MNIST_hand_written_digit_recognition.ipynb

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  "metadata": {
    "colab": {
      "name": "MNIST hand written digit recognition",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sasidharswarangi/Crowd-Counting/blob/master/MNIST_hand_written_digit_recognition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TPcE-cf03x1G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 79
        },
        "outputId": "415df8ef-8557-426f-d5e2-95fd9cd495c5"
      },
      "source": [
        "from __future__ import print_function\n",
        "import keras\n",
        "from keras.datasets import mnist\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Activation\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras import backend as K\n",
        "import matplotlib.pyplot as plt\n",
        "import random"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u9ykG_EHxT5y"
      },
      "source": [
        "### Tuning parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vk-aKTUq37Mp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "82ac2000-e086-4d61-fcc9-d49a2d5c2155"
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Q22VwhX38Uo"
      },
      "source": [
        "batch_size = 128\n",
        "num_classes = 10\n",
        "epochs = 5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "48yFsajKeKXg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "294fbb14-77bf-4687-c9e5-c726fac515c5"
      },
      "source": [
        "for i in range(0,10):\n",
        "  plt.imshow(x_train[i].\n",
        "          reshape(28, 28), cmap='Greys', interpolation='nearest')\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kOWq/qag3S1qc3V8saVOBvXxHu0zjXW2acRX82hU+/bm7t/xH0jwNHpH/VNK/F9FDlb7+\nUdI72c+uonuT9LQG39b9nwaPbSyRdJmkrZI+lvSypEvbqLf/kvSepHc1GKxJBfU2XYNv0d+VtCP7\nmVf0a5foqyWvG6fLAkFwgA4IgrADQRB2IAjCDgRB2IEgCDsQBGEHgvh//v1TaNV8b54AAAAASUVO\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gy4yrDZ67Mpc/LyPs70iaZGZXmtlwSXMkbSmhj28xsxHZiROZ2QhJP1H7LUW9RdL87PZ8\nSa+U2Ms3tMsy3nnLjKvk56705c/dveUXSTeq/4z8/0l6pIwecvr6B0l/zi57yu5N0kb1v6w7rv5z\nGwskXSJpq6QPJL0haWwb9fafknZJek/9wZpQUm/T1P8S/T1JO7PLjWU/d4m+WvK88XFZIAhO0AFB\nEHYgCMIOBEHYgSAIOxAEYQeCIOxAEP8PJdJc1jCDmVwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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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kQdiBJAg7kARhB5Ig7EAShB1I4v8Agm4e9UiNt68AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pGXSz264W9/O1dprGu69pxtUG+66V05+3IuxvSbrMzMaa2Xcl/VTS2hb08Q1mNjg7cSIz\nGyzpx2q/qajXSpqV3Z8l6cUW9vI32mUa71rTjKvF+67l05+7e9N/JN2knjPy/yvpn1rRQ42+LpX0\nP9nPu63uTdIq9bysO66ecxuzJV0saZ2kjyT9t6SL2qi3/5D0jqRt6gnWqBb1dq16XqJvk7Q1+7mp\n1fsu0VdT9hsflwWC4AQdEARhB4Ig7EAQhB0IgrADQRB2IAjCDgTxfxCyZWiOyYzMAAAAAElFTkSu\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAL9ElEQVR4nO3dUagU5xnG8efJaUWIJWhdRFSqFiGE\nQm1YTKEhpJQWI4hpLkK9KBZC7UUCFXLRkEIquQqlrSQghdNGeixtQqAN8ULaWimE3CTZyGliIq0x\nMVQxumJi9SZG+/bijOmJOTt73Jnd2eb9/2DZ2Xlnz7wMPs7ufLv7OSIE4NPvhqYbADAahB1IgrAD\nSRB2IAnCDiTxmVHubOnSpbF69epR7hJI5fjx4zp79qznqlUKu+2Nkh6XNCHp1xHxWNn2q1evVqfT\nqbJLACXa7XbP2sAv421PSNot6S5Jt0jaavuWQf8egOGq8p59g6Q3I+KtiLgk6WlJW+ppC0DdqoR9\nhaR/zXp8olj3Mba32+7Y7nS73Qq7A1DF0K/GR8RkRLQjot1qtYa9OwA9VAn7SUmrZj1eWawDMIaq\nhP1lSetsr7G9QNJ3JO2rpy0AdRt46C0iLtt+QNKfNTP0ticiXq+tMwC1qjTOHhH7Je2vqRcAQ8TH\nZYEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4k\nQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUii0iyuGH9Xrlwp\nrR87dqy0vmPHjtL6/v1M4vv/olLYbR+XdEHSFUmXI6JdR1MA6lfHmf3rEXG2hr8DYIh4zw4kUTXs\nIekvtl+xvX2uDWxvt92x3el2uxV3B2BQVcN+e0TcKukuSffbvuPaDSJiMiLaEdFutVoVdwdgUJXC\nHhEni/szkp6VtKGOpgDUb+Cw277R9ueuLkv6lqTDdTUGoF5VrsYvk/Ss7at/5/cR8adaukJtPvjg\ng9L6zTffXFpfuXJlaf3ixYul9UWLFpXWMToDhz0i3pL05Rp7ATBEDL0BSRB2IAnCDiRB2IEkCDuQ\nBF9xRakTJ06U1s+fP19aZ+htfHBmB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkGGdHqYhougXUhDM7\nkARhB5Ig7EAShB1IgrADSRB2IAnCDiTBODtKFT8V3lO/n6rG+ODMDiRB2IEkCDuQBGEHkiDsQBKE\nHUiCsANJMM6OSqanp0vra9euHVEn6Kfvmd32HttnbB+etW6J7QO2jxb3i4fbJoCq5vMy/jeSNl6z\n7iFJByNinaSDxWMAY6xv2CPieUnnrlm9RdJUsTwl6e6a+wJQs0Ev0C2LiFPF8ruSlvXa0PZ22x3b\nnW63O+DuAFRV+Wp8zPwiYc9fJYyIyYhoR0S71WpV3R2AAQ0a9tO2l0tScX+mvpYADMOgYd8naVux\nvE3Sc/W0A2BY+o6z235K0p2Slto+Ieknkh6T9Izt+yS9I+neYTaJwd1wQ/n/54sXl4+avvfee6X1\nI0eOXHdPaEbfsEfE1h6lb9TcC4Ah4uOyQBKEHUiCsANJEHYgCcIOJMFXXD/lFi5cWFrfvHlzaX3v\n3r11toMGcWYHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4k\nQdiBJPg+Oyo5e/Zs0y1gnjizA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EASjLOjkqmpqdL6rl27RtQJ\n+ul7Zre9x/YZ24dnrdtp+6Tt6eK2abhtAqhqPi/jfyNp4xzrd0XE+uK2v962ANStb9gj4nlJ50bQ\nC4AhqnKB7gHbrxYv8xf32sj2dtsd251ut1thdwCqGDTsv5T0RUnrJZ2S9PNeG0bEZES0I6LdarUG\n3B2AqgYKe0ScjogrEfEfSb+StKHetgDUbaCw214+6+G3JR3utS2A8dB3nN32U5LulLTU9glJP5F0\np+31kkLScUk/GGKPGKKNG+caaPkf5mf/9Ogb9ojYOsfqJ4fQC4Ah4uOyQBKEHUiCsANJEHYgCcIO\nJMFXXJNbs2ZNpedfunSptH7+/PmetZtuuqnSvnF9OLMDSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKM\nsyc3MTFR6fkRUVr/8MMPK/191IczO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kwTh7cu12u7S+fv36\n0vr09HRp/YknnuhZe/TRR0ufi3pxZgeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJBhnR6l77rmntP72\n22+X1h955JE620EFfc/stlfZ/pvtN2y/bvuHxfoltg/YPlrcLx5+uwAGNZ+X8ZclPRgRt0j6qqT7\nbd8i6SFJByNinaSDxWMAY6pv2CPiVEQcKpYvSDoiaYWkLZKmis2mJN09rCYBVHddF+hsr5b0FUkv\nSloWEaeK0ruSlvV4znbbHdudbrdboVUAVcw77LYXSfqDpB0R8e/ZtZj51cE5f3kwIiYjoh0R7Var\nValZAIObV9htf1YzQf9dRPyxWH3a9vKivlzSmeG0CKAO87kab0lPSjoSEb+YVdonaVuxvE3Sc/W3\nh3Fnu/Q2MTHR84bRms84+9ckfVfSa7avfnn5YUmPSXrG9n2S3pF073BaBFCHvmGPiBckuUf5G/W2\nA2BY+LgskARhB5Ig7EAShB1IgrADSfAVV1Ty/vvvl9ZfeumlnrXbbrut7nZQgjM7kARhB5Ig7EAS\nhB1IgrADSRB2IAnCDiTBODtKTU5OltYXLlxYWl+7dm2d7aACzuxAEoQdSIKwA0kQdiAJwg4kQdiB\nJAg7kATj7Ci1efPm0vqhQ4dK6wsWLKizHVTAmR1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkug7zm57\nlaS9kpZJCkmTEfG47Z2Svi+pW2z6cETsH1ajaMbu3bubbgE1mc+Hai5LejAiDtn+nKRXbB8oarsi\n4mfDaw9AXeYzP/spSaeK5Qu2j0haMezGANTrut6z214t6SuSXixWPWD7Vdt7bC/u8Zzttju2O91u\nd65NAIzAvMNue5GkP0jaERH/lvRLSV+UtF4zZ/6fz/W8iJiMiHZEtFutVg0tAxjEvMJu+7OaCfrv\nIuKPkhQRpyPiSkT8R9KvJG0YXpsAquobdtuW9KSkIxHxi1nrl8/a7NuSDtffHoC6zOdq/NckfVfS\na7ani3UPS9pqe71mhuOOS/rBUDoEUIv5XI1/QZLnKDGmDvwf4RN0QBKEHUiCsANJEHYgCcIOJEHY\ngSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJBwRo9uZ3ZX0zqxVSyWdHVkD12dcexvXviR6\nG1SdvX0hIub8/beRhv0TO7c7EdFurIES49rbuPYl0dugRtUbL+OBJAg7kETTYZ9seP9lxrW3ce1L\nordBjaS3Rt+zAxidps/sAEaEsANJNBJ22xtt/8P2m7YfaqKHXmwft/2a7WnbnYZ72WP7jO3Ds9Yt\nsX3A9tHifs459hrqbaftk8Wxm7a9qaHeVtn+m+03bL9u+4fF+kaPXUlfIzluI3/PbntC0j8lfVPS\nCUkvS9oaEW+MtJEebB+X1I6Ixj+AYfsOSRcl7Y2ILxXrfirpXEQ8VvxHuTgifjQmve2UdLHpabyL\n2YqWz55mXNLdkr6nBo9dSV/3agTHrYkz+wZJb0bEWxFxSdLTkrY00MfYi4jnJZ27ZvUWSVPF8pRm\n/rGMXI/exkJEnIqIQ8XyBUlXpxlv9NiV9DUSTYR9haR/zXp8QuM133tI+ovtV2xvb7qZOSyLiFPF\n8ruSljXZzBz6TuM9StdMMz42x26Q6c+r4gLdJ90eEbdKukvS/cXL1bEUM+/BxmnsdF7TeI/KHNOM\nf6TJYzfo9OdVNRH2k5JWzXq8slg3FiLiZHF/RtKzGr+pqE9fnUG3uD/TcD8fGadpvOeaZlxjcOya\nnP68ibC/LGmd7TW2F0j6jqR9DfTxCbZvLC6cyPaNkr6l8ZuKep+kbcXyNknPNdjLx4zLNN69phlX\nw8eu8enPI2LkN0mbNHNF/pikHzfRQ4++1kr6e3F7veneJD2lmZd1H2rm2sZ9kj4v6aCko5L+KmnJ\nGPX2W0mvSXpVM8Fa3lBvt2vmJfqrkqaL26amj11JXyM5bnxcFkiCC3RAEoQdSIKwA0kQdiAJwg4k\nQdiBJAg7kMR/AR+JpCjSKGTVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7EARhB4L4f74HIpmnqlYSAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fsl7cjoXxd4q"
      },
      "source": [
        "### Input image dimensions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3pmTy4WP39hn"
      },
      "source": [
        "img_rows, img_cols = 28, 28"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vKyX4Tya3-Zv"
      },
      "source": [
        "if K.image_data_format() == 'channels_first':\n",
        "    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
        "    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
        "    input_shape = (1, img_rows, img_cols)\n",
        "else:\n",
        "    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
        "    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
        "input_shape = (img_rows, img_cols, 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_MHiiXVJ35f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6862737b-7284-4959-8f84-dca2b6184a69"
      },
      "source": [
        "x_train[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2R_RYuFvxkxG"
      },
      "source": [
        "### Reshaping the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHL9_0sE3_1Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "0853be29-6d1c-41c6-8261-4a86813d8ff4"
      },
      "source": [
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "x_train /= 255\n",
        "x_test /= 255\n",
        "print('x_train shape:', x_train.shape)\n",
        "print(x_train.shape[0], 'train samples')\n",
        "print(x_test.shape[0], 'test samples')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train shape: (60000, 28, 28, 1)\n",
            "60000 train samples\n",
            "10000 test samples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQMRbujdxoS1"
      },
      "source": [
        "### Applying One hot encoding for the data\n",
        "    One hot encoding basically is this,\n",
        "For example you have digits 0 to 9 in your data and every image corresponds to a digit such as 3\n",
        "In order to make this data understandable by the neural network we need to convert our 3 to one hot encoding which is,\n",
        "One Hot : \t[0 0 0 1 0 0 0 0 0 0]\n",
        "Normal :\t[0 1 2 3 4 5 6 7 8 9]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tczq1zJJ4Bd0"
      },
      "source": [
        "# One hot encoding\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fytNaqPMy0uf"
      },
      "source": [
        "## Creating the actual NN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUShWbV24C4R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "d9c99d6d-9de0-4239-d9c8-cf7098616ada"
      },
      "source": [
        "model = Sequential()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bEzSoYEB4EOl"
      },
      "source": [
        "import tensorflow as tf "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eR3qIzvRy4jy"
      },
      "source": [
        "### adding layers to the NN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p5P1W5284GnQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "db89dc31-b1e0-4b55-b863-db83a4965003"
      },
      "source": [
        "model.add(Conv2D(32, kernel_size=(3, 3),\n",
        "                 activation='relu',\n",
        "input_shape=input_shape))\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(num_classes, activation=Activation(tf.nn.softmax)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/activations.py:235: UserWarning: Do not pass a layer instance (such as Activation) as the activation argument of another layer. Instead, advanced activation layers should be used just like any other layer in a model.\n",
            "  identifier=identifier.__class__.__name__))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uJxeGY6wy9SY"
      },
      "source": [
        "### compiling the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xf9mmKWK4QMh"
      },
      "source": [
        "model.compile(loss=keras.losses.categorical_crossentropy,\n",
        "              optimizer=keras.optimizers.Adadelta(),\n",
        "metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W1o7VFdvzCcY"
      },
      "source": [
        "### training the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRMGGn5g4RdW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "outputId": "9595bb86-6f19-49df-cfc9-1ac56f3b857b"
      },
      "source": [
        "model.fit(x_train, y_train,\n",
        "          batch_size=batch_size,\n",
        "          epochs=epochs,\n",
        "          verbose=1,\n",
        "validation_data=(x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 365s 6ms/step - loss: 0.2375 - acc: 0.9259 - val_loss: 0.0450 - val_acc: 0.9847\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 364s 6ms/step - loss: 0.0750 - acc: 0.9776 - val_loss: 0.0327 - val_acc: 0.9879\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 363s 6ms/step - loss: 0.0585 - acc: 0.9824 - val_loss: 0.0288 - val_acc: 0.9899\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 364s 6ms/step - loss: 0.0470 - acc: 0.9862 - val_loss: 0.0254 - val_acc: 0.9905\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 365s 6ms/step - loss: 0.0396 - acc: 0.9880 - val_loss: 0.0243 - val_acc: 0.9911\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fe39b790240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bUFlA5EvzFDB"
      },
      "source": [
        "### evaulating result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R6U6JC6Z4Scj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "818db0f1-4cd1-4bb0-e0ee-6e854678dd3a"
      },
      "source": [
        "score = model.evaluate(x_test, y_test, verbose=0)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.024344487878857762\n",
            "Test accuracy: 0.9911\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}