{
  "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": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1vuQoR42AF_N",
        "outputId": "5c473da6-0bf9-42ac-8e7a-993d4522623f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n",
            "Epoch 1/10\n",
            "468/468 [==============================] - 100s 210ms/step - loss: 0.3633 - accuracy: 0.8851 - val_loss: 0.0526 - val_accuracy: 0.9829\n",
            "Epoch 2/10\n",
            "468/468 [==============================] - 96s 204ms/step - loss: 0.1228 - accuracy: 0.9621 - val_loss: 0.0363 - val_accuracy: 0.9879\n",
            "Epoch 3/10\n",
            "468/468 [==============================] - 98s 209ms/step - loss: 0.0898 - accuracy: 0.9730 - val_loss: 0.0298 - val_accuracy: 0.9896\n",
            "Epoch 4/10\n",
            "468/468 [==============================] - 99s 210ms/step - loss: 0.0719 - accuracy: 0.9786 - val_loss: 0.0198 - val_accuracy: 0.9935\n",
            "Epoch 5/10\n",
            "468/468 [==============================] - 100s 213ms/step - loss: 0.0680 - accuracy: 0.9791 - val_loss: 0.0193 - val_accuracy: 0.9938\n",
            "Epoch 6/10\n",
            "468/468 [==============================] - 97s 207ms/step - loss: 0.0590 - accuracy: 0.9816 - val_loss: 0.0175 - val_accuracy: 0.9942\n",
            "Epoch 7/10\n",
            "468/468 [==============================] - 101s 216ms/step - loss: 0.0547 - accuracy: 0.9830 - val_loss: 0.0181 - val_accuracy: 0.9941\n",
            "Epoch 8/10\n",
            "468/468 [==============================] - 101s 215ms/step - loss: 0.0511 - accuracy: 0.9845 - val_loss: 0.0155 - val_accuracy: 0.9946\n",
            "Epoch 9/10\n",
            "468/468 [==============================] - 100s 214ms/step - loss: 0.0476 - accuracy: 0.9858 - val_loss: 0.0188 - val_accuracy: 0.9937\n",
            "Epoch 10/10\n",
            "468/468 [==============================] - 100s 213ms/step - loss: 0.0444 - accuracy: 0.9858 - val_loss: 0.0182 - val_accuracy: 0.9948\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7ef01c2c8520>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "# Load the pre prepared data from TensofFlow\n",
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
        "\n",
        "# Preprocess the data\n",
        "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
        "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "x_train /= 255\n",
        "x_test /= 255\n",
        "\n",
        "# Define a CNN model with data augmentation\n",
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv2D(64, (3, 3), activation=\"relu\", input_shape=(28, 28, 1)),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    tf.keras.layers.Conv2D(64, (3, 3), activation=\"relu\"),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(512, activation=\"relu\"),\n",
        "    # Dropout to avoid from the overfitting\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "\n",
        "    # There are 10 different (0-9) output.\n",
        "    # So use 10 units dense layer with softmax activation\n",
        "    tf.keras.layers.Dense(10, activation=\"softmax\"),\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Data Augmentation\n",
        "datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "    rotation_range=10,\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1,\n",
        "    shear_range=0.1,\n",
        "    zoom_range=0.1\n",
        ")\n",
        "\n",
        "datagen.fit(x_train)\n",
        "\n",
        "# Train the model with data augmentation\n",
        "model.fit(datagen.flow(x_train, y_train, batch_size=128),\n",
        "          steps_per_epoch=len(x_train) / 128,\n",
        "          epochs=10,\n",
        "          validation_data=(x_test, y_test))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, acc = model.evaluate(x=x_test, y=y_test)\n",
        "print(\"Loss:\", loss)\n",
        "print(\"Accuracy:\", acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3O5b1ShcIE7-",
        "outputId": "6ef12f6b-eaa2-4c85-97da-db2ee7d81b6e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 4s 13ms/step - loss: 0.0182 - accuracy: 0.9948\n",
            "Loss: 0.018189573660492897\n",
            "Accuracy: 0.9947999715805054\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save the model and model weights to a avaliable path\n"
      ],
      "metadata": {
        "id": "2pNqtozRN5wj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(\"/content/model/model.h5\")\n",
        "model.save_weights(\"/content/model/model_weights.h5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Fp3_wYEKMF-",
        "outputId": "14213539-b8bf-4774-8916-5a0ea28b23a2"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    }
  ]
}