tf.keras.random.SeedGenerator

Generates variable seeds upon each call to a RNG-using function.

tf.keras.random.SeedGenerator(     seed=None, name=None, **kwargs )

In Keras, all RNG-using methods (such as keras.random.normal()) are stateless, meaning that if you pass an integer seed to them (such as seed=42), they will return the same values at each call. In order to get different values at each call, you must use a SeedGenerator instead as the seed argument. The SeedGenerator object is stateful.

Example:

seed_gen = keras.random.SeedGenerator(seed=42) values = keras.random.normal(shape=(2, 3), seed=seed_gen) new_values = keras.random.normal(shape=(2, 3), seed=seed_gen)

Usage in a layer:

class Dropout(keras.Layer):     def __init__(self, **kwargs):         super().__init__(**kwargs)         self.seed_generator = keras.random.SeedGenerator(1337)      def call(self, x, training=False):         if training:             return keras.random.dropout(                 x, rate=0.5, seed=self.seed_generator             )         return x

Methods

from_config

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@classmethod from_config(     config )

get_config

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get_config()

next

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next(     ordered=True )