A preprocessing layer which randomly varies image height during training.
Inherits From: Layer
, Module
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.
Args |
factor
|
A positive float (fraction of original height), or a tuple of size 2
representing lower and upper bound for resizing vertically. When
represented as a single float, this value is used for both the upper and
lower bound. For instance, factor=(0.2, 0.3) results in an output with
height changed by a random amount in the range [20%, 30%] .
factor=(-0.2, 0.3) results in an output with height changed by a random
amount in the range [-20%, +30%] . factor=0.2 results in an output with
height changed by a random amount in the range [-20%, +20%] .
|
interpolation
|
String, the interpolation method. Defaults to "bilinear" .
Supports "bilinear" , "nearest" , "bicubic" , "area" ,
"lanczos3" , "lanczos5" , "gaussian" , "mitchellcubic" .
|
seed
|
Integer. Used to create a random seed.
|
|
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) .
|