tf.keras.layers.Layer

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Base layer class.

Inherits From: Module

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.Layer, `tf.compat.v2.keras.layers.Layer`

tf.keras.layers.Layer(
    trainable=True, name=None, dtype=None, dynamic=False, **kwargs
)

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 variables
• build(): Called once from __call__, when we know the shapes of inputs and dtype. Should have the calls to add_weight(), and then call the super's build() (which sets self.built = True, which is nice in case the user wants to call build() manually before the first __call__).
• call(): Called in __call__ after making sure build() 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	The dtype of the layer's computations and weights (default of None means use tf.keras.backend.floatx in TensorFlow 2, or the type of the first input in TensorFlow 1).
dynamic	Set this to True if your layer should only be run eagerly, and should not be used to generate a static computation graph. This would be the case for a Tree-RNN or a recursive network, for example, or generally for any layer that manipulates tensors using Python control flow. If False, we assume that the layer can safely be used to generate a static computation graph.

Read-only properties: name: The name of the layer (string). dtype: The dtype of the layer's computations and weights. If mixed precision is used with a tf.keras.mixed_precision.experimental.Policy, this is instead just the dtype of the layer's weights, as the computations are done in a different dtype. 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.

Dtypes and casting

Each layer has a dtype, which is typically the dtype of the layer's computations and variables. A layer's dtype can be queried via the Layer.dtype property. The dtype is specified with the dtype constructor argument. In TensorFlow 2, the dtype defaults to tf.keras.backend.floatx() if no dtype is passed. floatx() itself defaults to "float32". Additionally, layers will cast their inputs to the layer's dtype in TensorFlow 2. For example:

x = tf.ones((4, 4, 4, 4), dtype='float64')
layer = tf.keras.layers.Conv2D(filters=4, kernel_size=2)
print(layer.dtype)  # float32

# `layer` casts it's inputs to layer.dtype, which is float32, and does
# computations in float32.
y = layer(x)

Currently, only tensors in the first argument to the layer's call method are casted. For example:

class MyLayer(tf.keras.layers.Layer):
  # Bug! `b` will not be casted.
  def call(self, a, b):
    return a + 1., b + 1.

a = tf.constant(1., dtype="float32")
b = tf.constant(1., dtype="float32")

layer = MyLayer(dtype="float64")
x, y = layer(a, b)
print(x.dtype)  # float64
print(y.dtype)  # float32. Not casted since `b` was not passed to first input

It is recommended to accept tensors only in the first argument. This way, all tensors are casted to the layer's dtype. MyLayer should therefore be written as:

class MyLayer(tf.keras.layers.Layer):
  # Now, all tensor inputs will be casted.
  def call(self, inputs):
    a, b = inputs
    return a + 1., b + 1.

a = tf.constant(1., dtype="float32")
b = tf.constant(1., dtype="float32")

layer = MyLayer(dtype="float64")
x, y = layer((a, b))
print(x.dtype)  # float64
print(y.dtype)  # float64.

In a future minor release, tensors in other arguments may be casted as well.

Currently, other arguments are not automatically casted for technical reasons, but this may change in a future minor release.

A layer subclass can prevent its inputs from being autocasted by passing autocast=False to the layer constructor. For example:

class MyLayer(tf.keras.layers.Layer):

  def __init__(self, **kwargs):
    kwargs['autocast']=False
    super(MyLayer, self).__init__(**kwargs)

  def call(self, inp):
    return inp

x = tf.ones((4, 4, 4, 4), dtype='float64')
layer = MyLayer()
print(layer.dtype)  # float32.
y = layer(x)  # MyLayer will not cast inputs to it's dtype of float32
print(y.dtype)  # float64

Running models in float64 in TensorFlow 2

If you want to run a Model in float64, you can set floatx to be float64 by calling tf.keras.backend.set_floatx('float64'). This will cause all layers to default to float64 instead of float32:

tf.keras.backend.set_floatx('float64')
layer1 = tf.keras.layers.Dense(4)
layer2 = tf.keras.layers.Dense(4)

x = tf.ones((4, 4))
y = layer2(layer1(x))  # Both layers run in float64

Alternatively, you can pass dtype='float64' to each individual layer. Note that if you have any layers which contain other layers as members, you must ensure each sublayer gets dtype='float64' passed to it's constructor as well:

layer1 = tf.keras.layers.Dense(4, dtype='float64')
layer2 = tf.keras.layers.Dense(4, dtype='float64')

x = tf.ones((4, 4))
y = layer2(layer1(x))  # Both layers run in float64

class NestedLayer(tf.keras.layers.Layer):
  def __init__(self, **kwargs):
    super(NestedLayer, self).__init__(**kwargs)
    self.dense = tf.keras.layers.Dense(4, dtype=kwargs.get('dtype'))

  def call(self, inp):
    return self.dense(inp)

layer3 = NestedLayer(dtype='float64')
z = layer3(x)  # layer3's dense layer runs in float64, since NestedLayer
               # correcty passed it's dtype to it's dense layer

Attributes
activity_regularizer	Optional regularizer function for the output of this layer.
dtype
dynamic
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_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.
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_spec
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.
metrics
name	Returns the name of this module as passed or determined in the ctor. Note: This is not the same as the self.name_scope.name which includes parent module names.
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_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.
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.
trainable
trainable_variables	Sequence of variables owned by this module and it's submodules. Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change.
trainable_weights
updates
variables	Returns the list of all layer variables/weights. Alias of self.weights.
weights	Returns the list of all layer variables/weights.

Methods

add_loss

View source

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.

This method can be used inside a subclassed layer or model's call function, in which case losses should be a Tensor or list of Tensors.

Example:

class MyLayer(tf.keras.layers.Layer):
  def call(inputs, self):
    self.add_loss(tf.abs(tf.reduce_mean(inputs)), inputs=True)
    return inputs

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model's Inputs. These losses become part of the model's topology and are tracked in get_config.

Example:

inputs = tf.keras.Input(shape=(10,))
x = tf.keras.layers.Dense(10)(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Actvity regularization.
model.add_loss(tf.abs(tf.reduce_mean(x)))

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model's layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model's topology since they can't be serialized.

Example:

inputs = tf.keras.Input(shape=(10,))
x = tf.keras.layers.Dense(10)(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Weight regularization.
model.add_loss(lambda: tf.reduce_mean(x.kernel))

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

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. 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).

add_metric

View source

add_metric(
    value, aggregation=None, name=None
)

Adds metric tensor to the layer.

Args
value	Metric tensor.
aggregation	Sample-wise metric reduction function. If aggregation=None, it indicates that the metric tensor provided has been aggregated already. eg, bin_acc = BinaryAccuracy(name='acc') followed by model.add_metric(bin_acc(y_true, y_pred)). If aggregation='mean', the given metric tensor will be sample-wise reduced using mean function. eg, model.add_metric(tf.reduce_sum(outputs), name='output_mean', aggregation='mean').
name	String metric name.

Raises
ValueError	If aggregation is anything other than None or mean.

add_update

View source

add_update(
    updates, inputs=None
)

Add update op(s), potentially dependent on layer inputs. (deprecated arguments)

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, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode.
inputs	Deprecated, will be automatically inferred.

add_weight

View source

add_weight(
    name=None, shape=None, 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.

Arguments
name	Variable name.
shape	Variable shape. Defaults to scalar if unspecified.
dtype	The type of the variable. Defaults to self.dtype or float32.
initializer	Initializer instance (callable).
regularizer	Regularizer instance (callable).
trainable	Boolean, whether the variable should be part of the layer's "trainable_variables" (e.g. variables, biases) or "non_trainable_variables" (e.g. BatchNorm mean and variance). Note that trainable cannot be True if synchronization is set to ON_READ.
constraint	Constraint instance (callable).
partitioner	Partitioner to be passed to the Trackable API.
use_resource	Whether to use ResourceVariable.
synchronization	Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True.
aggregation	Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation.
**kwargs	Additional keyword arguments. Accepted values are getter and collections.

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 partitioned 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 as ON_READ.

build

View source

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 of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).

call

View source

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.

compute_mask

View source

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).

compute_output_shape

View source

compute_output_shape(
    input_shape
)

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

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.

compute_output_signature

View source

compute_output_signature(
    input_signature
)

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn't implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Args
input_signature	Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer.

Returns
Single TensorSpec or nested structure of TensorSpec objects, describing how the layer would transform the provided input.

Raises
TypeError	If input_signature contains a non-TensorSpec object.

count_params

View source

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).

from_config

View source

@classmethod
from_config(
    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.

get_config

View source

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.

get_input_at

View source

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.

get_input_mask_at

View source

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).

get_input_shape_at

View source

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.

get_losses_for

View source

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.

get_output_at

View source

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.

get_output_mask_at

View source

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).

get_output_shape_at

View source

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.

get_updates_for

View source

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.

get_weights

View source

get_weights()

Returns the current weights of the layer.

Returns
Weights values as a list of numpy arrays.

set_weights

View source

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 of get_weights).

Raises
ValueError	If the provided weights list does not match the layer's specifications.

__call__

View source

__call__(
    inputs, *args, **kwargs
)

Wraps call, applying pre- and post-processing steps.

Arguments
inputs	input tensor(s).
*args	additional positional arguments to be passed to self.call.
**kwargs	additional keyword arguments to be passed to self.call.

Returns
Output tensor(s).

Note:

• The following optional keyword arguments are reserved for specific uses:
  • training: Boolean scalar tensor of Python boolean indicating whether the call is meant for training or inference.
  • mask: Boolean input mask.
• If the layer's call method takes a mask argument (as some Keras layers do), its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support.

Raises
ValueError	if the layer's call method returns None (an invalid value).