keras: layer_average_pooling_3d – R documentation

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keras

layer_average_pooling_3d

Average pooling operation for 3D data (spatial or spatio-temporal).

Description

Average pooling operation for 3D data (spatial or spatio-temporal).

Usage

layer_average_pooling_3d(
  object,
  pool_size = c(2L, 2L, 2L),
  strides = NULL,
  padding = "valid",
  data_format = NULL,
  batch_size = NULL,
  name = NULL,
  trainable = NULL,
  weights = NULL
)

Arguments

`object`	Model or layer object
`pool_size`	list of 3 integers, factors by which to downscale (dim1, dim2, dim3). (2, 2, 2) will halve the size of the 3D input in each dimension.
`strides`	list of 3 integers, or NULL. Strides values.
`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, spatial_dim1, spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`. 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".
`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': 5D tensor with shape: (batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)
If data_format='channels_first': 5D tensor with shape: (batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)

Output shape

If data_format='channels_last': 5D tensor with shape: (batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels)
If data_format='channels_first': 5D tensor with shape: (batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)