Class Layer
Inherits From: CheckpointableBase
Defined in tensorflow/python/keras/engine/base_layer.py
.
Base layer class.
This is the class from which all layers inherit.
A layer is a class implementing common neural networks operations, such as convolution, batch norm, etc. These operations require managing weights, losses, updates, and inter-layer connectivity.
Users will just instantiate a layer and then treat it as a callable.
We recommend that descendants of Layer
implement the following methods:
__init__()
: Save configuration in member variablesbuild()
: Called once from__call__
, when we know the shapes of inputs anddtype
. Should have the calls toadd_weight()
, and then call the super'sbuild()
(which setsself.built = True
, which is nice in case the user wants to callbuild()
manually before the first__call__
).call()
: Called in__call__
after making surebuild()
has been called once. Should actually perform the logic of applying the layer to the input tensors (which should be passed in as the first argument).
Arguments:
trainable
: Boolean, whether the layer's variables should be trainable.name
: String name of the layer.dtype
: Default dtype of the layer's weights (default ofNone
means use the type of the first input).
Read-only properties:
* name
: The name of the layer (string).
* dtype
: Default dtype of the layer's weights (default of None
means use the
type of the first input).
* updates
: List of update ops of this layer.
* losses
: List of losses added by this layer.
* trainable_weights
: List of variables to be included in backprop.
* non_trainable_weights
: List of variables that should not be
included in backprop.
* weights
: The concatenation of the lists trainable_weights and
non_trainable_weights (in this order).
Mutable properties:
* trainable
: Whether the layer should be trained (boolean).
* input_spec
: Optional (list of) InputSpec
object(s) specifying the
constraints on inputs that can be accepted by the layer.
__init__
__init__(
trainable=True,
name=None,
dtype=None,
**kwargs
)
Properties
activity_regularizer
Optional regularizer function for the output of this layer.
dtype
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.
Returns:
Input tensor or list of input tensors.
Raises:
AttributeError
: if the layer is connected to more than one incoming layers.
Raises:
RuntimeError
: If called in Eager mode.AttributeError
: If no inbound nodes are found.
input_mask
Retrieves the input mask tensor(s) of a layer.
Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.
Returns:
Input mask tensor (potentially None) or list of input mask tensors.
Raises:
AttributeError
: if the layer is connected to more than one incoming layers.
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.
Returns:
Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).
Raises:
AttributeError
: if the layer has no defined input_shape.RuntimeError
: if called in Eager mode.
losses
Losses which are associated with this Layer
.
Variable regularization tensors are created when this property is accessed,
so it is eager safe: accessing losses
under a tf.GradientTape
will
propagate gradients back to the corresponding variables.
Returns:
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.
Returns:
Output tensor or list of output tensors.
Raises:
AttributeError
: if the layer is connected to more than one incoming layers.RuntimeError
: if called in Eager mode.
output_mask
Retrieves the output mask tensor(s) of a layer.
Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.
Returns:
Output mask tensor (potentially None) or list of output mask tensors.
Raises:
AttributeError
: if the layer is connected to more than one incoming layers.
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.
Returns:
Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).
Raises:
AttributeError
: if the layer has no defined output shape.RuntimeError
: if called in Eager mode.
trainable_variables
trainable_weights
updates
variables
Returns the list of all layer variables/weights.
Alias of self.weights
.
Returns:
A list of variables.
weights
Returns the list of all layer variables/weights.
Returns:
A list of variables.
Methods
tf.keras.layers.Layer.__call__
__call__(
inputs,
*args,
**kwargs
)
Wraps call
, applying pre- and post-processing steps.
Arguments:
inputs
: input tensor(s).*args
: additional positional arguments to be passed toself.call
.**kwargs
: additional keyword arguments to be passed toself.call
.
Returns:
Output tensor(s).
Raises:
ValueError
: if the layer'scall
method returns None (an invalid value).
tf.keras.layers.Layer.__setattr__
__setattr__(
name,
value
)
Implement setattr(self, name, value).
tf.keras.layers.Layer.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()
).
Arguments:
losses
: Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor.inputs
: Ignored when executing eagerly. 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. IfNone
is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).
tf.keras.layers.Layer.add_metric
add_metric(
value,
aggregation=None,
name=None
)
Adds metric tensor to the layer.
Args:
value
: Metric tensor.aggregation
: Sample-wise metric reduction function. Ifaggregation=None
, it indicates that the metric tensor provided has been aggregated already. eg,model.add_metric(BinaryAccuracy(name='acc')(y_true, y_pred))
. If aggregation='mean', the given metric tensor will be sample-wise reduced usingmean
function. eg,model.add_metric( tf.reduce_mean(outputs), name='output_mean', aggregation='mean')
.name
: String metric name.
Raises:
ValueError
: Ifaggregation
is anything other than None ormean
.
tf.keras.layers.Layer.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 when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).
Arguments:
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.
tf.keras.layers.Layer.add_variable
add_variable(
*args,
**kwargs
)
Alias for add_weight
.
tf.keras.layers.Layer.add_weight
add_weight(
name,
shape,
dtype=None,
initializer=None,
regularizer=None,
trainable=None,
constraint=None,
partitioner=None,
use_resource=None,
synchronization=tf.VariableSynchronization.AUTO,
aggregation=tf.VariableAggregation.NONE,
**kwargs
)
Adds a new variable to the layer, or gets an existing one; returns it.
Arguments:
name
: variable name.shape
: variable shape.dtype
: The type of the variable. Defaults toself.dtype
orfloat32
.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.trainable
defaults toTrue
unlesssynchronization
is set toON_READ
.constraint
: constraint instance (callable).partitioner
: Partitioner to be passed to theCheckpointable
API.use_resource
: Whether to useResourceVariable
.synchronization
: Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the classtf.VariableSynchronization
. By default the synchronization is set toAUTO
and the currentDistributionStrategy
chooses when to synchronize. Ifsynchronization
is set toON_READ
,trainable
must not be set toTrue
.aggregation
: Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the classtf.VariableAggregation
.**kwargs
: Additional keyword arguments. Accepted values aregetter
andcollections
.
Returns:
The created variable. Usually either a Variable
or ResourceVariable
instance. If partitioner
is not None
, a PartitionedVariable
instance is returned.
Raises:
RuntimeError
: If called with partioned variable regularization and eager execution is enabled.ValueError
: When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set asON_READ
.
tf.keras.layers.Layer.apply
apply(
inputs,
*args,
**kwargs
)
Apply the layer on a input.
This is an alias of self.__call__
.
Arguments:
inputs
: Input tensor(s).*args
: additional positional arguments to be passed toself.call
.**kwargs
: additional keyword arguments to be passed toself.call
.
Returns:
Output tensor(s).
tf.keras.layers.Layer.build
build(input_shape)
Creates the variables of the layer (optional, for subclass implementers).
This is a method that implementers of subclasses of Layer
or Model
can override if they need a state-creation step in-between
layer instantiation and layer call.
This is typically used to create the weights of Layer
subclasses.
Arguments:
input_shape
: Instance ofTensorShape
, or list of instances ofTensorShape
if the layer expects a list of inputs (one instance per input).
tf.keras.layers.Layer.call
call(
inputs,
**kwargs
)
This is where the layer's logic lives.
Arguments:
inputs
: Input tensor, or list/tuple of input tensors.**kwargs
: Additional keyword arguments.
Returns:
A tensor or list/tuple of tensors.
tf.keras.layers.Layer.compute_mask
compute_mask(
inputs,
mask=None
)
Computes an output mask tensor.
Arguments:
inputs
: Tensor or list of tensors.mask
: Tensor or list of tensors.
Returns:
None or a tensor (or list of tensors, one per output tensor of the layer).
tf.keras.layers.Layer.compute_output_shape
compute_output_shape(input_shape)
Computes the output shape of the layer.
Assumes that the layer will be built to match that input shape provided.
Arguments:
input_shape
: Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.
Returns:
An input shape tuple.
tf.keras.layers.Layer.count_params
count_params()
Count the total number of scalars composing the weights.
Returns:
An integer count.
Raises:
ValueError
: if the layer isn't yet built (in which case its weights aren't yet defined).
tf.keras.layers.Layer.from_config
@classmethod
from_config(
cls,
config
)
Creates a layer from its config.
This method is the reverse of get_config
,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights
).
Arguments:
config
: A Python dictionary, typically the output of get_config.
Returns:
A layer instance.
tf.keras.layers.Layer.get_config
get_config()
Returns the config of the layer.
A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.
The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by Network
(one layer of abstraction above).
Returns:
Python dictionary.
tf.keras.layers.Layer.get_input_at
get_input_at(node_index)
Retrieves the input tensor(s) of a layer at a given node.
Arguments:
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.
Returns:
A tensor (or list of tensors if the layer has multiple inputs).
Raises:
RuntimeError
: If called in Eager mode.
tf.keras.layers.Layer.get_input_mask_at
get_input_mask_at(node_index)
Retrieves the input mask tensor(s) of a layer at a given node.
Arguments:
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.
Returns:
A mask tensor (or list of tensors if the layer has multiple inputs).
tf.keras.layers.Layer.get_input_shape_at
get_input_shape_at(node_index)
Retrieves the input shape(s) of a layer at a given node.
Arguments:
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.
Returns:
A shape tuple (or list of shape tuples if the layer has multiple inputs).
Raises:
RuntimeError
: If called in Eager mode.
tf.keras.layers.Layer.get_losses_for
get_losses_for(inputs)
Retrieves losses relevant to a specific set of inputs.
Arguments:
inputs
: Input tensor or list/tuple of input tensors.
Returns:
List of loss tensors of the layer that depend on inputs
.
Raises:
RuntimeError
: If called in Eager mode.
tf.keras.layers.Layer.get_output_at
get_output_at(node_index)
Retrieves the output tensor(s) of a layer at a given node.
Arguments:
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.
Returns:
A tensor (or list of tensors if the layer has multiple outputs).
Raises:
RuntimeError
: If called in Eager mode.
tf.keras.layers.Layer.get_output_mask_at
get_output_mask_at(node_index)
Retrieves the output mask tensor(s) of a layer at a given node.
Arguments:
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.
Returns:
A mask tensor (or list of tensors if the layer has multiple outputs).
tf.keras.layers.Layer.get_output_shape_at
get_output_shape_at(node_index)
Retrieves the output shape(s) of a layer at a given node.
Arguments:
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.
Returns:
A shape tuple (or list of shape tuples if the layer has multiple outputs).
Raises:
RuntimeError
: If called in Eager mode.
tf.keras.layers.Layer.get_updates_for
get_updates_for(inputs)
Retrieves updates relevant to a specific set of inputs.
Arguments:
inputs
: Input tensor or list/tuple of input tensors.
Returns:
List of update ops of the layer that depend on inputs
.
Raises:
RuntimeError
: If called in Eager mode.
tf.keras.layers.Layer.get_weights
get_weights()
Returns the current weights of the layer.
Returns:
Weights values as a list of numpy arrays.
tf.keras.layers.Layer.set_weights
set_weights(weights)
Sets the weights of the layer, from Numpy arrays.
Arguments:
weights
: a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output ofget_weights
).
Raises:
ValueError
: If the provided weights list does not match the layer's specifications.