tensorflow (version 2.16.0)

use_session_with_seed: Use a session with a random seed

Description

Set various random seeds required to ensure reproducible results. The provided seed value will establish a new random seed for R, Python, NumPy, and TensorFlow. GPU computations and CPU parallelism will also be disabled by default.

Usage

use_session_with_seed(
  seed,
  disable_gpu = TRUE,
  disable_parallel_cpu = TRUE,
  quiet = FALSE
)

Value

TensorFlow session object, invisibly

Arguments

seed

A single value, interpreted as an integer

disable_gpu

TRUE to disable GPU execution (see Parallelism below).

disable_parallel_cpu

TRUE to disable CPU parallelism (see Parallelism below).

quiet

TRUE to suppress printing of messages.

Parallelism

By default the use_session_with_seed() function disables GPU and CPU parallelism, since both can result in non-deterministic execution patterns (see https://stackoverflow.com/questions/42022950/). You can optionally enable GPU or CPU parallelism by setting the disable_gpu and/or disable_parallel_cpu parameters to FALSE.

Details

This function must be called at the very top of your script (i.e. immediately after library(tensorflow), library(keras), etc.). Any existing TensorFlow session is torn down via tf$reset_default_graph().

This function takes all measures known to promote reproducible results from TensorFlow sessions, however it's possible that various individual TensorFlow features or dependent libraries escape its effects. If you encounter non-reproducible results please investigate the possible sources of the problem, contributions via pull request are very welcome!

Packages which need to be notified before and after the seed is set can register for the "tensorflow.on_before_use_session" and "tensorflow.on_use_session" hooks (see setHook()) for additional details on hooks).

Examples

if (FALSE) {
library(tensorflow)
use_session_with_seed(42)
}