layer_upsampling_3d {keras} | R Documentation |
Upsampling layer for 3D inputs.
Description
Repeats the 1st, 2nd and 3rd dimensions of the data by size[[0]]
, size[[1]]
and
size[[2]]
respectively.
Usage
layer_upsampling_3d(
object,
size = c(2L, 2L, 2L),
data_format = NULL,
batch_size = NULL,
name = NULL,
trainable = NULL,
weights = NULL
)
Arguments
object |
What to compose the new
|
size |
int, or list of 3 integers. The upsampling factors for dim1, dim2 and dim3. |
data_format |
A string, one of |
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
5D tensor with shape:
If
data_format
is"channels_last"
:(batch, dim1, dim2, dim3, channels)
If
data_format
is"channels_first"
:(batch, channels, dim1, dim2, dim3)
Output shape
5D tensor with shape:
If
data_format
is"channels_last"
:(batch, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels)
If
data_format
is"channels_first"
:(batch, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3)
See Also
Other convolutional layers:
layer_conv_1d()
,
layer_conv_1d_transpose()
,
layer_conv_2d()
,
layer_conv_2d_transpose()
,
layer_conv_3d()
,
layer_conv_3d_transpose()
,
layer_conv_lstm_2d()
,
layer_cropping_1d()
,
layer_cropping_2d()
,
layer_cropping_3d()
,
layer_depthwise_conv_1d()
,
layer_depthwise_conv_2d()
,
layer_separable_conv_1d()
,
layer_separable_conv_2d()
,
layer_upsampling_1d()
,
layer_upsampling_2d()
,
layer_zero_padding_1d()
,
layer_zero_padding_2d()
,
layer_zero_padding_3d()