tf.keras.layers.GlobalAveragePooling2D

Global average pooling operation for 2D data.

Inherits From: Layer, Operation

View aliases

Main aliases

tf.keras.layers.GlobalAvgPool2D

tf.keras.layers.GlobalAveragePooling2D(
    data_format=None, keepdims=False, **kwargs
)

Used in the notebooks

Used in the guide	Used in the tutorials
Estimators Pruning for on-device inference w/ XNNPACK	Transfer learning and fine-tuning TFF simulations with accelerators

Args
`data_format`	string, either `"channels_last"` or `"channels_first"`. The ordering of the dimensions in the inputs. `"channels_last"` corresponds to inputs with shape `(batch, height, width, channels)` while `"channels_first"` corresponds to inputs with shape `(batch, features, height, weight)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be `"channels_last"`.
`keepdims`	A boolean, whether to keep the temporal dimension or not. If `keepdims` is `False` (default), the rank of the tensor is reduced for spatial dimensions. If `keepdims` is `True`, the spatial dimension are retained with length 1. The behavior is the same as for `tf.reduce_mean` or `np.mean`.

Args

data_format string, either "channels_last" or "channels_first". The ordering of the dimensions in the inputs. "channels_last" corresponds to inputs with shape (batch, height, width, channels) while "channels_first" corresponds to inputs with shape (batch, features, height, weight). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".

keepdims A boolean, whether to keep the temporal dimension or not. If keepdims is False (default), the rank of the tensor is reduced for spatial dimensions. If keepdims is True, the spatial dimension are retained with length 1. The behavior is the same as for tf.reduce_mean or np.mean.

Input shape:

If data_format='channels_last': 4D tensor with shape: (batch_size, height, width, channels)
If data_format='channels_first': 4D tensor with shape: (batch_size, channels, height, width)

Output shape:

If keepdims=False: 2D tensor with shape (batch_size, channels).
If keepdims=True:
- If data_format="channels_last": 4D tensor with shape (batch_size, 1, 1, channels)
- If data_format="channels_first": 4D tensor with shape (batch_size, channels, 1, 1)

Example:

x = np.random.rand(2, 4, 5, 3)
y = keras.layers.GlobalAveragePooling2D()(x)
y.shape
(2, 3)

Attributes
`input`	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
`output`	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.

Attributes

input

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

Only returns the tensor(s) corresponding to the first time the operation was called.

output

Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

`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).

Args
`config`	A Python dictionary, typically the output of get_config.

Returns
A layer instance.

`symbolic_call`

View source

symbolic_call(
    *args, **kwargs
)