RevBlock
Inherits From: Layer
Defined in tensorflow/contrib/layers/python/layers/rev_block_lib.py
.
Block of reversible layers. See rev_block.
activity_regularizer
Optional regularizer function for the output of this layer.
dtype
graph
input
Retrieves the input tensor(s) of a layer.
Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.
Input tensor or list of input tensors.
AttributeError
: if the layer is connected to more than one incoming layers.RuntimeError
: If called in Eager mode.AttributeError
: If no inbound nodes are found.input_shape
Retrieves the input shape(s) of a layer.
Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.
Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).
AttributeError
: if the layer has no defined input_shape.RuntimeError
: if called in Eager mode.losses
Losses which are associated with this Layer
.
Note that when executing eagerly, getting this property evaluates regularizers. When using graph execution, variable regularization ops have already been created and are simply returned here.
A list of tensors.
name
non_trainable_variables
non_trainable_weights
output
Retrieves the output tensor(s) of a layer.
Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.
Output tensor or list of output tensors.
AttributeError
: if the layer is connected to more than one incoming layers.RuntimeError
: if called in Eager mode.output_shape
Retrieves the output shape(s) of a layer.
Only applicable if the layer has one output, or if all outputs have the same shape.
Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).
AttributeError
: if the layer has no defined output shape.RuntimeError
: if called in Eager mode.scope_name
trainable_variables
trainable_weights
updates
variables
Returns the list of all layer variables/weights.
A list of variables.
weights
Returns the list of all layer variables/weights.
A list of variables.
__init__
__init__( f, g, num_layers=1, f_side_input=None, g_side_input=None, use_efficient_backprop=True, name='revblock', **kwargs )
Initialize self. See help(type(self)) for accurate signature.
__call__
__call__( inputs, *args, **kwargs )
Wraps call
, applying pre- and post-processing steps.
inputs
: input tensor(s).*args
: additional positional arguments to be passed to self.call
.**kwargs
: additional keyword arguments to be passed to self.call
. Note: kwarg scope
is reserved for use by the layer.Output tensor(s).
Note: - If the layer'scall
method takes ascope
keyword argument, this argument will be automatically set to the current variable scope. - If the layer'scall
method takes amask
argument (as some Keras layers do), its default value will be set to the mask generated forinputs
by the previous layer (ifinput
did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support.
ValueError
: if the layer's call
method returns None (an invalid value).__deepcopy__
__deepcopy__(memo)
add_loss
add_loss( losses, inputs=None )
Add loss tensor(s), potentially dependent on layer inputs.
Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a
and b
, some entries in layer.losses
may be dependent on a
and some on b
. This method automatically keeps track of dependencies.
The get_losses_for
method allows to retrieve the losses relevant to a specific set of inputs.
Note that add_loss
is not supported when executing eagerly. Instead, variable regularizers may be added through add_variable
. Activity regularization is not supported directly (but such losses may be returned from Layer.call()
).
losses
: Loss tensor, or list/tuple of tensors.inputs
: If anything other than None is passed, it signals the losses are conditional on some of the layer's inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If None
is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).RuntimeError
: If called in Eager mode.add_update
add_update( updates, inputs=None )
Add update op(s), potentially dependent on layer inputs.
Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a
and b
, some entries in layer.updates
may be dependent on a
and some on b
. This method automatically keeps track of dependencies.
The get_updates_for
method allows to retrieve the updates relevant to a specific set of inputs.
This call is ignored in Eager mode.
updates
: Update op, or list/tuple of update ops.inputs
: If anything other than None is passed, it signals the updates are conditional on some of the layer's inputs, and thus they should only be run where these inputs are available. This is the case for BatchNormalization updates, for instance. If None, the updates will be taken into account unconditionally, and you are responsible for making sure that any dependency they might have is available at runtime. A step counter might fall into this category.add_variable
add_variable( name, shape, dtype=None, initializer=None, regularizer=None, trainable=True, constraint=None, partitioner=None )
Adds a new variable to the layer, or gets an existing one; returns it.
name
: variable name.shape
: variable shape.dtype
: The type of the variable. Defaults to self.dtype
or float32
.initializer
: initializer instance (callable).regularizer
: regularizer instance (callable).trainable
: whether the variable should be part of the layer's "trainable_variables" (e.g. variables, biases) or "non_trainable_variables" (e.g. BatchNorm mean, stddev). Note, if the current variable scope is marked as non-trainable then this parameter is ignored and any added variables are also marked as non-trainable.constraint
: constraint instance (callable).partitioner
: (optional) partitioner instance (callable). If provided, when the requested variable is created it will be split into multiple partitions according to partitioner
. In this case, an instance of PartitionedVariable
is returned. Available partitioners include tf.fixed_size_partitioner
and tf.variable_axis_size_partitioner
. For more details, see the documentation of tf.get_variable
and the "Variable Partitioners and Sharding" section of the API guide.The created variable. Usually either a Variable
or ResourceVariable
instance. If partitioner
is not None
, a PartitionedVariable
instance is returned.
RuntimeError
: If called with partioned variable regularization and eager execution is enabled.apply
apply( inputs, *args, **kwargs )
Apply the layer on a input.
This simply wraps self.__call__
.
inputs
: Input tensor(s).*args
: additional positional arguments to be passed to self.call
.**kwargs
: additional keyword arguments to be passed to self.call
.Output tensor(s).
backward
backward( y1, y2 )
build
build(_)
Creates the variables of the layer.
call
call( inputs, forward=True )
The logic of the layer lives here.
inputs
: input tensor(s).**kwargs
: additional keyword arguments.Output tensor(s).
compute_output_shape
compute_output_shape(input_shape)
Computes the output shape of the layer given the input shape.
input_shape
: A (possibly nested tuple of) TensorShape
. It need not be fully defined (e.g. the batch size may be unknown).A (possibly nested tuple of) TensorShape
.
TypeError
: if input_shape
is not a (possibly nested tuple of) TensorShape
.ValueError
: if input_shape
is incomplete or is incompatible with the the layer.count_params
count_params()
Count the total number of scalars composing the weights.
An integer count.
ValueError
: if the layer isn't yet built (in which case its weights aren't yet defined).forward
forward( x1, x2 )
get_input_at
get_input_at(node_index)
Retrieves the input tensor(s) of a layer at a given node.
node_index
: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0
will correspond to the first time the layer was called.A tensor (or list of tensors if the layer has multiple inputs).
RuntimeError
: If called in Eager mode.get_input_shape_at
get_input_shape_at(node_index)
Retrieves the input shape(s) of a layer at a given node.
node_index
: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0
will correspond to the first time the layer was called.A shape tuple (or list of shape tuples if the layer has multiple inputs).
RuntimeError
: If called in Eager mode.get_losses_for
get_losses_for(inputs)
Retrieves losses relevant to a specific set of inputs.
inputs
: Input tensor or list/tuple of input tensors.List of loss tensors of the layer that depend on inputs
.
RuntimeError
: If called in Eager mode.get_output_at
get_output_at(node_index)
Retrieves the output tensor(s) of a layer at a given node.
node_index
: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0
will correspond to the first time the layer was called.A tensor (or list of tensors if the layer has multiple outputs).
RuntimeError
: If called in Eager mode.get_output_shape_at
get_output_shape_at(node_index)
Retrieves the output shape(s) of a layer at a given node.
node_index
: Integer, index of the node from which to retrieve the attribute. E.g. node_index=0
will correspond to the first time the layer was called.A shape tuple (or list of shape tuples if the layer has multiple outputs).
RuntimeError
: If called in Eager mode.get_updates_for
get_updates_for(inputs)
Retrieves updates relevant to a specific set of inputs.
inputs
: Input tensor or list/tuple of input tensors.List of update ops of the layer that depend on inputs
.
RuntimeError
: If called in Eager mode.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/layers/RevBlock