{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Machine Learning\n",
    "\n",
    "This notebook is an example of a CNN for recognizing handwritten characters.\n",
    "\n",
    "Most of this code is from https://keras.io/examples/vision/mnist_convnet/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# Hide GPU from visible devices\n",
    "tf.config.set_visible_devices([], 'GPU')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "# Model / data parameters\n",
    "num_classes = 10\n",
    "input_shape = (28, 28, 1)\n",
    "\n",
    "# Load the data and split it between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
    "\n",
    "# Scale images to the [0, 1] range\n",
    "x_train = x_train.astype(\"float32\") / 255\n",
    "x_test = x_test.astype(\"float32\") / 255\n",
    "\n",
    "# Make sure images have shape (28, 28, 1)\n",
    "x_train = np.expand_dims(x_train, -1)\n",
    "x_test = np.expand_dims(x_test, -1)\n",
    "print(\"x_train shape:\", x_train.shape)\n",
    "print(x_train.shape[0], \"train samples\")\n",
    "print(x_test.shape[0], \"test samples\")\n",
    "\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "# [1, 2, 3, 4] -> [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 13, 13, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 11, 11, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1600)              0         \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 1600)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 10)                16010     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 34,826\n",
      "Trainable params: 34,826\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential(\n",
    "    [\n",
    "        keras.Input(shape=input_shape),\n",
    "        layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
    "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "        layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
    "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "        layers.Flatten(),\n",
    "        layers.Dropout(0.5),\n",
    "        layers.Dense(num_classes, activation=\"softmax\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "epochs = 15\n",
    "\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "422/422 [==============================] - 9s 20ms/step - loss: 0.3573 - accuracy: 0.8919 - val_loss: 0.0857 - val_accuracy: 0.9777\n",
      "Epoch 2/15\n",
      "422/422 [==============================] - 8s 19ms/step - loss: 0.1184 - accuracy: 0.9636 - val_loss: 0.0608 - val_accuracy: 0.9825\n",
      "Epoch 3/15\n",
      "422/422 [==============================] - 8s 19ms/step - loss: 0.0862 - accuracy: 0.9733 - val_loss: 0.0496 - val_accuracy: 0.9868\n",
      "Epoch 4/15\n",
      "422/422 [==============================] - 8s 20ms/step - loss: 0.0724 - accuracy: 0.9778 - val_loss: 0.0424 - val_accuracy: 0.9883\n",
      "Epoch 5/15\n",
      "422/422 [==============================] - 8s 19ms/step - loss: 0.0656 - accuracy: 0.9793 - val_loss: 0.0398 - val_accuracy: 0.9895\n",
      "Epoch 6/15\n",
      "422/422 [==============================] - 8s 20ms/step - loss: 0.0591 - accuracy: 0.9816 - val_loss: 0.0361 - val_accuracy: 0.9912\n",
      "Epoch 7/15\n",
      "422/422 [==============================] - 8s 20ms/step - loss: 0.0522 - accuracy: 0.9833 - val_loss: 0.0315 - val_accuracy: 0.9922\n",
      "Epoch 8/15\n",
      "422/422 [==============================] - 8s 20ms/step - loss: 0.0485 - accuracy: 0.9846 - val_loss: 0.0319 - val_accuracy: 0.9910\n",
      "Epoch 9/15\n",
      "422/422 [==============================] - 9s 20ms/step - loss: 0.0447 - accuracy: 0.9858 - val_loss: 0.0331 - val_accuracy: 0.9917\n",
      "Epoch 10/15\n",
      "422/422 [==============================] - 9s 21ms/step - loss: 0.0416 - accuracy: 0.9871 - val_loss: 0.0309 - val_accuracy: 0.9922\n",
      "Epoch 11/15\n",
      "422/422 [==============================] - 8s 20ms/step - loss: 0.0397 - accuracy: 0.9877 - val_loss: 0.0281 - val_accuracy: 0.9932\n",
      "Epoch 12/15\n",
      "422/422 [==============================] - 9s 20ms/step - loss: 0.0393 - accuracy: 0.9874 - val_loss: 0.0308 - val_accuracy: 0.9908\n",
      "Epoch 13/15\n",
      "422/422 [==============================] - 8s 20ms/step - loss: 0.0373 - accuracy: 0.9882 - val_loss: 0.0276 - val_accuracy: 0.9928\n",
      "Epoch 14/15\n",
      "422/422 [==============================] - 8s 19ms/step - loss: 0.0357 - accuracy: 0.9879 - val_loss: 0.0265 - val_accuracy: 0.9935\n",
      "Epoch 15/15\n",
      "422/422 [==============================] - 8s 19ms/step - loss: 0.0334 - accuracy: 0.9886 - val_loss: 0.0298 - val_accuracy: 0.9927\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2242fa4c220>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This line can be run multiple times, but keep in mind that the model will probably be over fitting\n",
    "\n",
    "model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the Trained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.02596166729927063\n",
      "Test accuracy: 0.9919000267982483\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print(\"Test loss:\", score[0])\n",
    "print(\"Test accuracy:\", score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Model (h5 format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"mnist.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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