layer_average_pooling_1d: Average pooling for temporal data.

In dfalbel/keras: R Interface to 'Keras'

Description

Average pooling for temporal data.

Usage

layer_average_pooling_1d(
  object,
  pool_size = 2L,
  strides = NULL,
  padding = "valid",
  data_format = "channels_last",
  batch_size = NULL,
  name = NULL,
  trainable = NULL,
  weights = NULL
)

Arguments

object	Model or layer object
pool_size	Integer, size of the average pooling windows.
strides	Integer, or NULL. Factor by which to downscale. E.g. 2 will halve the input. If NULL, it will default to pool_size.
padding	One of "valid" or "same" (case-insensitive).
data_format	One of channels_last (default) or channels_first. The ordering of the dimensions in the inputs.
batch_size	Fixed batch size for layer
name	An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
trainable	Whether the layer weights will be updated during training.
weights	Initial weights for layer.

Input shape

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

Output shape

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

See Also

Other pooling layers: layer_average_pooling_2d(), layer_average_pooling_3d(), layer_global_average_pooling_1d(), 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()

dfalbel/keras documentation built on Nov. 27, 2019, 8:16 p.m.