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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": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare the data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build the Model"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/15\n",
"422/422 [==============================] - 6s 3ms/step - loss: 0.3744 - accuracy: 0.8868 - val_loss: 0.0892 - val_accuracy: 0.9763\n",
"Epoch 2/15\n",
"422/422 [==============================] - 1s 3ms/step - loss: 0.1177 - accuracy: 0.9634 - val_loss: 0.0660 - val_accuracy: 0.9817\n",
"Epoch 3/15\n",
"422/422 [==============================] - 1s 3ms/step - loss: 0.0876 - accuracy: 0.9732 - val_loss: 0.0480 - val_accuracy: 0.9865\n",
"Epoch 4/15\n",
"422/422 [==============================] - 1s 3ms/step - loss: 0.0738 - accuracy: 0.9774 - val_loss: 0.0462 - val_accuracy: 0.9872\n",
"Epoch 5/15\n",
"422/422 [==============================] - 1s 3ms/step - loss: 0.0642 - accuracy: 0.9805 - val_loss: 0.0440 - val_accuracy: 0.9872\n",
"Epoch 6/15\n",
"422/422 [==============================] - 1s 2ms/step - loss: 0.0585 - accuracy: 0.9818 - val_loss: 0.0373 - val_accuracy: 0.9898\n",
"Epoch 7/15\n",
"422/422 [==============================] - 1s 3ms/step - loss: 0.0544 - accuracy: 0.9832 - val_loss: 0.0348 - val_accuracy: 0.9908\n",
"Epoch 8/15\n",
"422/422 [==============================] - 1s 3ms/step - loss: 0.0495 - accuracy: 0.9845 - val_loss: 0.0342 - val_accuracy: 0.9907\n",
"Epoch 9/15\n",
"422/422 [==============================] - 1s 2ms/step - loss: 0.0462 - accuracy: 0.9853 - val_loss: 0.0313 - val_accuracy: 0.9910\n",
"Epoch 10/15\n",
"422/422 [==============================] - 1s 2ms/step - loss: 0.0444 - accuracy: 0.9858 - val_loss: 0.0320 - val_accuracy: 0.9907\n",
"Epoch 11/15\n",
"422/422 [==============================] - 1s 2ms/step - loss: 0.0418 - accuracy: 0.9872 - val_loss: 0.0303 - val_accuracy: 0.9913\n",
"Epoch 12/15\n",
"422/422 [==============================] - 1s 3ms/step - loss: 0.0410 - accuracy: 0.9874 - val_loss: 0.0276 - val_accuracy: 0.9922\n",
"Epoch 13/15\n",
"422/422 [==============================] - 1s 3ms/step - loss: 0.0381 - accuracy: 0.9875 - val_loss: 0.0292 - val_accuracy: 0.9912\n",
"Epoch 14/15\n",
"422/422 [==============================] - 1s 2ms/step - loss: 0.0368 - accuracy: 0.9879 - val_loss: 0.0291 - val_accuracy: 0.9920\n",
"Epoch 15/15\n",
"422/422 [==============================] - 1s 2ms/step - loss: 0.0356 - accuracy: 0.9888 - val_loss: 0.0257 - val_accuracy: 0.9925\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x1c9d4871f40>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"batch_size = 128\n",
"epochs = 15\n",
"\n",
"model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\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: 0.026043808087706566\n",
"Test accuracy: 0.9907000064849854\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": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"model.save(\"mnist.h5\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example GUI"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n"
]
}
],
"source": [
"from tkinter import *\n",
"from PIL import ImageGrab\n",
"import imageio\n",
"import tkinter.font as font\n",
"\n",
"class Paint(object):\n",
" def __init__(self):\n",
" self.root=Tk()\n",
" self.root.title('Playing with numbers')\n",
" # self.root.wm_iconbitmap('44143.ico')\n",
" self.root.configure(background='light salmon')\n",
" self.c = Canvas(self.root,bg='light cyan', height=330, width=400)\n",
" self.label = Label(self.root, text='Draw any numer', font=20, bg='light salmon')\n",
" self.label.grid(row=0, column=3)\n",
" self.c.grid(row=1, columnspan=9)\n",
" self.c.create_line(0,0,400,0,width=20,fill='midnight blue')\n",
" self.c.create_line(0,0,0,330,width=20,fill='midnight blue')\n",
" self.c.create_line(400,0,400,330,width=20,fill='midnight blue')\n",
" self.c.create_line(0,330,400,330,width=20,fill='midnight blue')\n",
" self.myfont = font.Font(size=20,weight='bold')\n",
" self.predicting_button=Button(self.root,text='Predict', fg='white', bg='blue', height=2, width=6, font=self.myfont, command=lambda:self.classify(self.c))\n",
" self.predicting_button.grid(row=2,column=1)\n",
" self.clear=Button(self.root,text='Clear', fg='white', bg='orange', height=2, width=6, font=self.myfont, command=self.clear)\n",
" self.clear.grid(row=2,column=5)\n",
" self.prediction_text = Text(self.root, height=5, width=5)\n",
" self.prediction_text.grid(row=4, column=3)\n",
" self.label=Label(self.root, text=\"Predicted Number is\", fg=\"black\", font=30, bg='light salmon')\n",
"\n",
" self.label.grid(row=3,column=3)\n",
" self.model=model\n",
" self.setup()\n",
" self.root.mainloop()\n",
"\n",
"\n",
" def setup(self):\n",
" self.old_x=None\n",
" self.old_y=None\n",
" self.color='black'\n",
" self.linewidth=15\n",
" self.c.bind('<B1-Motion>', self.paint)\n",
" self.c.bind('<ButtonRelease-1>', self.reset)\n",
"\n",
"\n",
" def paint(self,event):\n",
" paint_color=self.color\n",
" if self.old_x and self.old_y:\n",
" self.c.create_line(self.old_x,self.old_y,event.x,event.y,fill=paint_color,width=self.linewidth,capstyle=ROUND,\n",
" smooth=TRUE,splinesteps=48)\n",
" self.old_x=event.x\n",
" self.old_y=event.y\n",
"\n",
"\n",
" def clear(self):\n",
" \"\"\"Clear drawing area\"\"\"\n",
" self.c.delete(\"all\")\n",
"\n",
" def reset(self, event):\n",
" \"\"\"reset old_x and old_y if the left mouse button is released\"\"\"\n",
" self.old_x, self.old_y = None, None\n",
"\n",
"\n",
" def classify(self,widget):\n",
" x=self.root.winfo_rootx()+widget.winfo_x()\n",
" y=self.root.winfo_rooty()+widget.winfo_y()\n",
" x1=widget.winfo_width()\n",
" y1=widget.winfo_height()\n",
" ImageGrab.grab().crop((x,y,x1,y1)).resize((28,28)).save('classify.png')\n",
" img=imageio.imread('classify.png', as_gray=True, pilmode='P')\n",
" img=np.array(img)\n",
" img=np.reshape(img,(1,28,28,1))\n",
" img[img==0] = 255\n",
" img[img==225] = 0\n",
" # Predict digit\n",
" pred = self.model.predict([img])\n",
" # Get index with highest probability\n",
" pred = np.argmax(pred)\n",
" print(pred)\n",
" self.prediction_text.delete(\"1.0\", END)\n",
" self.prediction_text.insert(END, pred)\n",
" labelfont = ('times', 30, 'bold')\n",
" self.prediction_text.config(font=labelfont)\n",
"\n",
"if __name__ == '__main__':\n",
" Paint()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.7 ('base')",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"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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