TensorFlow 1 version
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View source on GitHub
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Base layer class.
Inherits From: Module
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 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
|
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).
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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.
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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)InputSpecobject(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. When mixed
precision is used, layers may have different computation and variable dtypes.
See tf.keras.mixed_precision.experimental.Policy for details on layer
dtypes.
Attributes | |
|---|---|
activity_regularizer
|
Optional regularizer function for the output of this layer. |
dtype
|
|
dynamic
|
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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
|
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losses
|
Losses which are associated with this Layer.
Variable regularization tensors are created when this property is accessed,
so it is eager safe: accessing |
metrics
|
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name
|
Returns the name of this module as passed or determined in the ctor. |
non_trainable_variables
|
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non_trainable_weights
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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
|
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trainable_variables
|
Sequence of trainable variables owned by this module and its submodules. |
trainable_weights
|
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updates
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variables
|
Returns the list of all layer variables/weights.
Alias of |
weights
|
Returns the list of all layer variables/weights. |
Methods
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.
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).
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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. 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').
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name
|
String metric name. |
| Raises | |
|---|---|
ValueError
|
If aggregation is anything other than None or mean.
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add_update
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.
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inputs
|
Deprecated, will be automatically inferred. |
add_weight
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.compat.v1.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.
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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.
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constraint
|
Constraint instance (callable). |
partitioner
|
Partitioner to be passed to the Trackable API.
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use_resource
|
Whether to use ResourceVariable.
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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.
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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,
collections, experimental_autocast and caching_device.
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| Returns | |
|---|---|
The created variable. Usually either a Variable or ResourceVariable
instance. If partitioner is not None, a PartitionedVariable
instance is returned.
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| 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.
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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 of TensorShape, or list of instances of
TensorShape if the layer expects a list of inputs
(one instance per input).
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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. |
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). |
compute_output_shape
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
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
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
@classmethodfrom_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
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
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.
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| Returns | |
|---|---|
| A tensor (or list of tensors if the layer has multiple inputs). |
| Raises | |
|---|---|
RuntimeError
|
If called in Eager mode. |
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). |
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. |
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.
|
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. |
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). |
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. |
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.
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get_weights
get_weights()
Returns the current weights of the layer.
| Returns | |
|---|---|
| Weights values as a list of numpy arrays. |
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 of get_weights).
|
| Raises | |
|---|---|
ValueError
|
If the provided weights list does not match the layer's specifications. |
__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 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 thecallis meant for training or inference.mask: Boolean input mask.
- If the layer's
callmethod takes amaskargument (as some Keras layers do), its default value will be set to the mask generated forinputsby the previous layer (ifinputdid 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).
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