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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Fareed-Ahmad7/tensorflow-learning/blob/main/image_classification_using_CNN's_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# setup data"
      ],
      "metadata": {
        "id": "UkyqayD57pHV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNpKoZetyaXC",
        "outputId": "c81f0005-3a75-46be-ca42-051e884d6743"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tensorflow_datasets in /usr/local/lib/python3.7/dist-packages (4.0.1)\n",
            "Collecting tensorflow_datasets\n",
            "  Downloading tensorflow_datasets-4.6.0-py3-none-any.whl (4.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.3 MB 29.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (4.64.0)\n",
            "Requirement already satisfied: protobuf>=3.12.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (3.17.3)\n",
            "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (5.8.0)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (1.1.0)\n",
            "Requirement already satisfied: etils[epath] in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (0.6.0)\n",
            "Collecting toml\n",
            "  Downloading toml-0.10.2-py2.py3-none-any.whl (16 kB)\n",
            "Requirement already satisfied: termcolor in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (1.1.0)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (2.23.0)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (0.3.5.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (1.21.6)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (1.15.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (4.1.1)\n",
            "Requirement already satisfied: promise in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (2.3)\n",
            "Requirement already satisfied: tensorflow-metadata in /usr/local/lib/python3.7/dist-packages (from tensorflow_datasets) (1.9.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->tensorflow_datasets) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->tensorflow_datasets) (2022.6.15)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->tensorflow_datasets) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->tensorflow_datasets) (1.24.3)\n",
            "Requirement already satisfied: zipp in /usr/local/lib/python3.7/dist-packages (from etils[epath]->tensorflow_datasets) (3.8.1)\n",
            "Requirement already satisfied: googleapis-common-protos<2,>=1.52.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-metadata->tensorflow_datasets) (1.56.4)\n",
            "Installing collected packages: toml, 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-4.6.0 toml-0.10.2\n"
          ]
        }
      ],
      "source": [
        "!pip install -U tensorflow_datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from __future__ import absolute_import, division, print_function\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "# tf.logging.set_verbosity(tf.logging.Error)\n",
        "\n",
        "import math\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# improve progress bardisplay\n",
        "import tqdm\n",
        "import tqdm.auto\n",
        "tqdm.tqdm = tqdm.auto.tqdm\n",
        "\n",
        "print(tf.__version__)\n",
        "\n",
        "# tf.enable_eager_execution()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O7p2XUCL6zvL",
        "outputId": "7f31b065-bbf1-4cec-d03b-3f815e46622e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.8.2\n"
          ]
        }
      ]
    },
    {
      "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": {
        "colab": {
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          "height": 147,
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        },
        "id": "55ZS07p664rH",
        "outputId": "48602f73-73a7-4553-a095-3a5c34989f8a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDownloading and preparing dataset 29.45 MiB (download: 29.45 MiB, generated: 36.42 MiB, total: 65.87 MiB) to ~/tensorflow_datasets/fashion_mnist/3.0.1...\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f29a6823142740af83b3b20439b974a1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d249a1cbedcf4097a8aa34af3724996c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extraction completed...: 0 file [00:00, ? file/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e76f390a5e92455cadb748036eafd192"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating splits...:   0%|          | 0/2 [00:00<?, ? splits/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f83b11251a064e66b6e5e7fe17f1e299"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train examples...:   0%|          | 0/60000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "108a598898d74abdbc7f4fccaf0aecf2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling ~/tensorflow_datasets/fashion_mnist/3.0.1.incompleteBUHIKB/fashion_mnist-train.tfrecord*...:   0%|  …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b30354add64f4098b25c400b2bad63cd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test examples...:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "396b49f36f334d1fa051ac844929eb36"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling ~/tensorflow_datasets/fashion_mnist/3.0.1.incompleteBUHIKB/fashion_mnist-test.tfrecord*...:   0%|   …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5a3ccd36e87c4a4f84ad07bc63191da1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDataset fashion_mnist downloaded and prepared to ~/tensorflow_datasets/fashion_mnist/3.0.1. Subsequent calls will reuse this data.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "metadata": {
        "id": "E2MmeHWQ7BV1"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_train_examples = metadata.splits['train'].num_examples\n",
        "num_test_examples = metadata.splits['test'].num_examples\n",
        "\n",
        "print(\"no of training examples: \", num_train_examples)\n",
        "print(\"no of testing examples: \", num_test_examples)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a8DvbKYA7O8n",
        "outputId": "822df814-f8d2-4ea7-9b94-82ffe984d545"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "no of training examples:  60000\n",
            "no of testing examples:  10000\n"
          ]
        }
      ]
    },
    {
      "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 functionapplies the normalize function to each element in the trian and test dataset\n",
        "train_dataset = train_dataset.map(normalize)\n",
        "test_dataset = test_dataset.map(normalize)"
      ],
      "metadata": {
        "id": "tUbWPi657Rz9"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# take 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\n",
        "plt.figure()\n",
        "plt.imshow(image, cmap=plt.cm.binary)\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.xlabel(class_names[label])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "trQFvJTK7ad3",
        "outputId": "5acef34f-3867-4818-a6b5-e6d9d1e21a3b"
      },
      "execution_count": 7,
      "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": "code",
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "i = 0\n",
        "for (image,label) in 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",
        "  i += 1\n",
        "plt.show()  \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "qrCZ-mmR7eXG",
        "outputId": "26265b96-feb4-45a8-f255-8991ff50f8d1"
      },
      "execution_count": 8,
      "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 Model"
      ],
      "metadata": {
        "id": "p3skC93v7keB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3,3), padding='same', activation = tf.nn.relu, input_shape=(28, 28, 1)),             \n",
        "    tf.keras.layers.MaxPooling2D((2,2), strides = 2),\n",
        "    tf.keras.layers.Conv2D(64, (3,3), padding='same', activation = tf.nn.relu),\n",
        "    tf.keras.layers.MaxPooling2D((2,2), strides = 2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128, activation = tf.nn.relu),\n",
        "    tf.keras.layers.Dense(10, activation = tf.nn.softmax)\n",
        "])"
      ],
      "metadata": {
        "id": "kT1PyhYw7nzY"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    loss = 'sparse_categorical_crossentropy',\n",
        "    optimizer= 'adam',\n",
        "    metrics = ['accuracy']\n",
        "    )      "
      ],
      "metadata": {
        "id": "QdnYdqG-9XlT"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 32\n",
        "train_dataset = train_dataset.repeat().shuffle(num_train_examples).batch(BATCH_SIZE)\n",
        "test_dataset = test_dataset.batch(BATCH_SIZE)"
      ],
      "metadata": {
        "id": "kgRWXpK49d8d"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_dataset, epochs = 10, steps_per_epoch=math.ceil(num_train_examples/BATCH_SIZE))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "40p62taC95xY",
        "outputId": "a56ecabb-2b00-4150-e4a2-473c5f0b7669"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 25s 5ms/step - loss: 0.3994 - accuracy: 0.8552\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.2648 - accuracy: 0.9038\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.2188 - accuracy: 0.9199\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1868 - accuracy: 0.9304\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1559 - accuracy: 0.9432\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1403 - accuracy: 0.9476\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 14s 7ms/step - loss: 0.1166 - accuracy: 0.9585\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.0984 - accuracy: 0.9636\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.0861 - accuracy: 0.9689\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.0709 - accuracy: 0.9729\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7faadb8b7e10>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_accuracy = model.evaluate(test_dataset, steps=num_test_examples/32)\n",
        "print(\"Accuracy on test dataset: \", test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5sAQ5ohF-s59",
        "outputId": "21ca5dfe-785d-4ef2-f0fa-12d2b965e2c6"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "312/312 [==============================] - 2s 4ms/step - loss: 0.3118 - accuracy: 0.9159\n",
            "Accuracy on test dataset:  0.9158999919891357\n"
          ]
        }
      ]
    },
    {
      "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": {
        "id": "-F0PyGer-vwx"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "40GO0cbr_CSw",
        "outputId": "286e5308-e3f9-4d84-c480-4660130e15f7"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tq83UhHO_Ftw",
        "outputId": "ad21ebae-512a-4a62-9853-dfb35816566e"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2.2571409e-10, 1.1906054e-10, 4.0432994e-04, 6.2269753e-13,\n",
              "       9.9367803e-01, 7.0337859e-12, 5.9176628e-03, 2.6984938e-13,\n",
              "       1.6049591e-12, 7.3766222e-11], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(predictions[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5zqtakR9_JBS",
        "outputId": "c69b237f-763c-46d5-8088-8c6dd21f47b0"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UXR-O4Ms_NRH",
        "outputId": "4764884f-d2aa-4bba-819f-ef8d4e394613"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can graph this to look at the full set of 10 class predictions"
      ],
      "metadata": {
        "id": "fDZ49A8r_m5g"
      }
    },
    {
      "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": "vPDAbMtC_p_P"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "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": 203
        },
        "id": "QkUZkm9__tiR",
        "outputId": "5a1b2f71-6877-4e2a-cd65-f831d805152f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x216 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": 203
        },
        "id": "Ra9TJXrK_wpP",
        "outputId": "aaae456d-eff5-4fd7-83bf-7783f02b6630"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": 589
        },
        "id": "nrWz_Uca_zgC",
        "outputId": "d86c827d-2b56-465b-8231-0db08316fdfb"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 30 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": "bSIIrcBQ_2NN",
        "outputId": "ee13c62a-04ab-470f-c778-5d6f6948b11b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "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": "QYPnicBJ_5Md",
        "outputId": "e657b75c-a05b-43a1-83ab-8dc4d5f22d5f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_single = model.predict(img)\n",
        "\n",
        "print(predictions_single)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8AGdBje_9Nc",
        "outputId": "5698125e-4cee-414c-980c-0d421f1d260c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2.2571409e-10 1.1905986e-10 4.0432840e-04 6.2269395e-13 9.9367803e-01\n",
            "  7.0337052e-12 5.9176311e-03 2.6984732e-13 1.6049408e-12 7.3765521e-11]]\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": 300
        },
        "id": "2fP-OWZ5ABVf",
        "outputId": "76149b4b-5fc2-4951-ed30-a41c182c4cb2"
      },
      "execution_count": 26,
      "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": "code",
      "source": [
        "np.argmax(predictions_single[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s3Q21OTU_9vo",
        "outputId": "c013b1d0-80eb-4806-e6ae-aa564a20c43d"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    }
  ]
}