tf.keras.layers.experimental.preprocessing.RandomWidth

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Randomly vary the width of a batch of images during training.

Inherits From: Layer

tf.keras.layers.experimental.preprocessing.RandomWidth(
    factor, interpolation='bilinear', seed=None, name=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.

Arguments:

  • factor: A positive float (fraction of original width), or a tuple of size 2 representing lower and upper bound for resizing horizontally. 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 width varying in the range [original + 20%, original + 30%]. factor=(-0.2, 0.3) results in an output width varying in the range [original - 20%, original + 30%]. factor=0.2 results in an output width varying in the range [original - 20%, original + 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.
  • name: A string, the name of the layer.

Input shape:

4D tensor with shape: (samples, height, width, channels) (data_format='channels_last').

Output shape:

4D tensor with shape: (samples, random_height, width, channels).