tf.keras.layers.RandomWidth

A preprocessing layer which randomly varies image width during training.

Inherits From: Layer, Module

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.

factor A positive float (fraction of original width), 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 width changed by a random amount in the range [20%, 30%]. factor=(-0.2, 0.3) results in an output with width changed by a random amount in the range [-20%, +30%]. factor=0.2 results in an output with width 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.

3D unbatched) or 4D (batched) tensor with shape

(..., height, random_width, channels).

auto_vectorize Control whether automatic vectorization occurs.

By default the call() method leverages the tf.vectorized_map() function. Auto-vectorization can be disabled by setting self.auto_vectorize = False in your __init__() method. When disabled, call() instead relies on tf.map_fn(). For example:

class SubclassLayer(BaseImageAugmentationLayer):
  def __init__(self):
    super().__init__()
    self.auto_vectorize = False