def __init__(self,
units,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(Dense, self).__init__(**kwargs)
self.units = int(units)
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
# Not implemented arguments
default_args_check(kernel_regularizer, "kernel_regularizer", "Dense")
default_args_check(bias_regularizer, "bias_regularizer", "Dense")
default_args_check(activity_regularizer, "activity_regularizer",
"Dense")
default_args_check(kernel_constraint, "kernel_constraint", "Dense")
default_args_check(bias_constraint, "bias_constraint", "Dense")
def __init__(self,
kernel_size,
strides=(1, 1),
padding='valid',
depth_multiplier=1,
data_format=None,
activation=None,
use_bias=True,
depthwise_initializer='glorot_uniform',
bias_initializer='zeros',
depthwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
bias_constraint=None,
**kwargs):
super(DepthwiseConv2D, self).__init__(**kwargs)
self.rank = 2
self.kernel_size = conv_utils.normalize_tuple(
kernel_size, self.rank, 'kernel_size')
if self.kernel_size[0] != self.kernel_size[1]:
raise NotImplementedError("TF Encrypted currently only supports same "
"stride along the height and the width."
"You gave: {}".format(self.kernel_size))
self.strides = conv_utils.normalize_tuple(strides, self.rank, 'strides')
self.padding = conv_utils.normalize_padding(padding).upper()
self.depth_multiplier = depth_multiplier
self.data_format = conv_utils.normalize_data_format(data_format)
if activation is not None:
logger.info("Performing an activation before a pooling layer can result "
"in unnecessary performance loss. Check model definition in "
"case of missed optimization.")
self.activation = activations.get(activation)
self.use_bias = use_bias
self.depthwise_initializer = initializers.get(depthwise_initializer)
self.bias_initializer = initializers.get(bias_initializer)
# Not implemented arguments
default_args_check(depthwise_regularizer,
"depthwise_regularizer",
"DepthwiseConv2D")
default_args_check(bias_regularizer,
"bias_regularizer",
"DepthwiseConv2D")
default_args_check(activity_regularizer,
"activity_regularizer",
"DepthwiseConv2D")
default_args_check(depthwise_constraint,
"depthwise_constraint",
"DepthwiseConv2D")
default_args_check(bias_constraint,
"bias_constraint",
"DepthwiseConv2D")
def __init__(self,
units,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(Dense, self).__init__(**kwargs)
self.units = int(units)
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
if kernel_regularizer:
raise NotImplementedError(
arg_not_impl_msg.format("kernel_regularizer", "Dense"), )
if bias_regularizer:
raise NotImplementedError(
arg_not_impl_msg.format("bias_regularizer", "Dense"), )
if activity_regularizer:
raise NotImplementedError(
arg_not_impl_msg.format("activity_regularizer", "Dense"), )
if kernel_constraint:
raise NotImplementedError(
arg_not_impl_msg.format("kernel_constraint", "Dense"), )
if bias_constraint:
raise NotImplementedError(
arg_not_impl_msg.format(" bias_constraint", "Dense"), )
def __init__(self,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=None,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(Conv2D, self).__init__(**kwargs)
self.rank = 2
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, self.rank,
'kernel_size')
if self.kernel_size[0] != self.kernel_size[1]:
raise NotImplementedError(
"TF Encrypted currently only supports same "
"stride along the height and the width."
"You gave: {}".format(self.kernel_size))
self.strides = conv_utils.normalize_tuple(strides, self.rank,
'strides')
self.padding = conv_utils.normalize_padding(padding).upper()
self.data_format = conv_utils.normalize_data_format(data_format)
if activation is not None:
logger.info(
"Performing an activation before a pooling layer can result "
"in unnecessary performance loss. Check model definition in "
"case of missed optimization.")
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
if dilation_rate:
raise NotImplementedError(
arg_not_impl_msg.format("dilation_rate", "Conv2d"), )
if kernel_regularizer:
raise NotImplementedError(
arg_not_impl_msg.format("kernel_regularizer", "Conv2d"), )
if bias_regularizer:
raise NotImplementedError(
arg_not_impl_msg.format("bias_regularizer", "Conv2d"), )
if activity_regularizer:
raise NotImplementedError(
arg_not_impl_msg.format("activity_regularizer", "Conv2d"), )
if kernel_constraint:
raise NotImplementedError(
arg_not_impl_msg.format("kernel_constraint", "Conv2d"), )
if bias_constraint:
raise NotImplementedError(
arg_not_impl_msg.format("bias_constraint", "Conv2d"), )
def __init__(self, activation, **kwargs):
super(Activation, self).__init__(**kwargs)
self.activation_identifier = activation
self.activation = activations.get(self.activation_identifier)
