tf.keras.Layer

View source on GitHub

This is the class from which all layers inherit.

Inherits From: Operation

tf.keras.Layer(
    *,
    activity_regularizer=None,
    trainable=True,
    dtype=None,
    autocast=True,
    name=None,
    **kwargs
)

Used in the notebooks

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 __init__(), for instance via self.add_weight();
• 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.

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 or build() method.

Users will just instantiate a layer and then treat it as a callable.

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 keras.DTypePolicy, which allows the computation and weight dtype to differ. Defaults to None. None means to use keras.config.dtype_policy(), which is a float32 policy unless set to different value (via keras.config.set_dtype_policy()).

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, using add_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), using add_weight(), or other state. __call__() will automatically build the layer (if it has not been built yet) by calling build().
• call(self, *args, **kwargs): Called in __call__ after making sure build() has been called. call() performs the logic of applying the layer to the input arguments. Two reserved keyword arguments you can optionally use in call() are: 1. training (boolean, whether the call is in inference mode or training mode). 2. mask (boolean tensor encoding masked timesteps in the input, used e.g. in RNN layers). A typical signature for this method is call(self, inputs), and user could optionally add training and mask if the layer need them.
• 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.

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().__init__()
        self.units = units

    # Create the state of the layer (weights)
    def build(self, input_shape):
        self.kernel = self.add_weight(
            shape=(input_shape[-1], self.units),
            initializer="glorot_uniform",
            trainable=True,
            name="kernel",
        )
        self.bias = self.add_weight(
            shape=(self.units,),
            initializer="zeros",
            trainable=True,
            name="bias",
        )

    # Defines the computation
    def call(self, inputs):
        return ops.matmul(inputs, self.kernel) + self.bias

# Instantiates the layer.
linear_layer = SimpleDense(4)

# This will also call `build(input_shape)` and create the weights.
y = linear_layer(ops.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

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 = self.add_weight(
        shape=(),
        initializer="zeros",
        trainable=False,
        name="total",
      )

  def call(self, inputs):
      self.total.assign(self.total + ops.sum(inputs))
      return self.total

my_sum = ComputeSum(2)
x = ops.ones((2, 2))
y = my_sum(x)

assert my_sum.weights == [my_sum.total]
assert my_sum.non_trainable_weights == [my_sum.total]
assert my_sum.trainable_weights == []

name The name of the layer (string). dtype Dtype of the layer's weights. Alias of layer.variable_dtype. variable_dtype Dtype of the layer's weights. 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 keras.DTypePolicy, this will be different than variable_dtype. 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. dtype_policy

input Retrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called. input_dtype The dtype layer inputs should be converted to. losses List of scalar losses from add_loss, regularizers and sublayers. metrics List of all metrics. metrics_variables List of all metric variables. non_trainable_variables List of all non-trainable layer state.

This extends layer.non_trainable_weights to include all state used by the layer including state for metrics and SeedGenerators. output Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called. supports_masking Whether this layer supports computing a mask using compute_mask. trainable_variables List of all trainable layer state.

This is equivalent to layer.trainable_weights. variables List of all layer state, including random seeds.

This extends layer.weights to include all state used by the layer including SeedGenerators.

Note that metrics variables are not included here, use metrics_variables to visit all the metric variables.

Methods

add_loss

View source

add_loss(
    loss
)

Can be called inside of the call() method to add a scalar loss.

Example:

class MyLayer(Layer):
    ...
    def call(self, x):
        self.add_loss(ops.sum(x))
        return x

add_metric

View source

add_metric()

add_variable

View source

add_variable(
    shape,
    initializer,
    dtype=None,
    trainable=True,
    autocast=True,
    regularizer=None,
    constraint=None,
    name=None
)

Add a weight variable to the layer.

Alias of add_weight().

add_weight

View source

add_weight(
    shape=None,
    initializer=None,
    dtype=None,
    trainable=True,
    autocast=True,
    regularizer=None,
    constraint=None,
    aggregation='mean',
    name=None
)

Add a weight variable to the layer.

shape Shape tuple for the variable. Must be fully-defined (no None entries). Defaults to () (scalar) if unspecified. initializer Initializer object to use to populate the initial variable value, or string name of a built-in initializer (e.g. "random_normal"). If unspecified, defaults to "glorot_uniform" for floating-point variables and to "zeros" for all other types (e.g. int, bool). dtype Dtype of the variable to create, e.g. "float32". If unspecified, defaults to the layer's variable dtype (which itself defaults to "float32" if unspecified). trainable Boolean, whether the variable should be trainable via backprop or whether its updates are managed manually. Defaults to True. autocast Boolean, whether to autocast layers variables when accessing them. Defaults to True. regularizer Regularizer object to call to apply penalty on the weight. These penalties are summed into the loss function during optimization. Defaults to None. constraint Contrainst object to call on the variable after any optimizer update, or string name of a built-in constraint. Defaults to None. aggregation String, one of 'mean', 'sum', 'only_first_replica'. Annotates the variable with the type of multi-replica aggregation to be used for this variable when writing custom data parallel training loops. name String name of the variable. Useful for debugging purposes.

build

View source

build(
    input_shape
)

build_from_config

View source

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.

config Dict containing the input shape associated with this layer.

call

View source

call(
    *args, **kwargs
)

compute_mask

View source

compute_mask(
    inputs, previous_mask
)

compute_output_shape

View source

compute_output_shape(
    *args, **kwargs
)

compute_output_spec

View source

compute_output_spec(
    *args, **kwargs
)

count_params

View source

count_params()

Count the total number of scalars composing the weights.

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).

config A Python dictionary, typically the output of get_config.

get_build_config

View source

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.

get_config

View source

get_config()

Returns the config of the object.

An object config is a Python dictionary (serializable) containing the information needed to re-instantiate it.

get_weights

View source

get_weights()

Return the values of layer.weights as a list of NumPy arrays.

load_own_variables

View source

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().

store Dict from which the state of the model will be loaded.

quantize

View source

quantize(
    mode
)

quantized_call

View source

quantized_call(
    *args, **kwargs
)

save_own_variables

View source

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().

store Dict where the state of the model will be saved.

set_weights

View source

set_weights(
    weights
)

Sets the values of layer.weights from a list of NumPy arrays.

stateless_call

View source

stateless_call(
    trainable_variables,
    non_trainable_variables,
    *args,
    return_losses=False,
    **kwargs
)

Call the layer without any side effects.

trainable_variables List of trainable variables of the model. non_trainable_variables List of non-trainable variables of the model. *args Positional arguments to be passed to call(). return_losses If True, stateless_call() will return the list of losses created during call() as part of its return values. **kwargs Keyword arguments to be passed to call().

Example:

model = ...
data = ...
trainable_variables = model.trainable_variables
non_trainable_variables = model.non_trainable_variables
# Call the model with zero side effects
outputs, non_trainable_variables = model.stateless_call(
    trainable_variables,
    non_trainable_variables,
    data,
)
# Attach the updated state to the model
# (until you do this, the model is still in its pre-call state).
for ref_var, value in zip(
    model.non_trainable_variables, non_trainable_variables
):
    ref_var.assign(value)

symbolic_call

View source

symbolic_call(
    *args, **kwargs
)

__call__

View source

__call__(
    *args, **kwargs
)

Call self as a function.