Copy_of_Coding_Tutorial_Week_2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ananthrn/Coursera-gettings-started-with-tensorflow-2/blob/main/Copy_of_Coding_Tutorial_Week_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-EeEuETAGtZK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb0350bf-ce85-4434-e6d4-d5bfb8de5304"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.8.2\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I8Ip_m-SGtZN"
      },
      "source": [
        "# The Sequential model API"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rKoUdepmGtZP"
      },
      "source": [
        " ## Coding tutorials\n",
        " #### [1. Building a Sequential model](#coding_tutorial_1)\n",
        " #### [2. Convolutional and pooling layers](#coding_tutorial_2)\n",
        " #### [3. The compile method](#coding_tutorial_3)\n",
        " #### [4. The fit method](#coding_tutorial_4)\n",
        " #### [5. The evaluate and predict methods](#coding_tutorial_5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Txxn75yGtZP"
      },
      "source": [
        "***\n",
        "<a id=\"coding_tutorial_1\"></a>\n",
        "## Building a Sequential model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dqqTz4u3GtZQ"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten, Softmax"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3KO5Jq1DGtZR"
      },
      "source": [
        "#### Build a feedforward neural network model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T8zI_6HWGtZR"
      },
      "outputs": [],
      "source": [
        "# Build the Sequential feedforward neural network model\n",
        "\n",
        "model = Sequential(\n",
        "      [ Flatten(input_shape=(28, 28)),\n",
        "        Dense(16, activation='relu'),\n",
        "        Dense(16, activation='relu'),\n",
        "        Dense(10, activation='softmax')\n",
        "      ]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "azkwCzqgGtZS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f1906a6-9036-40f8-ba96-9cce8f802116"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten (Flatten)           (None, 784)               0         \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 16)                12560     \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 16)                272       \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 10)                170       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 13,002\n",
            "Trainable params: 13,002\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Print the model summary\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4LXOCLvoGtZS"
      },
      "source": [
        "***\n",
        "<a id=\"coding_tutorial_2\"></a>\n",
        "## Convolutional and pooling layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LAGRNZYPGtZT"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3PXtZAhQGtZU"
      },
      "source": [
        "#### Build a convolutional neural network model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FWNaTUrVGtZU"
      },
      "outputs": [],
      "source": [
        "# Build the Sequential convolutional neural network model\n",
        "\n",
        "conv_model = Sequential(\n",
        "    [\n",
        "     Conv2D(16, (3, 3), activation='relu', strides = 2, padding = 'SAME', input_shape=(28, 28, 1)),\n",
        "     MaxPooling2D((3, 3), strides = 2),\n",
        "     Flatten(),\n",
        "     Dense(16, activation='relu'),\n",
        "     Dense(10, activation='softmax')\n",
        "    ]\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HQJQxm26GtZV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54bb4b2e-4aa4-4b4a-82d2-83bc3fb57e3c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 14, 14, 16)        160       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 6, 6, 16)         0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 576)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 16)                9232      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                170       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 9,562\n",
            "Trainable params: 9,562\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Print the model summary\n",
        "conv_model.summary()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZCC3iuKTGtZV"
      },
      "source": [
        "***\n",
        "<a id=\"coding_tutorial_3\"></a>\n",
        "## The compile method"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UYNFAuKsGtZV"
      },
      "source": [
        "#### Compile the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QkTf83m3GtZW"
      },
      "outputs": [],
      "source": [
        "# Define the model optimizer, loss function and metrics\n",
        "conv_model.compile(\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    optimizer='adam',\n",
        "    metrics=['sparse_categorical_accuracy']\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4nGEize0GtZW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53782b7f-d293-410a-b9c7-564985b6a20a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conv model loss: sparse_categorical_crossentropy\n",
            "conv model optimizer: <keras.optimizer_v2.adam.Adam object at 0x7fdeed4dffd0>\n",
            "conv model metrics: []\n"
          ]
        }
      ],
      "source": [
        "# Print the resulting model attributes\n",
        "print(\"conv model loss:\", conv_model.loss)\n",
        "print(\"conv model optimizer:\", conv_model.optimizer)\n",
        "print(\"conv model metrics:\", conv_model.metrics)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UAFczeU4GtZW"
      },
      "source": [
        "***\n",
        "<a id=\"coding_tutorial_4\"></a>\n",
        "## The fit method"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m4283c7RGtZX"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing import image\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y5JaTuIMGtZX"
      },
      "source": [
        "#### Load the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A_OK6tGcGtZX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3cceb918-9c76-40dd-f682-ad812e508491"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "40960/29515 [=========================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "26435584/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "16384/5148 [===============================================================================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n",
            "4431872/4422102 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "# Load the Fashion-MNIST dataset\n",
        "\n",
        "fashion_mnist_data = tf.keras.datasets.fashion_mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist_data.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RosA3DHeGtZY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0c83dea-a149-4be5-b626-d551489400dc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train data shape: (60000, 28, 28)\n",
            "test data shape:  (10000, 28, 28)\n",
            "train labels shape:  (60000,)\n",
            "Test labels shape:  (10000,)\n"
          ]
        }
      ],
      "source": [
        "# Print the shape of the training data\n",
        "\n",
        "print(\"train data shape:\", train_images.shape)\n",
        "print(\"test data shape: \", test_images.shape)\n",
        "print(\"train labels shape: \", train_labels.shape)\n",
        "print(\"Test labels shape: \", test_labels.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vn96PQ2dGtZY"
      },
      "outputs": [],
      "source": [
        "# Define the labels\n",
        "\n",
        "labels = [\n",
        "    'T-shirt/top',\n",
        "    'Trouser',\n",
        "    'Pullover',\n",
        "    'Dress',\n",
        "    'Coat',\n",
        "    'Sandal',\n",
        "    'Shirt',\n",
        "    'Sneaker',\n",
        "    'Bag',\n",
        "    'Ankle boot'\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2_oTrsCrGtZY"
      },
      "outputs": [],
      "source": [
        "# Rescale the image values so that they lie in between 0 and 1.\n",
        "\n",
        "train_images = train_images/255.0\n",
        "test_images = test_images/255.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EPWx4IreGtZZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "50b275f1-f89d-4459-ee3b-4a6beff3f61f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ankle boot\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Display one of the images\n",
        "plt.imshow(train_images[0])\n",
        "print(labels[train_labels[0]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Yv_5_9iGtZZ"
      },
      "source": [
        "#### Fit the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fKQEUKh0GtZZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "475d24c9-b102-4feb-d90d-6cc04b9d3475"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.3423 - sparse_categorical_accuracy: 0.8781\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 10s 6ms/step - loss: 0.3293 - sparse_categorical_accuracy: 0.8827\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.3205 - sparse_categorical_accuracy: 0.8849\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.3135 - sparse_categorical_accuracy: 0.8868\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.3073 - sparse_categorical_accuracy: 0.8899\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.3015 - sparse_categorical_accuracy: 0.8907\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.2970 - sparse_categorical_accuracy: 0.8926\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 14s 8ms/step - loss: 0.2937 - sparse_categorical_accuracy: 0.8933\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 17s 9ms/step - loss: 0.2890 - sparse_categorical_accuracy: 0.8950\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 12s 7ms/step - loss: 0.2851 - sparse_categorical_accuracy: 0.8955\n"
          ]
        }
      ],
      "source": [
        "# Fit the model\n",
        "history = conv_model.fit(\n",
        "    x=train_images,\n",
        "    y=train_labels,\n",
        "    batch_size=32,\n",
        "    epochs=10\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ae1CyQz7GtZZ"
      },
      "source": [
        "#### Plot training history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gKYYkb-1GtZZ"
      },
      "outputs": [],
      "source": [
        "# Load the history into a pandas Dataframe\n",
        "history_df = pd.DataFrame.from_dict(history.history)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CsmlP_m7YRfm",
        "outputId": "14094f82-9bde-4556-deb5-6e8f86165b6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       loss  sparse_categorical_accuracy\n",
              "0  0.342289                     0.878150\n",
              "1  0.329335                     0.882700\n",
              "2  0.320501                     0.884900\n",
              "3  0.313537                     0.886783\n",
              "4  0.307274                     0.889867"
            ],
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0b70a43b-9177-4557-8ce7-e707593a8aa9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0b70a43b-9177-4557-8ce7-e707593a8aa9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l1uZeUG7GtZa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "6c7abad9-8c27-437c-a86b-c62328c6d4a6"
      },
      "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"
          }
        }
      ],
      "source": [
        "# Make a plot for the loss\n",
        "loss_plot = history_df.plot(y='loss', title='loss vs epochs', legend=False)\n",
        "loss_plot.set(xlabel='epochs', ylabel='loss')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n6HSokMgGtZa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "611bac86-5184-4f3f-8363-6aaa8d33b726"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'sparse_categorical_accuracy'), Text(0.5, 0, 'epochs')]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Make a plot for the accuracy\n",
        "accuracy_plot = history_df.plot(y='sparse_categorical_accuracy', title='sparse_categorical_accuracy vs epochs', legend=False)\n",
        "accuracy_plot.set(xlabel='epochs', ylabel='sparse_categorical_accuracy')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3LHAAkWXGtZa"
      },
      "outputs": [],
      "source": [
        "# Make a plot for the additional metric\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nztVX6KTGtZa"
      },
      "source": [
        "***\n",
        "<a id=\"coding_tutorial_5\"></a>\n",
        "## The evaluate and predict methods"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jFjyul6yGtZa"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jO5DGJsyGtZa"
      },
      "source": [
        "#### Evaluate the model on the test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ADc6i7-_GtZb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d70fc4c8-90bc-42b7-bf97-ab9941e7c79e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3325 - sparse_categorical_accuracy: 0.8821\n"
          ]
        }
      ],
      "source": [
        "# Evaluate the model\n",
        "\n",
        "evaluation = conv_model.evaluate(\n",
        "    x=test_images,\n",
        "    y=test_labels\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XK22fsSfZq42",
        "outputId": "f3d94ca3-5d53-4209-e770-39b8098264bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.33245575428009033, 0.882099986076355]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "poiV36NJGtZb"
      },
      "source": [
        "#### Make predictions from the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nL5GSbnJGtZb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "3a0ddec9-ec94-40ca-8f9b-f71b9dfb98ec"
      },
      "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"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label: Dress\n"
          ]
        }
      ],
      "source": [
        "# Choose a random test image\n",
        "\n",
        "random_inx = np.random.choice(test_images.shape[0])\n",
        "\n",
        "test_image = test_images[random_inx]\n",
        "plt.imshow(test_image)\n",
        "plt.show()\n",
        "print(f\"Label: {labels[test_labels[random_inx]]}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vx6yGPuoGtZb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e8101d6-54a2-41b4-d7fe-f16ba9e58847"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "new_test_image.shape (1, 28, 28, 1)\n",
            "Model prediction:  Coat\n"
          ]
        }
      ],
      "source": [
        "# Get the model predictions\n",
        "new_test_image = test_image[np.newaxis, ..., np.newaxis]\n",
        "print(\"new_test_image.shape\", new_test_image.shape)\n",
        "\n",
        "model_pred = conv_model.predict(\n",
        "    x=new_test_image\n",
        ")\n",
        "\n",
        "print(\"Model prediction: \", labels[np.argmax(model_pred[0])])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conv_model(new_test_image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rGZft0UgZ7qM",
        "outputId": "bf46a6de-ac42-4ceb-afb2-6f6303446899"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 10), dtype=float32, numpy=\n",
              "array([[1.2905137e-02, 7.1122532e-04, 8.1539117e-03, 1.3521095e-01,\n",
              "        6.1038798e-01, 2.7072433e-08, 2.3234203e-01, 1.3295093e-05,\n",
              "        2.7410986e-04, 1.3221926e-06]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "wCEhzG7acPGz"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.1"
    },
    "colab": {
      "name": "Copy of Coding Tutorial - Week 2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}