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

View source

@classmethod
from_config(
    config
)

get_config

View source

get_config()

next

View source

next(
    ordered=True
)