Basic_classification.ipynb

{
  "metadata": {
    "kernelspec": {
      "language": "python",
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.7.12",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat_minor": 0,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JebaMercy14/AI-Spartificial-Course/blob/main/Basic_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <center> Basic classification: Classify images of clothing"
      ],
      "metadata": {
        "id": "qWGYeXVdMF3e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# Using tensorflow\n",
        "\n",
        "# Also make sure you have linked kaggle account to your phone number and enable the internet option in the settings of notebook to run this code successfully"
      ],
      "metadata": {
        "id": "jYysdyb-CaWM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "ynPlvfT-auwg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's start**\n",
        "* 1. Import libraries\n",
        "* 2. Import dataset - already as train and test data\n",
        "* 3. Make list of class names and explore the dataset\n",
        "* 4. Preprocess the dataset - scale the data between (0,1)\n",
        "* 5. Build the model\n",
        "* 6. Compile the model\n",
        "* 7. Train the model\n",
        "* 8. Feed the model on test data\n",
        "* 9. Test accuracy\n",
        "* 10. Let's start"
      ],
      "metadata": {
        "id": "FbVhjPpzn6BM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# IMPORT LIBRARY\n",
        "# TensorFlow and tf.keras\n",
        "import tensorflow as tf\n",
        "\n",
        "# Helper libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(tf.__version__)"
      ],
      "metadata": {
        "id": "dzLKpmZICaWN",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:16:21.181169Z",
          "iopub.execute_input": "2022-03-14T15:16:21.181486Z",
          "iopub.status.idle": "2022-03-14T15:16:26.154166Z",
          "shell.execute_reply.started": "2022-03-14T15:16:21.181454Z",
          "shell.execute_reply": "2022-03-14T15:16:26.153109Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c92d6e71-3e3d-4d4a-cc4a-b466c41c5c94"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.11.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import the Fashion MNIST dataset"
      ],
      "metadata": {
        "id": "yR0EdgrLCaWR"
      }
    },
    {
      "cell_type": "markdown",
      "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 by 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 a format identical to that of the articles of clothing you'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",
        "Here, 60,000 images are used 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. Import and [load the Fashion MNIST data](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/fashion_mnist/load_data) directly from TensorFlow:"
      ],
      "metadata": {
        "id": "DLdCchMdCaWQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "metadata": {
        "id": "7MqDQO0KCaWS",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:16:30.547578Z",
          "iopub.execute_input": "2022-03-14T15:16:30.548290Z",
          "iopub.status.idle": "2022-03-14T15:16:32.702851Z",
          "shell.execute_reply.started": "2022-03-14T15:16:30.548254Z",
          "shell.execute_reply": "2022-03-14T15:16:32.701925Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa37dd96-eeb6-4dfc-bdb7-eb3cb6e5a58b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "29515/29515 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26421880/26421880 [==============================] - 1s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "5148/5148 [==============================] - 0s 1us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4422102/4422102 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loading the dataset returns four NumPy arrays:\n",
        "\n",
        "* The `train_images` and `train_labels` arrays are the *training set*—the data the model uses to learn.\n",
        "* The model is tested against the *test set*, the `test_images`, and `test_labels` arrays.\n",
        "\n",
        "The images are 28x28 NumPy arrays, with pixel values ranging from 0 to 255. The *labels* are an array of integers, ranging from 0 to 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:"
      ],
      "metadata": {
        "id": "t9FDsUlxCaWW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "metadata": {
        "id": "IjnLH5S2CaWx",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:16:50.114615Z",
          "iopub.execute_input": "2022-03-14T15:16:50.114922Z",
          "iopub.status.idle": "2022-03-14T15:16:50.119359Z",
          "shell.execute_reply.started": "2022-03-14T15:16:50.114887Z",
          "shell.execute_reply": "2022-03-14T15:16:50.118598Z"
        },
        "trusted": true
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "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, with each image represented as 28 x 28 pixels:"
      ],
      "metadata": {
        "id": "Brm0b_KACaWX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_images.shape"
      ],
      "metadata": {
        "id": "zW5k_xz1CaWX",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:16:58.253577Z",
          "iopub.execute_input": "2022-03-14T15:16:58.254040Z",
          "iopub.status.idle": "2022-03-14T15:16:58.261523Z",
          "shell.execute_reply.started": "2022-03-14T15:16:58.254010Z",
          "shell.execute_reply": "2022-03-14T15:16:58.260714Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50621df5-87f4-417c-fb66-64cf3bf3f7ef"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Likewise, there are 60,000 labels in the training set:"
      ],
      "metadata": {
        "id": "cIAcvQqMCaWf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_labels)"
      ],
      "metadata": {
        "id": "TRFYHB2mCaWb",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:17:05.146167Z",
          "iopub.execute_input": "2022-03-14T15:17:05.146467Z",
          "iopub.status.idle": "2022-03-14T15:17:05.151873Z",
          "shell.execute_reply.started": "2022-03-14T15:17:05.146425Z",
          "shell.execute_reply": "2022-03-14T15:17:05.151059Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d2d0cf7-412b-4205-9a1a-0f3faa249823"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_images"
      ],
      "metadata": {
        "id": "fB3s-JJZF63c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69478070-3c6b-4274-a3df-8fc1e4c7f22b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0]],\n",
              "\n",
              "       [[0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0]],\n",
              "\n",
              "       [[0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0]],\n",
              "\n",
              "       [[0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0]],\n",
              "\n",
              "       [[0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Each label is an integer between 0 and 9:"
      ],
      "metadata": {
        "id": "YSlYxFuRCaWk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels\n"
      ],
      "metadata": {
        "id": "XKnCTHz4CaWg",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:17:07.669771Z",
          "iopub.execute_input": "2022-03-14T15:17:07.670033Z",
          "iopub.status.idle": "2022-03-14T15:17:07.680371Z",
          "shell.execute_reply.started": "2022-03-14T15:17:07.670006Z",
          "shell.execute_reply": "2022-03-14T15:17:07.679614Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36c5a574-b2fd-456d-ed67-29f57c15e806"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are 10,000 images in the test set. Again, each image is represented as 28 x 28 pixels:"
      ],
      "metadata": {
        "id": "TMPI88iZpO2T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_images.shape"
      ],
      "metadata": {
        "id": "2KFnYlcwCaWl",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:17:14.277739Z",
          "iopub.execute_input": "2022-03-14T15:17:14.278035Z",
          "iopub.status.idle": "2022-03-14T15:17:14.283074Z",
          "shell.execute_reply.started": "2022-03-14T15:17:14.277989Z",
          "shell.execute_reply": "2022-03-14T15:17:14.282251Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4e83286-942a-48db-e2da-f2b4da157f79"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And the test set contains 10,000 images labels:"
      ],
      "metadata": {
        "id": "rd0A0Iu0CaWq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len(test_labels)"
      ],
      "metadata": {
        "id": "iJmPr5-ACaWn",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:17:16.898915Z",
          "iopub.execute_input": "2022-03-14T15:17:16.899217Z",
          "iopub.status.idle": "2022-03-14T15:17:16.904772Z",
          "shell.execute_reply.started": "2022-03-14T15:17:16.899171Z",
          "shell.execute_reply": "2022-03-14T15:17:16.904211Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15c9d2eb-0b00-4547-806a-df08fc587112"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocess the data\n",
        "\n",
        "The data must be preprocessed before training the network. If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255:"
      ],
      "metadata": {
        "id": "ES6uQoLKCaWr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[0])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "m4VEw8Ud9Quh",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:17:24.873028Z",
          "iopub.execute_input": "2022-03-14T15:17:24.873711Z",
          "iopub.status.idle": "2022-03-14T15:17:25.149075Z",
          "shell.execute_reply.started": "2022-03-14T15:17:24.873663Z",
          "shell.execute_reply": "2022-03-14T15:17:25.148494Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "b5f040d2-78b8-442a-8bc6-aa93f1387954"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scale these values to a range of 0 to 1 before feeding them to the neural network model. To do so, divide the values by 255. It's important that the *training set* and the *testing set* be preprocessed in the same way:"
      ],
      "metadata": {
        "id": "Wz7l27Lz9S1P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_images = train_images / 255.0\n",
        "\n",
        "test_images = test_images / 255.0"
      ],
      "metadata": {
        "id": "bW5WzIPlCaWv",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:17:31.647160Z",
          "iopub.execute_input": "2022-03-14T15:17:31.647597Z",
          "iopub.status.idle": "2022-03-14T15:17:31.841445Z",
          "shell.execute_reply.started": "2022-03-14T15:17:31.647560Z",
          "shell.execute_reply": "2022-03-14T15:17:31.840541Z"
        },
        "trusted": true
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To verify that the data is in the correct format and that you're ready to build and train the network, let's display the first 25 images from the *training set* and display the class name below each image."
      ],
      "metadata": {
        "id": "Ee638AlnCaWz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[train_labels[i]])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "oZTImqg_CaW1",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:17:33.786300Z",
          "iopub.execute_input": "2022-03-14T15:17:33.786573Z",
          "iopub.status.idle": "2022-03-14T15:17:34.766757Z",
          "shell.execute_reply.started": "2022-03-14T15:17:33.786545Z",
          "shell.execute_reply": "2022-03-14T15:17:34.765811Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "72addf5d-6739-400d-a4ab-077d0ab1a721"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Build the model\n",
        "\n",
        "Building the neural network requires configuring the layers of the model, then compiling the model."
      ],
      "metadata": {
        "id": "59veuiEZCaW4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set up the layers\n",
        "\n",
        "The basic building block of a neural network is the [*layer*](https://www.tensorflow.org/api_docs/python/tf/keras/layers). Layers extract representations from the data fed into them. Hopefully, these representations are meaningful for the problem at hand.\n",
        "\n",
        "Most of deep learning consists of chaining together simple layers. Most layers, such as `tf.keras.layers.Dense`, have parameters that are learned during training."
      ],
      "metadata": {
        "id": "Gxg1XGm0eOBy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dense(10)\n",
        "])"
      ],
      "metadata": {
        "id": "9ODch-OFCaW4",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:17:45.932178Z",
          "iopub.execute_input": "2022-03-14T15:17:45.932977Z",
          "iopub.status.idle": "2022-03-14T15:17:46.049271Z",
          "shell.execute_reply.started": "2022-03-14T15:17:45.932927Z",
          "shell.execute_reply": "2022-03-14T15:17:46.048368Z"
        },
        "trusted": true
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first layer in this network, `tf.keras.layers.Flatten`, transforms the format of the images from a two-dimensional array (of 28 by 28 pixels) to a one-dimensional array (of 28 * 28 = 784 pixels). Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn; it only reformats the data.\n",
        "\n",
        "After the pixels are flattened, the network consists of a sequence of two `tf.keras.layers.Dense` layers. These are densely connected, or fully connected, neural layers. The first `Dense` layer has 128 nodes (or neurons). The second (and last) layer returns a logits array with length of 10. Each node contains a score that indicates the current image belongs to one of the 10 classes.\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*](https://www.tensorflow.org/api_docs/python/tf/keras/Model#compile) step:\n",
        "\n",
        "* [*Loss function*](https://www.tensorflow.org/api_docs/python/tf/keras/losses) —This measures how accurate the model is during training. You want to minimize this function to \"steer\" the model in the right direction.\n",
        "* [*Optimizer*](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers) —This is how the model is updated based on the data it sees and its loss function.\n",
        "* [*Metrics*](https://www.tensorflow.org/api_docs/python/tf/keras/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": "gut8A_7rCaW6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "Lhan11blCaW7",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:17:53.113303Z",
          "iopub.execute_input": "2022-03-14T15:17:53.113601Z",
          "iopub.status.idle": "2022-03-14T15:17:53.130395Z",
          "shell.execute_reply.started": "2022-03-14T15:17:53.113571Z",
          "shell.execute_reply": "2022-03-14T15:17:53.129391Z"
        },
        "trusted": true
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train the model\n",
        "\n",
        "Training the neural network model requires the following steps:\n",
        "\n",
        "1. Feed the training data to the model. In this example, the training data is in the `train_images` and `train_labels` arrays.\n",
        "2. The model learns to associate images and labels.\n",
        "3. You ask the model to make predictions about a test set—in this example, the `test_images` array.\n",
        "4. Verify that the predictions match the labels from the `test_labels` array.\n"
      ],
      "metadata": {
        "id": "qKF6uW-BCaW-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Feed the model\n",
        "\n",
        "To start training,  call the [`model.fit`](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit) method—so called because it \"fits\" the model to the training data:"
      ],
      "metadata": {
        "id": "Z4P4zIV7E28Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels.shape"
      ],
      "metadata": {
        "id": "KSHQ-_QE7jdb",
        "outputId": "331ff764-78c5-4c60-9c4a-bde00d5ff6be",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000,)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_images, train_labels, epochs=10)"
      ],
      "metadata": {
        "id": "xvwvpA64CaW_",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:13:33.742979Z",
          "iopub.status.idle": "2022-03-14T15:13:33.743261Z",
          "shell.execute_reply.started": "2022-03-14T15:13:33.743123Z",
          "shell.execute_reply": "2022-03-14T15:13:33.743137Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "badebac1-8e80-4804-9c09-520f9a16d3db"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.4984 - accuracy: 0.8255\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3799 - accuracy: 0.8631\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.3371 - accuracy: 0.8761\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 8s 5ms/step - loss: 0.3150 - accuracy: 0.8828\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2965 - accuracy: 0.8907\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.2836 - accuracy: 0.8955\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2707 - accuracy: 0.8992\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2580 - accuracy: 0.9031\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.2498 - accuracy: 0.9067\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2410 - accuracy: 0.9109\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f49de0a4ac0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As the model trains, the loss and accuracy metrics are displayed. This model reaches an accuracy of about 0.91 (or 91%) on the training data."
      ],
      "metadata": {
        "id": "W3ZVOhugCaXA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluate accuracy\n",
        "\n",
        "Next, compare how the model performs on the test dataset:"
      ],
      "metadata": {
        "id": "wCpr6DGyE28h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
        "\n",
        "print('\\nTest accuracy:', test_acc)"
      ],
      "metadata": {
        "id": "VflXLEeECaXC",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:18:09.196576Z",
          "iopub.execute_input": "2022-03-14T15:18:09.197381Z",
          "iopub.status.idle": "2022-03-14T15:18:10.494611Z",
          "shell.execute_reply.started": "2022-03-14T15:18:09.197334Z",
          "shell.execute_reply": "2022-03-14T15:18:10.493692Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12c26b89-b49c-42a4-d292-5def198b8189"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 2s - loss: 0.3516 - accuracy: 0.8785 - 2s/epoch - 5ms/step\n",
            "\n",
            "Test accuracy: 0.8784999847412109\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It turns out that the accuracy on the test dataset is a little less than the accuracy on the training dataset. This gap between training accuracy and test accuracy represents *overfitting*. Overfitting happens when a machine learning model performs worse on new, previously unseen inputs than it does on the training data. An overfitted model \"memorizes\" the noise and details in the training dataset to a point where it negatively impacts the performance of the model on the new data. For more information, see the following:\n",
        "*   [Demonstrate overfitting](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit#demonstrate_overfitting)\n",
        "*   [Strategies to prevent overfitting](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit#strategies_to_prevent_overfitting)"
      ],
      "metadata": {
        "id": "yWfgsmVXCaXG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Make predictions\n",
        "\n",
        "With the model trained, you can use it to make predictions about some images.\n",
        "Attach a softmax layer to convert the model's linear outputs—[logits](https://developers.google.com/machine-learning/glossary#logits)—to probabilities, which should be easier to interpret."
      ],
      "metadata": {
        "id": "v-PyD1SYE28q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "probability_model = tf.keras.Sequential([model, \n",
        "                                         tf.keras.layers.Softmax()])"
      ],
      "metadata": {
        "id": "DnfNA0CrQLSD",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:18:20.833765Z",
          "iopub.execute_input": "2022-03-14T15:18:20.834247Z",
          "iopub.status.idle": "2022-03-14T15:18:20.854292Z",
          "shell.execute_reply.started": "2022-03-14T15:18:20.834182Z",
          "shell.execute_reply": "2022-03-14T15:18:20.853532Z"
        },
        "trusted": true
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = probability_model.predict(test_images)"
      ],
      "metadata": {
        "id": "Gl91RPhdCaXI",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:18:22.505323Z",
          "iopub.execute_input": "2022-03-14T15:18:22.505803Z",
          "iopub.status.idle": "2022-03-14T15:18:23.036014Z",
          "shell.execute_reply.started": "2022-03-14T15:18:22.505753Z",
          "shell.execute_reply": "2022-03-14T15:18:23.035151Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22320022-a46e-483a-99f6-6b65a234de7b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "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": "x9Kk1voUCaXJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[0]"
      ],
      "metadata": {
        "id": "3DmJEUinCaXK",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:18:24.584898Z",
          "iopub.execute_input": "2022-03-14T15:18:24.586403Z",
          "iopub.status.idle": "2022-03-14T15:18:24.595621Z",
          "shell.execute_reply.started": "2022-03-14T15:18:24.586346Z",
          "shell.execute_reply": "2022-03-14T15:18:24.594670Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1458a974-05ab-4096-c77b-49448d5c2054"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.12875425e-08, 5.40789247e-10, 2.68525358e-09, 6.06469736e-11,\n",
              "       7.55642304e-08, 1.47874455e-03, 2.65628000e-08, 9.62710194e-03,\n",
              "       1.18889136e-06, 9.88892853e-01], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A prediction is an array of 10 numbers. They represent the model's \"confidence\" that the image corresponds to each of the 10 different articles of clothing. You can see which label has the highest confidence value:"
      ],
      "metadata": {
        "id": "-hw1hgeSCaXN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(predictions[0])"
      ],
      "metadata": {
        "id": "qsqenuPnCaXO",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:18:30.006603Z",
          "iopub.execute_input": "2022-03-14T15:18:30.006909Z",
          "iopub.status.idle": "2022-03-14T15:18:30.014009Z",
          "shell.execute_reply.started": "2022-03-14T15:18:30.006877Z",
          "shell.execute_reply": "2022-03-14T15:18:30.013211Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f1f0dff-f18f-4aa9-9d8a-a56e6a09b0a2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, the model is most confident that this image is an ankle boot, or `class_names[9]`. Examining the test label shows that this classification is correct:"
      ],
      "metadata": {
        "id": "E51yS7iCCaXO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels[0]"
      ],
      "metadata": {
        "id": "Sd7Pgsu6CaXP",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:18:35.396560Z",
          "iopub.execute_input": "2022-03-14T15:18:35.396827Z",
          "iopub.status.idle": "2022-03-14T15:18:35.403211Z",
          "shell.execute_reply.started": "2022-03-14T15:18:35.396800Z",
          "shell.execute_reply": "2022-03-14T15:18:35.402263Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82a77904-ba63-4322-dd06-748f94511369"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Graph this to look at the full set of 10 class predictions."
      ],
      "metadata": {
        "id": "ygh2yYC972ne"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_image(i, predictions_array, true_label, img):\n",
        "  true_label, img = true_label[i], img[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "\n",
        "  plt.imshow(img, 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",
        "  true_label = true_label[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks(range(10))\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": "DvYmmrpIy6Y1",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:18:37.023173Z",
          "iopub.execute_input": "2022-03-14T15:18:37.023808Z",
          "iopub.status.idle": "2022-03-14T15:18:37.033759Z",
          "shell.execute_reply.started": "2022-03-14T15:18:37.023768Z",
          "shell.execute_reply": "2022-03-14T15:18:37.032882Z"
        },
        "trusted": true
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Verify predictions\n",
        "\n",
        "With the model trained, you can use it to make predictions about some images."
      ],
      "metadata": {
        "id": "Zh9yABaME29S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's look at the 0th image, predictions, and prediction array. Correct prediction labels are blue and incorrect prediction labels are red. The number gives the percentage (out of 100) for the predicted label."
      ],
      "metadata": {
        "id": "d4Ov9OFDMmOD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "i = 0\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions[i], test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions[i],  test_labels)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "HV5jw-5HwSmO",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:18:45.718751Z",
          "iopub.execute_input": "2022-03-14T15:18:45.719205Z",
          "iopub.status.idle": "2022-03-14T15:18:45.896647Z",
          "shell.execute_reply.started": "2022-03-14T15:18:45.719159Z",
          "shell.execute_reply": "2022-03-14T15:18:45.895651Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "outputId": "95674a4e-c059-4818-8335-652df4f465cd"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "i = 12\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions[i], test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions[i],  test_labels)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Ko-uzOufSCSe",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:18:50.853911Z",
          "iopub.execute_input": "2022-03-14T15:18:50.854404Z",
          "iopub.status.idle": "2022-03-14T15:18:51.030070Z",
          "shell.execute_reply.started": "2022-03-14T15:18:50.854368Z",
          "shell.execute_reply": "2022-03-14T15:18:51.028740Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "outputId": "10197cde-eb1e-4289-cc99-2af4ddaf3449"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's plot several images with their predictions. Note that the model can be wrong even when very confident."
      ],
      "metadata": {
        "id": "kgdvGD52CaXR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the first X test images, their predicted labels, and the true labels.\n",
        "# Color correct predictions in blue and 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[i], test_labels, test_images)\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
        "  plot_value_array(i, predictions[i], test_labels)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "hQlnbqaw2Qu_",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:18:58.311931Z",
          "iopub.execute_input": "2022-03-14T15:18:58.312257Z",
          "iopub.status.idle": "2022-03-14T15:19:00.549864Z",
          "shell.execute_reply.started": "2022-03-14T15:18:58.312221Z",
          "shell.execute_reply": "2022-03-14T15:19:00.547114Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "outputId": "40a3d412-397d-4283-ad43-ccabdcbdf17c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 30 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Use the trained model\n",
        "\n",
        "Finally, use the trained model to make a prediction about a single image."
      ],
      "metadata": {
        "id": "R32zteKHCaXT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Grab an image from the test dataset.\n",
        "img = test_images[1]\n",
        "\n",
        "print(img.shape)"
      ],
      "metadata": {
        "id": "yRJ7JU7JCaXT",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:19:03.191817Z",
          "iopub.execute_input": "2022-03-14T15:19:03.192124Z",
          "iopub.status.idle": "2022-03-14T15:19:03.197150Z",
          "shell.execute_reply.started": "2022-03-14T15:19:03.192088Z",
          "shell.execute_reply": "2022-03-14T15:19:03.196266Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9bc95447-0ee3-4c4e-a876-908ac5647bd9"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`tf.keras` models are optimized to make predictions on a *batch*, or collection, of examples at once. Accordingly, even though you're using a single image, you need to add it to a list:"
      ],
      "metadata": {
        "id": "vz3bVp21CaXV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Add the image to a batch where it's the only member.\n",
        "img = (np.expand_dims(img,0))\n",
        "\n",
        "print(img.shape)"
      ],
      "metadata": {
        "id": "lDFh5yF_CaXW",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:19:05.125308Z",
          "iopub.execute_input": "2022-03-14T15:19:05.125644Z",
          "iopub.status.idle": "2022-03-14T15:19:05.130661Z",
          "shell.execute_reply.started": "2022-03-14T15:19:05.125612Z",
          "shell.execute_reply": "2022-03-14T15:19:05.129968Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17be959a-4791-459d-862e-19d6fa11d2d0"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now predict the correct label for this image:"
      ],
      "metadata": {
        "id": "EQ5wLTkcCaXY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_single = probability_model.predict(img)\n",
        "\n",
        "print(predictions_single)"
      ],
      "metadata": {
        "id": "o_rzNSdrCaXY",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:19:09.708337Z",
          "iopub.execute_input": "2022-03-14T15:19:09.708667Z",
          "iopub.status.idle": "2022-03-14T15:19:09.770921Z",
          "shell.execute_reply.started": "2022-03-14T15:19:09.708630Z",
          "shell.execute_reply": "2022-03-14T15:19:09.770266Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8b72fe8-97cb-4169-e8df-0b16838ec0c3"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 32ms/step\n",
            "[[1.2614818e-04 5.7081826e-16 9.9636722e-01 7.7250272e-12 3.3899765e-03\n",
            "  3.1778772e-08 1.1662273e-04 7.8783657e-16 4.3428183e-10 1.3751081e-12]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_value_array(1, predictions_single[0], test_labels)\n",
        "_ = plt.xticks(range(10), class_names, rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "6Ai-cpLjO-3A",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:19:11.307233Z",
          "iopub.execute_input": "2022-03-14T15:19:11.307652Z",
          "iopub.status.idle": "2022-03-14T15:19:11.457555Z",
          "shell.execute_reply.started": "2022-03-14T15:19:11.307623Z",
          "shell.execute_reply": "2022-03-14T15:19:11.456454Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "77925771-9051-420d-f60e-17d356955e6b"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`tf.keras.Model.predict` returns a list of lists—one list for each image in the batch of data. Grab the predictions for our (only) image in the batch:"
      ],
      "metadata": {
        "id": "cU1Y2OAMCaXb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(predictions_single[0])"
      ],
      "metadata": {
        "id": "2tRmdq_8CaXb",
        "execution": {
          "iopub.status.busy": "2022-03-14T15:19:16.238617Z",
          "iopub.execute_input": "2022-03-14T15:19:16.240681Z",
          "iopub.status.idle": "2022-03-14T15:19:16.248554Z",
          "shell.execute_reply.started": "2022-03-14T15:19:16.240638Z",
          "shell.execute_reply": "2022-03-14T15:19:16.247479Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d19c94c-022a-4792-f2b2-8bbeec8ab941"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And the model predicts a label as expected."
      ],
      "metadata": {
        "id": "YFc2HbEVCaXd"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gQjnZ0bubih1"
      },
      "execution_count": 33,
      "outputs": []
    }
  ]
}