Repeats the 1st, 2nd and 3rd dimensions of the data by size[[0]], size[[1]] and
size[[2]] respectively.
layer_upsampling_3d(object, size = c(2L, 2L, 2L), data_format = NULL,
batch_size = NULL, name = NULL, trainable = NULL, weights = NULL)Model or layer object
int, or list of 3 integers. The upsampling factors for dim1, dim2 and dim3.
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".
Fixed batch size for layer
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.
Whether the layer weights will be updated during training.
Initial weights for layer.
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)
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)
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