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► Keras 3 API documentation / Layers API / Pooling layers / GlobalAveragePooling2D layer

GlobalAveragePooling2D layer

[source]

GlobalAveragePooling2D class

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

Global average pooling operation for 2D data.

Arguments

  • 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)
GlobalAveragePooling2D layer
GlobalAveragePooling2D class