Generates variable seeds upon each call to a function generating random numbers.

In Keras, all random number generators (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 for repeated calls. To get different values for each call, a SeedGenerator providing the state of the random generator has to be used.

Note that all the random number generators have a default seed of NULL, which implies that an internal global SeedGenerator is used. If you need to decouple the RNG from the global state you can provide a local StateGenerator with either a deterministic or random initial state.

Remark concerning the JAX backen: Note that the use of a local StateGenerator as seed argument is required for JIT compilation of RNG with the JAX backend, because the use of global state is not supported.

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)

Generates variable seeds upon each call to a function generating random numbers.

Usage

Arguments

Value

Examples

See also