if self.data_format == 'channels_first':
height = self.size[0] * input_shape[2] if input_shape[
2] is not None else None
width = self.size[1] * input_shape[3] if input_shape[
3] is not None else None
return tensor_shape.TensorShape(
[input_shape[0], input_shape[1], height, width])
else:
height = self.size[0] * input_shape[1] if input_shape[
1] is not None else None
width = self.size[1] * input_shape[2] if input_shape[
2] is not None else None
return tensor_shape.TensorShape(
[input_shape[0], height, width, input_shape[3]])
def call(self, inputs):
return resize_images_bilinear(inputs, self.size[0], self.size[1],
self.data_format)
def get_config(self):
config = {'size': self.size, 'data_format': self.data_format}
base_config = super(BilinearUpSampling2D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
# add this to custom objects for restoring model save files
get_custom_objects().update({
'SeparableConv2DKeras': SeparableConv2DKeras,
'BilinearUpSampling2D': BilinearUpSampling2D
})
height = self.size[0] * input_shape[2] if input_shape[
2] is not None else None
width = self.size[1] * input_shape[3] if input_shape[
3] is not None else None
return tensor_shape.TensorShape(
[input_shape[0], input_shape[1], height, width])
else:
height = self.size[0] * input_shape[1] if input_shape[
1] is not None else None
width = self.size[1] * input_shape[2] if input_shape[
2] is not None else None
return tensor_shape.TensorShape(
[input_shape[0], height, width, input_shape[3]])
def call(self, inputs):
output_layer = depth_to_space(inputs, block_size=self.size)
return output_layer
def get_config(self):
config = {'size': self.size, 'data_format': self.data_format}
base_config = super(DepthToSpace, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
# add this to custom objects for restoring model save files
get_custom_objects().update({
'SeparableConv2DKeras': SeparableConv2DKeras,
'BilinearUpSampling2D': BilinearUpSampling2D,
'DepthToSpace': DepthToSpace
})
activations.serialize(self.activation),
'use_bias':
self.use_bias,
'depthwise_initializer':
initializers.serialize(self.depthwise_initializer),
'pointwise_initializer':
initializers.serialize(self.pointwise_initializer),
'bias_initializer':
initializers.serialize(self.bias_initializer),
'depthwise_regularizer':
regularizers.serialize(self.depthwise_regularizer),
'pointwise_regularizer':
regularizers.serialize(self.pointwise_regularizer),
'bias_regularizer':
regularizers.serialize(self.bias_regularizer),
'activity_regularizer':
regularizers.serialize(self.activity_regularizer),
'depthwise_constraint':
constraints.serialize(self.depthwise_constraint),
'pointwise_constraint':
constraints.serialize(self.pointwise_constraint),
'bias_constraint':
constraints.serialize(self.bias_constraint)
}
base_config = super(SeparableConv2DKeras, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
# add this to custom objects for restoring model save files
get_custom_objects().update({'SeparableConv2DKeras': SeparableConv2DKeras})
cols = input_shape[2]
rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
self.padding, self.strides[0])
cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
self.padding, self.strides[1])
if self.data_format == 'channels_first':
return tensor_shape.TensorShape(
[input_shape[0], self.filters, rows, cols])
else:
return tensor_shape.TensorShape(
[input_shape[0], rows, cols, self.filters])
def get_config(self):
config = super(DepthWiseConv2D, self).get_config()
config.pop('kernel_initializer')
config.pop('kernel_regularizer')
config.pop('kernel_constraint')
config['depth_multiplier'] = self.depth_multiplier
config['depthwise_initializer'] = initializers.serialize(
self.depthwise_initializer)
config['depthwise_regularizer'] = regularizers.serialize(
self.depthwise_regularizer)
config['depthwise_constraint'] = constraints.serialize(
self.depthwise_constraint)
return config
# add this to custom objects for restoring model save files
get_custom_objects().update({'DepthWiseConv2D': DepthWiseConv2D})