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.

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.

Methods

from_config

View source

@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

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

Returns
An operation instance.