View source on GitHub |
A preprocessing layer which randomly varies image width during training.
tf.keras.layers.RandomWidth(
factor, interpolation='bilinear', seed=None, **kwargs
)
This layer will randomly adjusts the width of a batch of images of a
batch of images by a random factor. The input should be a 3D (unbatched) or
4D (batched) tensor in the "channels_last"
image data format. Input pixel
values can be of any range (e.g. [0., 1.)
or [0, 255]
) and of interger or
floating point dtype. By default, the layer will output floats.
By default, this layer is inactive during inference.
For an overview and full list of preprocessing layers, see the preprocessing guide.
Input shape | |
---|---|
3D
|
unbatched) or 4D (batched) tensor with shape
|
Output shape | |
---|---|
3D
|
unbatched) or 4D (batched) tensor with shape
|