Max pooling operation for temporal data.

layer_max_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 max 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

A string, one of "channels_last" (default) 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).

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

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

See also