cnn - GitPress.io

Example Python program cnn.py Python version 3.x or newer. To check the Python version use:

python --version

Modules

from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Activation

Methods

def load_data():
def build_model():
def main():

Code

Python example

# for mnist data
from keras.datasets import mnist
from keras.utils import np_utils

# for CNN models
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Activation


def load_data():
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
    x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train = x_train / 255
    x_test = x_test / 255

    y_train = np_utils.to_categorical(y_train, 10)
    y_test = np_utils.to_categorical(y_test, 10)

    return (x_train, y_train), (x_test, y_test)


def build_model():
    model = Sequential()

    # initializing CNN
    model.add(Convolution2D(
        input_shape=(28, 28, 1),
        filters=64,
        kernel_size=5,
        strides=1,
        padding='same',
        data_format='channels_first'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(
        pool_size=2,
        strides=2,
        padding='same',
        data_format='channels_first'))

    # Second convolutional layer
    model.add(Convolution2D(
        32, 
        5, 
        strides=1,
        padding='same',
        data_format='channels_first'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(
        2,
        2,
        'same',
        data_format='channels_first'))

    # flatten
    model.add(Flatten())

    # fully connected networks
    model.add(Dense(1024))
    model.add(Activation('relu'))

    model.add(Dense(10))
    model.add(Activation('softmax'))

    # summary CNN model
    model.summary()

    return model


def main():
    (x_train, y_train), (x_test, y_test) = load_data()

    model = build_model()

    # compiling the CNN
    model.compile(
        optimizer='adam',
        loss='binary_crossentropy',
        metrics=['accuracy'])

    # start training
    model.fit(
        x_train,
        y_train,
        epochs=1,
        batch_size=64)

    score = model.evaluate(x_train, y_train)
    print('\nTrain Accuracy: ', score)

    score = model.evaluate(x_test, y_test)
    print('\nTest Accuracy: ', score)


if __name__ == '__main__':
    main()

Useful Links

Articles: https://python-commandments.org/
Python shell: https://bsdnerds.org/learn-python/
Tutorial: https://pythonprogramminglanguage.com/