# tf.keras.layers.UpSampling3D
|---|---|
| [ TensorFlow 1 version](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/layers/UpSampling3D) | [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.0.0/tensorflow/python/keras/layers/convolutional.py#L2000-L2069) |
Upsampling layer for 3D inputs.
Inherits From: [`Layer`](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/Layer)
#### View aliases
**Compat aliases for migration**
See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.
[`tf.compat.v1.keras.layers.UpSampling3D`](https://www.tensorflow.org/api_docs/python/tf/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.
| ## 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, spatial_dim1, spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds to inputs with shape `(batch, 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, dim1, dim2, dim3, channels)`
- If `data_format` is `"channels_first"`: `(batch, channels, dim1, dim2, dim3)`
#### Output shape:
5D tensor with shape:
- If `data_format` is `"channels_last"`: `(batch, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels)`
- If `data_format` is `"channels_first"`: `(batch, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3)`