tf.keras.layers.experimental.preprocessing.RandomRotation

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Randomly rotate each image.

Inherits From: Layer

tf.keras.layers.experimental.preprocessing.RandomRotation(
    factor, fill_mode='reflect', interpolation='bilinear', seed=None, name=None,
    **kwargs
)

By default, random rotations are only applied during training. At inference time, the layer does nothing. If you need to apply random rotations at inference time, set training to True when calling the layer.

Input shape:

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

Output shape:

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

Attributes:

  • factor: a positive float represented as fraction of 2pi, or a tuple of size 2 representing lower and upper bound for rotating clockwise and counter-clockwise. When represented as a single float, lower = upper.
  • fill_mode: Points outside the boundaries of the input are filled according to the given mode (one of {'constant', 'reflect', 'wrap'}).
    • reflect: (d c b a | a b c d | d c b a) The input is extended by reflecting about the edge of the last pixel.
    • constant: (k k k k | a b c d | k k k k) The input is extended by filling all values beyond the edge with the same constant value k = 0.
    • wrap: (a b c d | a b c d | a b c d)
  • interpolation: Interpolation mode. Supported values: "nearest", "bilinear".
  • 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, height, width, channels), data_format='channels_last'.

Raise:

  • ValueError: if lower bound is not between [0, 1], or upper bound is negative.