tf.keras.layers.experimental.preprocessing.RandomFlip

Randomly flip each image horizontally and vertically.

Inherits From: PreprocessingLayer, Layer, Module

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.experimental.preprocessing.RandomFlip

tf.keras.layers.experimental.preprocessing.RandomFlip(
    mode=HORIZONTAL_AND_VERTICAL, seed=None, name=None, **kwargs
)

This layer will flip the images 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 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
mode	String indicating which flip mode to use. Can be "horizontal", "vertical", or "horizontal_and_vertical". Defaults to "horizontal_and_vertical". "horizontal" is a left-right flip and "vertical" is a top-bottom flip.
seed	Integer. Used to create a random seed.
name	A string, the name of the layer.

Methods

adapt

View source

adapt(
    data, reset_state=True
)

Fits the state of the preprocessing layer to the data being passed.

Arguments
data	The data to train on. It can be passed either as a tf.data Dataset, or as a numpy array.
reset_state	Optional argument specifying whether to clear the state of the layer at the start of the call to adapt, or whether to start from the existing state. This argument may not be relevant to all preprocessing layers: a subclass of PreprocessingLayer may choose to throw if 'reset_state' is set to False.