l03c01_classifying_images_of_clothing.ipynb

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      "source": [
        "##### Copyright 2018 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ckMIh7O7s6D"
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      "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."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vasWnqRgy1H4"
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      "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",
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        "# Software is furnished to do so, subject to the following conditions:\n",
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        "# The above copyright notice and this permission notice shall be included in\n",
        "# all copies or substantial portions of the Software.\n",
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        "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
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        "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
        "# DEALINGS IN THE SOFTWARE."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "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",
      "metadata": {
        "id": "P7mUJVqcINSM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "31b9a640-3298-4039-e5e6-567043c37613"
      },
      "source": [
        "!pip install -U tensorflow_datasets"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow_datasets in /usr/local/lib/python3.7/dist-packages (4.0.1)\n",
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            "  Downloading tensorflow_datasets-4.4.0-py3-none-any.whl (4.0 MB)\n",
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            "Installing collected packages: tensorflow-datasets\n",
            "  Attempting uninstall: tensorflow-datasets\n",
            "    Found existing installation: tensorflow-datasets 4.0.1\n",
            "    Uninstalling tensorflow-datasets-4.0.1:\n",
            "      Successfully uninstalled tensorflow-datasets-4.0.1\n",
            "Successfully installed tensorflow-datasets-4.4.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FxXYSCXGQqQ"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1UbK0Uq7GWaO"
      },
      "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"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "590z76KRGtKk"
      },
      "source": [
        "import logging\n",
        "logger = tf.get_logger()\n",
        "logger.setLevel(logging.ERROR)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "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",
      "metadata": {
        "id": "7MqDQO0KCaWS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e0d24f4-4486-4df0-9e8a-baa135a49a49"
      },
      "source": [
        "dataset, metadata = tfds.load('fashion_mnist', as_supervised=True, with_info=True)\n",
        "train_dataset, test_dataset = dataset['train'], dataset['test']"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDownloading and preparing dataset fashion_mnist/3.0.1 (download: 29.45 MiB, generated: 36.42 MiB, total: 65.87 MiB) to /root/tensorflow_datasets/fashion_mnist/3.0.1...\u001b[0m\n",
            "Shuffling and writing examples to /root/tensorflow_datasets/fashion_mnist/3.0.1.incompleteVY0BAS/fashion_mnist-train.tfrecord\n",
            "Shuffling and writing examples to /root/tensorflow_datasets/fashion_mnist/3.0.1.incompleteVY0BAS/fashion_mnist-test.tfrecord\n",
            "\u001b[1mDataset fashion_mnist downloaded and prepared to /root/tensorflow_datasets/fashion_mnist/3.0.1. Subsequent calls will reuse this data.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "IjnLH5S2CaWx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f450973-eaf6-4c37-ee2a-58f83ee6289f"
      },
      "source": [
        "class_names = metadata.features['label'].names\n",
        "print(\"Class names: {}\".format(class_names))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class names: ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n"
          ]
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "MaOTZxFzi48X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8fd4fce-5680-4232-f6b9-217d4196db86"
      },
      "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": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of training examples: 60000\n",
            "Number of test examples:     10000\n"
          ]
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "nAsH3Zm-76pB"
      },
      "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()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "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",
      "metadata": {
        "id": "oSzE9l7PjHx0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "4d4437a7-0cf0-4480-8944-ba5dd2e0e069"
      },
      "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": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "oZTImqg_CaW1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "d852af46-abdb-4a73-d2bc-c7912862a10d"
      },
      "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()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "9ODch-OFCaW4"
      },
      "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": 11,
      "outputs": []
    },
    {
      "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",
      "metadata": {
        "id": "Lhan11blCaW7"
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "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",
      "metadata": {
        "id": "o_Dp8971McQ1"
      },
      "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)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvwvpA64CaW_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1957860-006f-4cc7-e4e8-a5bed0e373b5"
      },
      "source": [
        "model.fit(train_dataset, epochs=5, steps_per_epoch=math.ceil(num_train_examples/BATCH_SIZE))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 19s 3ms/step - loss: 0.4998 - accuracy: 0.8227\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3781 - accuracy: 0.8634\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3366 - accuracy: 0.8773\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3158 - accuracy: 0.8835\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2959 - accuracy: 0.8913\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f810694eb10>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "VflXLEeECaXC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60fd2b29-2dc9-4f58-b2c5-189a62dc73cd"
      },
      "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": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 3s 8ms/step - loss: 0.3569 - accuracy: 0.8730\n",
            "Accuracy on test dataset: 0.8730000257492065\n"
          ]
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "Ccoz4conNCpl"
      },
      "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": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gl91RPhdCaXI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ec6446a-de7a-4340-f300-bdb3bb6ae604"
      },
      "source": [
        "predictions.shape\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "3DmJEUinCaXK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25cf6ea3-1363-4c7d-d250-69fb2d51d8c4"
      },
      "source": [
        "predictions[0]"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.3311079e-04, 2.2973318e-05, 1.0426888e-02, 1.4420123e-05,\n",
              "       9.6058333e-01, 6.1446448e-10, 2.8717143e-02, 2.5679658e-10,\n",
              "       1.0210300e-04, 3.0981209e-09], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "qsqenuPnCaXO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae5bd593-3150-40f8-cfda-6a2a6d774360"
      },
      "source": [
        "np.argmax(predictions[0])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "Sd7Pgsu6CaXP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11324610-16ad-40e5-d2ee-a2f55589beb3"
      },
      "source": [
        "test_labels[0]"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ygh2yYC972ne"
      },
      "source": [
        "We can graph this to look at the full set of 10 class predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DvYmmrpIy6Y1"
      },
      "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": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d4Ov9OFDMmOD"
      },
      "source": [
        "Let's look at the 0th image, predictions, and prediction array. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HV5jw-5HwSmO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "152f59f7-8257-4dce-db1b-0985ac55a399"
      },
      "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": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAC6CAYAAACQs5exAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAARO0lEQVR4nO3dfazW5XnA8e8tyMvhTeVQkLceAV/qYLpC1bZKykKWzdp13f5Ypsk2l23pbOyaNbV2bdLYLdUte+mypFvs1sw/utlmUtrZaocdM51OKViYCFgQD4MKBxAUldfCvT+en43zd932OTlybg5+P8mJcj3X83t50Isfz3W/pJwzkqThd07tC5CktyoLsCRVYgGWpEoswJJUiQVYkiqxAEtSJaNrX4BUW29vb+7r66t9GTpLrVu3bn/OeVr0mgVYb3l9fX2sXbu29mXoLJVS2lF6za8gJKkSC7AkVWIBlqRKLMCSVMmgmnBnc7f40KFDrdi+ffvC3AkTJgzpXK+88korNn78+DB35syZQzrXSNLf38/+/ftT7euQhsugCvBI6xafPHmyFRs1alSY+53vfKcVu/vuu8PcJUuWtGKjR3f/UT766KOt2KJFi8Lcz33uc10fN1rZLqWRU8+iz1Wn34wZMDDQXe706bBnz+m9nrcSv4KQ3uK6Lb6DzdVPZwGWpEpG3ESMwfw1u/R1Q+See+5pxUrfd0ff137zm99sxXp6esL3z58/vxXbvn17mPvyyy+3YhMnTgxzo8/hzVhwfyR9jSGNJD4BS1IlFmBJqsQCLEmVWIAlqRILsCRVMuJGQQymI3///fe3YtEkCIATJ060YgcPHgxzly5d2ootW7asqxjA6tWrW7GNGzeGubfeemsrduONN4a5y5cvb8UG83m9GSMmJHXPJ2BJqsQCLEmVWIAlqRILsCRVMqxNuFKTZzCNopUrV7Zia9asCXOjZR9L04OjlbgOHDgQ5j755JOt2EMPPdSKHTlyJHz/nmA5qWuvvTbMjVZ0W7FiRZj74IMPtmKlact33HFHK1b6fRjpq6xJZyqfgCWpEguwJFViAZakSizAklSJBViSKhnWURCnTp0K49HC6Zs3bw5zo9EGM2bMCHN7e3tbsdIoiGjh89mzZ4e5kyZNasUWLFjQip1zTvzn21VXXdWKHT16NMyNTJkyJYxHx3j22WfD3DvvvLMV+9SnPtX1NUgaOp+AJakSC7AkVWIBlqRKLMCSVMmwNuEGs0vxfffdF8YnTJjQ9TEOHz7cikXr/kK5QdhtbrSD8rhx48L3R82yY8eOhbnRVOTS5xhdV6mRGE2Hjs71RueTNDQ+AUtSJRZgSarEAixJlViAJakSC7AkVXLG7op86NChMB4tBP7SSy91fdzSAuXRcUsLyEfxaARCadHyaKH20rmieyuNmDh+/HgrVho1EuVGC80DXHnlla3Ym7G4vvRW5xOwJFViAZakSizAklSJBViSKjkjmnB79+5txXbv3h3mzps3rxUrTaHdsWNHK3bRRReFuYOZ4hxNMY4aY6NHxx/v2LFjW7HSFOno3kprB0e7QA9mGvGqVavCeNSEs9kmDZ1PwJJUiQVYkiqxAEtSJRZgSarEAixJlZwRoyBWr17dikUdfYh3Ne7v7w9zBzM1NzpuaQTBUEdBRLsll0ZBROcq3cPAwEArFu32DDB16tRWbMuWLWGupNPDJ2BJqsQCLEmVWIAlqRILsCRVckY04TZt2tSKnXfeeWHujBkzWrHSjsZr1qxpxa6++uowN2pKlZpwUXMtWre3tPbw5MmTW7FSEy5q2JWaji+88EIr9p73vCfMjdYkjt4PsH///last7c3zJXUPZ+AJakSC7AkVWIBlqRKLMCSVIkFWJIqOSNGQRw8eLAVKy34HY0WKO2gvHnz5lZs165dYe6kSZNasdIoiGgqcJRbGp0RKY2CiKYSP/HEE2Hu97///VasNOojOl80MgLgkUceacU++MEPhrmSuucTsCRVYgGWpEoswJJUiQVYkio5I5pw0dq/s2bNCnOnTJnSipWm0EZNpWinZIh3Wy7tPhw1sHLOrVhpt+ZoF+h9+/aFuZHSdUXTqa+55powN7qH0vVG6wTbhJOGzidgSarEAixJlViAJakSC7AkVWIBlqRKzohRENFU5Llz54a50WiBaDF0gIULF7ZipenB0Q7K0U7JEO9UHI24KE0vjhabL11XNOIhOj/Eo0nWrVsX5kZTp0ujK0ojRyQNjU/AklSJBViSKrEAS1IlFmBJqmRYm3A7d+4M49H6updffnmYOzAw0IqVdlCOdiUuNbuiBtbhw4fD3Ki5Fh231ByMGmCl6dQvvvhiK3bgwIEwd9GiRa1YtJ4wwBVXXNHVdUH82UgaOp+AJakSC7AkVWIBlqRKLMCSVMkZ0YSLmjylBla08eQNN9wQ5kYNrFJjLdoENFp7GOJmVbQe8NixY8P3RxuAlppw0Qy9c86J/9xctmxZK1Zqwm3atCmMRwazVrGk7vkELEmVWIAlqRILsCRVYgGWpEoswJJUybCOgih106OpyNOmTQtzoxEIpZ1/V61a1YqVRhBESuv5nnvuuV3lRnkQ7z5cyo3ipREic+bMacVKoyCiNZjHjBnTda6kofMJWJIqsQBLUiUWYEmqxAIsSZUMaxOu1MzZv39/KxZNIy4pTeON1g5esmRJmButKVxaB/fYsWOtWNSEGz9+fPj+KDdqzAGcf/75rdill14a5m7YsKEVmz9/fpj73HPPtWJREw/KzUhJQ+MTsCRVYgGWpEoswJJUiQVYkiqxAEtSJcM6CqI0LXbu3LmtWGmn49mzZ7dizz//fJgbLWYeTXuGePH0aJF1gHHjxrViR48eDXMj0YiH0nVF046jzwviUR8l0RTnyy67LMx94IEHWrEf//jHYe7o0cP6n5Q0ovkELEmVWIAlqRILsCRVYgGWpErOiKnIUUNn/fr1Xedu3749zD116lQrVlrzNtotuTQFN5omHa0zXGpIRVOcS1Ovo3sorZW8ZcuWVqz02USfQ39/f5h76NChVqz02diEk7rnE7AkVWIBlqRKLMCSVIkFWJIqsQBLUiXD2rJOKYXxvr6+Vqy06PjOnTtbscceeyzMXbhwYStWWrx95syZrVg05RjikQl79+5txUojG6ZMmdKKlRZvj6ZDRwvYQzx9++mnnw5zly5d2opNnz49zI2mSR84cCDMnTVrVhiX1OYTsCRVYgGWpEoswJJUiQVYkioZ1ibc1q1bw/jUqVNbsajZBnFja9euXWFu1ISLpgxDvKZwtGYuxNN4o6m50RrDAKNGjWrFSusJR82unp6eMDe63h/96Edh7r59+1qx6PcB4s+mNK3cJpzUPZ+AJakSC7AkVWIBlqRKLMCSVIkFWJIqGdZREKURCNFOx5dcckmYG007LnXvo8XBSwuJT548uRWLFiIHOHbsWCsWLege7WgM8QiC0g7M0RTlaAQDxPc7b968MDcaoVHaiXrOnDmtWGkUhKTu+QQsSZVYgCWpEguwJFViAZakSoa1CVdaQzZqYEU7BwM8/vjjrVjUQIO4AbVu3bow9+KLL27Fent7w9xoZ+ZnnnmmFYvW/S1dV9TYg7gRuGfPnjA3aqxF04gBtm3b1orNnz8/zI2OsWPHjjD3uuuuC+OS2nwClqRKLMCSVIkFWJIqsQBLUiUWYEmqZFhHQZR2JJ44cWIrFk2rLcWjUQkQjyCYNm1amLtx48ZWrLRTcbS7c7TIejS6A2DDhg2t2MmTJ8PcaAfmSZMmhbnR1OfSovLRMUq50RTlI0eOhLmSuucTsCRVYgGWpEoswJJUiQVYkioZ1iZcqbEWNbt2794d5kaNrQ984ANhbtRYK5k5c2YrFjXWIL6P5557rutzXXDBBV2fK7rfzZs3h7nRGsqLFy8Oc7/73e+2YsuXLw9zo6ZjqcEoqXs+AUtSJRZgSarEAixJlViAJakSC7AkVTKsoyB6enrCeDQFtrQj8YIFC1qxW265petr2LlzZxh/8cUXW7HSzr8TJkxoxbZu3dqKlXaBjqYBjxkzJsy98MILW7FZs2aFuaWF6SMPP/xwK1YayRHdb2mhd0nd8wlYkiqxAEtSJRZgSarEAixJlQxrE66/vz+MR2vhlnbdPX78+JCuYc6cOYOKd+ud73znkN4/3KLGZ2m95khpF2dJ3fMJWJIqsQBLUiUWYEmqxAIsSZVYgCWpkmEdBXH99deH8V27drVipR16b7755q7Pl3PuOre0K3GktHj660ULmUN5F+duj9Ht+d/IpZde2ooNDAyEudGU6tLi7ZK65xOwJFViAZakSizAklSJBViSKkmDaVSllPYB8RxhaejennOeNtwnXbJkSV67du1wn/aMUegVFw2iZAhIKa3LOS+JXhvUKIga/3NIOrvcfvvtXefeddddp/FK6vMrCEmqxAIsSZUM60SMN1tKzAC+ALwLeAEYAD6WMz8c5HHOA27MmS8WXv9D4PeABHwpZ77wmtduBT4CnAS+lTO3pcR7gb8DjgO/kTNbm3N8DfjFnDlVOM+/ArflzPaUmAj8JbC8ubeXgE/mzOODubfmuH+cM59v/n0M8BDw8znT/YwQDYvB/PUczv6/op/tBtWEO5OkRAIeBe7Jmb9vYlcAk3Pme4M8Vh9wf84sDF5bCNwLXEWnoD4IfDhntqXEMuDTwPtz5lhKvC1n9qbECuCjQB/woZz5eEr8RXOO/yxcw88Af5ozH2p+fS/wLPDpnDmVEhcBl+fMtwZzb82xXs6Zia/59WeBbTnzlcEe62xkc1mnWbG5PJKfgJcBJ14tvgA5swF+Upz/HPglINMpbF9tniq/AZwPnAt8Jme+AdwFzE+J9cCqnPnEa87zDuDxnDncHPth4Feb4/8BcFfOHGvOv7d5zwmgp/k5kRLzgTml4tu4qbk2mvyrgZtefVrOmWfpFGRS4o+A32ne9w+vPpGnxEpgDjAO+JucuTsl7gLGN/f2VM7cBKwE7gQLMNhcVkU55xH5A/mjkP+68NqvQV4FeRTk6ZD/F/KFkEdDntzk9ELeBjlB7oO8sXCsd0D+IeSpkHsg/zfkv21eWw/5DsiPQ34Y8rua+JWQH4O8GvJsyPdCvvin3M/DkBc1//7LkL9eyFsM+UnIEyBPhPwU5J9rXrug+ed4yBshT21+/fLrjjEK8r7av4f++PNW/xnJT8Bv5FrgX3LmJDDQPLW+C3gA+HxKLAVOAbOA6W90oJzZnBJ/Bvw78Aqwns73vdD5G8QFwDXN8b+WEvNyZn0ToznXbjrr6nyVztPxx3Pm9SvfXAjs6/Levp4zrzTHXwFcB/wA+GhKna8w6DwJXww8H9zTyZQ4nhKTcualLs4p6TQYyaMgngIWD/I9NwHTgMU5cyWdpt24n/amnPnHnFmcM0uBg/CTJt8uYEXzh9kaOkW999X3NV+FfAb4E+CzwG3Al+h8P/x6R15zLU8BV6RE18uepcT76DTs3p0zV9ApyG90b2OBo90eX9KbbyQX4P8AxqbE778aSImfTYnrgO8Bv54So1JiGrAUWANMAfbmzImmgfb25q0vAZNKJ0qJtzX/nEvn+99/bl5aSee7aFLiEmAMsP81b/1N4Ns5c4DO98Gnmp+e4DSbgQUAOfMMsBa4oynipERfSry/ubdfSYmelJgAfKiJTQEO5szhlLiM5gm8cSIlfrK+Z0pMBfbnzInSPUs6/UbsVxA5k5u/bn8hJT5J52muH/gY8F/Au4ENdJpwt+XMnpT4CvBvKfEknQK3pTnW8ynxSEpsBB7I/78JB3BfU7ROAB/JmVe3D/4y8OXmfceB38qZDJASPcBvA7/Q5P4V8O0m78bglr4FvI/OEDGA36UzDG1bShyhU9g/kTNPpMQ/0fkDBTpNuB+kxCbgwymxGXgaeOw1x74b+J+UeCJ3mnDLmvNJqmjEDkM726TEeGA18N7mu+vTea4VwO15kOOlJb25RvJXEGeVnDlC53viWafzPM1EjJUWX6k+n4AlqRKfgCWpEguwJFViAZakSizAklSJBViSKrEAS1Il/wftqdcLKvPtLgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ko-uzOufSCSe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "2219fdf8-5b95-4e25-f72a-ef9d366d0c9e"
      },
      "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": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "hQlnbqaw2Qu_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "0ea7f4ba-42f8-4618-b498-5380b44261fe"
      },
      "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": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 30 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R32zteKHCaXT"
      },
      "source": [
        "Finally, use the trained model to make a prediction about a single image. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yRJ7JU7JCaXT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3fbc49e-454c-4786-94e8-b03110b42fcb"
      },
      "source": [
        "# Grab an image from the test dataset\n",
        "img = test_images[0]\n",
        "\n",
        "print(img.shape)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "lDFh5yF_CaXW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18bc598d-8661-43f2-b689-d81f71dc8e86"
      },
      "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": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EQ5wLTkcCaXY"
      },
      "source": [
        "Now predict the image:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_rzNSdrCaXY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c70126c9-5b64-4ff0-83cb-a636d5d5d1bc"
      },
      "source": [
        "predictions_single = model.predict(img)\n",
        "\n",
        "print(predictions_single)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.3311079e-04 2.2973294e-05 1.0426878e-02 1.4420137e-05 9.6058333e-01\n",
            "  6.1446448e-10 2.8717158e-02 2.5679658e-10 1.0210319e-04 3.0981209e-09]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Ai-cpLjO-3A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "a4b8a791-4323-4eec-9224-5c4c543d121e"
      },
      "source": [
        "plot_value_array(0, predictions_single, test_labels)\n",
        "_ = plt.xticks(range(10), class_names, rotation=45)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "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",
      "metadata": {
        "id": "2tRmdq_8CaXb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20d2ae14-bf3f-4d78-97ad-766849392210"
      },
      "source": [
        "np.argmax(predictions_single[0])"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "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"
      ]
    }
  ]
}