Copy_of_Clothing_classifier_program_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Clothing classifier program 2",
      "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": [
        "<a href=\"https://colab.research.google.com/github/GHakshay/clothes_recommendation_chatbot/blob/master/Copy_of_Clothing_classifier_program_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bymuj7zJnkDE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "39cf82e4-bb7e-4bbd-ac86-7de9390b3e2a"
      },
      "source": [
        "!pip install -U tensorflow_datasets"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: tensorflow_datasets in /usr/local/lib/python3.6/dist-packages (1.0.2)\n",
            "Requirement already satisfied, skipping upgrade: promise in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets) (2.2.1)\n",
            "Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets) (2.21.0)\n",
            "Requirement already satisfied, skipping upgrade: tqdm in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets) (4.28.1)\n",
            "Requirement already satisfied, skipping upgrade: psutil in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets) (5.4.8)\n",
            "Requirement already satisfied, skipping upgrade: absl-py in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets) (0.7.1)\n",
            "Requirement already satisfied, skipping upgrade: termcolor in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets) (1.1.0)\n",
            "Requirement already satisfied, skipping upgrade: dill in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets) (0.3.0)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets) (1.12.0)\n",
            "Requirement already satisfied, skipping upgrade: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets) (3.7.1)\n",
            "Requirement already satisfied, skipping upgrade: wrapt in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets) (1.11.2)\n",
            "Requirement already satisfied, skipping upgrade: future in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets) (0.16.0)\n",
            "Requirement already satisfied, skipping upgrade: tensorflow-metadata in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets) (0.13.0)\n",
            "Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets) (1.16.4)\n",
            "Requirement already satisfied, skipping upgrade: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->tensorflow_datasets) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->tensorflow_datasets) (2.8)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->tensorflow_datasets) (2019.6.16)\n",
            "Requirement already satisfied, skipping upgrade: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->tensorflow_datasets) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow_datasets) (41.0.1)\n",
            "Requirement already satisfied, skipping upgrade: googleapis-common-protos in /usr/local/lib/python3.6/dist-packages (from tensorflow-metadata->tensorflow_datasets) (1.6.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsWEvpylnmZ-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "41ddf901-aa12-4e4d-89b4-46b9c98cd1ed"
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "\n",
        "# Import TensorFlow and TensorFlow Datasets\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
        "\n",
        "# Helper libraries\n",
        "import math\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Improve progress bar display\n",
        "import tqdm\n",
        "import tqdm.auto\n",
        "tqdm.tqdm = tqdm.auto.tqdm\n",
        "\n",
        "\n",
        "print(tf.__version__)\n",
        "\n",
        "# This will go away in the future.\n",
        "# If this gives an error, you might be running TensorFlow 2 or above\n",
        "# If so, the just comment out this line and run this cell again\n",
        "tf.enable_eager_execution()  "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.14.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqoCsj-enmcw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset, metadata = tfds.load('fashion_mnist', as_supervised=True, with_info=True)\n",
        "train_dataset, test_dataset = dataset['train'], dataset['test']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Fh9C_WwnmfX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \n",
        "               'Sandal',      'Shirt',   'Sneaker',  'Bag',   'Ankle boot']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "566CWU0wnmiD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "4b1f9e81-e1ed-43d6-c45f-6344a4bfc572"
      },
      "source": [
        "num_train_examples = metadata.splits['train'].num_examples\n",
        "num_test_examples = metadata.splits['test'].num_examples\n",
        "print(\"Number of training examples: {}\".format(num_train_examples))\n",
        "print(\"Number of test examples:     {}\".format(num_test_examples))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of training examples: 60000\n",
            "Number of test examples:     10000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zs5idqchnxu_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def normalize(images, labels):\n",
        "  images = tf.cast(images, tf.float32)\n",
        "  images /= 255\n",
        "  return images, labels\n",
        "\n",
        "# The map function applies the normalize function to each element in the train\n",
        "# and test datasets\n",
        "train_dataset =  train_dataset.map(normalize)\n",
        "test_dataset  =  test_dataset.map(normalize)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rettc9QQnmkd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "0a75b0e2-12bc-40d0-d603-9644913ff629"
      },
      "source": [
        "# Take a single image, and remove the color dimension by reshaping\n",
        "for image, label in test_dataset.take(1):\n",
        "  break\n",
        "image = image.numpy().reshape((28,28))\n",
        "\n",
        "# Plot the image - voila a piece of fashion clothing\n",
        "plt.figure()\n",
        "plt.imshow(image, cmap=plt.cm.binary)\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TU6ZMCcZjayMePHgwN3bo0KFg29iajidOnAjGY3NmhS7BY+seNjQ0BOOx9QOPHz8ejF9y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\nJSLJUeISkeQocYlIcpS4RCQ5/w/4YRjRG/67BQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zeV6RpOdnmm6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "fe7581e1-ee9d-408a-c3da-72741db7fc4b"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "i = 0\n",
        "for (image, label) in test_dataset.take(25):\n",
        "    image = image.numpy().reshape((28,28))\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(image, cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[label])\n",
        "    i += 1\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F8zjLYV6GGTVqVLCvueaaYHs5hKUu7kueH4D4vvnHP/4R7Tv//PODzXPHsccem/Mtmg++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I9xfb7t3F4O4ff+OvxsOF0CkPp1st+X37m+K6kPpAESQgghRO3QAkgIIYQQtaPtJjCvimNV1/jx\n46PsVekjR46Mss8iyyqy8847L8rejPbd73636XU5k7BnypQpUfYhvrnQUn/d66+/PsreBDZQQ3RL\nWUjvv//+KHOaASB9PqwuXXHFFZNy/B6VTCbcDi7nN8vk/uNjM2bMyNyFaEYp9JRN3F/+8pez5Urv\nzvDhw6P81FNPZcv5sVo3zj///OQzp/1gE0XJLOXHHKcfYZMHZ2X31xo7dmyUfYZ1dmvgselNlvxb\nwP3q+5jb5DPM9yVacXXgd9vPadx/PFf5tAD82ZvHuA6uv1U3jFbLcboSIH3fWk3N8OijjyafOSXO\nwoULm8qATGBCCCGEEC2hBZAQQgghakfbTWB33XVX8pk3vuOMzE888URSjjP37rHHHskxjrriOljF\nCqTZQZ9++uko+031uH5Wx3tVL7eJVf+8gSoAzJo1C3WjpCL92te+FmWOuAPSvihlm+UorpLJJJc1\n1avZeRNVzv7sN+MUbyeX+RdI+4kzunrVONdRMguzifTKK6/MlitFttQBzj4PpJl/+d33kTMcZXfV\nVVclx3g88hzpzStDhgyJMkeO+SzqPM54bvX977Na58rxnOMzvfclOu6n9I76DPQM3yfPfWx6BFI3\ngdIcybRarrQBLlPKEs7HShF9/lhug1YfUdyVuVsaICGEEELUDi2AhBBCCFE7tAASQgghRO1ouw+Q\nD4Vk+y6Hu3F2ZyC1CT755JPJsQkTJkTZh8IxnJGZ7Y/vf//7k3JcB9u9fUZK3pWWs0J7X6H58+dn\n21QHbrnlluQzh256/6ucXdj7KrA/CZfzdnD2T2A/LW+bZl80tjHvvvvuEGX4eXl7PYdj+7BqplXf\nAx6rZ555ZrZcHbM/M37+5M/sr+H9FdlPh315gDQcnceST1nCcyYf87t6czb3XJi+h69bCg/vq3Nu\nCCH7rj/88MNR5gzHvjyngmDfLpb9eaXx1WpIO9fRavqWkj8X+wP59vF7433HuE5+r/3awmcDb6m9\nnT5DCCGEEKKfowWQEEIIIWpH201gXi215pprRvm5556LsjdlcCZoH0LL5hHO/ujNJrwBam7DPiA1\ny3BGTt4Y1ddXykrL6kwfmu83WB2IePUmmzo/+MEPZs8rZfPlPmITjD+H+5bNIt4cw/3HoflDhw7N\ntmGg0dUQ2JK56ZxzzolyyZzY6rVZ/f2nP/0pyj7VxIYbbhhlHuutbujYH2HTuzc3sXmI79ObF+bO\nnRtlP0Zy2fh33XXXpBybn3iu9pl5eY5ntwZfjtvO49mHx7NZO7dxa29jZtn3jFPE7LDDDlG+++67\nk3KDBg2Kci5Ldivt6IDHVCmTPp/jn33unvz3bMLkecOP/5IZlF0o+J4PPPDApBz/7raKNEBCCCGE\nqB1aAAkhhBCidmgBJIQQQoja0XYfoGnTpiWf2ebIdltvw+YQPx/iyfZI9g3xNlH2PeGd53l3WQ/7\ngHhfIbZbsr3R27C5vbwTOlAPHyDengRIUwaUwiTZvu1tyWwjZh8gX47fB7Y/e5sz+y189atfbXIX\nA5+u+sCUQmLvueeeKJe2rijtIp+D/cdOOumk5BiHyLcastvf4e19vJ8kh6bzFgn+2fAu6iNGjEiO\njRs3Lsq8VYPfmoDfI/bz8OHyvo05/HkdeD8Rvm5pK4m+ykorrRRlftZ33nlnUo7vm3/j/O8TP3v/\nW5irw/v28PtR8tPj81o9pzTf5HyP/Ge+D/YnBlJfoVapx0whhBBCCEFoASSEEEKI2tF2E5gPTWf1\nJqtEeRdpX86HnLN6l1VnXkW6wQYbRJlNW75NDKsHvUott2Otz8g5e/bsKD/zzDPZaw1U/HNjE2FJ\n5VqC3weWSyY1PuZNrBxC+7nPfa6lNoilw2p4n0n9nXLkkUdGebfdduvWuvsjfs5keH7iMGrOZg8A\nm2yySdNzAOC1116LModp+3DjXAbikimDzWh+DPMczMdKodilHQF6m1yI91577RVlNsnzc/fnl7Iz\nlzJB556pJzd/tkqrYfWeUhh8Dv8eygQmhBBCCNECWgAJIYQQona03QTm1V6simNVnzdRsHrXe7Sz\n6Yy9//2Gl/yZr+VVxz6KK1eO1ce8EZvfYJDVmT6r56GHHtr0WgMJn5W1ZKbkfiltlufV37n6+DxW\nif7tb39Lyk2cODHK3oQ5kLnhhhuifPTRRyfHeKxyBnOv1ubn6sctR2ass846UfZqfc4KXFK7c9+w\nec2bPKZMmRJlNpn7+vgzb6oMpFGjv/71r6M8efJk9EX43S+Z60tRYPzsfXQXj8eS20Auy7Cftznq\nid8bb8rgSE6OdPLzO78PPiKqL1Iy/7/44ovZ87gc95GvrzSOctFZ/vtS9udWKJ3Tqjms1fq7I5O7\nNEBCCCGEqB1aAAkhhBCidmgBJIQQQoja0RYfIM5QyjsWA8Do0aOjzFmS2e4LpLY+72PDtkT2JfA2\nYj7GNvLFixdny7F/kd8dmcPv2R/Ih5Z2ZpfegYjP/l2yz3M/l7KIsk2bbf/e5swZhrnPvY19jz32\nyF5rIMPvsM84O3z48CiX/PO4Pz/5yU8mxx577LEo847fPgswj2m25ft3gP3JuK9/8YtfJOWOP/74\nptf1/n38+Y477kiOLViwIMreZ6kvUvKZY0q+Qjz38bgC8mHrPot3br7zvj08959++ulR/tSnPpWU\n22yzzZpe11+H2+f7i8t2JcS6p+HfmlKW9NJ9tXrPrWZkbhUel/66OZ+drmaM7kq5EtIACSGEEKJ2\naAEkhBBCiNrRFhPYCy+8EGUfSs7ZmVltyWYzIM1Q6k1MrMYvbQ7H6ji+ljebsJqVVcJeFckmNlbh\neRMBt68rmz72dy666KLkM2+G6tXYXQlfZdWnN1PmwkR9iC9vrMmUwkIHAttuu22U2SQBpOOFTVbe\nPM1m7XXXXTc5xn3P/e6zQnMdPK68Cp379+GHH46yN1+NHTu2abmRI0cm5UqZ3vnam2++Ofo6pazA\nuc0veX7z+LmqVTMg9x9fy783Dz74YJR5A2K/eTKnrGCzbCmbu5/TOU2Jf897C7/Ja2nz0lYoZcb2\n8x2/D/yulLJ1lzJB58yvvk250Hd/3VJ2/1x9/h67gjRAQgghhKgdWgAJIYQQona0xQTGWWS9+Yoj\nsAYPHhxlH6VTMh2x6ovV2D5ajM1vXB9nRgXy6jyvYmPTHkc4+OgzVtt7095A5fHHH28qA6kZwptE\nuS/8c2TYVMZ96dXsfIzP8aay8ePHN73OQDeBMT4akp+dj9piWEX9wAMPJMf4mXPf8tjxsGmj1c0U\n/XzB1y2ZoNms4/v2+eefj3IuO3xfgp+vv5fcxpjexMjPp2S+KEVe5kwg/tn734IOvvjFLyafDzzw\nwCiPGjUqyqWM0Z65c+dGua+YwHyUHXPttddG2Y897rNSpuWSianVzVDfKaX5s9WNckvvF8vdEW0t\nDZAQQgghaocWQEIIIYSoHVoACSGEEKJ2tMUHiG3p3k6by87s7Xm8o7oPV2XY9u/DNnkHY/YB8XZK\n9j1hOy37EgCpLZb9FrzvCtsz+VkMZO66664o+/7ivvWhoGzv5WO+HH9m3yzvU5QLg/c+QP5zBwPZ\n58fDO7cDwF//+tco8/PhDMlAGtJ+2GGHJcf+8Ic/RJn7xo859v/j98P73fF8seqqq0b54osvTsrx\nrvF8Lf9+ML5N7fSNaAc835XmyFZDm73vRauZphmu318rN7Z22WWX7HV5nvW+MVyf96/x72xvEUKI\nc1fJx3H69OlR5rEBpM+xlHXZX5dpdZf3rvgbtbrLeykTdCmlASMfICGEEEKId4gWQEIIIYSoHW0x\ngbH5ymdiZVUlm4eGDh2alOMwVK9KZfUmb5a42mqrJeVYnceqdV8fq49LpjIOsy+FNLKqft68eckx\nNjOMGzcuW0d/g0PfS6HHJRMY400huY3+fH0Mq89LppWS+WAgw1nZAeCpp56KMj9jr2rn8bf99tsn\nx3bdddcoX3PNNVEeMmRIUo5DotlM7s3Y3DfcDh+mzmOVzymlqPBh2ZMmTUJ/orSRcC502MPPymeJ\n5rHFc583PeRCrP08y595w1rO4g0AW221VZQ5VYN3p2D3Bz8fc3qGPffcE72FmWXNjv/1X/8VZR5T\nfqxwv5TMmaX3ITfPdofZt2TaKoXmM62a20qbNHcFaYCEEEIIUTu0ABJCCCFE7WiLCYxVU15dyuqs\nhQsXRvn73/9+Uo5Vgk888URyjFWCI0aMyLaD1busOvNqukGDBjVtn1erskq4pFbm+jz9LdKkVTji\nrpSV1pskWPVZUuHmIsR8Vm+ug1XmbCoF0o0ZJ0+enG37QMZnyOV3sxRhwcf88+I6OMrMm5t4o1Tu\nJ6/Wzm3IWTKV8Tvgs12zSf6ZZ55Jju20005Nr9VXKZl/c9mffb+yuZrnYyDdVHa33XaLss/CzXD/\n+/ZxO3jDU28C23nnnaP8u9/9run5Hn9fJReFniY351933XVLLQOk7zOPt5I7QSnijMu1uvGor6/V\naKyceawUgen7OXfPvKFyVxmYv8ZCCCGEEAW0ABJCCCFE7dACSAghhBC1oy0+QGwvLPlUsB2QQ8eB\ndBd1HyLH/gMcCulh2yTbhL19OJeF0tu611tvvShzWLXfpZjx9+99EgYKK620UpS9TZj72dutc7bk\nUqoCrqOUrZv9G3x9N9xwQ5TZB6hObLTRRsln9qthvxz/jHm8+Ky1/Mw5nNc/f/YZ4zHsy7HPDvsR\nlUK2+V307xun3vDjdocddkB/guegVneD93Mpf/Z+VfzsSr6RXD+3yaeX4Peo5L/x9a9/PcpXXHFF\n0+sA6bs2fPjw5Jh/L/si9957b5Q5PUNpN/ScPxCQjsuSj1zJj4gpZZ3OZYxu1R/I18fj3qdR4bL8\n7j333HPZa7WKNEBCCCGEqB1aAAkhhBCidrTFBMYqK7+BHauwWLXHWWiBNCSTNzoEUvU8q/a8OYxD\narmcV+lzfawWL2UP3nLLLaP87LPPZstxBlUAePTRR6O86aabYqCw+eabR/nGG29MjuU2vgRaD9fN\nbbjn1fG5zKM+0zhv3lpXJkyYkD3G774fw5yFefbs2dnzGK+uz2Vf92kN+DPLpVBZHsMlE7w30Wy3\n3XbZsn2RVjcoLY2rUug0uyHceuutUd56662TcjmzfilFgk+LkIPNmTNmzEiO8aa8Ps0Fb5Z76KGH\ntnStduNdKtgMyGPKm69yuxP4sca/a6Vs/FyuVZOVNznz2OHfTz9+c24o/rr8jpbMd4y/VleQBkgI\nIYQQtUMLICGEEELUjraYwFjF5iMtclmSN9xww+TzI488EuWXXnopOcaqRFa/lbJfssrNq47ZVMYq\nYb+R6dprrx3lww8/PMr77bdfUo4j2njTPyBvIujv8KaYxx57bLacV2fmosBazRjtyfXzmDFjknJz\n586N8qxZs6Ls38OBzDrrrJN85mfOKnSvTmfVszcns6qcN8jlyE3g7abhDrypks0wpSjBnMnOb5DM\nbfdmGP88+hOlzVBzWdSBtL+OOuqo5NgWW2wRZX5u3K/A2zcp7cCPUzZztBoNyxFhv/nNb5JjbML1\nZtopU6a0VH9P4jc55XeWn29pc+aSyYojK/344D7i65YiPEvfcx38++zfQ+4X7n/vrsJzTGlDVX6n\nuiPbtzRAQgghhKgdWgAJIYQQonZoASSEEEKI2tEWHyC2YXofoFzWZB8S/qc//SnKPgSP7ZFsL/R+\nBVyutOst+xSVbKzjxo2L8rrrrpstx2HxHMIIvD2kfyDi/Qz4fSiF7rJduZQxOrfDMJDalrmct6uz\nPfqmm26Kcp18gLzvBmdJ5h26fdZe9lfw42rEiBFNj/kxzP43pQyx3EYeS7ybOJD2L/v0lTJQ++zB\n/Y3SWMpl8fV+T5y+ws9V++67b9O6zzrrrOQz+9Cxn4v3ASqlO8jBvoUs90euvfba5PPo0aOjzFmN\nvc8d/yax7J8hP2/va8vvCo9F/3uXSwnj5woes+wD5N8hvtYLL7wQZT8f87gstZ1/0zlLfFeRBkgI\nIYQQtUMLICGEEELUjrZvhloyPTCscgeAhx9+OMo+3C23OZpXubIqvLQZKquFWd02ffr0pNykSZOi\n/L41+FkAAAtaSURBVI1vfKPJXbydxx57rKVy/R1Wg3oVJpsmS5lCcxs4Nvucg/uWzWFe1cupCliF\nX2fWXHPNKPNY4iy1QGoS8xl4+bzcZppAPkzbm6xyoa7eRJozcZc2ceRs7v0RNin4cGZ+3jwf+/Dz\n97///dn6efxwP/Cm0ECaVZ3TDviUH9xHrYbBlzZy5fpKmYVL2cDbzbx58/Dzn/8cAHDkkUcmx3i8\n8b34uS5nzvSh/zyn+RQu/Lk0l/Jczf3Pv6VA+r5x+/x8wHB93l2F78u/N9x/bB70a4auIA2QEEII\nIWqHFkBCCCGEqB1aAAkhhBCidrTFB2jBggVR9um/c/jw8CuvvDLKZ5xxRnKM7dFrrLFGlH1oLNsm\n2Ybpd+XlcDreMsFvccF+P7yVgofbN3HixOTYE088kT2vP8NhjN7vi+22pfDX3E7uQOs+QLk0Bt5/\ngPuoZLeuE2xT561oSj5d3keHxz73od8Kg3d8ztn4gdRv4Jlnnoky+zsA6fvR6u7Uflfz/gbvlM6p\nN4DUp4Kfje/LUaNGZevPbYswfvz45DPPrfy8S1salNKS5M7J+Y+W2trbLLfccnFceZ+dnI+in/ta\nnZ+4H/y1ctvX+HLcL6Vnn+uLUhqZUv/xb4bvy9mzZ0d5l112ibLfNqcrSAMkhBBCiNqhBZAQQggh\nakdb9IaHHXZYlO+9997k2Jw5c6L8gx/8IFsH78x8/PHHd1/juom11loryrvuumtyjLN6cmZcADjo\noIPa27Begk0aHjaT+HBdVs2ymcqrgVk1y+eUUiRwfd60kgstrTNs/uXUAP75sNrcm1RYfZ1LLwGk\nJu9cSDyQ9vXgwYObng+k/cnt8yG1XB+r0z1cX6vmmp6G55YZM2Ykx7gfeGx6l4TSjtq5+/YZtDfY\nYIMosyncmzK4b/t7Fu5WWXbZZaOpxs+RPBeW3rGuhPH7+jhNCbfD78zAfcbj3teXO+bbmmu7/57H\nm3eh2HbbbaN8wgknNK3P19EqfXNkCyGEEEK0ES2AhBBCCFE72mICK22Ixyq3jTbaKFsHq669Cr6d\nKmlub0mlxqpCnxn10ksvjbI3gfkMmAOFhx56qKkMlNXsrUZv5CISOJKihDeFzJ8/P8prr712S3UM\nBHKbygLAxhtvHOVzzjknyr6PeNPUVvEbqjKs8i6ZUhmf6ZbJbaAKpBE1W221VbaO3swe3CoLFy6M\n8uOPP54cY/MKm6V8Vu/ugMd3aSNobkcpGrQ/mB9bxczi+PHzIGfyLkVPMaXxyyZnH+m82267RZnb\n4V0DchG73oTN5sxSBBvXV5rrecwefPDBybFctnI/p/t7aYX+/XYJIYQQQnQBLYCEEEIIUTu0ABJC\nCCFE7Wh7+kzeHRhIw/FKmRxLIXjthK/lr5vLRsxhekBqj6+LfwmHFPudg9mvw2esZTsz25K9j8Ar\nr7wSZR+6ybBtmt+h0aNHJ+XYb2vPPffM1lcnDj300Chz+gqfxZnD0X2WWvY9yWUjBlI/D34/Shm/\nuW/9O5bb/fupp55KyvmUFTn6gw/QBz7wgSj7zNicJZr9QTh9h6e023qJnXbaKcoPPPBA0zYAqY+G\nby/TH559q6y44orYZpttAAC33XZbcozntNJOBXyMz2l2rQ78DgTjxo3rRKv7Fjwn8O8C+w11FWmA\nhBBCCFE7tAASQgghRO2wVjeZBAAzWwAgvwuoaAdrhRCGdXel6steQ/05cFBfDiy6vT/Vl71GS33Z\nqQWQEEIIIcRAQCYwIYQQQtQOLYCEEEIIUTv6/QLIzFY3swvN7Akzu8/M/mRmnYr5M7OVzeyQdrWx\nLpjZqmY2vfr3vJk9Q5/z+2E0zp1iZldmjv3GzDbIHDvczN7rvjvCzPYzs4/nzhM9g5m9WfX/A2Y2\nzcy2XfpZoid5J+NW9F80Nvu5D5A1EkbcAeDcEMJp1XebABgcQri1E/WMAXBlCGHDdrSzjpjZTwC8\nHEL4/1osPwXAd0IIH+3ENZYB8ASALUIIC+n7GwF8CsCJaPTrxZ1ouuhGzOzlEMKgSv4wgP8bQtih\nl5slMuTGbTXXWgghv0Fi97Zj2RDCG0svKbqKxmb/1wDtCOD1jsUPAIQQHgBwm5mdaGazzGymmX0a\nAMxskJldX612Z5rZHtVpxwFYp1oNn9jzt1EvzGwH+gvzfjPr2CF2kJldbGaPmNnvqkkXZnaTmW1R\nyS+b2c/N7AEA/wFgDQA3VosemNlgAO8BsB6AjwE4sbrOOmY2yczuMrMZZvZHM1uF6v//q3KzzCy/\nS6Z4JwwGsAgojkWY2Q/N7FEzu83MLjCz7/Rai2uMma1rZg+Z2e8APAhghJl9tuqvWWZ2TFVuWTP7\nO533GTP7DcmzKi3DjVT+F2Z2TzUWv1B9v0s1Fq8EMLPHb7je1HJstj0TdJvZEMB9Tb7fC8AkAJsA\nGArgXjO7BcACAHuGEP5hZkMB3GVmlwM4AsCGIYRJPdTuuvMdAF8LIdxuZoMAdKT33BTARADPArgd\nwHYAbnPnrgjg7hDCtwHAzA4CsCNpgHYBcH0I4Y6qb6MGyMxmAPh6COFmMzsSwI8BHF6d994QwiQz\n2x7AWWi8W+Kds4KZTQewPIARADrSBr+K5mNxCwCfQGPsvhvANDQf46JnWB/A/iGEqWY2CsDRaPTR\nSwCuM7OPArimcP6PAUwJIcwzs46t6L8EYH4IYSszWw6Nvr+2OrYFgA1CCH9ry90IpvZjs79rgHJM\nBnBBCOHNEMI8ADcD2BKAATim+iG8DsBIAKvlqxFt4nYAvzCzbwBYmVTd94QQnq7U7NMBjGly7psA\nLinUvSuAq/2XZrZSda2bq6/OBbA9FbkAAEIItwAYTJO1eGf8K4QwKYSwPhp9c16HOQXNx+J2AC4L\nIbwaQlgM4IreargAADwRQphayVsDuCGEsDCE8DqA85GOoWbcjkaffwFLfm8+BODA6sf3bgAro6Gx\nBYA7tfjpMWo/Nvv7AuhBAJt3ovx+AIYB2LzS9sxDY/Ur2oiZfY1MXmuEEI4D8AUAKwC43czWr4q+\nRqe9ieYayldDCG82+b6DrQDc04Vmeme4/usc10cJIdyJhkZ2GDQW+wv/bKHMW2j8aHbA/fhFNLRA\nYwBMq8zOBuCQ6sd3Ughh7RDC9Z24nuhm6jo2+/sC6AYAy5nZlzq+MLONAfwdwKfNbBkzG4bGXyn3\nAFgJDdXr62a2I4COnQEXA3gfRFsIIfyKJrtnzWydEMLMEMLxAO5FQ83eVWLfmdlEAI/QAikeCyG8\nBGCRmXXsIPk5NDSDHXT4iU0G8FJVXnQj1UJ3GQAvID8Wbwewu5ktX5lHW3aKF23nbgA7WiNqbFkA\nnwFwc6WxXWRm65nZuwDw7sJjQwh3AfghGj4mIwH8GcAhVR0ws/Fm9s53thRdpq5js1/7AIUQgpnt\nCeCXZvZ9NGyXc9Dw6xgE4AE0/pL/Xgjh+cqZ7wozmwlgKoBHqnpeMLPbzWwWgKtDCN/thdupE4dX\nA+stNLR4VwPYpot1nQHgGjN7FsBVSP0RLgTw68rUtjeAAwCcZo2w+ScBHEhlXzWz+9GwbR/UxbaI\nt9PhZwA0/vI/IITwZmEs3lv5G8xA4y/PmWj4m4heJoTwtJn9EMBNaPTlFSGEq6rD30djYTMfDb+Q\n5arvTzKztavy14YQZpnZwwDWBDC9YXHBfADR0Vb0GLUfm/06DF4Ixsz+gobD5nOdPO8mNELwpy6t\nrGg/ZjYohPBytVC9BcCXQgjTertdQtSdgTY2+7UGSAgmhPDB3m6D6BbOsEYCy+XRyPHVbydYIQYY\nA2psSgMkhBBCiNrR352ghRBCCCE6jRZAQgghhKgdWgAJIYQQonZoASSEEEKI2qEFkBBCCCFqhxZA\nQgghhKgd/w/NH5SWNsf0egAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZYdnZ9gnmpd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),\n",
        "    tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "    tf.keras.layers.Dense(10,  activation=tf.nn.softmax)\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tn9Uibj0nmr0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam', \n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CvUZTnT9oCWG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BATCH_SIZE = 32\n",
        "train_dataset = train_dataset.repeat().shuffle(num_train_examples).batch(BATCH_SIZE)\n",
        "test_dataset = test_dataset.batch(BATCH_SIZE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F6Y0VM_foEyN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "9eb9b49a-5b79-4a3e-b039-af954d4819de"
      },
      "source": [
        "model.fit(train_dataset, epochs=5, steps_per_epoch=math.ceil(num_train_examples/BATCH_SIZE))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 30s 16ms/step - loss: 0.4929 - acc: 0.8260\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 20s 11ms/step - loss: 0.3745 - acc: 0.8659\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 20s 11ms/step - loss: 0.3330 - acc: 0.8788\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 20s 11ms/step - loss: 0.3126 - acc: 0.8852\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 20s 11ms/step - loss: 0.2998 - acc: 0.8906\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fb8b9a88588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ptnROwFQoG-6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "f42b4b57-1818-4527-95fc-3d132056dd7e"
      },
      "source": [
        "test_loss, test_accuracy = model.evaluate(test_dataset, steps=math.ceil(num_test_examples/32))\n",
        "print('Accuracy on test dataset:', test_accuracy)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 2s 8ms/step - loss: 0.3404 - acc: 0.8786\n",
            "Accuracy on test dataset: 0.8786\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEVPIuoSoJa-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for test_images, test_labels in test_dataset.take(1):\n",
        "  test_images = test_images.numpy()\n",
        "  test_labels = test_labels.numpy()\n",
        "  predictions = model.predict(test_images)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkY7cEiwoM6H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "078a5ac2-aea2-4775-b840-e057d487d233"
      },
      "source": [
        "predictions.shape\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "koMguhbMoO7U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "1f143fc4-deb0-4975-abaa-89451124a081"
      },
      "source": [
        "predictions[0]"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4.7776786e-05, 3.2365980e-07, 1.3006718e-02, 6.5785472e-04,\n",
              "       1.4465573e-01, 4.3158079e-07, 8.4161407e-01, 6.4176103e-10,\n",
              "       1.7035500e-05, 3.7753170e-10], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yHajoFRAoRWT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e4501021-5e5d-4030-fa59-f7a508198580"
      },
      "source": [
        "np.argmax(predictions[0])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0Q_WKeeoVVg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f68ec012-44f0-4220-8252-1e086c4fa78b"
      },
      "source": [
        "test_labels[0]"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfMPRKWvoV92",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_image(i, predictions_array, true_labels, images):\n",
        "  predictions_array, true_label, img = predictions_array[i], true_labels[i], images[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  \n",
        "  plt.imshow(img[...,0], cmap=plt.cm.binary)\n",
        "\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "  if predicted_label == true_label:\n",
        "    color = 'blue'\n",
        "  else:\n",
        "    color = 'red'\n",
        "  \n",
        "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
        "                                100*np.max(predictions_array),\n",
        "                                class_names[true_label]),\n",
        "                                color=color)\n",
        "\n",
        "def plot_value_array(i, predictions_array, true_label):\n",
        "  predictions_array, true_label = predictions_array[i], true_label[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
        "  plt.ylim([0, 1]) \n",
        "  predicted_label = np.argmax(predictions_array)\n",
        " \n",
        "  thisplot[predicted_label].set_color('red')\n",
        "  thisplot[true_label].set_color('blue')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wuKLaweUoZE_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "outputId": "86f13dad-4981-4069-c351-4433c3e221ed"
      },
      "source": [
        "i = 0\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions, test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions,  test_labels)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAC9CAYAAACEXQdzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnKAgA4ATFGQAcIKCDABOUJABwIn/Bevto7Lh3jwbAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9vJzoTc_obqL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "outputId": "6192c189-7c85-4a4a-8edb-dce742619316"
      },
      "source": [
        "i = 12\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions, test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions,  test_labels)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9Jb1qpvND0AodcK6rCEEjJT1ppksO5/mdLYSwThKT1egsI8vOv3Vp/2sIulC1AiVJJml8\n8bhfCJoi6SxJcyVdZyarL0ghqEXSE5L+UNJ/SDpF0vQQtN5ME0PQSZJOlPR5SX8s6cIQ9ANJ10jq\nL+lnkvqoVshvNNOm4vjvSLpQtXNxo5nePmDYX5T0iiSZaW0IapY0VtLbIaifpNskfVfS80X+Hkk9\nQlAoXm+XpNsl/WOqGBeukfSDMudxrxD015L+UtJuSe+Z6Zt7x1y8t9MkPWKmR4v8FjP1K/4RvFfS\nJklfkDRP0ukhaIGk35rpDkkTJM0oXuPAc/0tSX8nKUj6tZm+v/f4kp6UdImk1ZK+aaZ1ZlpZ/BU1\n1Eyrq7zHrsBkNbJhZkf0S7J7JLs98bP/kuwrxeN+knWXbIJkmyUbIVk3yd6U7IIiZ4ZkY4vHJtm1\ndcdaIVlT3fffkOxHxeNnJJtU97OFkl1YPP6RZI/UHf/J4vF4yX7njPm7kr1QjHW0ZM2SXVP87KeS\nfV2yUfXPlexvJVsg2U8kGybZS4c4Z6Mlm+vE973/xPNWSdazeNxYd/5nSdZTsibJNkjWo/hZS/Hf\nCZJtk2x08X278RexRyX76oHnWrJTJPtfyQYV5+QVya6u+x19u3j8Q8keqzvek3vPG1988eV/dfWk\n3huSHi6uuhrNtLuIv22mj632McACSaOc57apdmWc8jXVPk5op/gIodFMrxahZ7X/ylyS/lWSzPSa\npJNCUOMBh/gXSR9LmiPpEUmzJLWFoHMknW6mXx34mmb6ezOdY6a/Ue1K9Ich6Dsh6Pniqv1AwyT5\n96E6uIWSfhmCrpP2nUupdtXaarWPGNZKGuI8920zfXiQY39F0kwnfp6kGVa78t0t6Zfafz73SPr3\n4vFzki6oe95a1a60ASR0akEOQd+rm6A6xUwPSPqOpN6S3ghBXyhS67dga5P/UcqnZopvRrff+VL0\ncUMZB36I3u57M+02061Fgb1KUqOkpZK+LGls8fnqTElnFB8T7BOCzlXtz/olkv7ETNeq9tHA5w94\nzR2Seh1qoMXk4oIQ9N9F6ApJj0v6kqTZxefBUrnzue0gr/N7kj4yU7ytXTX157KXau8TQEKnFmQz\nPV4UsnPMtCoEnW6md830oKTZ0r6CfDi2qvbZsELQmZLeryvY+35mps2SNoWgPyh+9ufSvqtlSfrT\n4hgXSNpc5O8TgvqEoL7F44sl7TbTe2b6JzOdYqZRql0JLjWLVmDcK+luST0k7d0zc48UrVhYKv+v\ngnbMdENxLi8PQd0knWqm6ZK+L6lBUr9DHSNh3/kqXCbpN4mfv63a5/NNxeTqt7T/fHZTbYJTkv5M\n7a+wz5D2r0QBEOvqTW1vCUETVStKi1T7iOHLh3msf5b0mxC0StKv1b6A/JukJ4uPRiZJ+gtJPyuW\nbn0g6Ya63E9D0HzViqa3sfJgSf8TgvZI+kS1gn5IIehqSXPMtKr4fkEIelfSQjO9U59rpm0haHkI\n+pyZlpU5vmoF/rniI5kg6VEzNYfD6H4204YQ9EaxdG+aav9Q/lVdyr5zbbVJvTslTdf+Sb29K0O2\nSTq/+Fhmrfb/Y9dD0uek8qtFgOPREV/2djSEoN9Kut5M/1fxeTNUcVlZZwlBX5c0xqzaSotOGEdP\nSW+YaexhPLfFLL5KL97bl8x095EYI3CsOiZu+2Cmi4/2GDrKTL8KQQMzGEerVL0YH0J3ST85wscE\njjnHxBUyABwL2MsCADJBQQaATFCQASATFGQAyAQFGQAyQUEGgEz8Pwgc9ujCdu4/AAAAAElFTkSu\nQmCC\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOfI--JGoeAE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "7997f259-b296-494b-d1df-7b51b54cc4dd"
      },
      "source": [
        "# Plot the first X test images, their predicted label, and the true label\n",
        "# Color correct predictions in blue, incorrect predictions in red\n",
        "num_rows = 5\n",
        "num_cols = 3\n",
        "num_images = num_rows*num_cols\n",
        "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
        "for i in range(num_images):\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
        "  plot_image(i, predictions, test_labels, test_images)\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
        "  plot_value_array(i, predictions, test_labels)\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5woQJMfZ5zVDMc/K5VemMRT4fslu3bqWvSdc3ZMiQGL/44ouFckcccUSMd9tttxj7nGQo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\nJEmSCtJQTpIkSZIkSZIKvgevv/tOJsx63wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x720 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRohOWClohfl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a7676a6a-094f-4c3f-a14a-33443223bd63"
      },
      "source": [
        "# Grab an image from the test dataset\n",
        "img = test_images[0]\n",
        "\n",
        "print(img.shape)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(28, 28, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_pOPXFbyojma",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "55258b1a-33d2-41de-e510-8c9a0e624388"
      },
      "source": [
        "# Add the image to a batch where it's the only member.\n",
        "img = np.array([img])\n",
        "\n",
        "print(img.shape)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 28, 28, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pOTKPLsdol9n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "ab01faef-5245-43c5-9ba1-da57dc36f7cd"
      },
      "source": [
        "predictions_single = model.predict(img)\n",
        "\n",
        "print(predictions_single)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[4.7776870e-05 3.2365975e-07 1.3006721e-02 6.5785454e-04 1.4465585e-01\n",
            "  4.3158153e-07 8.4161389e-01 6.4176336e-10 1.7035512e-05 3.7753234e-10]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTJsO7DEooTV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "0ecbfc50-56b8-4113-88c5-750b444c26e4"
      },
      "source": [
        "plot_value_array(0, predictions_single, test_labels)\n",
        "_ = plt.xticks(range(10), class_names, rotation=45)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T/fu8XPOjxC8mdKVm3CvufgtxT+PlCmNp2GNE596B7t7YLQB6wcw2bnvrcWJs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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "msWDt4-zorMD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6da765e0-9005-4ad7-cc2b-2b75e3f33828"
      },
      "source": [
        "np.argmax(predictions_single[0])"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    }
  ]
}