Copy_of_MNIST_Digit_Classification_using_MLP.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D4q7PQ1V0iaI"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.datasets import mnist\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "from matplotlib.ticker import MultipleLocator, FormatStrFormatter\n",
        "\n",
        "plt.rcParams[\"axes.titlesize\"] = 16\n",
        "plt.rcParams[\"axes.labelsize\"] = 14\n",
        "plt.rcParams[\"image.cmap\"] = \"gray\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.datasets import fashion_mnist"
      ],
      "metadata": {
        "id": "jFTSuzr07l5u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "SEED_VALUE = 42\n",
        "\n",
        "# Fix seed to make training deterministic.\n",
        "random.seed(SEED_VALUE)\n",
        "np.random.seed(SEED_VALUE)\n",
        "tf.random.set_seed(SEED_VALUE)"
      ],
      "metadata": {
        "id": "EUEICd9q7pEf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train_all, y_train_all), (X_test, y_test) = mnist.load_data()\n",
        "\n",
        "X_valid = X_train_all[:10000]\n",
        "X_train = X_train_all[10000:]\n",
        "\n",
        "y_valid = y_train_all[:10000]\n",
        "y_train = y_train_all[10000:]\n",
        "\n",
        "print(X_train.shape)\n",
        "print(X_valid.shape)\n",
        "print(X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LgzSynml7v_t",
        "outputId": "6ce90832-558d-4508-92c0-5d859438d68b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
            "(50000, 28, 28)\n",
            "(10000, 28, 28)\n",
            "(10000, 28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(18, 5))\n",
        "for i in range(3):\n",
        "    plt.subplot(1, 3, i + 1)\n",
        "    plt.axis(True)\n",
        "    plt.imshow(X_train[i], cmap=\"gray\")\n",
        "    plt.subplots_adjust(wspace=0.2, hspace=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "GyBZ2msd70sm",
        "outputId": "bf329405-ff57-4920-c7c8-b752696789ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train.reshape((X_train.shape[0], 28 * 28))\n",
        "X_train = X_train.astype(\"float32\") / 255\n",
        "\n",
        "X_test = X_test.reshape((X_test.shape[0], 28 * 28))\n",
        "X_test = X_test.astype(\"float32\") / 255\n",
        "\n",
        "X_valid = X_valid.reshape((X_valid.shape[0], 28 * 28))\n",
        "X_valid = X_valid.astype(\"float32\") / 255"
      ],
      "metadata": {
        "id": "3o_a60dg75a1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the Fashion MNIST dataset.\n",
        "((X_train_fashion, y_train_fashion), (_, _)) = fashion_mnist.load_data()\n",
        "\n",
        "# The labels in the Fashion MNIST dataset are encoded as integers.\n",
        "print(y_train_fashion[0:9])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMY91Hiw8EXL",
        "outputId": "16ab473c-f7a8-47ee-9457-f0c75d0b5b80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "\u001b[1m29515/29515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "\u001b[1m26421880/26421880\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "\u001b[1m5148/5148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "\u001b[1m4422102/4422102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
            "[9 0 0 3 0 2 7 2 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_onehot = to_categorical(y_train_fashion[0:9])\n",
        "print(y_train_onehot)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-vkCICq-8Jba",
        "outputId": "e927116e-f4e4-4111-8b91-11d09aa077b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert integer labels to one-hot encoded vectors.\n",
        "y_train = to_categorical(y_train)\n",
        "y_valid = to_categorical(y_valid)\n",
        "y_test  = to_categorical(y_test)"
      ],
      "metadata": {
        "id": "Lz9HIjfr8O_X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate the model.\n",
        "model = tf.keras.Sequential()\n",
        "\n",
        "# Build the model.\n",
        "model.add(Dense(128, activation='relu', input_shape=(X_train.shape[1],)))\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(10,  activation=\"softmax\"))\n",
        "\n",
        "# Display the model summary.\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "DRIsis1j8Tr-",
        "outputId": "31a7f34d-3a55-4469-bd83-1a348ab1bed3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │         \u001b[38;5;34m100,480\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │          \u001b[38;5;34m16,512\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │           \u001b[38;5;34m1,290\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">100,480</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m118,282\u001b[0m (462.04 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">118,282</span> (462.04 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m118,282\u001b[0m (462.04 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">118,282</span> (462.04 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer=\"rmsprop\",\n",
        "    loss=\"categorical_crossentropy\",\n",
        "    metrics=[\"accuracy\"],\n",
        ")"
      ],
      "metadata": {
        "id": "Kb2B9Jlf8Xmw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_results = model.fit(X_train,\n",
        "                             y_train,\n",
        "                             epochs=21,\n",
        "                             batch_size=64,\n",
        "                             validation_data=(X_valid, y_valid));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E2HmM4588bGf",
        "outputId": "638a7ffc-e6b9-4b6b-d5c6-9f44f0ce795c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4ms/step - accuracy: 0.8643 - loss: 0.4780 - val_accuracy: 0.9450 - val_loss: 0.1755\n",
            "Epoch 2/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9601 - loss: 0.1360 - val_accuracy: 0.9606 - val_loss: 0.1299\n",
            "Epoch 3/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9746 - loss: 0.0893 - val_accuracy: 0.9658 - val_loss: 0.1137\n",
            "Epoch 4/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9814 - loss: 0.0649 - val_accuracy: 0.9691 - val_loss: 0.1105\n",
            "Epoch 5/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9863 - loss: 0.0487 - val_accuracy: 0.9715 - val_loss: 0.1104\n",
            "Epoch 6/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9894 - loss: 0.0385 - val_accuracy: 0.9717 - val_loss: 0.1157\n",
            "Epoch 7/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9921 - loss: 0.0293 - val_accuracy: 0.9692 - val_loss: 0.1311\n",
            "Epoch 8/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9935 - loss: 0.0243 - val_accuracy: 0.9714 - val_loss: 0.1345\n",
            "Epoch 9/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9946 - loss: 0.0196 - val_accuracy: 0.9732 - val_loss: 0.1316\n",
            "Epoch 10/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9956 - loss: 0.0156 - val_accuracy: 0.9733 - val_loss: 0.1338\n",
            "Epoch 11/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.9968 - loss: 0.0121 - val_accuracy: 0.9697 - val_loss: 0.1635\n",
            "Epoch 12/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9966 - loss: 0.0112 - val_accuracy: 0.9705 - val_loss: 0.1805\n",
            "Epoch 13/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.9966 - loss: 0.0101 - val_accuracy: 0.9720 - val_loss: 0.1590\n",
            "Epoch 14/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9977 - loss: 0.0076 - val_accuracy: 0.9729 - val_loss: 0.1572\n",
            "Epoch 15/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9976 - loss: 0.0081 - val_accuracy: 0.9730 - val_loss: 0.1660\n",
            "Epoch 16/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 5ms/step - accuracy: 0.9976 - loss: 0.0072 - val_accuracy: 0.9765 - val_loss: 0.1508\n",
            "Epoch 17/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9985 - loss: 0.0048 - val_accuracy: 0.9764 - val_loss: 0.1523\n",
            "Epoch 18/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9984 - loss: 0.0051 - val_accuracy: 0.9711 - val_loss: 0.2137\n",
            "Epoch 19/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9983 - loss: 0.0050 - val_accuracy: 0.9738 - val_loss: 0.2051\n",
            "Epoch 20/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9988 - loss: 0.0041 - val_accuracy: 0.9752 - val_loss: 0.1951\n",
            "Epoch 21/21\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9981 - loss: 0.0053 - val_accuracy: 0.9743 - val_loss: 0.1942\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_results(metrics, title=None, ylabel=None, ylim=None, metric_name=None, color=None):\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(15, 4))\n",
        "\n",
        "    if not (isinstance(metric_name, list) or isinstance(metric_name, tuple)):\n",
        "        metrics = [metrics,]\n",
        "        metric_name = [metric_name,]\n",
        "\n",
        "    for idx, metric in enumerate(metrics):\n",
        "        ax.plot(metric, color=color[idx])\n",
        "\n",
        "    plt.xlabel(\"Epoch\")\n",
        "    plt.ylabel(ylabel)\n",
        "    plt.title(title)\n",
        "    plt.xlim([0, 20])\n",
        "    plt.ylim(ylim)\n",
        "    # Tailor x-axis tick marks\n",
        "    ax.xaxis.set_major_locator(MultipleLocator(5))\n",
        "    ax.xaxis.set_major_formatter(FormatStrFormatter('%d'))\n",
        "    ax.xaxis.set_minor_locator(MultipleLocator(1))\n",
        "    plt.grid(True)\n",
        "    plt.legend(metric_name)\n",
        "    plt.show()\n",
        "    plt.close()"
      ],
      "metadata": {
        "id": "m_HVEMQy85Yg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrieve training results.\n",
        "train_loss = training_results.history[\"loss\"]\n",
        "train_acc  = training_results.history[\"accuracy\"]\n",
        "valid_loss = training_results.history[\"val_loss\"]\n",
        "valid_acc  = training_results.history[\"val_accuracy\"]\n",
        "\n",
        "plot_results(\n",
        "    [train_loss, valid_loss],\n",
        "    ylabel=\"Loss\",\n",
        "    ylim=[0.0, 0.5],\n",
        "    metric_name=[\"Training Loss\", \"Validation Loss\"],\n",
        "    color=[\"g\", \"b\"],\n",
        ")\n",
        "\n",
        "plot_results(\n",
        "    [train_acc, valid_acc],\n",
        "    ylabel=\"Accuracy\",\n",
        "    ylim=[0.9, 1.0],\n",
        "    metric_name=[\"Training Accuracy\", \"Validation Accuracy\"],\n",
        "    color=[\"g\", \"b\"],\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
        },
        "id": "mFyIYk0Q9CSm",
        "outputId": "b4e75079-412f-4682-f130-636acf576dbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(X_test)\n",
        "index = 0  # up to 9999\n",
        "print(\"Ground truth for test digit: \", y_test[index])\n",
        "print(\"\\n\")\n",
        "print(\"Predictions for each class:\\n\")\n",
        "for i in range(10):\n",
        "    print(\"digit:\", i, \" probability: \", predictions[index][i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HTz73-y09Pw3",
        "outputId": "e536bcbf-3322-4233-d1ca-0dc891064fe7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n",
            "Ground truth for test digit:  [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            "\n",
            "\n",
            "Predictions for each class:\n",
            "\n",
            "digit: 0  probability:  8.729837e-25\n",
            "digit: 1  probability:  3.617277e-21\n",
            "digit: 2  probability:  3.460681e-17\n",
            "digit: 3  probability:  6.1899135e-16\n",
            "digit: 4  probability:  8.43316e-30\n",
            "digit: 5  probability:  5.053907e-25\n",
            "digit: 6  probability:  2.6305837e-36\n",
            "digit: 7  probability:  0.99999994\n",
            "digit: 8  probability:  2.8177708e-22\n",
            "digit: 9  probability:  4.4525763e-18\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate predictions for the test dataset.\n",
        "predictions = model.predict(X_test)\n",
        "\n",
        "# For each sample image in the test dataset, select the class label with the highest probability.\n",
        "predicted_labels = [np.argmax(i) for i in predictions]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p4Zlxdv49TFu",
        "outputId": "d8ebbf34-19f5-49ce-ac29-c3d0bf2ec469"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert one-hot encoded labels to integers.\n",
        "y_test_integer_labels = tf.argmax(y_test, axis=1)\n",
        "\n",
        "# Generate a confusion matrix for the test dataset.\n",
        "cm = tf.math.confusion_matrix(labels=y_test_integer_labels, predictions=predicted_labels)\n",
        "\n",
        "# Plot the confusion matrix as a heatmap.\n",
        "plt.figure(figsize=[15, 8])\n",
        "import seaborn as sn\n",
        "\n",
        "sn.heatmap(cm, annot=True, fmt=\"d\", annot_kws={\"size\": 14})\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Truth\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "tWT8cUE79V0n",
        "outputId": "c94abbb0-19b3-479a-fa0f-f4435305ef3e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    }
  ]
}