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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
)
Used in the notebooks
Used in the guide | Used in the tutorials |
---|---|
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), defined
either in the constructor __init__()
or in the build()
method.
Users will just instantiate a layer and then treat it as a callable.
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. 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 state variables that do not depend on input shapes, usingadd_weight()
.build(self, input_shape)
: This method can be used to create weights that depend on the shape(s) of the input(s), usingadd_weight()
.__call__()
will automatically build the layer (if it has not been built yet) by callingbuild()
.call(self, *args, **kwargs)
: Called in__call__
after making surebuild()
has been called.call()
performs the logic of applying the layer to the input tensors (which should be passed in as argument). Two reserved keyword arguments you can optionally use incall()
are:training
(boolean, whether the call is in inference mode or training mode)mask
(boolean tensor encoding masked timesteps in the input, used in RNN layers)
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 Writing custom layers and models with Keras
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
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 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))
Arguments | |
---|---|
losses
|
Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. |
**kwargs
|
Additional keyword arguments for backward compatibility. Accepted values: inputs - Deprecated, will be automatically inferred. |
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(x))
self.add_metric(tf.reduce_sum(x), 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.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 .
|
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 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 ,
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 (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
call(
inputs, **kwargs
)
This is where the layer's logic lives.
Note here that call()
method in tf.keras
is little bit different
from keras
API. In keras
API, you can pass support masking for
layers as additional arguments. Whereas tf.keras
has compute_mask()
method to support masking.
Arguments | |
---|---|
inputs
|
Input tensor, or list/tuple of input tensors. |
**kwargs
|
Additional keyword arguments. Currently unused. |
Returns | |
---|---|
A tensor or list/tuple of tensors. |