tf.keras.layers.RandomFlip

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A preprocessing layer which randomly flips images during training.

Inherits From: Layer, Operation

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

Used in the notebooks

Used in the guide	Used in the tutorials
Working with preprocessing layers	Image segmentation Image classification Data augmentation Transfer learning and fine-tuning Retraining an Image Classifier

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.

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.

Args
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.

Attributes
input	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
output	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

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@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
config	A Python dictionary, typically the output of get_config.

Returns
A layer instance.

symbolic_call

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symbolic_call(
    *args, **kwargs
)