tf.keras xception imagenet

xception代码

# @Author: ---chenzhenhua
# @E-mail: [email protected]


def Xception(classes):

	# Determine proper input shape
	inputs = Input(shape=input_shape)
	if ReShape:
	    X = Reshape(ReShape)(inputs)
	else:
	    X = inputs
	#img_input = Input(shape=input_shape)

	# Block 1
	x = Conv2D(32, (3, 3), strides=(2, 2), use_bias=False)(X)
	x = BatchNormalization()(x)
	x = Activation('relu')(x)
	x = Conv2D(64, (3, 3), use_bias=False)(x)
	x = BatchNormalization()(x)
	x = Activation('relu')(x)

	residual = Conv2D(128, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
	residual = BatchNormalization()(residual)

	# Block 2
	x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False)(x)
	x = BatchNormalization()(x)
	x = Activation('relu')(x)
	x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False)(x)
	x = BatchNormalization()(x)

	# Block 2 Pool
	x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
	x = layers.add([x, residual])

	residual = Conv2D(256, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
	residual = BatchNormalization()(residual)

	# Block 3
	x = Activation('relu')(x)
	x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False)(x)
	x = BatchNormalization()(x)
	x = Activation('relu')(x)
	x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False)(x)
	x = BatchNormalization()(x)

	# Block 3 Pool
	x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
	x = layers.add([x, residual])

	residual = Conv2D(728, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
	residual = BatchNormalization()(residual)

	# Block 4
	x = Activation('relu')(x)
	x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False)(x)
	x = BatchNormalization()(x)
	x = Activation('relu')(x)
	x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False)(x)
	x = BatchNormalization()(x)

	x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
	x = layers.add([x, residual])

	# Block 5 - 12
	for i in range(8):
		residual = x

		x = Activation('relu')(x)
		x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False)(x)
		x = BatchNormalization()(x)
		x = Activation('relu')(x)
		x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False)(x)
		x = BatchNormalization()(x)
		x = Activation('relu')(x)
		x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False)(x)
		x = BatchNormalization()(x)

		x = layers.add([x, residual])

	residual = Conv2D(1024, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
	residual = BatchNormalization()(residual)

	# Block 13
	x = Activation('relu')(x)
	x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False)(x)
	x = BatchNormalization()(x)
	x = Activation('relu')(x)
	x = SeparableConv2D(1024, (3, 3), padding='same', use_bias=False)(x)
	x = BatchNormalization()(x)

	# Block 13 Pool
	x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
	x = layers.add([x, residual])

	# Block 14
	x = SeparableConv2D(1536, (3, 3), padding='same', use_bias=False)(x)
	x = BatchNormalization()(x)
	x = Activation('relu')(x)

	# Block 14 part 2
	x = SeparableConv2D(2048, (3, 3), padding='same', use_bias=False)(x)
	x = BatchNormalization()(x)
	x = Activation('relu')(x)

	# Fully Connected Layer
	x = GlobalAveragePooling2D()(x)
	x = Dense(classes, activation='softmax')(x)

	#inputs = inputs

	# Create model
	model = Model(inputs, x, name='model_xception')

	# load weights
	#model.load_weights(weights_path)

	return model

model = Xception(class_num)