UpSampling3D layer
UpSampling3D class
keras.layers.UpSampling3D(size=(2, 2, 2), data_format=None, **kwargs)
Upsampling layer for 3D inputs.
Repeats the 1st, 2nd and 3rd dimensions
of the data by size[0], size[1] and size[2] respectively.
Example
>>> input_shape = (2, 1, 2, 1, 3)
>>> x = np.ones(input_shape)
>>> y = keras.layers.UpSampling3D(size=(2, 2, 2))(x)
>>> y.shape
(2, 2, 4, 2, 3)
Arguments
- size: Int, or tuple of 3 integers. The upsampling factors for dim1, dim2 and dim3.
- 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_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)while"channels_first"corresponds to inputs with shape(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3). When unspecified, usesimage_data_formatvalue found in your Keras config file at~/.keras/keras.json(if exists) else"channels_last". Defaults to"channels_last".
Input shape
5D tensor with shape:
- If data_format is "channels_last":
(batch_size, dim1, dim2, dim3, channels)
- If data_format is "channels_first":
(batch_size, channels, dim1, dim2, dim3)
Output shape
5D tensor with shape:
- If data_format is "channels_last":
(batch_size, upsampled_dim1, upsampled_dim2, upsampled_dim3,
channels)
- If data_format is "channels_first":
(batch_size, channels, upsampled_dim1, upsampled_dim2,
upsampled_dim3)