Randomly flip each image horizontally and vertically.
Inherits From: PreprocessingLayer
, Layer
, Module
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.
4D tensor with shape: (samples, height, width, channels)
, data_format='channels_last'.
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. |
adapt
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. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/layers/experimental/preprocessing/RandomFlip