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This is the class from which all layers inherit.
Inherits From: Module
tf.keras.layers.Layer(
trainable=True, name=None, dtype=None, dynamic=False, **kwargs
)
A layer is a callable object that takes as input one or more tensors and
that outputs one or more tensors. It involves computation, defined
in the call()
method, and a state (weight variables). State can be
created in various places, at the convenience of the subclass implementer:
- in
__init__()
; - in the optional
build()
method, which is invoked by the first__call__()
to the layer, and supplies the shape(s) of the input(s), which may not have been known at initialization time; - in the first invocation of
call()
, with some caveats discussed below.
Layers are recursively composable: If you assign a Layer instance as an
attribute of another Layer, the outer layer will start tracking the weights
created by the inner layer. Nested layers should be instantiated in the
__init__()
method.
Users will just instantiate a layer and then treat it as a callable.
Args | |
---|---|
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. Can also be a
tf.keras.mixed_precision.Policy , which allows the computation and
weight dtype to differ. Default of None means to use
tf.keras.mixed_precision.global_policy() , which is a float32 policy
unless set to different value.
|
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.
|
We recommend that descendants of Layer
implement the following methods:
__init__()
: Defines custom layer attributes, and creates layer weights that do not depend on input shapes, usingadd_weight()
, or other state.build(self, input_shape)
: This method can be used to create weights that depend on the shape(s) of the input(s), usingadd_weight()
, or other state.__call__()
will automatically build the layer (if it has not been built yet) by callingbuild()
.call(self, inputs, *args, **kwargs)
: Called in__call__
after making surebuild()
has been called.call()
performs the logic of applying the layer to theinputs
. The first invocation may additionally create state that could not be conveniently created inbuild()
; see its docstring for details. Two reserved keyword arguments you can optionally use incall()
are:training
(boolean, whether the call is in inference mode or training mode). See more details in the layer/model subclassing guidemask
(boolean tensor encoding masked timesteps in the input, used in RNN layers). See more details in the layer/model subclassing guide A typical signature for this method iscall(self, inputs)
, and user could optionally addtraining
andmask
if the layer need them.*args
and**kwargs
is only useful for future extension when more input parameters are planned to be added.
get_config(self)
: Returns a dictionary containing the configuration used to initialize this layer. If the keys differ from the arguments in__init__
, then overridefrom_config(self)
as well. This method is used when saving the layer or a model that contains this layer.
Examples:
Here's a basic example: a layer with two variables, w
and b
,
that returns y = w . x + b
.
It shows how to implement build()
and call()
.
Variables set as attributes of a layer are tracked as weights
of the layers (in layer.weights
).
class SimpleDense(Layer):
def __init__(self, units=32):
super(SimpleDense, self).__init__()
self.units = units
def build(self, input_shape): # Create the state of the layer (weights)
w_init = tf.random_normal_initializer()
self.w = tf.Variable(
initial_value=w_init(shape=(input_shape[-1], self.units),
dtype='float32'),
trainable=True)
b_init = tf.zeros_initializer()
self.b = tf.Variable(
initial_value=b_init(shape=(self.units,), dtype='float32'),
trainable=True)
def call(self, inputs): # Defines the computation from inputs to outputs
return tf.matmul(inputs, self.w) + self.b
# Instantiates the layer.
linear_layer = SimpleDense(4)
# This will also call `build(input_shape)` and create the weights.
y = linear_layer(tf.ones((2, 2)))
assert len(linear_layer.weights) == 2
# These weights are trainable, so they're listed in `trainable_weights`:
assert len(linear_layer.trainable_weights) == 2
Note that the method add_weight()
offers a shortcut to create weights:
class SimpleDense(Layer):
def __init__(self, units=32):
super(SimpleDense, self).__init__()
self.units = units
def build(self, input_shape):
self.w = self.add_weight(shape=(input_shape[-1], self.units),
initializer='random_normal',
trainable=True)
self.b = self.add_weight(shape=(self.units,),
initializer='random_normal',
trainable=True)
def call(self, inputs):
return tf.matmul(inputs, self.w) + self.b
Besides trainable weights, updated via backpropagation during training,
layers can also have non-trainable weights. These weights are meant to
be updated manually during call()
. Here's a example layer that computes
the running sum of its inputs:
class ComputeSum(Layer):
def __init__(self, input_dim):
super(ComputeSum, self).__init__()
# Create a non-trainable weight.
self.total = tf.Variable(initial_value=tf.zeros((input_dim,)),
trainable=False)
def call(self, inputs):
self.total.assign_add(tf.reduce_sum(inputs, axis=0))
return self.total
my_sum = ComputeSum(2)
x = tf.ones((2, 2))
y = my_sum(x)
print(y.numpy()) # [2. 2.]
y = my_sum(x)
print(y.numpy()) # [4. 4.]
assert my_sum.weights == [my_sum.total]
assert my_sum.non_trainable_weights == [my_sum.total]
assert my_sum.trainable_weights == []
For more information about creating layers, see the guide Making new Layers and Models via subclassing
Attributes | |
---|---|
name
|
The name of the layer (string). |
dtype
|
The dtype of the layer's weights. |
variable_dtype
|
Alias of dtype .
|
compute_dtype
|
The dtype of the layer's computations. Layers automatically
cast inputs to this dtype which causes the computations and output to
also be in this dtype. When mixed precision is used with a
tf.keras.mixed_precision.Policy , this will be different than
variable_dtype .
|
dtype_policy
|
The layer's dtype policy. See the
tf.keras.mixed_precision.Policy documentation for details.
|
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). |
trainable
|
Whether the layer should be trained (boolean), i.e. whether
its potentially-trainable weights should be returned as part of
layer.trainable_weights .
|
input_spec
|
Optional (list of) InputSpec object(s) specifying the
constraints on inputs that can be accepted by the layer.
|
activity_regularizer
|
Optional regularizer function for the output of this layer. |
dynamic
|
Whether the layer is dynamic (eager-only); set in the constructor. |
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. |
losses
|
List of losses added using the add_loss() API.
Variable regularization tensors are created when this property is
accessed, so it is eager safe: accessing
|
metrics
|
List of metrics added using the add_metric() API.
|
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. |
supports_masking
|
Whether this layer supports computing a mask using compute_mask .
|
Methods
add_loss
add_loss(
losses, **kwargs
)
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(self, inputs):
self.add_loss(tf.abs(tf.reduce_mean(inputs)))
return inputs
The same code works in distributed training: the input to add_loss()
is treated like a regularization loss and averaged across replicas
by the training loop (both built-in Model.fit()
and compliant custom
training loops).
The add_loss
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 Input
s.
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)
# Activity 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,))
d = tf.keras.layers.Dense(10)
x = d(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Weight regularization.
model.add_loss(lambda: tf.reduce_mean(d.kernel))
Args | |
---|---|
losses
|
Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. |
**kwargs
|
Used for backwards compatibility only. |
add_metric
add_metric(
value, name=None, **kwargs
)
Adds metric tensor to the layer.
This method can be used inside the call()
method of a subclassed layer
or model.
class MyMetricLayer(tf.keras.layers.Layer):
def __init__(self):
super(MyMetricLayer, self).__init__(name='my_metric_layer')
self.mean = tf.keras.metrics.Mean(name='metric_1')
def call(self, inputs):
self.add_metric(self.mean(inputs))
self.add_metric(tf.reduce_sum(inputs), name='metric_2')
return inputs
This method can also be called directly on a Functional Model during
construction. In this case, any tensor passed to this Model must
be symbolic and be able to be traced back to the model's Input
s. These
metrics become part of the model's topology and are tracked when you
save the model via save()
.
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)
model.add_metric(math_ops.reduce_sum(x), name='metric_1')
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)
model.add_metric(tf.keras.metrics.Mean()(x), name='metric_1')
Args | |
---|---|
value
|
Metric tensor. |
name
|
String metric name. |
**kwargs
|
Additional keyword arguments for backward compatibility.
Accepted values:
aggregation - When the value tensor provided is not the result
of calling a keras.Metric instance, it will be aggregated by
default using a keras.Metric.Mean .
|
add_weight
add_weight(
name=None,
shape=None,
dtype=None,
initializer=None,
regularizer=None,
trainable=None,
constraint=None,
use_resource=None,
synchronization=tf.VariableSynchronization.AUTO
,
aggregation=tf.VariableAggregation.NONE
,
**kwargs
)
Adds a new variable to the layer.
Args | |
---|---|
name
|
Variable name. |
shape
|
Variable shape. Defaults to scalar if unspecified. |
dtype
|
The type of the variable. Defaults to self.dtype .
|
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). |
use_resource
|
Whether to use a ResourceVariable or not.
See this guide
for more information.
|
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 ,
collections , experimental_autocast and caching_device .
|
Returns | |
---|---|
The variable created. |
Raises | |
---|---|
ValueError
|
When giving unsupported dtype and no initializer or when
trainable has been set to True with synchronization set as
ON_READ .
|
build
build(
input_shape
)
Creates the variables of the layer (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. It is invoked automatically before
the first execution of call()
.
This is typically used to create the weights of Layer
subclasses
(at the discretion of the subclass implementer).
Args | |
---|---|
input_shape
|
Instance of TensorShape , or list of instances of
TensorShape if the layer expects a list of inputs
(one instance per input).
|
build_from_config
build_from_config(
config
)
Builds the layer's states with the supplied config dict.
By default, this method calls the build(config["input_shape"])
method,
which creates weights based on the layer's input shape in the supplied
config. If your config contains other information needed to load the
layer's state, you should override this method.
Args | |
---|---|
config
|
Dict containing the input shape associated with this layer. |
call
call(
inputs, *args, **kwargs
)
This is where the layer's logic lives.
The call()
method may not create state (except in its first
invocation, wrapping the creation of variables or other resources in
tf.init_scope()
). It is recommended to create state, including
tf.Variable
instances and nested Layer
instances,
in __init__()
, or in the build()
method that is
called automatically before call()
executes for the first time.
Args | |
---|---|
inputs
|
Input tensor, or dict/list/tuple of input tensors.
The first positional inputs argument is subject to special rules:
|
*args
|
Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above. |
**kwargs
|
Additional keyword arguments. May contain tensors, although
this is not recommended, for the reasons above.
The following optional keyword arguments are reserved:
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, 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).
|
Returns | |
---|---|
A tensor or list/tuple of tensors. |
compute_mask
compute_mask(
inputs, mask=None
)
Computes an output mask tensor.
Args | |
---|---|
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.
This method will cause the layer's state to be built, if that has not happened before. This requires that the layer will later be used with inputs that match the input shape provided here.
Args | |
---|---|
input_shape
|
Shape tuple (tuple of integers) or tf.TensorShape ,
or structure of shape tuples / tf.TensorShape instances
(one per output tensor of the layer).
Shape tuples can include None for free dimensions,
instead of an integer.
|
Returns | |
---|---|
A tf.TensorShape instance
or structure of tf.TensorShape instances.
|
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
@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
).
Args | |
---|---|
config
|
A Python dictionary, typically the output of get_config. |
Returns | |
---|---|
A layer instance. |
get_build_config
get_build_config()
Returns a dictionary with the layer's input shape.
This method returns a config dict that can be used by
build_from_config(config)
to create all states (e.g. Variables and
Lookup tables) needed by the layer.
By default, the config only contains the input shape that the layer was built with. If you're writing a custom layer that creates state in an unusual way, you should override this method to make sure this state is already created when Keras attempts to load its value upon model loading.
Returns | |
---|---|
A dict containing the input shape associated with the 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).
Note that get_config()
does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.
Returns | |
---|---|
Python dictionary. |
get_weights
get_weights()
Returns the current weights of the layer, as NumPy arrays.
The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of NumPy arrays, which can in turn be used to load state into similarly parameterized layers.
For example, a Dense
layer returns a list of two values: the kernel
matrix and the bias vector. These can be used to set the weights of
another Dense
layer:
layer_a = tf.keras.layers.Dense(1,
kernel_initializer=tf.constant_initializer(1.))
a_out = layer_a(tf.convert_to_tensor([[1., 2., 3.]]))
layer_a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
layer_b = tf.keras.layers.Dense(1,
kernel_initializer=tf.constant_initializer(2.))
b_out = layer_b(tf.convert_to_tensor([[10., 20., 30.]]))
layer_b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
layer_b.set_weights(layer_a.get_weights())
layer_b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
Returns | |
---|---|
Weights values as a list of NumPy arrays. |
load_own_variables
load_own_variables(
store
)
Loads the state of the layer.
You can override this method to take full control of how the state of
the layer is loaded upon calling keras.models.load_model()
.
Args | |
---|---|
store
|
Dict from which the state of the model will be loaded. |
save_own_variables
save_own_variables(
store
)
Saves the state of the layer.
You can override this method to take full control of how the state of
the layer is saved upon calling model.save()
.
Args | |
---|---|
store
|
Dict where the state of the model will be saved. |
set_weights
set_weights(
weights
)
Sets the weights of the layer, from NumPy arrays.
The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer's weights must be instantiated before calling this function, by calling the layer.
For example, a Dense
layer returns a list of two values: the kernel
matrix and the bias vector. These can be used to set the weights of
another Dense
layer:
layer_a = tf.keras.layers.Dense(1,
kernel_initializer=tf.constant_initializer(1.))
a_out = layer_a(tf.convert_to_tensor([[1., 2., 3.]]))
layer_a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
layer_b = tf.keras.layers.Dense(1,
kernel_initializer=tf.constant_initializer(2.))
b_out = layer_b(tf.convert_to_tensor([[10., 20., 30.]]))
layer_b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
layer_b.set_weights(layer_a.get_weights())
layer_b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
Args | |
---|---|
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__(
*args, **kwargs
)
Wraps call
, applying pre- and post-processing steps.
Args | |
---|---|
*args
|
Positional arguments to be passed to self.call .
|
**kwargs
|
Keyword arguments to be passed to self.call .
|
Returns | |
---|---|
Output tensor(s). |
Note | |
---|---|
|
Raises | |
---|---|
ValueError
|
if the layer's call method returns None (an invalid
value).
|
RuntimeError
|
if super().__init__() was not called in the
constructor.
|