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"`.

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)`

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)`

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.

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 a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

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

Returns
A layer instance.

`symbolic_call`

View source

symbolic_call(
    *args, **kwargs
)