A preprocessing layer which randomly flips images during training.
tf.keras.layers.RandomFlip( mode=HORIZONTAL_AND_VERTICAL, seed=None, **kwargs )
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 interger or floating point dtype. By default, the layer will output floats.
For an overview and full list of preprocessing layers, see the preprocessing guide.
3D (unbatched) or 4D (batched) tensor with shape: (..., height, width, channels)
, in "channels_last"
format.
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" . 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. |
Attributes | |
---|---|
auto_vectorize | Control whether automatic vectorization occurs. By default the class SubclassLayer(BaseImageAugmentationLayer): def __init__(self): super().__init__() self.auto_vectorize = False |
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Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/keras/layers/RandomFlip