from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
def build_cae_model(height=32, width=32, channel=3):
"""
build convolutional autoencoder model
"""
input_img = Input(shape=(height, width, channel))
# encoder
net = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
net = MaxPooling2D((2, 2), padding='same')(net)
net = Conv2D(8, (3, 3), activation='relu', padding='same')(net)
net = MaxPooling2D((2, 2), padding='same')(net)
net = Conv2D(4, (3, 3), activation='relu', padding='same')(net)
encoded = MaxPooling2D((2, 2), padding='same', name='enc')(net)
# decoder
net = Conv2D(4, (3, 3), activation='relu', padding='same')(encoded)
net = UpSampling2D((2, 2))(net)
net = Conv2D(8, (3, 3), activation='relu', padding='same')(net)
net = UpSampling2D((2, 2))(net)
net = Conv2D(16, (3, 3), activation='relu', padding='same')(net)
net = UpSampling2D((2, 2))(net)
decoded = Conv2D(channel, (3, 3), activation='sigmoid', padding='same')(net)
return Model(input_img, decoded)
