Upsampling layer for 3D inputs.
Repeats the 1st, 2nd and 3rd dimensions of the data by size[[0]], size[[1]] and
size[[2]] respectively.
[[0]: R:[0 [[1]: R:[1 [[2]: R:[2
layer_upsampling_3d(object, size = c(2L, 2L, 2L), data_format = NULL,
batch_size = NULL, name = NULL, trainable = NULL, weights = NULL)Arguments
| object | Model or layer object |
| 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_formatis"channels_last":(batch, dim1, dim2, dim3, channels)If
data_formatis"channels_first":(batch, channels, dim1, dim2, dim3)
Output shape
5D tensor with shape:
If
data_formatis"channels_last":(batch, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels)If
data_formatis"channels_first":(batch, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3)
See also
Other convolutional layers: layer_conv_1d,
layer_conv_2d_transpose,
layer_conv_2d,
layer_conv_3d_transpose,
layer_conv_3d,
layer_conv_lstm_2d,
layer_cropping_1d,
layer_cropping_2d,
layer_cropping_3d,
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