random_seed_generator {keras3} | R Documentation |
Generates variable seeds upon each call to a RNG-using function.
Description
In Keras, all RNG-using methods (such as 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.
Usage
random_seed_generator(seed = NULL, name = NULL, ...)
Arguments
seed |
Initial seed for the random number generator |
name |
String, name for the object |
... |
For forward/backward compatability. |
Value
A SeedGenerator
instance, which can be passed as the seed =
argument to other random tensor generators.
Examples
seed_gen <- random_seed_generator(seed = 42) values <- random_normal(shape = c(2, 3), seed = seed_gen) new_values <- random_normal(shape = c(2, 3), seed = seed_gen)
Usage in a layer:
layer_dropout2 <- new_layer_class( "dropout2", initialize = function(...) { super$initialize(...) self$seed_generator <- random_seed_generator(seed = 1337) }, call = function(x, training = FALSE) { if (training) { return(random_dropout(x, rate = 0.5, seed = self$seed_generator)) } return(x) } ) out <- layer_dropout(rate = 0.8) out(op_ones(10), training = TRUE)
## tf.Tensor([0. 5. 5. 0. 0. 0. 0. 0. 0. 0.], shape=(10), dtype=float32)
See Also
Other random:
random_beta()
random_binomial()
random_categorical()
random_dropout()
random_gamma()
random_integer()
random_normal()
random_shuffle()
random_truncated_normal()
random_uniform()