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Randomly vary the width of a batch of images during training.
Compat aliases for migration
See Migration guide for more details.
tf.keras.layers.experimental.preprocessing.RandomWidth( factor, interpolation='bilinear', seed=None, **kwargs )
Adjusts the width of a batch of images by a random factor. The input should be a 4-D tensor in the "channels_last" image data format.
By default, this layer is inactive during inference.
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, |