToZCefA5mc1xg6HxUHg0Hx7R798A+zdCy7GTixzInMzrHVFePTGZTR1RXvep2LjzKI+O48KyYEc6f3Lhud193XwkuGJm92t01AavjPrtJ2fXZ5XyRtZsNjCTVSem8WLjFOrau/+3yEgzJhYGY1GVhu+TinKYWJhDSUE2scNNVNUcZk1NAxv3HDl5Oe+kohwqoxGWjD/Kxe2riR58kYxd/xM0a2bmwYy3w+x3Bq/IWd48ei5aGuH1/w7CYNsKwINWgYobYf7fQW5kyKukAEimjo7gjGD7n4Ij3N527rljU669+5y1NAZnCyfPFNYFIXGioWuZsTO6+hQ6wyEydfhfvdHWElyS3NlG39lef3R31zK5Y2HC/KBZbOL8YHrCXMgObrxraGqlOtZwsvlobayBvUeagWDnO6e0gAvLIlRGi7iwLMLsCfmn3NfQ1NLOnoambkfpwVF888mj+APHTuAOBTSyIO0NFtgbVKa/QUX6Dsp8z8ltHc2dzPHicnxSJTnT3kTRjMWkjUms87+5tZ0NNfvZV72CnB3PMrP+L0z14G+xvWMSVbkXcXjS2ymY83YunDGR2RMKSB8ON/gd2R00EVX9Eg5sgvRsmPtuqPhQ0CLQo9+htb2D2sNN7Dh4nJ0HG8PXcXYcPM6jn7iYib2d/SRAASCpwT3onI9vPtq3LrjCpbMdO7swbAIJm4+KZ4GlBet6R7Cce/jeETdNL2W9rdOzjAS27cFZ4r4NwVH9gc3BFVMQ9JeUzA129BPmdfWFFEzqd5DtO9Ic9iUEgbCmpv7kMyFyM9NPXn7auXNvaGo9ZRtFuZmnHLFPCkeVLS3KYVJhLoW5GcHwHE31sGdN9z6Fwzu6NjZ2eveO5kmV3Y+Q62uCJp2tfwwPoBqDneiMSzg+9TLW5l7ES4eLqAp/S2d987LSKZ9SROXUCJVlESqnRpL7FDz34LeveQyv/g+s6RAncsazecJVPJ/3Tl5pnMzOg8eJHW6iPe7Gxbys9GD8rXF5fOVvLyA6Lu+svl4BIKntxLGgqWRfdVc47FvfrelkWCiKdnXGdrbTF88atPtO3J0dBxu7hUJLW0fvO/dwp3/O9yc0HgpCIb5PobPNHrrO2g5sDc7wIDhrm/2uoFln+iWQdeqOsPO3VNUcpmpXPVWxBjbsbqC1PdjHTSzMpiIMg8qyCBdGI+RnD869Fo0tbXFH78F75xF9XcNR3mGv8f70F7ksbTVZ1s729POoGnc1e6e+h5JJUaaPH8O04jxK8rMHZJwrBYBITx0dwY7n8I7wSNq6v1vaGcoI39N6WfdMZZx+ueyClLrCqZvGQ90DYW91uNO/MniNn31WzXYn2trZsPtI0GEevnYcbASCzc2ekN8VCtEIcyYWJDy8R0NTK7sONobNNd139PuPnui2bPGYLKYV5zG9eAxTw/dpxXnMyG2maPsybM0vg99u6cEVSpU3wvlXQ+bZNfn0pAAQEQEOH29hTSwIg85gONwYNB3lZKYFTUfhVUfzJhVS39Qa7OAPdB3R7zrUePL+jE4TC7OZVjyG6cV54bDpXTv8wkSenrf/9aDjeO0TQV9PThEs+EDQeVx20Tn1WykARER64e7sOtR48gyhqqae9buP0NJjHCgzmFyUy/TxeSd39FPHjWH6+DymjssbuKE7OtrhjT8HHccb/yu4QW/cTFj6i6Dv5yzomcAiIr0w63rQ0bWVwWNJWto6eH3vEV7fezRsvhlDdFzu0Azgl5beNUz9iaOw4bew/jcwdtqAf5XOAERERrnTnQGkxq2TIiJyCgWAiEiKUgCIiKQoBYCISIpKKADM7Coz22RmW83srl7mZ5vZ4+H8V8xselj+TjN71cyqw/fL4tb5U7jNqvA1AgcaFxEZufq8DNTM0oH7gHcCMWClmS1z9w1xi30MOOzus8JnAn8bWAocAN7r7rvNbAGwHJgSt95N7q7LekREkiCRM4DFwFZ33+7uLcBjwLU9lrkWeCicfhK43MzM3V9z987hC9cDuWaWImMYi4gMb4kEwBSgJu5zjO5H8d2Wcfc2oAEo7rHMB4DV7h4/SMbPwuafr9lpRjwys9vMbJWZraqrq0uguiIikoghuRPYzOYTNAtdGVd8k7vXmlkB8CvgZuDhnuu6+/3A/eF26sxsZ89lEjSeoElKAvp7dNHfojv9PbobDX+PXm8jTiQAaoFo3OeysKy3ZWJmlgEUAQcBzKwM+A1wi7tv61zB3WvD96Nm9ihBU9MpARDP3c/64aNmtqq3O+FSlf4eXfS36E5/j+5G898jkSaglcBsM5thZlnADcCyHsssAz4STl8HPOfubmYR4CngLnf/n86FzSzDzMaH05nAe4B15/ZTRESkP/oMgLBN/3aCK3g2Ak+4+3ozu8fMrgkX+ylQbGZbgS8AnZeK3g7MAr7e43LPbGC5ma0FqgjOIP59IH+YiIic2YgaDO5cmNltYX+CoL9HPP0tutPfo7vR/PdImQAQEZHuNBSEiEiKUgCIiKSolAiAvsYyShVmFjWzFWa2wczWm9lnk12n4cDM0s3sNTP772TXJdnMLGJmT5rZ62a20czekuw6JYuZfT78d7LOzH5pZgPzhPZhZNQHQNxYRlcD84AbzezsHqw58rUBX3T3ecDFwKdT+G8R77MEV7gJ/DPwe3efC1SQon8XM5sC3AEscvcFQDrBJfCjyqgPABIbyygluPsed18dTh8l+Mfdc1iPlBLeqPi3wE+SXZdkM7MiYAnBZd24e4u71ye3VkmVQTB+WQaQB+zuY/kRJxUCIJGxjFJOOGT3QuCV5NYk6X4A/D9AR7IrMgzMAOoIxuh6zcx+YmZjkl2pZAhHKvh/gV3AHqDB3Z9Jbq0GXioEgPRgZvkE4y99zt2PJLs+yWJm7wH2u/urya7LMJEB/A3wQ3dfCByn66bOlGJmYwlaCmYAk4ExZvbh5NZq4KVCACQyllHKCIfe+BXwiLv/Otn1SbK3AdeY2Q6CpsHLzOwXya1SUsWAmLt3nhU+SRAIqegK4A13r3P3VuDXwFuTXKcBlwoBkMhYRikhHHL7p8BGd/9esuuTbO7+ZXcvc/fpBP9fPOfuo+4oL1HuvheoMbM5YdHlwIYzrDKa7QIuNrO88N/N5YzCDvEhGQ46mdy9zcw6xzJKBx5w9/VJrlayvI1g2O1qM6sKy/7B3Z9OYp1kePkM8Eh4sLQd+GiS65MU7v6KmT0JrCa4eu41wmHpRxMNBSEikqJSoQlIRER6oQAQEUlRCgARkRSlABARSVEKABGRFKUAEBkiZvYOjTgqw4kCQEQkRSkARHowsw+b2V/NrMrMfhw+L+CYmX0/HB/+WTMrCZetNLOXzWytmf0mHEMGM5tlZn80szVmttrMZoabz48bb/+R8C5TkaRQAIjEMbMLgKXA29y9EmgHbgLGAKvcfT7wZ+Ab4SoPA19y9wuB6rjyR4D73L2CYAyZPWH5QuBzBM+mOI/g7myRpBj1Q0GI9NPlwJuAleHBeS6wn2C46MfDZX4B/DocPz/i7n8Oyx8C/sPMCoAp7v4bAHdvBgi391d3j4Wfq4DpwIuD/7NETqUAEOnOgIfc/cvdCs2+1mO5sx1D5UTcdDv6NyhJpCYgke6eBa4zswkAZjbOzKYR/Fu5LlzmQ8CL7t4AHDazS8Lym4E/h09bi5nZ+8JtZJtZ3pD+CpEE6OhDJI67bzCzrwLPmFka0Ap8muDhKIvDefsJ+gkAPgL8KNzBx4+eeTPwYzO7J9zG9UP4M0QSotFARRJgZsfcPT/Z9RAZSGoCEhFJUToDEBFJUToDEBFJUQoAEZEUpQAQEUlRCgARkRSlABARSVH/P00Av1wzK6OOAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xTNUBzzb6Qp3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "f0582df8-39c1-493d-8a9d-5b5f1e5454c1"
      },
      "source": [
        "import requests\n",
        "from PIL import Image\n",
        "#url = 'https://printables.space/files/uploads/download-and-print/large-printable-numbers/3-a4-1200x1697.jpg'\n",
        "url = \"https://patchion.com/1723/the-digit-4-white.jpg\"\n",
        "response = requests.get(url, stream = True) # so the reqiest doesn' wait for image to be downloaded completely\n",
        "print(response)\n",
        "img = Image.open(response.raw) # identify raw content of the image\n",
        "plt.imshow(img, cmap = plt.get_cmap(\"gray\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<Response [200]>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f33a4604e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gtFHxzMv68hQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "3056dadb-5423-4720-d2e5-2a2de1b5f3d3"
      },
      "source": [
        "import cv2\n",
        "img_array = np.asarray(img)\n",
        "print(img_array.shape) # depth of 3 RGB \n",
        "resized = cv2.resize(img_array, (28, 28)) # our network was trained on 28 x 28 grayscale images\n",
        "gray_scale = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)\n",
        "#print(gray_scale.shape)\n",
        "image = cv2.bitwise_not(gray_scale)\n",
        "plt.imshow(image, cmap = plt.get_cmap(\"gray\")) # if bitwise not is not performed then this will give an image of white back. and black digit\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1600, 1600, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f33a4072a58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+evK68XFZIAlO0AFJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEv8H3ktnY7BOsN0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TodReP5Z7lwd"
      },
      "source": [
        "image = image/255\n",
        "image = image.reshape(1, 28, 28, 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eIiDSNls7rjE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "82fe6346-5a82-41b7-d33d-742500dc21fb"
      },
      "source": [
        "prediction = model.predict_classes(image)\n",
        "print(\"Predicted digit : \",str(prediction))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-14-c9773d381961>:1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
            "Instructions for updating:\n",
            "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "Predicted digit :  [4]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lc6z3hzL7vgu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "7a0b459f-81ff-4ec5-baa8-8941fbce7c0c"
      },
      "source": [
        "score = model.evaluate(X_test, y_test, verbose = 0)\n",
        "print(type(score))\n",
        "print('Test score:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'list'>\n",
            "Test score: 0.03330440819263458\n",
            "Test accuracy: 0.989300012588501\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o42qVa2AyrYF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "906d2f7c-8ae1-4465-b195-2bdb77831a52"
      },
      "source": [
        "layer1 = Model(inputs=model.layers[0].input, outputs=model.layers[0].output)\n",
        "layer2 = Model(inputs=model.layers[0].input, outputs=model.layers[2].output)\n",
        " \n",
        "visual_layer1, visual_layer2 = layer1.predict(image), layer2.predict(image)\n",
        " \n",
        "print(visual_layer1.shape)\n",
        "print(visual_layer2.shape)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 24, 24, 30)\n",
            "(1, 10, 10, 15)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_8y7wBfs3YH6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "82038452-1900-4638-824a-7c3f210314ff"
      },
      "source": [
        "plt.figure(figsize = (10, 6))\n",
        "for i in range(30):\n",
        "  plt.subplot(6, 5, i+1)\n",
        "  plt.imshow(visual_layer1[0, :, :, i], cmap = plt.get_cmap('jet'))\n",
        "  plt.axis(\"off\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OzMWmG5DL54dd/CPnqxyjI/W4zbRIBQkJ4kDg1lzzipy1gVsV5odZZAnKChTEAl5oWAprMtnh6M8O/hWT3Cgi7eLD5AVXEjir3xH+NoIloqfA612ubrw6lKKhB9vahNmR7etJCFKgIBYJRsYqFSwSFQ00zlzJimA7ZURr+r1YLjp5Xw6/3x+VDYmIiEjy0fRIERERCUmBgoiIiISkQEFERERCUqAgIiIiISlQEBERkZAUKIiIiEhIChREREQkJAUKIiIiEpICBREREQmp1RLODofHtmUb/X6PVhlqRvszeWhfJhftz+SRjPtSPQoiIiISkhaFEhFJGm7MMo/tXSnQBfQGMoFdRGtRIbE39SiIiCSFTCAPyG7n+91ANyi8FuaOBkZFq2FicwoURESShpf29yYETISM2YeA/Gg0SJKAAgURkaRQC5QB1e18fz0AP/7pbziQ1gOGpUerYWJzylEQEbE1F+ZUHkluQqM8ysnc7oWsiDclSUI9CiIittYRM0zQLQrbctGX3fAm0CUKm5OkoEBBRAKCyXCZFrdD2s4duK8nOHQQqc/4EnTCdFCIzQ0HJgA5EW1FgYKIBPQBJgXuJfEFAzs3keUmNOXlE841X4G6KGxOLOSGIaNh9gDgkoi2pEBBJOVNg4ke6DIWKEdz5xOdCzMF0o3ZV+H2JGQCPWjsjThdGV/FOxioiaCJkgBcMA6Y6scElO2nQEEk1a3M4fPfOjir9DjwPLDH6hZJqzpifvJnAwcIvychHyjEBAvN+XiNy1neqch0UoiNubng3vd5/Ks/gu6dI9qSZj1IjEQ3E1tiYSgwFirgmuxn+PzOc6xukLRJPSY4aG/PT/DYbNlnnM0/Obud25ZE0oXDnMv/QVpk21GPgsRIR0wCTcvdm5IAssZCRQOUwEuO/4NH51vdImmTekyvz4EIttF6puJJOkSwbUkUX+ET8vgQMiLbjgIFibJCYA4wAHNCU29C4hkOGR4zbOlJgzVgfp1qX6WGKkwuyrEWnnMyimJu4JlIh7UlHrI8MNsDzGjx6d30ZRtD4XBkH6NAQaKreyEsc2EChiqiNWVLoihjNJ0OV8JAYNl8wGNxgyS+yoG3Mcdncy5u5zG6TqqDIfFtlYSv66cfUT/XAQ+0NKXZy743L+Z3/BAqI/scBQoSXZVV8CjAe1a3RL7ADeRD3QmO3todVoB6EezAhUk8jGwufNt4eY3L4WcomdEGOnKMtE8IObTQaUQl32ZzxFU2lcwoUfYElFrdBmlZDiaB8W1Y/rbVjZE264gZyqum5V6AaPLxOD8lfXg97IzxR0nE0jkBBwmRrOhi1NnF/JDfcX/eLyPanwoURFJGLSYJLhqFeSR+6jHDBfEYxqtl/w96c/vEZYGLz92QG1gcquI5NHU2sVSRQ8NAoKSlZ+tZ/co0PvxuXsRBn4YeRFJGNbADc9ER+wjOciiPz2et8MD4hSZQmJ4OJV7O2n4cGBSHz5dwHPrb+Ww95xKoaOnZehgHO84bAayN6HMUKEiM9AD6Y4rCiIi91MPeE7ABxvZ8mTtzHsEUapJE0v3CDxh5cFuIGSpeoAQq9hHZVFoFChIzecAI4pOAJSLR5QVehL0VXMMf+MXJX0IXh9WNkmb6shv+QCsxXDHweyLNbVGOgkRJH3PLG2Dqi48D15BavPnXQ43H4rZJePICt0h+ibjQjAo7c0NWEUyBm3eugGcxF6MI5+NLdH2LP8GDwOaGmH6OehQkSvIhYwAsg+8t+m+euXw8eztfCLdZ3S4JXx6mDkZeO9/feolgsQM33AbdH/kAZsK+h1ABpgQ0nD/x4IdQ0ONvMf0cHc0SPT5gNjyb/2882+/fzIll3pnelImZ31+NfoEmin2YfVHezvdrP9rfMZgHldsvYN+buRRsrYAiq9skzY3960Ze93+b2dMuxoxBtCQTJs6AMcBGYC+wfTWwq82fo0BBomt7CWwvDuMNTZfL1QUmMRwg0uQn7Uu78wIe2NCHIlZwxdBXVYApEQ2EyyZvhpUftvKijuS+sI/beYynb/ohpe9/A/r1QYGChCFQrY96zJnAFXhsFIwaAMOAyZjeggbMGvWVQC6Q7wXnSc5ynuTzVeeYNQOKCzAJNK1xQdock+uYEdjuht3ANswFShcZkcRwgG3fuJ5tvUcC661ujHzBG7ByAObcGYqbiazi36sX863sP7H5on/hHh4J61MUKKQ8N1CA6frfj/lKBCrBLYZLL1zHL/gVJ+lADVlU0Y2POZ8L+RtD2E4HTgJw80+XUlI2Goq7neHzXGb7twJTAw/VABv6Bj6/CgUK8eJq9rf+v0tztbDdA9utboe07O3ArTUuvsWfcPwPjOizg69/Zyf3dH8krPUfFCikPDem3kE1Zky6PwwZDGOg64UfAfAugzmPj+nLbi7/52ucs/Nz2AS8CiyAhUOmUfLKaHj0CC1/aV0wcA7cChfc8j5f5y+c4EVqyOKdH1waWHNgfeDzW1/+VqJhguktWgCPXnwLx+jIETrz6HwPzPW08r67oV+62U11AOGNc4qIFQ4w6UcvQxGc1e84n1ekQ+W+sLagQCHluSHDAXWdMUWSBptf+wOhS2Au1IfkkUUN3TjIOZs+hyXgfQsWHYUZe+FPQ74VWKp4UeiPuQsmff8plp68mcwXvay77lLWcA3vrLoUrV4YbwNgLjx58Q3829efhXOBAtj66FDemdvK2/qlw3RgC2aUamMuChREEpkLqIfl82G5m8+5E9IcwNawtqJAIeV1NtmwXQDnaDNTYQiQ5aXqZA6fdsjiY87j71zIx5zH7sv68r+XfZvPTp7NiTo3M5dnmxIKe0NFqIFV71bA874beb7hRpOTcB3mYtOgcc/4WwuFV3HTmGe46dFnTO9AHXAewP+GfltpCUwdhBkeqkYrhIoksky4dQZMAZZh1nvY/iE07CXcZGUFCinPbYKD3pgAIQ3IMMU7qiu6gc9Jpc/Bwbwcjp3dkXdeuhTGA7wLvNzC9oJz6INJkTnmVoIp1rK9hDMnO0ps7TC3DZjbF7gx01bB7MtqTLJrMdp3qcSNOY5rrW6ItIuTtAequavTAuZ1vw9WuWB7NeH2JpgtSYrbAwt6Yi4M2YCDltYsPUp33snqDjUnMBeLUCVB74GJDvq98Gfu5BFumvYMLHkC6qphe0fUVW0DS2ax/JbrGcpWen+yH8cf/VDksbpVElfZMO8O09s45EPgKasbJGFzcV6nj+lFOXhcsLyC1mdHhKZAIeVV0eaTQE1LDwZ7DtyAE0Y44C54glsZMWsHN01+BpZUEWmtcYkHN5DNyFs2cOOk5znxKjx3HAb5S9ihYjspxk3POXu5gWe43/lL5RjblDMwK810BC5r93ZUwlkiNAcemMEF/n/hYf9fmLl5HkVDl3LpkT/iON8PhZpyZxtTZnGj38eG42OhAVbWfY/Jq/zscIywumUSd26uZC1TWarSzbZVy56CQdz0o2egIvzhhqbUoyDt0KSW/xDoNL2S2TzAzZtW8OeR/VjLlXgfyIQFq4m8wp/EzXj4f9xN2tvA1+Bnx38DEz1Wt0os0oUjnHu8sqWRSLGFeigrgbIemDnN2cAx2lMvRYGChGkoMNb80wmMMv/88VNP8+NVT5tFoCqAmncxBZTqrWiktMf4D+k27JiZAdMdyDhhdYvEMgfxTHsQz6gHoXS31Y2RdnEBwyHfAeN6mSq487bSngqbChQkTN2gX5M/0+BoRQ4sAEo9Lbw+mL9Qj4lkg3/7An8H78V6T5kaCSLUwxI/rHSgBGQ7c5jAfwym7P68Hu3aigIFCdNgU3RnCbB9LZTmgKcHZsodmO6tHEzy4jFgGuRlQvlGTNXGWWaNh3LMdMmG9zCrFZah3geRROEFnoIaN+1fRVSs5QWWw5Y82FBopr+3c6qrAgUJX3cCWdA7aOwhADPFMgdTZAnABVmZUAgs7wPsg4GYv7djhihKswPvC1QQE5EEUW51AyRi5UA1lBaaoQeOtWsrChQkTO/B3AGw04sJCK6Cfjlm6mQNppsri0CeAmbhpzFAWg6U3mxyGnIx6zuU12J6GQ6goi4iIrFQC8VroTiTxp7f8ChQkDBVw97AfbAHoTcmMHBigoSMwD2B+yxML8RhTPnmysDr2RfYjoIEEZHYaV+AEKRAQcLkDQw7dAOGm4dKgb21nJ5NG0hQXBmo1OMBWEvjMEU5phtMww0iIolMgYKEqR58fhov8H4od9CYkNhsBsNezGIk7CbSqFZEROJPgYKEaQ8mpyAYELigwUnIQh6+pbAqExVeEhGxJwUKEqZ6whsuOICCBBER+3L4/X6r2yAiIiIJSotCiYiISEgKFERERCQkBQoiIiISkgIFERERCUmBgoiIiISkQEFERERCUqAgIiIiISlQEBERkZBarczocHhsW43J7/c4rG5DotH+TB6O97HvvrwI7ctmdGwmj2TclyrhLBFwYb5CLsw6D1oJ0lYaArfDgVu4MoA0IA/I8EODrhciyUiBgrSTG8jGXCXygPeAXRa2R8JWDmwHVgAVz4X5Zidwrdn1i6HTqCqOVnaGOld02ygillOgIGGaBCP6QgnAEzQuEuWztFUSBl/gVoHZjxUnMKuChisPygfBdgdHM7o39jCIRfKBQZjl3rWku0SPkhklPBUX4Pc6mOZfCFRjehHWY05OYgt1mN6EVUDJUmBJOzdUDDwCnneh8IT5sw7FjFbpUsRZlT1h3FVWt0SSjHoUpI0GAVfRqUslzIStXIK5MoDJTxBbCPYkbMQMO0S0BHiwN2mPua8ZoSDBEi7ADTXw+c5zzP4ViSIFCtI2w65i2jsLeWLTDByT/grss7pFEq7gkMMaYOVS4FiUNlwGVMPhEaZHIQ2dWeKqI1AAvtUw5hhQa3WDJMnYaOhhLDAV6GF1Q1KQC3rDNfwB8rzAiyhx0YYqgXXAFjA9CdG8oPgaAxGJMx9wAjMUWB64F4kemwQKmTB7KOzMBSZY3ZgU4wI6whgYvayEcT3XBB4T21kHeNZD+dLYbL8O5ShYohYTuEcyjCQSmk0CBSeMgZEXbwA6W92YFHM7LL+DntfvZcXUa1n37nUoJ8FmajD5CHvBDGDH8BenggSRpGOTkUQX145cwTzm0mdIOWwPFviRmNuSyfvDHHhvLGCgYwHwmNUtknDtBRYAe08AB4nZsaOhB5GkZJNAwUkWNXSjSvO04+Z2WNOZy4a+RN8PoJDHgL+i6os20oDpTSgD9voxsxNiHGDb5Ixib3nAKEw+QnGrrxSJBtsc1t04SPaHDYFAQb0JMbemM5vHO/h6w1k4LnidU1PgxD6CQw7FAI8Q82x4JzY6o9jZKFicCytzoWQTOh9KrNnmsP6ML8HZqGsz5sbC1KFkjDrEAJ+LqzoEZzhoypVtBIcAyoGVwE6IS5Bnm7OJXWUC/QG32a8lFjdHUoZtDu0TpHO881mmO1ViZ8RQDi7tyL/zEJ0czwJ/R0GCzfgwvQlbgJ2PEdfpcrY5o9hRPnQfDZV+KHkQs6PVmyCxZ5vD+iQdOOnsYHUzkthwGDMaxsE41rFt9UhgIQoSbKgCWIapvqjhoiTgBvoA2VB5BNNVpP0q8WObQEFiLGM0D6//CcuYyjbHx8B89GvFhnyYvIR1TwBVFjdGoiMHuAoTAS5Bx6XEm20ChXP5hMw9XrNCnURfd7iSl9lNX/aQgU5GNlSJWehpI1jyi7MCM8OiO+bMonyiKKkF3sUMIem4lPizScElF1/lH2bMNcvqtiSpfChYXsF4/oDp6hTbKQOWfAh71xP/ISOvqdewHZNHlOa10c+QRFcNvAy8bXVDJEXZJFCo5QevreJbU980v5gkiobDZA9Mhu9MeZkr334T1Yq3mQbMr/lygP1Ys3ygDw5/aI7PMqDGpR4FkSRhk5i/GsZ4eAeA/7W4LclmNLc9+xCv8l3ectQAHqsbJOHyESiqBKbehRW5CV7gKSAHSqeZmkDBIQgRsTUdximvhMXz7zZdxrxpdWOkPeow+68UrM+Gr4XFmDyJqUA+JmBQRVUR21KgkPKKYa7KwNpaHab4Tpkf65Pd6gEPlObAxmnmoSwUKIjYmE0DBRdwI5ANPI9ZXtXqE6SINPLCYcxMDOUqiNiaTQwFb58AACAASURBVJIZm3NDv1yYmI4ZDLVpvCOSzA4HbgoURGzNpoFCT763679Z88LlkFeICRZc1jZJxCoZwDAgz0HiHAc+9SiItMiN3aag2zRQyOGZ12/i6q+9DkXmb7v9jxeJmjRgINAPEuo4qEE9CiKncWEW90qg47QNbBoouOAg1H5EoFKjhh4khWUBozAzW7dMg9keTP6OxbqgKZIip/FiiqEds7ohYbFpoAAcgap/opOQSJqfrhd/xIjBb/DM0PGMvH8D0ANrhyGcJoDpgo7RqHDTtv3pauPrxDr12C353qaBQjUv/mwsBXuADWAKzNjrf7xI1Dh9dMDHhfyN7614iY3bxzLX/y4X+MdDvge4m/hfPFwmdSgfTY2MVHcPL/tf5zJ/P8z/1KAcYCxQCPSHWz38h/+fsHyOBY2UZGbTQKGWx7iDhb2nBQoFHUMDoZLKnJzkK3wCDwOT4Fc33M/eo/1gNjAsHTMUkdnsFstxUqcZdtDQQ+SmwPmOt3jtrfGcPqSUDQzCLEGdS/biA/z6hl/xvRv/24pWShKz6SFczab517Np2Bio2Yd6FCSl+ZzUHM+i5pwsOAdO7IOyZ2HALvAvc+B/BbZmX0wHTtKRY/yTL/EZZ3PJgT9D7gNRbowbmAZdMgMzMVCPQqQeqODivX5YA/BYkycOAGsxXdn1VGcMxbHSD/1BpdglmmwaKNSbZdk3gKltb3XZWhEL+RycqEun/px0yAaXE7YC75XCoGHm0l3Q4a/UnjSXlkzgK8B/+W/mx/QmukG2C3IzTW94HiZHQSK0DHq39HgtsKvxzwYPjI9PiyS12DdQqHgDKjIxq+GIpDAffF6Xzis5V3Df2o/pzBE6c5iTOHkbNz468Bln04GTpHOCMvLZzLcpuW80MfnlmRG42fTsIiKns+GhHBxX3Ra415CDCA0OKt+/AE9aYCjB5wjcc/q9E7MuxDJg+4nYtMWJLc8sItIymx3OLmAGZLigbitQHrhp6EEE+GKAENT0SM/CFGfamR6DHGCvWcUyDVPbQeLIhfkhZb/pd5LYbDbrwQm9XYFxuHxM1q/mDIuc4uPMF/8MIBcTMJy6uGQSvWPphCnd3BClzUkY1J2TXHKAoZi6KNaxWaCQTc89e5n79D0wojMJUX1OxG66A2OAcQA3QMYsmDwDuCdKH5BuPkOzHeLMi5kqrh7WpLFlGv7nr6Cn/9uWNsNmgYKbK1nLj/gf06GAC0XPImHKAPIazPTF3r1gIqaXLtcRhY27zPaz0KFpCQ05JJNBQ0t4dxLcwDOWtsNmgQKcx8dc8ElloHu1GhVaEglTmpdOXWp4+Jaf4J/pYPX/XGGmMmZFafu5mOl86lEQicgOxyCGTPdzv+OXlrbDnjH/SQLxgZJ2RMLmPMmXzv6M71DMezfDhBHryeh3iLr8rlCah5mfX485yNpxfGWhHgVLuYCOBAsxiZ09BI9a3Qbb9Sj4eJXvsuK8awOlmytQj4JImHwdqD/uppxeDBgOvAkfnHMBRX9YCounwJA7gEl8scpPJmde0t2pNR4sNwZy7wAmWN0QSRI2i/m97KYvW7nEZFWrR0EkfD4nPl8HasiCAuAodH29jqsuW0vxT79DZcMFUNELKoNrQgT1wWRf78DUeAz+Yg32QGQDOaYao3oULOQ2+6Ai84yvFGkLmx3Kxzh08/ksLrwb6nZjchREJCw+Bw01HflbpwvhGkzyYTZcd3Ad3+62mT/MHM+amdfwT87mJB0A6MBJ7uNaRu7dxp29f83//PNHHF3ZHXYS6BqtgMJcGIipn6DFoCxUDDt3YdbAEYmczQ5lL2wEasD8ohGRsPmABhef8BW8I+GkEz5Lc9HBd5LOR49yXqeP6c8uOnAycDPDeyM/2QbPw7d/sZm/nX0h60ddARlpptJjaS6MwAQKXdCwg6Wq0Y8oiSabBQr1ULYcytyYiowi0i4+eObI99nc+V8AOEkHOnQ4CWfDMTpy7GgGAE7nyVNveebc75P1i0/5mPM5UtUZ6tJMPsI8THGlYIBgs7OKiLTOhod0udUNEEkK3spMPqi8qM2vr6PrFx9Mw0yHFJGk5fD7/Va3QURERBKUzaZHioiISDwpUBAREZGQFCiIiIhISAoUREREJCQFCiIiIhKSAgUREREJSYGCiIiIhKRAQUREREJqtTKjw+GxbTUmv9/jsLoNiUb7M3loXyYX7c/kkYz70oYlnMUeMgF34N9OwMXpX7dqGpcpFhGRRKVAQaLMHbgNB/oA6ebhXMxyxk7M6oV7K4A9wC6g1oJ2iohIW1gYKPQB+mMuFHusa4bESLC3wGVuNZgAIYvAty4XyMYsF65AIbEEe398gNfitoiI1awLFIZcD2saoLAvlHksa4ZEWzBA2AS8jbnoAHVOqHNB5UigAKa6oHc63HUJWhE00WQD3TBBXLXFbRERq8UhUMjEnHi8NF5E6qES2Jlm7iUJeWnc503tB1zgLIAucCqQkATixfQmnEk2ZpgpmG8iIskoDoFCf3COBt8JzBBDmbmvWAjjstEvllSzA9gHFQVmOEJd2wmomrYdl2OBAmAF5rgWkWQUh0ChB4wAStLB1/SXRy0am05FXuAYbA/+fdDCtkhkqoEq2tb7ICJ2FftAIa0vLACmAyXb0C9IAS9UPgjr3MAxqxsjp4SbxFgcuNcxLZLMYhgoTILxfc049CpgJ+iEkorcmAtQPafvf9VQSHyTIKsv1GwF1rfwvI7n5JSDOW6r0DEqEMsSzo/2xT/dwcilG+CBx6DuwZh9lCSyHMxU2I5WN0TOKJh8GggAFvRl5qfzoGgojQGfJL/hwJWYY1ckJj0KhZBfCLkwb+RMNr0yBtgS/Y8Rm6gHjqBxbDuZCgNzIQ0eO3JHYGbSWEwiarmlLZNYc2GCwmyrGyIJJPo9CkMK+dW+u6A3/NzRD8YtjPpHiJ1UYTLilbhqG0tyWf6X66EOvF2Wm1SE8QMwvzQl+WUDDhpLsEuqi2KgkA/MgXx4hhtgDajqnkgicmHqm4QYSlgBP/vnb8ysR6oBLxwG5SSkCGdnGALmOyIS1aGHsbDBBaWwp/8gKD2BTiwiicgN9KBxemMzJfM5muamMZGtHEoL0FTWVOCEQmAMsL0nsNXa5khCiGKg4DMFdMoJBAk7ordpEYkiL61XUwxW1QzS2nEpZS+BqqkqhidGFM8AtVCWAyUAj6DeBJFE1Y6pqYoVUoQPKipgZS5KXJWgKBz+w2HYaPPPZUC5hhxEEktwqCHoGKcPOXwfhhXAliPAohben2l+YR4OldwWqlaG2I8X890I7k+RaCQzOkcz7p0X4FGg/DHgoYg3KSLRlA0MxcxaGI5JPG5iTAFXv/MsTO8c+v3dIXQtjBxMIKJuh+RQDryHqqZKUARH9gRYMgDSYN3D15nqizGPQF3ADUAu8HvUNWZ3bhg4yyRPVQRuW1YDuyxtVfI5hlmQDcwxFOhNGObhsnde4o9Hannpvu+ZsekMD9S9C7zc+PY8B0wENha0sO1MmD0FioDxQFlF4L0tJEmKTRzEfGfUOyRG+3sUeg+g4pbOTLtxIdy1HLZ4iH2g4IbCXnCXC1PtT+wtmwv+8j6LHpnKTS8s5pJ3NmGiBomuWkxy8Q5MFns5ABe/s4VLHeNZ2XkyeDxmoa41QO7g099eCF1/+hF0b2k6ZUcevv8n+Pc7OKvkOIzJ5Qs9FmIzVZjviIYexGh/j8LeCnL/eiRQL+GxqDWodfWwsRY2ZgL74vSZEjvH+OCai7h98jKyJx7gpK8DqrsRP3/9wTB+6J/GtasfBzxweDfc2hcqmh1by+FQ5flQ2dIxV8/Mdx/nT1d8i8+fPCf2jRaRuItg6GEZDIxeQ9rGC6jSY/KohTUeWJND9YJpMAzM2KjExQoPM1d0AzyBB543RTS/wAMbQm3kGAzxsjqvyHQkKE1BJOnosJYEUAuzMaknp8bSJX56AHmYSqrlLTyfiUlkbKn2ghfYCuV5UJ5NY7Z8DqeqOoqIrSlQkARQDz6PclMtk4vJDXmblndCNiaY8NHyuPU2TAJqMCjIAbphhpEUKIjYnQIFkZRXjln5qSLE88GegVD5I83rJwRnPGjFUHsbhAn6hgb+Xo6i+dSkQEEk5VXR+nTGWlpPMm1e8lnZ8vbnBvoDvWAqkAUsyEOBQmpSoCAiklQyMcNFYAK4Y7R9NlEO/fxDuJ/ZvEs1VeRwkG78jQspXdAH2BiD9kqiU6AgIpJUOtKYU3IicN/WQCGbXVsv4YlhcO91b8EVsG9KLi9zJTN5PFYNlgRnw0AhWPRFSVKx40L/f6VRHubCU4YqLtpBNSY4CB7D4QwFHcBR6ucC//v85N2LoBJ4E3PPu9FtpthGHAMFF2bcy8vpX+L2cEb4fgktuJ+C+yr4WEuisS+DCw1Fuh2JnRxMkYQz5TJI/LX0w6kdq4OeUgtTPXwwFeAFzPE5A5wuVFo9dcUvUFg2hyM3ubmX+1j82hyYAlR62rGh5olTEh35QBGMAZY1kNvjY/qym6/yD/6VP/IVPuFC/k6HQCb74/yUn++/Hya7AuW7z+RGoBewFlNKGCAHSqdx6UXreKv/OCh9DzMGWh3t/ziJyD5MgNDSfnEBkzC9Dlp/Jb7GwoihJsauAfaGWv0zXBMw54NgEOICn867qSwOgYIL6Mi4m15ghaOBRUtnUT81nScn3gaLY//p0lY5MAq4DRb1uI3+7GLkwW2wCXgYTpTCi8cbf/fPvedhOs4/xvSpv4Utbdl+LxOEbOhBY6DQkV9ddBdzqh/mrCI/zO6DWYtAEks1rQdvBdAb2JuDAoV46mN+cPkwQwNLOgeGCFpzpmFFF9DfLAQWvDqUgclx0HTXVBX7QGHIHC7780sMZTMj/QV8jRfY1+tiKNdaDYllDxRnQnF/bh+2zEyHysUsL3w70CXw74AfFwPfALYfaeP21waChG1NHjvAz7++gJ8PWQDLKjAFf9SbYC9eYDXs7YzWX4m3tTB1LI1Ji3vP8PpRwAhgPa0H5NVQ0Rl8uzErSR7BDGXo2ExVsQ8UJsJT3MhzXM8E/sAH110E5Z6Yf6yEqxpzAlnfxh6CcAVXL2yqHnZ6YGcsPk/iR2PX1igjvKGGPDOiUNbtDK+rBV9nzPHa4uIfkmJiHyjM3sdXimtMj2QZqGtZRMQK66Esl9aHh7yYKp1uzNofInHJUfi9+d5JEtN0SpHEd5CWF/ZqrhpzTAdvWvo91dmwjoIklv6YmvA7UBe0SCIbAwzGzCzaGOI1LmCWWfI9C2gANp4pp0GS3VlWN0DsLh2T9ZhpdUNEpFV5MBDMyp4tyTTPDcTkPI4P3EK+XlKFehQkQnswqw4es7ohItIqX2CGY0vTHF2QMcN0OhwGtgMTgdwGmJ4bxzZKIlKPgkSoFjP2qXFMkcR2whRmCpVPlAf0A+ow+Y4N8WmVJD71KEiE8oEBmJ6FPW18j5IfReLvZajIoeXZDF4oLYHSPEyuUS0UTgDSgFVxbKMkIgUK0k4uzNcnD5PQGPwqBe+D3ZvBgCCTxnUdwJysgjXpvbS/Nr2ItM0xzHHZ/FgLHssHAq/ZF3jNRsxxq0JLqU6BgrRTb2AE9MuByUCXvpDV11Rv7IJJWziM6cb0QVpRNX077eYTzqXmaBYNc0eb81DpEUyBja3ohCQSS8MDt600rgRaD1yC6RnMJFhy3xyLwzHJynvQsZnaFChIOwVWmXRibhmYICHXT1qXT2nwZZvHA4FCTqeDnMv/AXB2p8/Y3yXbvAc3+hqKxIOTxtVhCfy7nsZ6CU2FWjFWUpHO0NJOe4By2JkNOzvTeBJy0nAqB6Fx6ej95LCfgYG/P8OsIlmL+aXiQ8mQIrH2Nib/oOmQH5gehveavC44NLEPc0yrNyHVKVCQdgqebKpRPXgROwges+E8LgIOv99vdRtEREQkQamOgoiIiISkQEFERERCUqAgIiIiISlQEBERkZAUKIiIiEhIChREREQkJAUKIiIiEpICBREREQmp1cqMDofHttWY/H6Pw+o2JBrtz+ShfZlctD+TRzLuS/UoiIiISEgKFERERCQkBQoiIiISkgIFERERCUmBgoiIiISkQEHC5AKyAbfVDRERkThQoCBhcGNm1PoCN0lN2UAeChZFUoMCBWkjN5ATuK8FvNY2RyxUCNyICRZEJNkpUJA2ygEGoYuDQDVQDtRb3A6RVFMI3AsMj+unKlCQNuoDAwcAo6xuiFhuI/AMJlgQkbgpLKSr/2MYODquH9tqCWeRxiEHYCdAmYVtkcShoSeR+JkBEzMhDw795/mBczGYHt4emPNyVcw+XYGCnEEPYCzmizgfXSBEROJsQyafDXPwpXI/DFxI47DfBOiXCaXZwMsx+3gNPUgIbkyQ4MYECRW0HCT0xyS29Y9f00REUsIE6O2BBpjQ6Xl4FE5LJp+cSaftldBlcExboUBBQsjGJMxkAuuBXSFedy3M7mXuRUQkeiYO4Md7fgN7YZ3jY1g+/7Snv/nsW9Ss/Qosjm0zNPQgIRwD9gNHQjw/AfIHmHIKKwF2x6thIiKpoQT+6x93wBowPQmnG8x2WAYsDLWBTEyvcDWRDBurR0FCqAW2EjJ5sXAAI/dtgDFAuQd4Pm4tExFJCZXrId8HWza2+PR3eZUHX4eCi/4aYgM9gXwiLY6mQEECXDRGn22w0c+m+WNgeQybJCKSklyYc/ERzA+2ihZf9R79uf0cKD/SK8R2MolGyX0NPUiAE5O8WE/b5sffB3Nj2iARkRTlxFzgD9LalPSN/Ct3/2Ix3i2ZIV6RDeQCOyJqjXoUJCATU3kxv9njoRaBCvZAeOA2j7kXEZEIDMWcSydg8sSCeQVXQlbw8UYfc57JOa9saVsu6D4YxucABRG1SoGCBGRDWgEmWHA1eTxYcCm72evd5rEFsGjRVHggTs0UEUlao2AFMLAAkycWqJeQOxiKMQnkTZQd/Sr7h3cN0QnshunA4gbMeb39NPQgAVXQ8J65Py07th6TMdu8rn89UAseuD1rGcyLTyslGoLjn160XoNIIugDXAm4YDZQEaiyOMLDNze/xTvvA0v4wihEVqcazq8+BFktbbPedE6sSQM2R9Q6BQoSUA282MLjXlouDeo176nzwNRYtkuiLzj+2eQXi4hYqD8UpZtpkBWeU4/23LyXP+V8h8VVN3H74mVAyWnv+jI1OPYRIlDwQoMHtkTeOgUKKc+NyUuo5/RwdTj0Gw2lYEo39wbGQkY6DMGsC8RzmAtONrCn2ft7ADngvApGAAOBLsDcCszEX7GOj0jnVYtINO2CFQXAe6c9un90by6vWsPrD16NCRJO71LY89IgFl49DRa09XPcmB7FL9ZkaI0ChZQXTGKsxhRYCl48Ck0hpcXAEjcwACamwyjInnqA6tk9YEEfIB/S0qHBx+lf4lygP8yDrrM+4gpeoRfleFY+GAg+xDpeFCSIJJI9mB9kzRR7eN0B8JeW37YMZg5ZCBva+jmZmMt+PeGcAxQopLxaYBtfvHgUw5ixUBEcx94Bq7JhVWeql/SAnX5M9HsAGjryxSk85cAJmJ3LoZXn81S/aaZHoXRfzP+LRJLDWHhgqFkpcOUTxHJ1QLGZ3h5YAt8c+RbfZjMPjbi3lWDBBcwxv90qKjDn5qYzKs5MgULKq8dEs81thYqtTf7e0/i6nU1fF2qOb1Xgtsu8fmeIl4lICEOZPut+XuEK9q3shoaL5JTF8ObIb5FDFW5O8NCQ1gIFN0wBRgHTc+GwC7N2T9uHH9ofKGR5ePLTG/gtt7DN8TGnd1tL8uiBmds7AAoxuQajMN+cNOgzcgfjWcMypnLowfPN6maVG2FMIdwGjAPVWBBphzQoZCNZ1OCZ+CCsAxoeRAmowjj4zl1/MullXTjDrDMvbA/883AF8Dbxy1G4DS52PMvWV57FwR+BAyhQSEY5wABzwZ8Hgy4u4fs8g5sTpFPPjdufhx/AfVt+xfdmPc1qimD2IFjs5+Gv/pSZEx+HVVb/N4jYUBYM5C9kUWMuBF2AJS4UKAgNnkBwEMgTa7Xyogv2YnKY2UHLPcita3+gMK+Wy3wVVK/sATyBvrzJqhxYD+vyYUMBO/JGsGPgCPPNccL8p+9h5Z7vMYEX2f/N3rDlBPAGDLyWmWMeh1UfWtt8EbuqhMf5KZ/xJchogKw0q1skCacaM/x7rIXnMuG2GTARGA/sfZe2lef/oghyFBZSrQyHFFCNWZRkK/hcUOaEssbKjfsaZjDjhYXsv643bPE0vq1ul3oSRCJyhJe5EjcnOMt5ks/TQGllcrpaQhdOc9J10UfcySPc43wEeLndn6JvnYTgArphurZGQHcgH7I3HmBvh97UkEUV3ejIErpwhNxbjygwEImqjez5+rWQBjQQSAhWz600VY8ZU2hp2L+eQ987n3vGPwKHI+vZVaAgITgxc24LYAxmGGwY/L7D91nlqKMPdQzvVIHrCmAyuIbXhshQCfY+KH/Fflw0zrmW+CuDnauBdExRs5ZKqYuEOrf6TC2cLWBmObSfAgUJwYdJUH0DNvQBMmFxNmMbNrLcfz0PcT3rX5lglkl/B7g61HbuhH7pULoVWB+fpkuUzDEzVxbXAgutbkwKqsfMJnNigrYzBdsuoGPgfQooUkOw0mJLBZS8wGooz6S1parbQoGChBAswFTLaV+yWz1MyX8O7gK2e868me7pJpGmNC8GbZSYGoYJFFZkQo3VjUlV4UxjywbyMPVLNAstuWVigkJf4O9Q1VYj60kI0jLTEh7fPrOk9HZ/215fWQLzjgBvxLJVEgtbNkLvE1Cz1uqWSFuMmcZt/l1QMgW4G1P4RJLSghlM9x+GydOIx5CUAgUJ04HAAmbNu7KCSxe7mj2+A7Mq5YHYN02ibCPwEK3P0ZaEUQSLts+iaPhSyHMBPa1ukcSIa0otj3xyT6CgXex7jhQoSJjeg7rVQHGzx6+B7rMwmY9NHQMOojFTkdjKnnyAqm/Ab7jDFOPp3svqJkmMePtl4njDD0Xx+TwLAgU3ptqfO/4fLVFQjRn3ar5ATS70A7Nvm9JKhfaVD4wN3Gfyxd4iSSQ5HapYC2RvbzDHYnerWyQxU7nVrN8QpyFdCwKFWfDANHMvSeQ5KF5PJEU9JLFc7M/Hv2AYg/x50H0GMNLqJkkr9rw2iJuXwOIhN5lpcVrOPYltBZYTrWTFM4l/oNAFk02dG/dPlpg6gPnyaincZHE5r/HhXXA5r5tkejItbpG0qhSqb0ljI4UmhcjXxoRjsaFqTDnm8BZ3aq/4T488vAIKB9CehSlEJH4ectzLQ5PvBQfAapSQmuAegK5Fh/l8xTmBFCKdYyU6LKijUEakxR9EJB48pgtb7OFwLZ8vyDSV+Gr8mCRikcip4JKISFL4PSzIw3RHq9yzRI8CBRGRpFCFcoQkFlRHQUREREJSoCAiIiIhOfx+TaERERGRlqlHQUREREJSoCAiIiIhKVAQERGRkBQoiIiISEgKFERERCQkBQoiIiISkgIFERERCUmBgoiIiITU6loPDofHttWY/H6Pw+o2JBrtz+ShfZlctD+TRzLuS/UoiIiISEgKFERERCQkBQoiIiISkgIFEZGk4gIyA/cikVOgICKSNFxAb+AaoL/FbZFkoUBBRCQpuIFsyLoW1vSCvKusbpAkiVanR4qIiF1cCcP6wjAgDZ3dJWrUoyAikgzm9aX8nW7c+MgTsBc4bHWDJFkoUBBJWW4gB5P4JvY1FPCQPfsAPdceogtHYA1QU2t1wyRJKFAQSVk9gFFAvtUNkUjkj+VS/zq2dBgGs6ADJ2HjQmCR1S2TJKFRLJGUczcsSYcVQEkJoF+e9tQDGA6zYSk305E6vFvg8eM/Af7T6sZJElGPgkiq2ZKOf4mDX2++E3gbKLO6RdIuo2B6Xx686XbedFTS8XgdXxrlpy5Dp3WJLvUoiKSMCTBxAGflHWfTXy7hngP3Aw9Y3SgJmxvoAQN7cdkjL/EvbGbYpbD0nCLYfgLYZXUDJckoUBBJFRMHsO2F/lzHCxQ6pgK/sbpF0i45wGhYCa9NHw9XwbNvXs2P330amA94LW6fJBsFCiJJrxAGFkI/mMCLVPxHAbAaXVDsagIUZ1J04VIYCSWXDuKG/S+AB7RPJRYUKIgkuy6FFP1lKSs23UyFYz3wPLqg2NjkTPxfc+Cvhj9f04+rT66FvEUoKVViRYGCSLI7XMGK394MKwGOoSDBrq6EWweT+8Q+2Al/PO+bjDmyAe9dmUC91Y2TJKZAQSTpLYNbrW6DRKz3YFY/cQWD2Q7V8Dt+gLfLcqDa6pZJktM8GpGklQNMAgotbodEpj9098BimPDJeuroyOWXruGpJ6ehngSJBwUKIkkrH4b0xQQKLovbIu03Akr8zPzOPPglHKYLrzsGw9TVKFCQeNDQg0jSmQYVnWBLGkz0AztQXoKNdc/h5a9+hwv5O7X/6eJxfgLsAQ5a3TJJEepREEk2o3Lwv+FmzbWXY2Y4vGx1iyQSo2DchLco+M8KZnRYyPNv3ogpqlRldcskRShQEEk2xUfoeN1Bxj/5GrqY2FkOMA3GAceBofDkfbfBFIjukEM+MBzIjuI2JZlo6EEk6SyiLsPqNkjkesCwHDLGHYJFcGhIBnxjNdEv0dwncKtCMyikJepREEkaY+FRDxR50K/DZJAPC6D/ObuoLOnEv/MQpg5GNLlg4mAoSQeuj/K2JVkoUBBJGkO5+2f30efpHShQSAZ5XDb8Jb7G37iVJ3hqa4ymQ86GucPvgX6aGSMt09CDSNJ4g4em3QulABstbotEzs1X+IT/41xe//nVsApiUqZ5JTza+87A90biIwcYCeRBbjo0AIcB9gG/BzywAdKGVTO00zY2TRsDSzxWNVaBgkjy2AZLDmB+daruv/05+DI17KEvzKsFthKTHoViqOveFaiI/rYlhGxw9oWBQBFQA5QB6BzPZAAABGZJREFUqwpM0FAEj18+5f+3d/8hcddxHMefBx3sxvSP3Ww2JTZWqTFjDIkRWxsi/WJplIFCdNcYzI0UjcFoCR3rtmUMcjC4QdK0hLQZDGxFWrGhJomuw0k6qM1Coxw7NlsoeHD98T1z/Thvd7vvfa+71wO+KNx9v583HPp93+fz/ry/lNNNXmuAjb4xrpyyLlolCiJpYwHtckgnV2n+6QD024HPgUkTxlgA/wj4i4ABE64v/20agr0wvBaGi1hK7seNl9un2B9sZX8hsAnYAHDmLse0A2VAFvAlsRSuxpkoOG4bGP6d5S5eNhj+qWYvIsmhTn3pYxpaNoAfjClps3YkdKNeG8k2y98TMztL91UH0AYdib5v3gMUAyuBIUxOFF5jS2iUMr6ijnc4xV68RUdhDVAF5AObF2Debqy5NALnPbEPIyKS0S6B90GMWaJE73aQlOJ+g6nTTi5TQBeV+NoOgtuT4EHmMBJCB7HOPMaeKORmc45d5J64ySf18FbTMbwvHYV8eNR1gY38yGN8w+9kMVNwL817XlddlUjKctx2BDH+mcyhWcBU8Bvgi/GcxW+mWRif5wJLn6mkrAMwYgtQvm6Q0uODXHY9xNduO4n/OxyP66zYE4Vfe7mv+oaxbjKMsdTROAU4GKrfwdCqHXyUv9soyJgHJq7HFZiImK0Wapxs8/XS98sT+Na5eJPDXNt+P/R7rA5OYvY8bH6EnO9+ZoIC+nicL3gSX8NBo7+GpK5NUDEcMupJh4Ecwr+khjiWHgagI7y20viPl26EDxXPiqS+NU5WvB2ggyo682DfkTZWHprDXdkJ/VYHJ7Erhhr4gJf51jZPxTM9VBzpwfdqAzRbHZsszwMlVscQmRouiWQqL8zNOjnNK1SdDWG7FsJd3An1V62OTOJRY+Pjvc/ylP8CT5fAmXO7sJ0IwQPB6OeKLEPbI0UyVSHwIfQd2g7PeayORu7WVnjR/6mxDF0K79IArU0s7T4TiY9mFEQy1c4pbOMhemwVVkciiVAItMOV6lwebhphsLoUFaZKIiRhRmE1kAfMoGYwIqmkBdqtjkESZgVwE0YoYbx2C3RYHZCkC/NnFMrqcIVm4Pg+04cSEclYXXDxvSJ2//E+nOxHWaAkivmJQiGU0w1bTR9JRCRzjUEXldzqysHYt/6D1RFJmjA/UTjZywv2z2DbqOlDiYhkrLPXOVZ7GPZYHYikmyTUKAxAcIg7L6hZHT4Wn4InIiLRXYLWnRD83upAJOmyMTpyBjCjeDVJxYzrMQoZp6O/Pb8O3IA3nvalIiKZ6iLcmkSPGM9ELljvhMnzmPHMhCT1UbBHf8uiVRgPmPrrSVoiIhLdLEoSMpXDuHeadN9MQqIQAEa546YfE61QvxbjsaoiIiKyvBYYy8asFgRJmlGIpdZgMnyIiIhIdIHwYQ5bKBQy7eIiIiLy/6YWziIiIhKREgURERGJSImCiIiIRKREQURERCJSoiAiIiIRKVEQERGRiP4E6KjAP4W7iDEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mrcUH08X8j6N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "bd1fb019-684c-43d8-a192-19f37f17a64c"
      },
      "source": [
        "plt.figure(figsize = (10, 6))\n",
        "for i in range(15):\n",
        "  plt.subplot(3, 5, i+1)\n",
        "  plt.imshow(visual_layer2[0, :, :, i], cmap = plt.get_cmap('jet'))\n",
        "  plt.axis(\"off\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 15 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}