Copy_of_mnsit_tutorial_keras.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of mnsit_tutorial_keras.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/kasireddybhimireddy/datascience/blob/master/Copy_of_mnsit_tutorial_keras.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cjLBfGq449Vf",
        "colab_type": "code",
        "outputId": "bac40488-45e9-4b2a-e5cc-6ac775ba4819",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "from tensorflow.keras.callbacks import TensorBoard\n",
        "import time\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i6oo0Dr1STCl",
        "colab_type": "code",
        "outputId": "1d606a9e-a8f5-4217-8e19-fa0697990df8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "mnist = tf.keras.datasets.mnist #28*28 image of handwritten of 0-9 \n",
        "(x_train, y_train),(x_test,y_test) = mnist.load_data()"
      ],
      "execution_count": 0,
      "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51gWMWV8U0v2",
        "colab_type": "code",
        "outputId": "9670b86d-fd12-4a65-c561-b11dffed969c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print(\"Training Data Shape is {}\".format(x_train.shape))\n",
        "print(\"Training Labels Shape is {}\".format(y_train.shape))\n",
        "print(\"Testing Data Shape is {}\".format(x_test.shape))\n",
        "print(\"Testing Labels Shape is {}\".format(y_test.shape))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Data Shape is (60000, 28, 28)\n",
            "Training Labels Shape is (60000,)\n",
            "Testing Data Shape is (10000, 28, 28)\n",
            "Testing Labels Shape is (10000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5z4YMympU_p3",
        "colab_type": "code",
        "outputId": "a3493480-c9f0-4b29-f4c2-a371e09f063c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 969
        }
      },
      "source": [
        "print(\"Sample Training Data is {}\".format(x_train[0]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sample Training Data is [[  0   0   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]\n",
            " [  0   0   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]\n",
            " [  0   0   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]\n",
            " [  0   0   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]\n",
            " [  0   0   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]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   3  18  18  18 126 136\n",
            "  175  26 166 255 247 127   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  30  36  94 154 170 253 253 253 253 253\n",
            "  225 172 253 242 195  64   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0  49 238 253 253 253 253 253 253 253 253 251\n",
            "   93  82  82  56  39   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0  18 219 253 253 253 253 253 198 182 247 241\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  80 156 107 253 253 205  11   0  43 154\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0  14   1 154 253  90   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0 139 253 190   2   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0  11 190 253  70   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0  35 241 225 160 108   1\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0  81 240 253 253 119\n",
            "   25   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  45 186 253 253\n",
            "  150  27   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  16  93 252\n",
            "  253 187   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   0 249\n",
            "  253 249  64   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  46 130 183 253\n",
            "  253 207   2   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0  39 148 229 253 253 253\n",
            "  250 182   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0  24 114 221 253 253 253 253 201\n",
            "   78   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0  23  66 213 253 253 253 253 198  81   2\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0  18 171 219 253 253 253 253 195  80   9   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0  55 172 226 253 253 253 253 244 133  11   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0 136 253 253 253 212 135 132  16   0   0   0   0   0   0\n",
            "    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   0\n",
            "    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   0\n",
            "    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   0\n",
            "    0   0   0   0   0   0   0   0   0   0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HiHbQF0LUs-M",
        "colab_type": "code",
        "outputId": "03c7ede7-0b0c-492b-aa7b-978b5df70ea1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"Sample Training label is {}\".format(y_train[0:5]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sample Training label is [5 0 4 1 9]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3RgMo0SNVEzV",
        "colab_type": "code",
        "outputId": "ae76b497-c6fa-45d5-9691-7d0c535179be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for i in range(0,20):\n",
        "  plt.imshow(x_train[i], cmap = plt.cm.binary)\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HmY3OXoiRmY2WNEftt/rwNkmLssuLJL1SYC//pF1Wbq60srQKvu/absVrd2/5j6Q7NfCK\n/yeSVhfRQ4W+/lXS/2U/+4vuTdJmDTwM/FoDr408JOn7knZK+kjS65LGtVFv/y3pfUm9GghaZ0G9\n3aSBh/S9kvZmP3cWfd8l+irkfuMdfkBQvOAHBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiCo/wcm\nwWArzGoGmwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UbWbfNLMJkpZJ2lFAH19hZpdkF2JkZpdI+p7ab/bhHZJ6ssc9krYX2MvfaZeZmyvNLK2C\nj13bzXjt7i3/kbRIw1f8/1fSvxXRQ4W+/lHSu9nPB0X3JmmLhl8G/p+Gr43cJelySbskfSTpvyRd\n1ka9PS9pn6T3NBy0zoJ6u1nDL+nfk9Sf/Swq+tgl+irkuPEJPyAoLvgBQRF+ICjCDwRF+IGgCD8Q\nFOEHgiL8QFCEHwjq/wF6JnTe716qdQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IPxAU4QeCIvxAUP8HF8NDxhA0MHUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+I5LGD3j87WxZW3D3I9nfXkmb1H6zDx+/OElq9re34H7+rJ1mbh5sZmm1wWvXTjNeFxH+\n3ZJuMLPvmNlQST+UtKWAPr7GzIZnJ2JkZsMlzVT7zT68RdKi7P4iSZsL7OVL2mXm5kozS6vg167t\nZrx295bfJM1R/xn//5X0L0X0UKGvv5H0XnbbX3Rvktar/23g5+o/N3KfpL+StEPSQUn/KenqNurt\n3yV1S9qr/qCNK6i36ep/S79X0rvZbU7Rr12ir0JeN77hBwTFCT8gKMIPBEX4gaAIPxAU4QeCIvxA\nUIQfCIrwA0H9H7Eqh22M6C7eAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LentS0rCk1zUatDkF9bZIo5f0r0s6lP0sL/rYJfoq5LjxDj8gKF7wA4Ii/EBQhB8IivAD\nQRF+ICjCDwRF+IGgCD8Q1P8DS+Y5YpiksR0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiCo/webFEk6bu28lwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QhB8IivADQRF+ICjCDwRF+IGg/h+py0p9Jsq34QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gfr7WTis3D7WytNrgvmunFa+LCP8uSbeY2UQzGy1psaStBfTxLWZ2bfZCjMzsWkk/UPut\nPrxV0pLs8hJJrxfYy0XaZeXmSitLq+D7ru1WvHb3lv9IelgDr/j/r6S/L6KHCn39laQPsp+Piu5N\n0kYNPAz8Pw28NvKEpL+QtF3Sp5L+U9INbdTbv0jaL2mfBoLWWVBv92jgIf0+SXuzn4eLvu8SfRVy\nv/EOPyAoXvADgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxDU/wOhSHNQbYxDHAAAAABJRU5ErkJg\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1+vobSW9kt7er7k3Ssxr7GPh/Gvtt5H5JMyUdknRS0n9JuqaDenta0puSjmssaHMq6m2p\nxj7SH5d0LLutqPq9S/RVyfvGEX5AUPzgBwRF+IGgCD8QFOEHgiL8QFCEHwiK8ANBEX4gqP8H511c\n7+OrvLUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XqT4ouf8P5gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bfyTdoJF3/P9P0n8U0UOVvmZK2pn99BXdm6RnNPI08JhG3hu5XdKFkrZI+lDS7yVN6aDe\nnpL0vqT3NBK0aQX1dq1GntK/J2lH9nND0Y9doq9CHjc+4QcExRt+QFCEHwiK8ANBEX4gKMIPBEX4\ngaAIPxAU4QeC+n9TsFv2oRSITQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JjT8N/Ejj743cIulsSUOS3pH0K0m9XdTbf0raLekNjQetv6TeLtf4U/o3JO3Kfq4u+7FL\n9FXK48Yn/ICgeMMPCIrwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQ/w9ayVj3tIRW7wAAAABJRU5E\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nvwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRfiBpAg/kBThB5Ii/EBShB9IivADSf0//vEeuvzxFbAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YRKEzCQL9Bcy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JeMHNaPLUrRm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = tf.keras.utils.normalize(x_train, axis = 1)\n",
        "x_test = tf.keras.utils.normalize(x_test,axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QC6iVuk2UgHU",
        "colab_type": "code",
        "outputId": "611ad8d7-968a-4bf0-fe6f-83bbeae489b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(\"Training Data after normalizing is {}\".format(x_train[0]))\n",
        "print(\"Testing  Data after normalizing is {}\".format(x_test[0]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Data after normalizing is [[0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.00393124 0.02332955 0.02620568 0.02625207 0.17420356 0.17566281\n",
            "  0.28629534 0.05664824 0.51877786 0.71632322 0.77892406 0.89301644\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.05780486 0.06524513 0.16128198 0.22713296\n",
            "  0.22277047 0.32790981 0.36833534 0.3689874  0.34978968 0.32678448\n",
            "  0.368094   0.3747499  0.79066747 0.67980478 0.61494005 0.45002403\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.12250613 0.45858525 0.45852825 0.43408872 0.37314701\n",
            "  0.33153488 0.32790981 0.36833534 0.3689874  0.34978968 0.32420121\n",
            "  0.15214552 0.17865984 0.25626376 0.1573102  0.12298801 0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.04500225 0.4219755  0.45852825 0.43408872 0.37314701\n",
            "  0.33153488 0.32790981 0.28826244 0.26543758 0.34149427 0.31128482\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.1541463  0.28272888 0.18358693 0.37314701\n",
            "  0.33153488 0.26569767 0.01601458 0.         0.05945042 0.19891229\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.0253731  0.00171577 0.22713296\n",
            "  0.33153488 0.11664776 0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.20500962\n",
            "  0.33153488 0.24625638 0.00291174 0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.01622378\n",
            "  0.24897876 0.32790981 0.10191096 0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.04586451 0.31235677 0.32757096 0.23335172 0.14931733 0.00129164\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.10498298 0.34940902 0.3689874  0.34978968 0.15370495\n",
            "  0.04089933 0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.06551419 0.27127137 0.34978968 0.32678448\n",
            "  0.245396   0.05882702 0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.02333517 0.12857881 0.32549285\n",
            "  0.41390126 0.40743158 0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.32161793\n",
            "  0.41390126 0.54251585 0.20001074 0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.06697006 0.18959827 0.25300993 0.32678448\n",
            "  0.41390126 0.45100715 0.00625034 0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.05110617 0.19182076 0.33339444 0.3689874  0.34978968 0.32678448\n",
            "  0.40899334 0.39653769 0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.04117838 0.16813739\n",
            "  0.28960162 0.32790981 0.36833534 0.3689874  0.34978968 0.25961929\n",
            "  0.12760592 0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.04431706 0.11961607 0.36545809 0.37314701\n",
            "  0.33153488 0.32790981 0.36833534 0.28877275 0.111988   0.00258328\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.05298497 0.42752138 0.4219755  0.45852825 0.43408872 0.37314701\n",
            "  0.33153488 0.25273681 0.11646967 0.01312603 0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.37491383 0.56222061\n",
            "  0.66525569 0.63253163 0.48748768 0.45852825 0.43408872 0.359873\n",
            "  0.17428513 0.01425695 0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.92705966 0.82698729\n",
            "  0.74473314 0.63253163 0.4084877  0.24466922 0.22648107 0.02359823\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]]\n",
            "Testing  Data after normalizing is [[0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.        ]\n",
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            "  0.         0.         0.         0.         0.33861822 0.450819\n",
            "  0.40358052 0.330929   0.07161837 0.         0.         0.\n",
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            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.33861822 0.450819\n",
            "  0.32890224 0.02719964 0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.        ]\n",
            " [0.         0.         0.         0.         0.         0.\n",
            "  0.         0.         0.         0.         0.         0.\n",
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            "  0.         0.         0.         0.        ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tWIs89KTWlh0",
        "colab_type": "code",
        "outputId": "6746cf5d-90e8-4617-8f66-393223e9a682",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Flatten()) \n",
        "model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu))\n",
        "model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu))\n",
        "model.add(tf.keras.layers.Dense(10,activation=tf.nn.softmax)) # softmax for probability distribution\n",
        "model.compile(optimizer = \"adam\" , loss = 'sparse_categorical_crossentropy' , metrics = ['accuracy'] )\n",
        "model.fit(x_train,y_train,epochs = 3 )\n",
        "predictions = model.predict([x_test])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "60000/60000 [==============================] - 5s 83us/sample - loss: 0.2663 - acc: 0.9223\n",
            "Epoch 2/3\n",
            "60000/60000 [==============================] - 4s 67us/sample - loss: 0.1053 - acc: 0.9672\n",
            "Epoch 3/3\n",
            "60000/60000 [==============================] - 4s 66us/sample - loss: 0.0728 - acc: 0.9776\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IvfQVcuWSewk",
        "colab_type": "code",
        "outputId": "88484d8b-c570-445c-904b-b6a8a8b5d9b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print(\"Probability distribution for a multi-class classification\")\n",
        "predictions[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Probability distribution for a multi-class classification\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4.1246079e-11, 3.1149664e-08, 9.4184941e-08, 2.1265657e-06,\n",
              "       1.6074571e-10, 1.4645993e-08, 8.5587949e-14, 9.9999774e-01,\n",
              "       1.0664741e-10, 5.5634601e-08], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0GLNwMw2YgSO",
        "colab_type": "code",
        "outputId": "fa4e57f7-77c9-4567-a748-c74861f990c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(np.argmax(predictions[0]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fMzJJrGCYrf9",
        "colab_type": "code",
        "outputId": "e64fecae-e1b2-4ad1-bbb6-af819d1e48a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "plt.imshow(x_test[0], cmap = plt.cm.binary)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H8EUA3M_SoCF",
        "colab_type": "code",
        "outputId": "0fdbde9d-6489-4d64-e6b8-7301785c0452",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "val_loss, val_acc = model.evaluate(x_test, y_test)\n",
        "print(val_loss, val_acc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 0s 43us/sample - loss: 0.0949 - acc: 0.9695\n",
            "0.09493139761416242 0.9695\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dhh9W4N-SwFz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = model.predict([x_test])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_t4-R7phS8kB",
        "colab_type": "code",
        "outputId": "229dfbcc-f947-4ec7-db87-a8a6487dcafa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "plt.imshow(x_test[0], cmap = plt.cm.binary)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rscobu5vS35U",
        "colab_type": "code",
        "outputId": "7414fcd7-65ed-4a54-e182-9a8f3ca8768e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "print(np.argmax(predictions[0]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KoYIgCIOY-os",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from IPython.display import display, Javascript\n",
        "from google.colab.output import eval_js\n",
        "from base64 import b64decode\n",
        "\n",
        "def take_photo(filename='photo.jpg', quality=0.8):\n",
        "  js = Javascript('''\n",
        "    async function takePhoto(quality) {\n",
        "      const div = document.createElement('div');\n",
        "      const capture = document.createElement('button');\n",
        "      capture.textContent = 'Capture';\n",
        "      div.appendChild(capture);\n",
        "\n",
        "      const video = document.createElement('video');\n",
        "      video.style.display = 'block';\n",
        "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
        "\n",
        "      document.body.appendChild(div);\n",
        "      div.appendChild(video);\n",
        "      video.srcObject = stream;\n",
        "      await video.play();\n",
        "\n",
        "      // Resize the output to fit the video element.\n",
        "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
        "\n",
        "      // Wait for Capture to be clicked.\n",
        "      await new Promise((resolve) => capture.onclick = resolve);\n",
        "\n",
        "      const canvas = document.createElement('canvas');\n",
        "      canvas.width = video.videoWidth;\n",
        "      canvas.height = video.videoHeight;\n",
        "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
        "      stream.getVideoTracks()[0].stop();\n",
        "      div.remove();\n",
        "      return canvas.toDataURL('image/jpeg', quality);\n",
        "    }\n",
        "    ''')\n",
        "  display(js)\n",
        "  data = eval_js('takePhoto({})'.format(quality))\n",
        "  binary = b64decode(data.split(',')[1])\n",
        "  with open(filename, 'wb') as f:\n",
        "    f.write(binary)\n",
        "  return filename"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rvpKkDQ8Y-qq",
        "colab_type": "code",
        "outputId": "e6ed55d8-02ab-4093-ecb7-f2601108688a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        }
      },
      "source": [
        "from IPython.display import Image\n",
        "try:\n",
        "  filename = take_photo()\n",
        "  print('Saved to {}'.format(filename))\n",
        "  \n",
        "  # Show the image which was just taken.\n",
        "  display(Image(filename))\n",
        "except Exception as err:\n",
        "  # Errors will be thrown if the user does not have a webcam or if they do not\n",
        "  # grant the page permission to access it.\n",
        "  print(str(err))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function takePhoto(quality) {\n",
              "      const div = document.createElement('div');\n",
              "      const capture = document.createElement('button');\n",
              "      capture.textContent = 'Capture';\n",
              "      div.appendChild(capture);\n",
              "\n",
              "      const video = document.createElement('video');\n",
              "      video.style.display = 'block';\n",
              "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
              "\n",
              "      document.body.appendChild(div);\n",
              "      div.appendChild(video);\n",
              "      video.srcObject = stream;\n",
              "      await video.play();\n",
              "\n",
              "      // Resize the output to fit the video element.\n",
              "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
              "\n",
              "      // Wait for Capture to be clicked.\n",
              "      await new Promise((resolve) => capture.onclick = resolve);\n",
              "\n",
              "      const canvas = document.createElement('canvas');\n",
              "      canvas.width = video.videoWidth;\n",
              "      canvas.height = video.videoHeight;\n",
              "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
              "      stream.getVideoTracks()[0].stop();\n",
              "      div.remove();\n",
              "      return canvas.toDataURL('image/jpeg', quality);\n",
              "    }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saved to photo.jpg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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zUOl1wRNWodmBFhpPVqDzaNFw1uz37NKzuL67bQfDlpStaN3d/ePc0OgmAF2lTVNJ01rm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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vy-aBLhbrFZH",
        "colab_type": "code",
        "outputId": "1e43fe03-86e6-4493-8e56-e951c4c9cc71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        }
      },
      "source": [
        "from PIL import Image\n",
        "user_test = filename\n",
        "col = Image.open(user_test)\n",
        "gray = col.convert('L')\n",
        "bw = gray.point(lambda x: 0 if x<100 else 255, '1')\n",
        "bw.save(\"bw_image.jpg\")\n",
        "bw\n",
        "img_array = cv2.imread(\"bw_image.jpg\", cv2.IMREAD_GRAYSCALE)\n",
        "img_array = cv2.bitwise_not(img_array)\n",
        "print(img_array.size)\n",
        "plt.imshow(img_array, cmap = plt.cm.binary)\n",
        "plt.show()\n",
        "img_size = 28\n",
        "new_array = cv2.resize(img_array, (img_size,img_size))\n",
        "plt.imshow(new_array, cmap = plt.cm.binary)\n",
        "plt.show()\n",
        "user_test = tf.keras.utils.normalize(new_array, axis = 1)\n",
        "predicted = model.predict([[user_test]])\n",
        "a = predicted[0][0]\n",
        "for i in range(0,10):\n",
        "  b = predicted[0][i]\n",
        "  print(\"Probability Distribution for\",i,b)\n",
        "\n",
        "print(\"The Predicted Value is\",np.argmax(predicted[0]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "307200\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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URdjd3ZWLdzweQykltaurzG3CKeY2bIIAZp7k/HKXNwmujBiS4oqHy2dezIwdDodD+fmq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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vSvp+9vK2knzyPVuVhmsamrm5U6aZWfovynzump3xumhlhP+4pFVT7n85W1YJ7n48+35K\n0h5Vb/bhkxcmSc2+nyq5n7+o0szN080srQo8d1Wa8bqM8B+UtNrMvmJmiyR9R9LeEvr4AjNbkn0Q\nIzNbIukbqt7sw3slbctub5P0XIm9XKQqMzfXm1laJT93lZvx2t07/iXpDk1+4v+epH8so4c6fV0j\n6X+zr7fK7k3SM5p8GXhek5+N3C9phaR9ko5I+m9J3RXq7d8kHZb0hiaDtrKk3m7R5Ev6NyQNZF93\nlP3cJfoq5XnjCD8gKD7wA4Ii/EBQhB8IivADQRF+ICjCDwRF+IGgCD8Q1P8DtCvWRhBAydUAAAAA\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Probability Distribution for 0 0.025624145\n",
            "Probability Distribution for 1 2.8128096e-07\n",
            "Probability Distribution for 2 0.0043100123\n",
            "Probability Distribution for 3 0.0031843842\n",
            "Probability Distribution for 4 0.0006469589\n",
            "Probability Distribution for 5 0.0029254246\n",
            "Probability Distribution for 6 7.786982e-05\n",
            "Probability Distribution for 7 0.0059413826\n",
            "Probability Distribution for 8 0.051996563\n",
            "Probability Distribution for 9 0.9052929\n",
            "The Predicted Value is 9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sjjtGv0DVSej",
        "colab_type": "text"
      },
      "source": [
        "1. Youtube Link: https://youtu.be/Bx4o4ySijSo\n",
        "2. Github: https://github.com/anmolchawla/MNSIT-Webcam-Tutorial\n",
        "3. Google Collab Link: https://colab.research.google.com/drive/1KTYo80VpJ6VCwa558NZxbdP1UIkv3OGV\n",
        "4. Meduim Article: https://medium.com/@anmol.chawlatrojan/mnsit-data-a-real-life-test-83f220e5422a\n",
        "5. Website: http://anmolchawla.me\n",
        "6. Email: anmol.chawlatrojan@gmail.com\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e06xE0IxVWta",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}