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► Keras 3 API documentation / Layers API / Pooling layers / GlobalAveragePooling1D layer

GlobalAveragePooling1D layer

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GlobalAveragePooling1D class

keras.layers.GlobalAveragePooling1D(data_format=None, keepdims=False, **kwargs)

Global average pooling operation for temporal 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, steps, features) while "channels_first" corresponds to inputs with shape (batch, features, steps). 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 temporal dimension are retained with length 1. The behavior is the same as for tf.reduce_mean or np.mean.

Call arguments

  • inputs: A 3D tensor.
  • mask: Binary tensor of shape (batch_size, steps) indicating whether a given step should be masked (excluded from the average).

Input shape

  • If data_format='channels_last': 3D tensor with shape: (batch_size, steps, features)
  • If data_format='channels_first': 3D tensor with shape: (batch_size, features, steps)

Output shape

  • If keepdims=False: 2D tensor with shape (batch_size, features).
  • If keepdims=True: - If data_format="channels_last": 3D tensor with shape (batch_size, 1, features) - If data_format="channels_first": 3D tensor with shape (batch_size, features, 1)

Example

>>> x = np.random.rand(2, 3, 4)
>>> y = keras.layers.GlobalAveragePooling1D()(x)
>>> y.shape
(2, 4)
GlobalAveragePooling1D layer
GlobalAveragePooling1D class