layer_average_pooling_3d {keras} | R Documentation

## 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)`

### See Also

Other pooling layers: `layer_average_pooling_1d`, `layer_average_pooling_2d`, `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`

[Package keras version 2.2.4 Index]