l03c01_classifying_images_of_clothing.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "punL79CN7Ox6"
      },
      "source": [
        "##### Copyright 2018 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "_ckMIh7O7s6D"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "vasWnqRgy1H4"
      },
      "outputs": [],
      "source": [
        "#@title MIT License\n",
        "#\n",
        "# Copyright (c) 2017 François Chollet\n",
        "#\n",
        "# Permission is hereby granted, free of charge, to any person obtaining a\n",
        "# copy of this software and associated documentation files (the \"Software\"),\n",
        "# to deal in the Software without restriction, including without limitation\n",
        "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
        "# and/or sell copies of the Software, and to permit persons to whom the\n",
        "# Software is furnished to do so, subject to the following conditions:\n",
        "#\n",
        "# The above copyright notice and this permission notice shall be included in\n",
        "# all copies or substantial portions of the Software.\n",
        "#\n",
        "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
        "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
        "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
        "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
        "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
        "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
        "# DEALINGS IN THE SOFTWARE."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jYysdyb-CaWM"
      },
      "source": [
        "# Classifying Images of Clothing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S5Uhzt6vVIB2"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_for_deep_learning/l03c01_classifying_images_of_clothing.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_for_deep_learning/l03c01_classifying_images_of_clothing.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FbVhjPpzn6BM"
      },
      "source": [
        "In this tutorial, we'll build and train a neural network to classify images of clothing, like sneakers and shirts.\n",
        "\n",
        "It's okay if you don't understand everything. This is a fast-paced overview of a complete TensorFlow program, with explanations along the way. The goal is to get the general sense of a TensorFlow project, not to catch every detail.\n",
        "\n",
        "This guide uses [tf.keras](https://www.tensorflow.org/guide/keras), a high-level API to build and train models in TensorFlow."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0tMfX2vR0uD"
      },
      "source": [
        "## Install and import dependencies\n",
        "\n",
        "We'll need [TensorFlow Datasets](https://www.tensorflow.org/datasets/), an API that simplifies downloading and accessing datasets, and provides several sample datasets to work with. We're also using a few helper libraries."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "P7mUJVqcINSM",
        "outputId": "eb9c2f22-63ff-403f-9096-8a11daa4f5d7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow_datasets in /usr/local/lib/python3.10/dist-packages (4.9.2)\n",
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            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->tensorflow_datasets) (1.26.16)\n",
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          ]
        }
      ],
      "source": [
        "!pip install -U tensorflow_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "_FxXYSCXGQqQ"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "1UbK0Uq7GWaO"
      },
      "outputs": [],
      "source": [
        "# Import TensorFlow Datasets\n",
        "import tensorflow_datasets as tfds\n",
        "tfds.disable_progress_bar()\n",
        "\n",
        "# Helper libraries\n",
        "import math\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "590z76KRGtKk"
      },
      "outputs": [],
      "source": [
        "import logging\n",
        "logger = tf.get_logger()\n",
        "logger.setLevel(logging.ERROR)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yR0EdgrLCaWR"
      },
      "source": [
        "## Import the Fashion MNIST dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DLdCchMdCaWQ"
      },
      "source": [
        "This guide uses the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset, which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 $\\times$ 28 pixels), as seen here:\n",
        "\n",
        "<table>\n",
        "  <tr><td>\n",
        "    <img src=\"https://tensorflow.org/images/fashion-mnist-sprite.png\"\n",
        "         alt=\"Fashion MNIST sprite\" width=\"600\">\n",
        "  </td></tr>\n",
        "  <tr><td align=\"center\">\n",
        "    <b>Figure 1.</b> <a href=\"https://github.com/zalandoresearch/fashion-mnist\">Fashion-MNIST samples</a> (by Zalando, MIT License).<br/>&nbsp;\n",
        "  </td></tr>\n",
        "</table>\n",
        "\n",
        "Fashion MNIST is intended as a drop-in replacement for the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset—often used as the \"Hello, World\" of machine learning programs for computer vision. The MNIST dataset contains images of handwritten digits (0, 1, 2, etc) in an identical format to the articles of clothing we'll use here.\n",
        "\n",
        "This guide uses Fashion MNIST for variety, and because it's a slightly more challenging problem than regular MNIST. Both datasets are relatively small and are used to verify that an algorithm works as expected. They're good starting points to test and debug code.\n",
        "\n",
        "We will use 60,000 images to train the network and 10,000 images to evaluate how accurately the network learned to classify images. You can access the Fashion MNIST directly from TensorFlow, using the [Datasets](https://www.tensorflow.org/datasets) API:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "7MqDQO0KCaWS",
        "outputId": "5057b132-74c9-452e-ecae-c923a755639e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset 29.45 MiB (download: 29.45 MiB, generated: 36.42 MiB, total: 65.87 MiB) to /root/tensorflow_datasets/fashion_mnist/3.0.1...\n",
            "Dataset fashion_mnist downloaded and prepared to /root/tensorflow_datasets/fashion_mnist/3.0.1. Subsequent calls will reuse this data.\n"
          ]
        }
      ],
      "source": [
        "dataset, metadata = tfds.load('fashion_mnist', as_supervised=True, with_info=True)\n",
        "train_dataset, test_dataset = dataset['train'], dataset['test']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t9FDsUlxCaWW"
      },
      "source": [
        "Loading the dataset returns metadata as well as a *training dataset* and *test dataset*.\n",
        "\n",
        "* The model is trained using `train_dataset`.\n",
        "* The model is tested against `test_dataset`.\n",
        "\n",
        "The images are 28 $\\times$ 28 arrays, with pixel values in the range `[0, 255]`. The *labels* are an array of integers, in the range `[0, 9]`. These correspond to the *class* of clothing the image represents:\n",
        "\n",
        "<table>\n",
        "  <tr>\n",
        "    <th>Label</th>\n",
        "    <th>Class</th>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>0</td>\n",
        "    <td>T-shirt/top</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>1</td>\n",
        "    <td>Trouser</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>2</td>\n",
        "    <td>Pullover</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>3</td>\n",
        "    <td>Dress</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>4</td>\n",
        "    <td>Coat</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>5</td>\n",
        "    <td>Sandal</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>6</td>\n",
        "    <td>Shirt</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>7</td>\n",
        "    <td>Sneaker</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>8</td>\n",
        "    <td>Bag</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>9</td>\n",
        "    <td>Ankle boot</td>\n",
        "  </tr>\n",
        "</table>\n",
        "\n",
        "Each image is mapped to a single label. Since the *class names* are not included with the dataset, store them here to use later when plotting the images:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "IjnLH5S2CaWx",
        "outputId": "79265e2b-6058-462c-89e8-b439a9cbfb89",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class names: ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n"
          ]
        }
      ],
      "source": [
        "class_names = metadata.features['label'].names\n",
        "print(\"Class names: {}\".format(class_names))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Brm0b_KACaWX"
      },
      "source": [
        "### Explore the data\n",
        "\n",
        "Let's explore the format of the dataset before training the model. The following shows there are 60,000 images in the training set, and 10000 images in the test set:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "MaOTZxFzi48X",
        "outputId": "7759388f-440d-4b13-89c7-7d903035641c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of training examples: 60000\n",
            "Number of test examples:     10000\n"
          ]
        }
      ],
      "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))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ES6uQoLKCaWr"
      },
      "source": [
        "## Preprocess the data\n",
        "\n",
        "The value of each pixel in the image data is an integer in the range `[0,255]`. For the model to work properly, these values need to be normalized to the range `[0,1]`. So here we create a normalization function, and then apply it to each image in the test and train datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "nAsH3Zm-76pB"
      },
      "outputs": [],
      "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)\n",
        "\n",
        "# The first time you use the dataset, the images will be loaded from disk\n",
        "# Caching will keep them in memory, making training faster\n",
        "train_dataset =  train_dataset.cache()\n",
        "test_dataset  =  test_dataset.cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lIQbEiJGXM-q"
      },
      "source": [
        "### Explore the processed data\n",
        "\n",
        "Let's plot an image to see what it looks like."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "oSzE9l7PjHx0",
        "outputId": "b84a3127-24d5-420d-faa2-ce2cb940a3f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ee638AlnCaWz"
      },
      "source": [
        "Display the first 25 images from the *training set* and display the class name below each image. Verify that the data is in the correct format and we're ready to build and train the network."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "oZTImqg_CaW1",
        "outputId": "70f060ae-2945-4147-a74c-05380edd7ae6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "for i, (image, label) in enumerate(train_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",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59veuiEZCaW4"
      },
      "source": [
        "## Build the model\n",
        "\n",
        "Building the neural network requires configuring the layers of the model, then compiling the model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gxg1XGm0eOBy"
      },
      "source": [
        "### Setup the layers\n",
        "\n",
        "The basic building block of a neural network is the *layer*. A layer extracts a representation from the data fed into it. Hopefully, a series of connected layers results in a representation that is meaningful for the problem at hand.\n",
        "\n",
        "Much of deep learning consists of chaining together simple layers. Most layers, like `tf.keras.layers.Dense`, have internal parameters which are adjusted (\"learned\") during training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "9ODch-OFCaW4"
      },
      "outputs": [],
      "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",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gut8A_7rCaW6"
      },
      "source": [
        "This network has three layers:\n",
        "\n",
        "* **input** `tf.keras.layers.Flatten` — This layer transforms the images from a 2d-array of 28 $\\times$ 28 pixels, to a 1d-array of 784 pixels (28\\*28). Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn, as it only reformats the data.\n",
        "\n",
        "* **\"hidden\"** `tf.keras.layers.Dense`— A densely connected layer of 128 neurons. Each neuron (or node) takes input from all 784 nodes in the previous layer, weighting that input according to hidden parameters which will be learned during training, and outputs a single value to the next layer.\n",
        "\n",
        "* **output**  `tf.keras.layers.Dense` — A 128-neuron, followed by 10-node *softmax* layer. Each node represents a class of clothing. As in the previous layer, the final layer takes input from the 128 nodes in the layer before it, and outputs a value in the range `[0, 1]`, representing the probability that the image belongs to that class. The sum of all 10 node values is 1.\n",
        "\n",
        "> Note: Using `softmax` activation and `SparseCategoricalCrossentropy()` has issues and which are patched by the `tf.keras` model. A safer approach, in general, is to use a linear output (no activation function) with `SparseCategoricalCrossentropy(from_logits=True)`.\n",
        "\n",
        "\n",
        "### Compile the model\n",
        "\n",
        "Before the model is ready for training, it needs a few more settings. These are added during the model's *compile* step:\n",
        "\n",
        "\n",
        "* *Loss function* — An algorithm for measuring how far the model's outputs are from the desired output. The goal of training is this measures loss.\n",
        "* *Optimizer* —An algorithm for adjusting the inner parameters of the model in order to minimize loss.\n",
        "* *Metrics* —Used to monitor the training and testing steps. The following example uses *accuracy*, the fraction of the images that are correctly classified."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Lhan11blCaW7"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKF6uW-BCaW-"
      },
      "source": [
        "## Train the model\n",
        "\n",
        "First, we define the iteration behavior for the train dataset:\n",
        "1. Repeat forever by specifying `dataset.repeat()` (the `epochs` parameter described below limits how long we perform training).\n",
        "2. The `dataset.shuffle(60000)` randomizes the order so our model cannot learn anything from the order of the examples.\n",
        "3. And `dataset.batch(32)` tells `model.fit` to use batches of 32 images and labels when updating the model variables.\n",
        "\n",
        "Training is performed by calling the `model.fit` method:\n",
        "1. Feed the training data to the model using `train_dataset`.\n",
        "2. The model learns to associate images and labels.\n",
        "3. The `epochs=5` parameter limits training to 5 full iterations of the training dataset, so a total of 5 * 60000 = 300000 examples.\n",
        "\n",
        "(Don't worry about `steps_per_epoch`, the requirement to have this flag will soon be removed.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "o_Dp8971McQ1"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE = 32\n",
        "train_dataset = train_dataset.cache().repeat().shuffle(num_train_examples).batch(BATCH_SIZE)\n",
        "test_dataset = test_dataset.cache().batch(BATCH_SIZE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "xvwvpA64CaW_",
        "outputId": "38da90e8-1f95-4862-caed-37919eeaa36d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 15s 3ms/step - loss: 0.4921 - accuracy: 0.8263\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3682 - accuracy: 0.8672\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3356 - accuracy: 0.8774\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3087 - accuracy: 0.8871\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.2885 - accuracy: 0.8928\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fcd8aa28910>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "model.fit(train_dataset, epochs=5, steps_per_epoch=math.ceil(num_train_examples/BATCH_SIZE))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W3ZVOhugCaXA"
      },
      "source": [
        "As the model trains, the loss and accuracy metrics are displayed. This model reaches an accuracy of about 0.88 (or 88%) on the training data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oEw4bZgGCaXB"
      },
      "source": [
        "## Evaluate accuracy\n",
        "\n",
        "Next, compare how the model performs on the test dataset. Use all examples we have in the test dataset to assess accuracy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "VflXLEeECaXC",
        "outputId": "4f141279-9d16-4b07-ad45-606b28aca64c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 2s 4ms/step - loss: 0.3511 - accuracy: 0.8741\n",
            "Accuracy on test dataset: 0.8741000294685364\n"
          ]
        }
      ],
      "source": [
        "test_loss, test_accuracy = model.evaluate(test_dataset, steps=math.ceil(num_test_examples/32))\n",
        "print('Accuracy on test dataset:', test_accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWfgsmVXCaXG"
      },
      "source": [
        "As it turns out, the accuracy on the test dataset is smaller than the accuracy on the training dataset. This is completely normal, since the model was trained on the `train_dataset`. When the model sees images it has never seen during training, (that is, from the `test_dataset`), we can expect performance to go down."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xsoS7CPDCaXH"
      },
      "source": [
        "## Make predictions and explore\n",
        "\n",
        "With the model trained, we can use it to make predictions about some images."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "Ccoz4conNCpl",
        "outputId": "abba7c73-d941-4ae0-cd9b-aa4f3a08b303",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 77ms/step\n"
          ]
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Gl91RPhdCaXI",
        "outputId": "40e37d96-efbc-419a-b923-d20fe635a18b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "predictions.shape\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x9Kk1voUCaXJ"
      },
      "source": [
        "Here, the model has predicted the label for each image in the testing set. Let's take a look at the first prediction:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "3DmJEUinCaXK",
        "outputId": "766fc31c-31b5-4c81-b4a8-bcf2af330adb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2.6347594e-05, 3.0610292e-07, 2.6000122e-02, 7.8421672e-06,\n",
              "       8.1844395e-01, 2.6875949e-08, 1.5542643e-01, 3.4447696e-09,\n",
              "       9.4914445e-05, 6.1130272e-08], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "predictions[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-hw1hgeSCaXN"
      },
      "source": [
        "A prediction is an array of 10 numbers. These describe the \"confidence\" of the model that the image corresponds to each of the 10 different articles of clothing. We can see which label has the highest confidence value:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "qsqenuPnCaXO",
        "outputId": "3b31124a-7d3b-4084-f81a-ff970975ad2b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "np.argmax(predictions[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E51yS7iCCaXO"
      },
      "source": [
        "So the model is most confident that this image is a shirt, or `class_names[6]`. And we can check the test label to see this is correct:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "Sd7Pgsu6CaXP",
        "outputId": "03fa4ea5-1f8d-44b0-ec51-2eddc090ee9b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "test_labels[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ygh2yYC972ne"
      },
      "source": [
        "We can graph this to look at the full set of 10 class predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "DvYmmrpIy6Y1"
      },
      "outputs": [],
      "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')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d4Ov9OFDMmOD"
      },
      "source": [
        "Let's look at the 0th image, predictions, and prediction array."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "HV5jw-5HwSmO",
        "outputId": "67d78220-b2c9-4792-d2bf-8a58f330c871",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAEECAYAAAD56kd7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAXa0lEQVR4nO3de2zW5fnH8astPfdpK5SN9YDlWAUVETxMNxyR6IanTaNmGJcty2aiMWaZOpwu6jadm1ucpzg185RsmctAZthEwTBmwIGHQUAKIkIpFAqFlp5baO/fH/ePX1vhvu7+eryg71fSYHs919Pv8y346f083+u5k5xzTgAAwLBKHu4DAAAABDIAACYQyAAAGEAgAwBgAIEMAIABBDIAAAYQyAAAGDBquA8AgE2dnZ1SVVUliURCkpKShvtwgJOWc04aGhqksLBQkpPD62ACGcAJVVVVSUlJyXAfBnDKqKyslOLi4mCdQAZwQolEQkT8/0Ryc3OH+WiAk1d9fb2UlJT837+pEAIZwAkde5o6NzeXQAYGQOylHy7qAgDAAAIZAAADCGQAAAwgkAEAMIBABgDAAAIZAAADejX2xDv2AAOjt+/YA2Dk6VUg8449wMCKvWMPgJGnV4E8Ut+xp6OjI1hLSUlRe1esWBGsvfzyy2rvzJkzg7VRo/r3Xi5r164N1qZPn6723nfffX3+vs65YG0kPevS23fsATDy9Or/7iP1HXv6E8jZ2dnBWmpqqtqbkZERrPU3kLXvnZ6ervb252dPIPc0Eh8zAB0vYgEAYACBDACAAQQyAAAGEMgAABhwSm+/qF1IJBK/sCZ24ZbmlVdeCdZKS0vV3szMzGDtjTfeUHuzsrLU+qRJk4K1zz77TO1tbGwM1nJyctRe7VzHfk79wcVTAE4WrJABADCAQAYAwAACGQAAAwhkAAAMIJABADCAQAYAwIBTeuypvyMvS5cuDdbWrFmj9h45ciRYq62tVXvnzJkTrM2dO1ftjdVXrlwZrG3atEntveOOO4K1BQsWqL3z5s0L1vr7cxrMsSkAGCqskAEAMIBABgDAAAIZAAADCGQAAAwgkAEAMIBABgDAAAIZAAADTMwha3Ok/ZlRXbJkiVpft26dWm9qagrWYtsczp49O1g7dOiQ2rtx48ZgbcWKFWpvS0uLWt+3b1+w9pWvfEXt7ejoCNYWL16s9i5btixYi23d+NBDD6n1/mztyPaMAKxghQwAgAEEMgAABhDIAAAYQCADAGAAgQwAgAEEMgAABpgYe+rs7AzWUlJS1N7y8vJgLTYiNG7cOLVeUFAQrMXGnhobG4O14uJitTeRSARrkydPVnuTk/XfsS644IJgrbW1Ve3V5OXlqXXtvnfs2KH2/upXv1Lr9957r1oHgJMBK2QAAAwwsUIGgJFo1y6Rmpq+9RYUiIwfP7DHg+FFIAPAMNi1S6SsTKSvrxRlZIhs3Uoon0p4yhoAhkFNTd/DWMT39nV1DZsIZAAADCCQAQAwgEAGAMAAExd1xWaNNYsWLQrWsrOz+3y/IiLNzc3B2pEjR9RebbY6RustLS1VezMyMtS6Ng/c1tam9mrbL8Z+htpjis1la1tG9ve4AMAKVsgAABhAIAMAYACBDACAAQQyAAAGEMgAABhAIAMAYICJsaf+qK+vD9aSkpLU3oaGhj5/35ycHLWufW/nnNqr1WPjVLHH3NLS0ufj0s5XbGSqvb09WIuNp2m9IiIbN24M1s4991y1V3vMsXMJAAOJFTIAAAYQyAAAGEAgAwBgAIEMAIABBDIAAAYQyAAAGEAgAwBggPk55P3796v1vXv3BmsTJ05Ue7Vt+0REKioqgrUJEyaovf3Z+lHbQjE27ztqlP4jTU9PD9ZiW0pq50vb1lFEpKmpKVjr7xaJy5cvD9Zic8jMGgOwghUyAAAGEMgAABhAIAMAYACBDACAAQQyAAAGEMgAABhAIAMAYID5OeSVK1eqdW2+NSsrS+3duXOnWu/PHr7a947N3Q7mHHJycvh3sNgcsnZcsfNRXV0drDU2Nqq9Y8aMUetbtmxR6wBwMmCFDACAAQQyAAAGEMgAABhAIAMAYACBDACAAQQyAAAGmB972rx5s1rPz88P1saNG6f2dnZ2qvV169YFaxdeeKHaq43qxMaetNGlhoYGtTcnJ0et5+bmBmuxsSdtZCo2QlZXVxesXXzxxWpvS0tLn++7pqZG7S0oKFDrADBUWCEDAGAAgQwAgAEEMgAABhDIAAAYQCADAGAAgQwAgAEEMgAABpifQ66trVXrSUlJwVpsrra+vl6tl5eXB2u7d+9WexOJRLAWm0PWtjKM9cZmqzWx86Vtk/jRRx+pve+//36wFpvpjh2XNqe8evVqtffaa69V6wAwVFghAwBgAIEMAIABBDIAAAYQyAAAGEAgAwBgAIEMAIAB5seempqa1HpRUVGwlpeXp/Zq2/aJ6OM0FRUVau/EiRODtdbWVrVXG/Nxzqm9HR0dan3//v3B2oEDB9ReTewxadtRXnTRRWpvbOxJe8xbtmxRexl7AmAFK2QAAAwgkAEAMIBABgDAAAIZAAADCGQAAAwgkAEAMIBABgDAAPNzyLHtF8ePHx+sxeZqGxoa1PpZZ50VrMW2OWxvbw/WsrKy1N6MjIxgTZuNFonP7Obn5wdrscekzRprxyyiz5N/+OGHam9sy0ntuGLz4gBgBStkAAAMIJABADCAQAYAwAACGQAAAwhkAAAMIJABADDAxNhTZWVlsJadna32Tps2LVirrq5We7URIBGRnJycYC02IqSN+TQ3N6u92uhS7PvGRrm0EaLYdpSHDx8O1g4dOqT2nn322cFaY2Oj2jtjxgy1rj2m2PadAGAFK2QAAAwgkAEAMIBABgDAAAIZAAADCGQAAAwgkAEAMIBABgDAAPNzyLE5Um3u9v3331d7r7rqKrWuzd3GZomTkpKCtby8PLVXm6t1zqm96enpaj2RSARrsTlkbUvJ5GT9d7u5c+cGa7E55M2bN6t1TWwLTgCwghUyAAAGEMgAABhAIAMAYACBDACAAQQyAAAGEMgAABhAIAMAYICJOWRtVjS2H/LYsWODNW2eV0TkoosuUuvLly8P1mJztxptv2MRkdTU1EHpFRHp6Ojoc69Wj+3DXFJSEqzF5pBra2vVelpaWp97AcAKVsgAABhAIAMAYACBDACAAQQyAAAGEMgAABhAIAMAYICJsSdtNKWmpkbt1bZIjIltN1hdXR2szZ49W+3Nz88P1mJbSra1tQVrsbGnzMxMta71ayNRIiKnnXZasFZWVqb2btiwIVibNGmS2ltVVaXWtZGq2PkCACtYIQMAYACBDACAAQQyAAAGEMgAABhAIAMAYACBDACAAQQyAAAGmJhD1rbfGz9+vNqrzfsWFxervQcPHlTr7e3twVpsW8j09PRgzTmn9mZkZARrra2tam+MNmsce0zaFouxn5M20x0T2xbyjDPOCNbefPNNtffo0aPB2qhRJv55ABghWCEDAGAAgQwAgAEEMgAABhDIAAAYQCADAGAAgQwAgAEm5jq07Re1sRQRkfXr1/e597PPPlPrnZ2dwVpaWpra29zcHKzFtgTUtpRMTtZ/h4qN6mhbP8a2stTOx9ixY9XeLVu2BGuxn0PsXO/cuTNYq6+vV3u1nwVjTwCGEitkAAAMIJABADCAQAYAwAACGQAAAwhkAAAMIJABADCAQAYAwAATg5ZJSUnBWmlpqdpbVlYWrFVWVqq9//nPf9T6WWedFazV1dWpvYWFhcGatr2iiD7vu3//frU3Nkucl5cXrGVmZqq92raRNTU1aq+2TebWrVvV3jlz5qj1L37xi8FabEvJQ4cOBWtFRUVqLwAMJFbIAAAYQCADAGAAgQwAgAEEMgAABhDIAAAYQCADAGCAibGnbdu2BWtjxoxRe7XRptgI0O7du9W6NvYU2wbx4MGDwVpqaqraq203GNu6MT09Xa2npKQEa62trWqvNiKUlZWl9mqPec+ePWrvgQMH1Lr2d0T7OYjoW38y9gRgKLFCBgDAAAIZAAADCGQAAAwgkAEAMIBABgDAAAIZAAADCGQAAAwwMYeszfQWFxervVOnTg3WYtsrxmacR40Kn57YPHBubm6wVl9fr/a2tbUFa83NzWpvQ0ODWtfmbrXtFUX07Rljs8LauZw4caLaG5ut1rZ2LCkpUXu18wEAQ4kVMgAABhDIAAAYQCADAGAAgQwAgAEEMgAABhDIAAAYQCADAGCAiTlkbZ/d2NxtU1NTsLZ27Vq1V5sVFtHnYz/88EO1d8qUKcFaQUGB2nv06NFgbfv27WpvXl6eWtcekzb/LKLPT+/bt0/t1WaJY3sWf/rpp2p90qRJfb7vioqKYO2rX/2q2gsAA4kVMgAABhDIAAAYQCADAGAAgQwAgAEEMgAABhDIAAAYYGLsqa6uLljLyclRe7Vt/bSaiD5eJKKP+YwdO1bt3bRpU7CmbWMoIpKUlBSspaSkqL2xMbENGzYEax0dHWpvZ2dnsJZIJNRebVvI1NRUtTd231q/tjWjiEhLS4taB4ChwgoZAAADCGQAAAwgkAEAMIBABgDAAAIZAAADCGQAAAwgkAEAMMDEHLI2Lxyb2d27d2+wFpvJvfrqq9W6NkscU1hYGKzFZom181FVVdXnYxIRGT16dLDWnxnn8vJytXfq1KnB2qxZs9Ted955R63PmzcvWNNmukXif0cAYKiwQgYAwAACGQAAAwhkAAAMMPEaMgDAloULF/ar/9FHHx2gIxk5WCEDAGAAgQwAgAEmnrLOysoK1mJb82lbJE6ePFntve222/QDU1RWVqr1w4cPB2u1tbVqb3Z2drC2bds2tTc5Wf8dS9vKMC0tTe390pe+FKwVFRWpvbm5uWpds2rVKrWujYJp51JE5ODBg306JgAYaKyQAQAwgEAGAMAAAhkAAAMIZAAADCCQAQAwgEAGAMAAAhkAAANMzCHv3LkzWOvo6FB7KyoqgrX29va+HlJUSUlJv+p9dd555w3K/VoWm0Wvq6vr8323tbX1uRcABhIrZAAADCCQAQAwgEAGAMAAAhkAAAMIZAAADCCQAQAwwMTY0/z584O13bt3q73aSMz3vve9Ph+TiIhzrs+9sXEtTUpKSp97k5KS1PrRo0cH5b77c8wxZWVlar26ujpYi21HOW/evD4dEwAMNFbIAAAYQCADAGAAgQwAgAEEMgAABhDIAAAYQCADAGBAr8aejo3/1NfXD8pBaDvuxMZ4tN6mpia1N/Z4GHvq/X0P5thTbNcubbTpyJEjaq/2d2Qw/r4fu8/+/N0CcGrqVSA3NDSIyOBtKThYnn322eE+BBj3+uuvD8v3bWhokLy8vGH53gBs6lUgFxYWSmVlpSQSiegKDECYc04aGhqksLBwuA8FgDG9CuTk5GQpLi4e7GMBRgRWxgBOhIu6AAAwwMR7WQPAYFi4cGG/+h999NEBOhIgjhUyAAAGEMgAABhAIKOHrVtFxo0T+d9Jt2HX3i5SWirywQfDfSQAMLhGXCDv2ydyxx0iEyeKpKeLlJSIXH21yDvvDOz3KS0V+f3ve3c8t9ziQzA7W+S880QWLeqq79wp8v3vi0yYIJKZKTJpksgDD/ig6n6bOXN8/5w5/vPurrqq531q7r3Xn59Eoutrzok8/7zIhReK5OSI5OeLzJ7tH19zc+/utzcefFDk3HN7fi0tTeSuu0R+8pOB+z4AYNGIuqhr506RSy7xgfLYYyJnny1y5IjIW2+J3H67yJYtQ39M3/mOSF2dyBtviBQUiPz5zyI33uhXhDNn+mPq7BR57jmRyZNFNm0S+cEPRJqaRH77W38fP/6xSFGRyB//KHL//T7A/vY3X3vtNZHkZJHrr48fy65dIkuXijz1VM+v33KLyOLF/r6fflpk7FiRDRt8IJeWinzzmwN3Pk7k5pv9Y/z4Y5Hp0wf3e6HLYL9D31DQ3smvNwbzsTc2Dsx9DNYhWj53J5tev0OfG0G+8Q3nioqca2w8vlZb2/XfFRXOXXONc9nZziUSzt1wg3P79nXVP/3U17/wBX+b2bOdW768q37ppc75dWXXR0h2tnOvvtrza6NHO/fCC+Ge3/zGuQkTuj4/80zn3nzT//c//+nctGldj2nyZOd27QrfV3ePPeYfS3evveaPf8mS42/f2elcXZ3/744O5x56yJ/ftDTnZszoOqZj7rnHuSlTnMvM9Md///3Otbf72ksvHX/OXnqpq3fuXH97DJ3KykonInzwwccAfVRWVqr/5kbMCvnQIZFly0Qeftg/tft5+fn+z85OkWuv9U/NrlolcvSoXz3fdJPIv/7lb9PYKDJ/vr+v9HSRV1/1T3tv3SoyfrxfTc6YIfLDH/rVrObii/0q9sor/TH89a8ira0iX/tauOfwYZHRo7s+nzFDZMUKkcsvF3n7bZFzzvFfv/tuf+y9fcfTd9/1T0V396c/iZSV+XPyeUlJIsfe4+KJJ0R+9zu/kp85U+TFF0WuucavaqdM8bdJJEReflmksFBk40Z/bhIJkXvu8ed30yb/M1qxwt+++/tnXHCBPz4MHd6hDxgYrrfv0Dc0v2sPv7Vr/apr8WL9dm+/7VxKSs9V5ccf+95168J906c799RTXZ+ffrpzjz8eP67aWucuv9zf/6hRzuXmOvfWW+Hbb9vmb/P8811f273buSuvdK6kxP+5e7dzq1b51e7Bg36FP2GCc7fe6lxbW/i+Z8xw7uc/7/m1M8/0zwbEFBY69/DDPb92/vnO3XZbuOexx5ybNavr8wce8MdwIk884Vxpafw4AOBkNWJWyL3dXKe83K8ou68qp03zq9fycpHzz/cr5AcfFPnHP0T27vWr6JYW/xrs/9fPfuZfQ16xwr+GvGSJfw353Xf9a9zd7dkj8vWvi9xwQ8+Vd1GRf+33mLY2kSuuEHnlFZFf/tKvQrdu9b3PPecv2jqRlhaRjIyeX+vNeauvF6mq8q/Pd3fJJf615mNee03kySdFtm/35/DoUZHc3Pj9i/gL2gbyAjIAsGbEXGU9ZYp/inUgLty66y6R118XeeQRH5zr1/vwjOwSeJzt2/1FUi++KHLZZf6p5wce8E8bP/NMz9tWVYnMneuf4n7+ef1+H3nEP309a5Z/mv3660VSU0Wuu67rafcTKSgQqa3t+bWpUwfmnL33nr84a/58/8vDf/8rct99vT9nhw75i8kA4FQ1YgJ59Gi/anzmGX+F8ufV1fk/zzxTpLLSfxyzebOvT5vmP1+9WuS73xX51rd8EI8bd/yoUVqaSGxL5GMrvs9v55uS4l/LPmbPHv+a8qxZIi+9dPztuysv91dq/+IX/vOODn8luYj/UzummTP9Y+1uwQKRTz4R+fvfj7+9c/717Nxc/7rw6tU966tXd52zNWtETj/dh/Ds2f4XpIqKnrfXztmmTf74AOBUNWICWcSHcUeHv0Bo0SKRbdt8gD35pMiXv+xvM2+eD9mbbxb56CORdev8aNKll3Zd8DRlir9wa/16/5TsggU9A1TEjwP9+98+TGtqTnw8Z5zhR5luvdV/n+3b/YVRy5d3jRIdC+Px4/2Y04EDfnZ5377j7885fyHZ4493Xbh2ySUiL7zgH+errx7/tHJ3V1zhV7LdQ/HGG/0FV9/+tl95f/CBD9KlS/25WrnS3+7uu0V+/Wv/tPTWrSILF/rzc+edXeds1y6Rv/zFP84nn/TPMnz+nO3Y4ftqavxT78e8+65f9QPAKWu4X8QealVVzt1+u7/oKi3Nj+lcc41zK1d23SY29rRjhx/Dycz0F1I9/bQfdbrzzq7bvPeec+ec41x6uj729Mknzl13nR+hysryPd3HoE40DiSBUao//MG566/v+bXqaucuu6zrcTQ1hY/lyBF/cdayZT2/3tHh3LPP+ou0srL8RWWzZvkLrZqbu27z4IP+fKamnnjs6e67nRszxrmcHOduuslf9JaX11VvbfXHn5/fc+xpzRr/tWPfCwBORUnO9fZyJ4wEzzzj36TkrbeG+0i63HSTf339pz8d7iMBgMEzYq6yRu/ceqt/vbyhoefbZw6X9nb/EsKPfjTcRwIAg4sVMgAABoyoi7oAALCKQAYAwAACGQAAAwhkAAAMIJABADCAQAYAwAACGQAAAwhkAAAMIJABADDgfwAimRcqGFw1YwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "Ko-uzOufSCSe",
        "outputId": "b476ff7d-9ee6-4357-d773-aa1810f74897",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kgdvGD52CaXR"
      },
      "source": [
        "Let's plot several images with their predictions. Correct prediction labels are blue and incorrect prediction labels are red. The number gives the percent (out of 100) for the predicted label. Note that it can be wrong even when very confident."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "hQlnbqaw2Qu_",
        "outputId": "c3c814c9-66f3-4af2-aa52-ddb3588bd4b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 30 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R32zteKHCaXT"
      },
      "source": [
        "Finally, use the trained model to make a prediction about a single image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "yRJ7JU7JCaXT",
        "outputId": "847bba8a-a02d-4f98-ac62-0ab0ed1892eb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(28, 28, 1)\n"
          ]
        }
      ],
      "source": [
        "# Grab an image from the test dataset\n",
        "img = test_images[0]\n",
        "\n",
        "print(img.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vz3bVp21CaXV"
      },
      "source": [
        "`tf.keras` models are optimized to make predictions on a *batch*, or collection, of examples at once. So even though we're using a single image, we need to add it to a list:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "lDFh5yF_CaXW",
        "outputId": "6ed4728a-23b5-47bb-f853-d677f0b8d650",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 28, 28, 1)\n"
          ]
        }
      ],
      "source": [
        "# Add the image to a batch where it's the only member.\n",
        "img = np.array([img])\n",
        "\n",
        "print(img.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EQ5wLTkcCaXY"
      },
      "source": [
        "Now predict the image:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "o_rzNSdrCaXY",
        "outputId": "08f5d2bf-0731-4977-de08-438b3860927e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 49ms/step\n",
            "[[2.6347570e-05 3.0610295e-07 2.6000125e-02 7.8421681e-06 8.1844407e-01\n",
            "  2.6875899e-08 1.5542637e-01 3.4447634e-09 9.4914452e-05 6.1130166e-08]]\n"
          ]
        }
      ],
      "source": [
        "predictions_single = model.predict(img)\n",
        "\n",
        "print(predictions_single)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "6Ai-cpLjO-3A",
        "outputId": "bdd8ef1e-d802-4d3d-f364-3f1e7f8e7199",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_value_array(0, predictions_single, test_labels)\n",
        "_ = plt.xticks(range(10), class_names, rotation=45)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cU1Y2OAMCaXb"
      },
      "source": [
        "`model.predict` returns a list of lists, one for each image in the batch of data. Grab the predictions for our (only) image in the batch:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "2tRmdq_8CaXb",
        "outputId": "37613190-cbe9-44ef-8a0e-676dc6dfaee4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "np.argmax(predictions_single[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YFc2HbEVCaXd"
      },
      "source": [
        "And, as before, the model predicts a label of 6 (shirt)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-KtnHECKZni_"
      },
      "source": [
        "# Exercises\n",
        "\n",
        "Experiment with different models and see how the accuracy results differ. In particular change the following parameters:\n",
        "*   Set training epochs set to 1\n",
        "*   Number of neurons in the Dense layer following the Flatten one. For example, go really low (e.g. 10) in ranges up to 512 and see how accuracy changes\n",
        "*   Add additional Dense layers between the Flatten and the final `Dense(10)`, experiment with different units in these layers\n",
        "*   Don't normalize the pixel values, and see the effect that has\n",
        "\n",
        "\n",
        "Remember to enable GPU to make everything run faster (Runtime -> Change runtime type -> Hardware accelerator -> GPU).\n",
        "Also, if you run into trouble, simply reset the entire environment and start from the beginning:\n",
        "*   Edit -> Clear all outputs\n",
        "*   Runtime -> Reset all runtimes"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "l03c01_classifying_images_of_clothing.ipynb",
      "toc_visible": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}