# tf.keras.layers.GlobalAveragePooling1D

## Class GlobalAveragePooling1D

### Aliases:

• Class tf.keras.layers.GlobalAveragePooling1D
• Class tf.keras.layers.GlobalAvgPool1D

Global average pooling operation for temporal data.

Input shape: 3D tensor with shape: (batch_size, steps, features).

Output shape: 2D tensor with shape: (batch_size, features)

## Properties

### activity_regularizer

Optional regularizer function for the output of this layer.

### 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.

#### Returns:

Input tensor or list of input tensors.

#### Raises:

• AttributeError: if the layer is connected to more than one incoming layers.

#### Raises:

• RuntimeError: If called in Eager mode.
• AttributeError: If no inbound nodes are found.

### 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.

#### Raises:

• AttributeError: if the layer is connected to more than one incoming layers.

### 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.

#### Returns:

Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).

#### Raises:

• AttributeError: if the layer has no defined input_shape.
• RuntimeError: if called in Eager mode.

### losses

Losses which are associated with this Layer.

Note that when executing eagerly, getting this property evaluates regularizers. When using graph execution, variable regularization ops have already been created and are simply returned here.

#### Returns:

A list of tensors.

### 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.

#### Returns:

Output tensor or list of output tensors.

#### Raises:

• AttributeError: if the layer is connected to more than one incoming layers.
• RuntimeError: if called in Eager mode.

### 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.

#### Raises:

• AttributeError: if the layer is connected to more than one incoming layers.

### 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.

#### Returns:

Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).

#### Raises:

• AttributeError: if the layer has no defined output shape.
• RuntimeError: if called in Eager mode.

### variables

Returns the list of all layer variables/weights.

#### Returns:

A list of variables.

### weights

Returns the list of all layer variables/weights.

#### Returns:

A list of variables.

## Methods

### __init__

__init__(**kwargs)


### __call__

__call__(
inputs,
*args,
**kwargs
)


Wrapper around self.call(), for handling internal references.

If a Keras tensor is passed: - We call self._add_inbound_node(). - If necessary, we build the layer to match the shape of the input(s). - We update the _keras_history of the output tensor(s) with the current layer. This is done as part of _add_inbound_node().

#### Arguments:

• inputs: Can be a tensor or list/tuple of tensors.
• *args: Additional positional arguments to be passed to call(). Only allowed in subclassed Models with custom call() signatures. In other cases, Layer inputs must be passed using the inputs argument and non-inputs must be keyword arguments.
• **kwargs: Additional keyword arguments to be passed to call().

#### Returns:

Output of the layer's call method.

#### Raises:

• ValueError: in case the layer is missing shape information for its build call.
• TypeError: If positional arguments are passed and this Layer is not a subclassed Model.

### __deepcopy__

__deepcopy__(memo)


### 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.

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Note that add_loss is not supported when executing eagerly. Instead, variable regularizers may be added through add_variable. Activity regularization is not supported directly (but such losses may be returned from Layer.call()).

#### Arguments:

• losses: Loss tensor, or list/tuple of tensors.
• inputs: 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).

#### Raises:

• RuntimeError: If called in Eager mode.

### add_update

add_update(
inputs=None
)


Add update op(s), potentially dependent on layer inputs.

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 in Eager mode.

#### Arguments:

• updates: Update op, or list/tuple of update ops.
• inputs: If anything other than None is passed, it signals the updates 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 BatchNormalization updates, for instance. If None, the updates will be taken into account unconditionally, and you are responsible for making sure that any dependency they might have is available at runtime. A step counter might fall into this category.

### add_variable

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


Adds a new variable to the layer, or gets an existing one; returns it.

#### Arguments:

• name: variable name.
• shape: variable shape.
• dtype: The type of the variable. Defaults to self.dtype or float32.
• initializer: initializer instance (callable).
• regularizer: regularizer instance (callable).
• trainable: whether the variable should be part of the layer's "trainable_variables" (e.g. variables, biases) or "non_trainable_variables" (e.g. BatchNorm mean, stddev). Note, if the current variable scope is marked as non-trainable then this parameter is ignored and any added variables are also marked as non-trainable.
• constraint: constraint instance (callable).
• partitioner: (optional) partitioner instance (callable). If provided, when the requested variable is created it will be split into multiple partitions according to partitioner. In this case, an instance of PartitionedVariable is returned. Available partitioners include tf.fixed_size_partitioner and tf.variable_axis_size_partitioner. For more details, see the documentation of tf.get_variable and the "Variable Partitioners and Sharding" section of the API guide.

#### Returns:

The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned.

#### Raises:

• RuntimeError: If called with partioned variable regularization and eager execution is enabled.

### add_weight

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


Adds a weight variable to the layer.

#### Arguments:

• name: String, the name for the weight variable.
• shape: The shape tuple of the weight.
• dtype: The dtype of the weight.
• initializer: An Initializer instance (callable).
• regularizer: An optional Regularizer instance.
• trainable: A boolean, whether the weight should be trained via backprop or not (assuming that the layer itself is also trainable).
• constraint: An optional Constraint instance.

#### Returns:

The created weight variable.

### apply

apply(
inputs,
*args,
**kwargs
)


Apply the layer on a input.

This simply wraps self.__call__.

#### 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).

### build

build(_)


Creates the variables of the layer.

### call

call(inputs)


### compute_mask

compute_mask(
inputs,
)


#### 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)


### 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

from_config(
cls,
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.

#### 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.

#### Raises:

• RuntimeError: If called in Eager mode.

### 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.

#### Raises:

• RuntimeError: If called in Eager mode.

### 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.