MNIST-Keras.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SendtexKerasMNIST.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/GokulDas027/ML-Codes/blob/master/MNIST-Keras.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "BMTV9_jOsYcP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# MNIST with tf.keras\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "MuFrL_TLd3bv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "mnist = tf.keras.datasets.mnist"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nZgjNERdebXe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "6f1ffb0b-8e26-482e-d12c-f3af50e02166"
      },
      "cell_type": "code",
      "source": [
        "(x_train, y_train),(x_test, y_test) = mnist.load_data()"
      ],
      "execution_count": 2,
      "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"
        }
      ]
    },
    {
      "metadata": {
        "id": "TnqXQVeVswpl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Mnist Dataset Sample"
      ]
    },
    {
      "metadata": {
        "id": "lhoNehkvsKFv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "0571c589-4bdc-424b-8f58-a19e5ec7b000"
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(x_train[0],cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J5ZwA4KheY3n06FGdOnVKXq9XbW1tWrBggZ566imtWbNGqampAzEjADiu11gmJSVpypQp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\nyUYlQKKNywAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f04a9d4f6d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1036
        },
        "outputId": "0a5c9b38-82d7-4369-dc14-e7a3afbb60f0",
        "id": "wFa3ypblrosd"
      },
      "cell_type": "code",
      "source": [
        "print(x_train[0])"
      ],
      "execution_count": 0,
      "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",
            " [  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"
        }
      ]
    },
    {
      "metadata": {
        "id": "qkSCuTw1s60x",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Normalising Dataset"
      ]
    },
    {
      "metadata": {
        "id": "ePpgyuD-ebbb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "Cidz7J3DebuC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "075afa35-2cbe-4a61-fb9b-733bb512a317"
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(x_train[0],cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUsAAAFKCAYAAACU6307AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJQAYEJYAYEBYAoABYQkABv8DDy4qOEZ/DE4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f04a98a9ac8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "7yiU2WipfnLB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2565
        },
        "outputId": "a1bac4ed-fb14-49cd-d9d2-ec133366d41d"
      },
      "cell_type": "code",
      "source": [
        "print(x_train[0])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "CUfEarRPtMlW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Defining Model"
      ]
    },
    {
      "metadata": {
        "id": "cvZegO7nebyQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = tf.keras.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))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZO85daiWv59a",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Compiling Model"
      ]
    },
    {
      "metadata": {
        "id": "Cx2qpQIGr4SM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "             loss='sparse_categorical_crossentropy',\n",
        "             metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "iA-tGLrfxWEV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Training the model"
      ]
    },
    {
      "metadata": {
        "id": "SNgCmZaur4dy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "outputId": "d1a36dd6-74b4-4ad8-80c9-937bed73b300"
      },
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train, epochs=5)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 8s 137us/step - loss: 0.0567 - acc: 0.9820\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 8s 127us/step - loss: 0.0417 - acc: 0.9861\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 8s 126us/step - loss: 0.0333 - acc: 0.9890\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 8s 126us/step - loss: 0.0269 - acc: 0.9908\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 8s 125us/step - loss: 0.0211 - acc: 0.9929\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f04a3916320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "metadata": {
        "id": "IB--ptgKyL46",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Testing the model"
      ]
    },
    {
      "metadata": {
        "id": "K_ZamePeyJU5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "2e264e11-855e-425d-ce59-85ed9aac325a"
      },
      "cell_type": "code",
      "source": [
        "loss_val, accuracy_val = model.evaluate(x_test, y_test)\n",
        "print('loss is',loss_val)\n",
        "print('accuracy is',accuracy_val)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 0s 34us/step\n",
            "loss is 0.10093973273078737\n",
            "accuracy is 0.9746\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bC53unXu9ucv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Prediction\n"
      ]
    },
    {
      "metadata": {
        "id": "QwB4qVHwy2Cm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "d7586815-cf25-43dc-851c-dc76d72b6ad4"
      },
      "cell_type": "code",
      "source": [
        "predictions = model.predict([x_test])\n",
        "predictions"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.4914976e-10, 3.6124864e-11, 2.2202578e-08, ..., 9.9998558e-01,\n",
              "        3.1797131e-07, 7.0218062e-09],\n",
              "       [2.7779916e-12, 1.6197548e-08, 1.0000000e+00, ..., 6.6735029e-13,\n",
              "        5.0200827e-13, 7.0571112e-17],\n",
              "       [1.2573163e-09, 9.9991524e-01, 2.8348603e-07, ..., 8.0339079e-05,\n",
              "        4.1025301e-06, 3.1954084e-09],\n",
              "       ...,\n",
              "       [1.3441980e-12, 3.7710959e-09, 1.6277374e-11, ..., 2.4200011e-07,\n",
              "        8.5099004e-11, 3.1396310e-06],\n",
              "       [7.4407034e-12, 3.6917228e-15, 4.7525989e-14, ..., 3.1116200e-11,\n",
              "        1.9037042e-06, 2.2901357e-12],\n",
              "       [1.6627334e-10, 8.4721674e-12, 3.6262579e-12, ..., 2.7442154e-13,\n",
              "        5.8331877e-09, 1.4061394e-12]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "metadata": {
        "id": "Z1actaE8-JlJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cf716c9f-2d69-4e9b-f42c-b43e71279ddc"
      },
      "cell_type": "code",
      "source": [
        "prediction = np.argmax(predictions[0])\n",
        "prediction"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "metadata": {
        "id": "BVbSdheW-eEe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "24e04ed9-d397-4f01-f3a0-34608cf371d9"
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(x_test[0])"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f04a38b5ac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f04a38e9c88>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "REIWRR7I_Q6s",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Tip\n",
        "\n",
        "## Saving a model\n",
        "```\n",
        "model.save('model_name.model')\n",
        "```\n",
        "## Loading a Saved model\n",
        "\n",
        "\n",
        "```\n",
        "loaded_model = tf.keras.models.load_models('model_name.model')\n",
        "```\n",
        "\n",
        "\n"
      ]
    }
  ]
}