Fashion_MNIST.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyPdfyR8X3agw3qmsuELID3e",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lakhya-jyoti/Tensorflow/blob/main/Fashion_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Installing the Dependencies**"
      ],
      "metadata": {
        "id": "Z1ALbR3lL6uH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U tensorflow_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lpuhxObPL-Tf",
        "outputId": "973f5813-993e-476e-e93c-9b2c260c23c0"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "yrfRK7k2NUUg"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **importing libraries**"
      ],
      "metadata": {
        "id": "yEYzDqLJMODG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "#import tensorflow and tensorflow dataset\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\n",
        "\n",
        "#helper libraries\n",
        "import math\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# improve progress bar display\n",
        "import tqdm\n",
        "import tqdm.auto\n",
        "tqdm.tqdm = tqdm.auto.tqdm\n",
        "\n",
        "\n",
        "print (tf.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rlagOfatMNHe",
        "outputId": "0758466e-a06a-46cb-d057-bab3a4811cb0"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.17.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Fashion MNIST dataset, which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28  ×  28 pixels)\n",
        "\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"
      ],
      "metadata": {
        "id": "fpzXhjUONfDd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset, metadata = tfds.load('fashion_mnist', as_supervised=True, with_info=True)\n",
        "train_dataset, test_dataset = dataset['train'], dataset['test']"
      ],
      "metadata": {
        "id": "IHq0POeONiHA"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The images are 28  ×  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",
        "\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:"
      ],
      "metadata": {
        "id": "4wTB6zkjOItl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = metadata.features['label'].names\n",
        "print(\"Class names: {}\".format(class_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1AmVb-nnOcgy",
        "outputId": "42b664eb-0ca2-43a3-ed7b-c4c843cc6bc0"
      },
      "execution_count": 74,
      "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",
      "source": [
        "## **Exploring the DATASET**"
      ],
      "metadata": {
        "id": "ZsT9ZDnHO2lg"
      }
    },
    {
      "cell_type": "code",
      "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))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNrCRt_eO7Pt",
        "outputId": "ff27f20c-0de6-4050-ff5f-22bdb02bd007"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of training examples: 60000\n",
            "Number of test examples:     10000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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."
      ],
      "metadata": {
        "id": "ZLA_oUuIPDBn"
      }
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "metadata": {
        "id": "_KXrIccDPEou"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's plot an image to see what it looks like."
      ],
      "metadata": {
        "id": "pXGqtDS7RWl_"
      }
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "NNYsIkCuRYfG",
        "outputId": "60062620-650e-42f5-84ed-b140c0fdedf8"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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."
      ],
      "metadata": {
        "id": "Xy9WykZ8R-QB"
      }
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "id": "PlTa7s5eR-6t",
        "outputId": "a6bb213e-0ba9-4f7b-c0d0-7dffa66f3a8d"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Build the model**"
      ],
      "metadata": {
        "id": "-p3VFnK6SHQW"
      }
    },
    {
      "cell_type": "markdown",
      "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."
      ],
      "metadata": {
        "id": "KYHfTSDWSI-d"
      }
    },
    {
      "cell_type": "code",
      "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",
        "])"
      ],
      "metadata": {
        "id": "c7E_p9taSoe5"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "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."
      ],
      "metadata": {
        "id": "Anri9v_6S0KR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "7Cs-uF4SS1C0"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "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.)"
      ],
      "metadata": {
        "id": "tDfuqPUKTB1C"
      }
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "QWFxmDlSTIcc"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_dataset, epochs=5, steps_per_epoch=math.ceil(num_train_examples/BATCH_SIZE))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G18ogRjQTDPo",
        "outputId": "a17d74e0-b146-4966-c4fd-07b3a0b76c53"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 3ms/step - accuracy: 0.7771 - loss: 0.6337\n",
            "Epoch 2/5\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8665 - loss: 0.3790\n",
            "Epoch 3/5\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8773 - loss: 0.3334\n",
            "Epoch 4/5\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 1ms/step - accuracy: 0.8840 - loss: 0.3147\n",
            "Epoch 5/5\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8891 - loss: 0.2969\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7d319044e530>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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."
      ],
      "metadata": {
        "id": "sG4OSPJWVCnb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_accuracy = model.evaluate(test_dataset, steps=math.ceil(num_test_examples/32))\n",
        "print('Accuracy on test dataset:', test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5pxPX3KVDj1",
        "outputId": "24ddb46f-cfb6-4346-8b70-66df857de9d6"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.8739 - loss: 0.3521\n",
            "Accuracy on test dataset: 0.8723000288009644\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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."
      ],
      "metadata": {
        "id": "3SFS1W3HVho3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Make predictions and explore\n",
        "\n",
        "With the model trained, we can use it to make predictions about some images."
      ],
      "metadata": {
        "id": "CkdjWXh5Vo8U"
      }
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "simgM9pkVpeK",
        "outputId": "cad458af-ccab-48bd-a7d3-ec38c4c2425a"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFduPeaOVuqL",
        "outputId": "d866fb3f-fd58-4edf-90a6-be51fa6124b1"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, the model has predicted the label for each image in the testing set. Let's take a look at the first prediction:"
      ],
      "metadata": {
        "id": "xmUA-ClCVxc1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJj73zLKVx8F",
        "outputId": "8cf9655a-1abf-4f10-cd0f-68ebd0be8a2f"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.1755080e-05, 3.3381330e-06, 3.6051287e-03, 3.4351069e-07,\n",
              "       9.8062724e-01, 9.8042668e-13, 1.5724644e-02, 1.2358496e-11,\n",
              "       2.7492362e-05, 5.0508813e-08], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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:"
      ],
      "metadata": {
        "id": "_I5LV9e4V4eQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(predictions[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N88n93HbV48h",
        "outputId": "b54983d5-a019-42cb-8c46-7975ffbd7816"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "check the test label to see this is correct:"
      ],
      "metadata": {
        "id": "PsfmSnpNWCRq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dyEfIuoDV-iV",
        "outputId": "ed2b6597-0b6e-4d43-b06d-2f2b3f5b4c87"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can graph this to look at the full set of 10 class predictions"
      ],
      "metadata": {
        "id": "OfJCdxhJWFnt"
      }
    },
    {
      "cell_type": "code",
      "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')"
      ],
      "metadata": {
        "id": "tT7nmQRgWHTh"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's look at the 0th image, predictions, and prediction array."
      ],
      "metadata": {
        "id": "b9fxMDfQXlRU"
      }
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "IuAJjTkaXmAt",
        "outputId": "805702f9-bbf5-4a02-9506-d8c20734cd8c"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "0AduDD-RXq_e",
        "outputId": "26dbf9b3-ebea-4232-ef1d-93d032494644"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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."
      ],
      "metadata": {
        "id": "fujdRNIfXu6P"
      }
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "id": "mAEs0EV_Xvrd",
        "outputId": "6c0d3a85-68f9-44ff-be2e-f37c24e1f6fa"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 30 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, use the trained model to make a prediction about a single image."
      ],
      "metadata": {
        "id": "V2yx42pHX3Er"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Grab an image from the test dataset\n",
        "img = test_images[0]\n",
        "\n",
        "print(img.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C_GvV8-yX3ft",
        "outputId": "38cca030-27f9-403b-e860-b4894bb1fb77"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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:"
      ],
      "metadata": {
        "id": "wQ4x70wfX9ve"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Add the image to a batch where it's the only member.\n",
        "img = np.array([img])\n",
        "\n",
        "print(img.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zrZu-8A1X-T4",
        "outputId": "820bfdc3-be31-4253-ce1f-184eb9a95512"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now predict the image"
      ],
      "metadata": {
        "id": "QdK-_TZYYCro"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_single = model.predict(img)\n",
        "\n",
        "print(predictions_single)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q1YOUuz6YDEE",
        "outputId": "bbee586e-4cca-43bb-bca8-a0a0841709cc"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
            "[[1.1755070e-05 3.3381300e-06 3.6051257e-03 3.4351075e-07 9.8062736e-01\n",
            "  9.8042679e-13 1.5724631e-02 1.2358449e-11 2.7492313e-05 5.0508721e-08]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_value_array(0, predictions_single, test_labels)\n",
        "_ = plt.xticks(range(10), class_names, rotation=45)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "8l04wEmGYG5r",
        "outputId": "82e367ca-d1e7-46c4-ebc8-cfb02e3133d8"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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:"
      ],
      "metadata": {
        "id": "21S4x8dEYNg1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(predictions_single[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "INEht4JkYN8L",
        "outputId": "be96ecaf-47d5-499b-f90d-7735cdabb07d"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "the model predicts a label of 4 (coat)"
      ],
      "metadata": {
        "id": "q03X2n_2YUmg"
      }
    }
  ]
}