tf.keras.layers.UpSampling3D

Upsampling layer for 3D inputs.

Inherits From: Layer, Operation

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.

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)

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). 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)

Attributes
input	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
output	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

View source

@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

Args
config	A Python dictionary, typically the output of get_config.

Returns
An operation instance.