def segnet(shape=224):
kernel = 3
filter_size = 64
pad = 1
pool_size = 2
model = Sequential()
model.add(Layer(input_shape=(shape, shape, 3)))
# encoder
model.add(ZeroPadding2D(padding=(pad, pad)))
model.add(Conv2D(filter_size, (kernel, kernel), padding='valid'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))
model.add(ZeroPadding2D(padding=(pad, pad)))
model.add(Conv2D(128, (kernel, kernel), padding='valid'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))
model.add(ZeroPadding2D(padding=(pad, pad)))
model.add(Conv2D(256, (kernel, kernel), padding='valid'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))
model.add(ZeroPadding2D(padding=(pad, pad)))
model.add(Conv2D(512, (kernel, kernel), padding='valid'))
model.add(BatchNormalization())
model.add(Activation('relu'))
# decoder
model.add(ZeroPadding2D(padding=(pad, pad)))
model.add(Conv2D(512, (kernel, kernel), padding='valid'))
model.add(BatchNormalization())
model.add(UpSampling2D(size=(pool_size, pool_size)))
model.add(ZeroPadding2D(padding=(pad, pad)))
model.add(Conv2D(256, (kernel, kernel), padding='valid'))
model.add(BatchNormalization())
model.add(UpSampling2D(size=(pool_size, pool_size)))
model.add(ZeroPadding2D(padding=(pad, pad)))
model.add(Conv2D(128, (kernel, kernel), padding='valid'))
model.add(BatchNormalization())
model.add(UpSampling2D(size=(pool_size, pool_size)))
model.add(ZeroPadding2D(padding=(pad, pad)))
model.add(Conv2D(filter_size, (kernel, kernel), padding='valid'))
model.add(BatchNormalization())
model.add(Conv2D(
2,
(1, 1),
padding='valid',
))
model.outputHeight = model.output_shape[-2]
model.outputWidth = model.output_shape[-3]
model.add(Activation('softmax'))
return model
