separableconv.py

print('SeparableConv2D is faster and yield better results than regular conv layers')

from keras.models import Sequential, Model
from keras import layers
height = 64
width = 64
channels = 3
num_classes = 10
model = Sequential()
model.add(layers.SeparableConv2D(32, 3,
                                 activation='relu',
                                 input_shape=(height, width, channels,)))
model.add(layers.SeparableConv2D(64, 3, activation='relu'))
model.add(layers.MaxPooling2D(2))
model.add(layers.SeparableConv2D(64, 3, activation='relu'))
model.add(layers.SeparableConv2D(128, 3, activation='relu'))
model.add(layers.MaxPooling2D(2))
model.add(layers.SeparableConv2D(64, 3, activation='relu'))
model.add(layers.SeparableConv2D(128, 3, activation='relu'))
model.add(layers.GlobalAveragePooling2D())
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dense(num_classes, activation='softmax'))
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')