Writing NMIST Dataset .ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Tensorflow_Assignment2.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMYdlCRCuJJKhRGZOD/vA4U",
      "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/Kushagratandon12/Introduction_to_tensorflow/blob/master/Tensorflow_Assignment2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2vj9tbrnzG4-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8c5b3815-161f-4c16-bf6c-044a90f59a2f"
      },
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tensorflow import keras\n",
        "print (tf.__version__)\n",
        "mnist = tf.keras.datasets.mnist"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ifRhnNY0zOM7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(training_data , training_labels),(test_data,test_label) = mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uei0Yle-5wnF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8d740e10-dc65-415f-e5fc-b8135c349f12"
      },
      "source": [
        "len(training_data[0])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IdDhyhOgzVp2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1395d85a-79cf-4ce6-ef66-971103e99bc3"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(training_data[5])\n",
        "print(training_data[5])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  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",
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            "    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  13  25 100\n",
            "  122   7   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0  33 151 208 252 252\n",
            "  252 146   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0  40 152 244 252 253 224 211\n",
            "  252 232  40   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0  15 152 239 252 252 252 216  31  37\n",
            "  252 252  60   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0  96 252 252 252 252 217  29   0  37\n",
            "  252 252  60   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0 181 252 252 220 167  30   0   0  77\n",
            "  252 252  60   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0  26 128  58  22   0   0   0   0 100\n",
            "  252 252  60   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 157\n",
            "  252 252  60   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 110 121 122 121 202\n",
            "  252 194   3   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0  10  53 179 253 253 255 253 253\n",
            "  228  35   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   5  54 227 252 243 228 170 242 252 252\n",
            "  231 117   6   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   6  78 252 252 125  59   0  18 208 252 252\n",
            "  252 252  87   7   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   5 135 252 252 180  16   0  21 203 253 247 129\n",
            "  173 252 252 184  66  49  49   0   0   0]\n",
            " [  0   0   0   0   0   3 136 252 241 106  17   0  53 200 252 216  65   0\n",
            "   14  72 163 241 252 252 223   0   0   0]\n",
            " [  0   0   0   0   0 105 252 242  88  18  73 170 244 252 126  29   0   0\n",
            "    0   0   0  89 180 180  37   0   0   0]\n",
            " [  0   0   0   0   0 231 252 245 205 216 252 252 252 124   3   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0 207 252 252 252 252 178 116  36   4   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0  13  93 143 121  23   6   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",
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            "    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"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zrgZ47+o5/Xk9wr5G0hQze5+ZXSDpU5JW1KGPtzGz4dmBE5nZcEkfUeNNRb1C0oLs/gJJT9Wxl9/TKNN4500zrjq/d3Wf/tzda/4n6Tr1HZH/jaS76tFDTl+TJD2f/W2ud2+SlqvvY90p9R3buFXSaEldkrZL+h9Joxqot0ckbZS0QX3BaqtTb3PU9xF9g6T12d919X7vEn3V5H3j57JAEBygA4Ig7EAQhB0IgrADQRB2IAjCDgRB2IEg/h8CIWRCsmbzCQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e5Q7W5Ey5YU4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6202c907-b696-47bd-d199-8d25d07577e3"
      },
      "source": [
        "training_data = training_data/255\n",
        "test_data=test_data/255\n",
        "print(training_labels)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[5 0 4 ... 5 6 8]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9TGUh5i6H0_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential([keras.layers.Flatten(),\n",
        "                                    keras.layers.Dense(1024,activation=tf.nn.relu),\n",
        "                                    keras.layers.Dense(10,activation=tf.nn.softmax)\n",
        "                                    ])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBHH6r4rfAZK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "    def on_epoch_end(self, epoch, logs={}):\n",
        "        if(logs.get('accuracy')>0.99):\n",
        "            print(\"\\nReached 99% accuracy so cancelling training!\")\n",
        "            self.model.stop_training = True\n",
        "\n",
        "\n",
        "#calling the class \n",
        "callbacks=myCallback()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wInNgCT2Vwyp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "d7996e28-cfab-40c9-d205-749c3f91f1de"
      },
      "source": [
        "model.compile(optimizer = tf.optimizers.Adam(),\n",
        "              loss = 'sparse_categorical_crossentropy',metrics=['accuracy'])\n",
        "model.fit(training_data,training_labels,epochs=10,batch_size=10,callbacks=[callbacks])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "6000/6000 [==============================] - 25s 4ms/step - loss: 0.0806 - accuracy: 0.9761\n",
            "Epoch 2/10\n",
            "6000/6000 [==============================] - 25s 4ms/step - loss: 0.0551 - accuracy: 0.9829\n",
            "Epoch 3/10\n",
            "6000/6000 [==============================] - 25s 4ms/step - loss: 0.0443 - accuracy: 0.9868\n",
            "Epoch 4/10\n",
            "6000/6000 [==============================] - 25s 4ms/step - loss: 0.0385 - accuracy: 0.9885\n",
            "Epoch 5/10\n",
            "5997/6000 [============================>.] - ETA: 0s - loss: 0.0318 - accuracy: 0.9907\n",
            "Reached 99% accuracy so cancelling training!\n",
            "6000/6000 [==============================] - 25s 4ms/step - loss: 0.0318 - accuracy: 0.9907\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f7e6c3b8630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LlvOYDTa8TU3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "40a42789-6080-47bb-eeee-9c0f7b4efdc1"
      },
      "source": [
        "model.evaluate(test_data,test_label)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0708 - accuracy: 0.9794\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.07080685347318649, 0.9793999791145325]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6t9mHpmw-pHn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}