tf.keras.layers.UpSampling3D

Upsampling layer for 3D inputs.

Inherits From: Layer, Module

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Compat aliases for migration

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`tf.compat.v1.keras.layers.UpSampling3D`

tf.keras.layers.UpSampling3D(
    size=(2, 2, 2), data_format=None, **kwargs
)

Repeats the 1st, 2nd and 3rd dimensions of the data by size[0], size[1] and size[2] respectively.

Examples:

input_shape = (2, 1, 2, 1, 3)
x = tf.constant(1, shape=input_shape)
y = tf.keras.layers.UpSampling3D(size=2)(x)
print(y.shape)
(2, 2, 4, 2, 3)

Args
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). 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".

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)