UpSampling3D layer

• Original Link : https://keras.io/api/layers/reshaping_layers/up_sampling3d/
• Last Checked at : 2024-11-25

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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, uses image_data_format value 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)