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{
"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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"kernelspec": {
"display_name": "Python 3.9.7 ('base')",
"language": "python",
"name": "python3"
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"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"vscode": {
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