View source on GitHub |
A preprocessing layer which randomly varies image height during training.
tf.keras.layers.RandomHeight(
factor, interpolation='bilinear', seed=None, **kwargs
)
This layer adjusts the height 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:
(..., height, width, channels) , in "channels_last" format.
|
Output shape | |
---|---|
3D (unbatched) or 4D (batched) tensor with shape:
(..., random_height, width, channels) .
|