|TensorFlow 1 version||View source on GitHub|
This is the class from which all layers inherit.
Compat aliases for migration
See Migration guide for more details.
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
call() method, and a state (weight variables), defined
either in the constructor
__init__() or in the
Users will just instantiate a layer and then treat it as a callable.
||Boolean, whether the layer's variables should be trainable.|
||String name of the layer.|
The dtype of the layer's computations and weights. Can also be a
Set this to
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, using
build(self, input_shape): This method can be used to create weights that depend on the shape(s) of the input(s), using
__call__()will automatically build the layer (if it has not been built yet) by calling
call(self, inputs, *args, **kwargs): Called in
__call__after making sure
build()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 in
training(boolean, whether the call is in inference mode or training mode). See more details in the layer/model subclassing guide
mask(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 is
call(self, inputs), and user could optionally add
maskif the layer need them.
**kwargsis 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 override
from_config(self)as well. This method is used when saving the layer or a model that contains this layer.
Here's a basic example: a layer with two variables,
y = w . x + b.
It shows how to implement
Variables set as attributes of a layer are tracked as weights
of the layers (in
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
||The name of the layer (string).|
||The dtype of the layer's weights.|
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
The layer's dtype policy. See the
||List of variables to be included in backprop.|
||List of variables that should not be included in backprop.|
||The concatenation of the lists trainable_weights and non_trainable_weights (in this order).|
Whether the layer should be trained (boolean), i.e. whether
its potentially-trainable weights should be returned as part of
Optional (list of)
||Optional regularizer function for the output of this layer.|
||Whether the layer is dynamic (eager-only); set in the constructor.|
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.
List of losses added using the |
Variable regularization tensors are created when this property is accessed,
so it is eager safe: accessing