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Showing content from https://keras.io/api/layers/preprocessing_layers/image_augmentation/random_flip below:

RandomFlip layer

[source]

keras.layers.RandomFlip(
    mode="horizontal_and_vertical", seed=None, data_format=None, **kwargs
)

A preprocessing layer which randomly flips images during training.

This layer will flip the images horizontally and or vertically based on the mode attribute. During inference time, the output will be identical to input. Call the layer with training=True to flip the input. Input pixel values can be of any range (e.g. [0., 1.) or [0, 255]) and of integer or floating point dtype. By default, the layer will output floats.

Note: This layer is safe to use inside a tf.data pipeline (independently of which backend you're using).

Input shape

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: (..., height, width, channels), in "channels_last" format.

Arguments

• mode: String indicating which flip mode to use. Can be "horizontal", "vertical", or "horizontal_and_vertical". "horizontal" is a left-right flip and "vertical" is a top-bottom flip. Defaults to "horizontal_and_vertical"
• seed: Integer. Used to create a random seed.
• **kwargs: Base layer keyword arguments, such as name and dtype.

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