livenessnet.py

from keras.models import Sequential
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers.core import Activation, Flatten, Dropout, Dense
from keras import backend as K

class LivenessNet:
	@staticmethod
	def build(width, height, depth, classes):
		model = Sequential()
		inputShape = (height, width, depth)
		chanDim = -1

		if K.image_data_format() == "channels_first":
			inputShape = (depth, height, width)
			chanDim = 1

		model.add(Conv2D(16, (3, 3), padding="same",
			input_shape=inputShape))
		model.add(Activation("relu"))
		model.add(BatchNormalization(axis=chanDim))
		model.add(Conv2D(16, (3, 3), padding="same"))
		model.add(Activation("relu"))
		model.add(BatchNormalization(axis=chanDim))
		model.add(MaxPooling2D(pool_size=(2, 2)))
		model.add(Dropout(0.25))

		model.add(Conv2D(32, (3, 3), padding="same"))
		model.add(Activation("relu"))
		model.add(BatchNormalization(axis=chanDim))
		model.add(Conv2D(32, (3, 3), padding="same"))
		model.add(Activation("relu"))
		model.add(BatchNormalization(axis=chanDim))
		model.add(MaxPooling2D(pool_size=(2, 2)))
		model.add(Dropout(0.25))

		model.add(Flatten())
		model.add(Dense(64))
		model.add(Activation("relu"))
		model.add(BatchNormalization())
		model.add(Dropout(0.5))

		model.add(Dense(classes))
		model.add(Activation("softmax"))

		return model