Global average pooling operation for temporal data.

layer_global_average_pooling_1d(
object,
data_format = "channels_last",
keepdims = FALSE,
...
)

## Arguments

object What to call the new Layer instance with. Typically a keras Model, another Layer, or a tf.Tensor/KerasTensor. If object is missing, the Layer instance is returned, otherwise, layer(object) is returned. One of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. A boolean, whether to keep the spatial dimensions or not. If keepdims is FALSE (default), the rank of the tensor is reduced for spatial dimensions. If keepdims is TRUE, the spatial dimensions are retained with length 1. The behavior is the same as for tf.reduce_mean or np.mean. standard layer arguments.

## Input shape

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

## Output shape

2D tensor with shape: (batch_size, channels)

Other pooling layers: layer_average_pooling_1d(), layer_average_pooling_2d(), layer_average_pooling_3d(), layer_global_average_pooling_2d(), layer_global_average_pooling_3d(), layer_global_max_pooling_1d(), layer_global_max_pooling_2d(), layer_global_max_pooling_3d(), layer_max_pooling_1d(), layer_max_pooling_2d(), layer_max_pooling_3d()