braindecode.util.set_random_seeds#

braindecode.util.set_random_seeds(seed, cuda, cudnn_benchmark=None)[source]#

Set seeds for python random module numpy.random and torch.

For more details about reproducibility in pytorch see https://pytorch.org/docs/stable/notes/randomness.html

Parameters:

  • seed (int) – Random seed.

  • cuda (bool) – Whether to set cuda seed with torch.

  • cudnn_benchmark (bool (default=None)) – Whether pytorch will use cudnn benchmark. When set to None it will not modify torch.backends.cudnn.benchmark (displays warning in the case of possible lack of reproducibility). When set to True, results may not be reproducible (no warning displayed). When set to False it may slow down computations.

Notes

In some cases setting environment variable PYTHONHASHSEED may be needed before running a script to ensure full reproducibility. See https://forums.fast.ai/t/solved-reproducibility-where-is-the-randomness-coming-in/31628/14

Using this function may not ensure full reproducibility of the results as we do not set torch.use_deterministic_algorithms(True).

Examples using braindecode.util.set_random_seeds#

Simple training on MNE epochs

Simple training on MNE epochs

Cropped Decoding on BCIC IV 2a Dataset

Cropped Decoding on BCIC IV 2a Dataset

Basic Brain Decoding on EEG Data

Basic Brain Decoding on EEG Data

How to train, test and tune your model?

How to train, test and tune your model?

Hyperparameter tuning with scikit-learn

Hyperparameter tuning with scikit-learn

Convolutional neural network regression model on fake data.

Convolutional neural network regression model on fake data.

Training a Braindecode model in PyTorch

Training a Braindecode model in PyTorch

Benchmarking eager and lazy loading

Benchmarking eager and lazy loading

Multiple discrete targets with the TUH EEG Corpus

Multiple discrete targets with the TUH EEG Corpus

Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset

Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset

Data Augmentation on BCIC IV 2a Dataset

Data Augmentation on BCIC IV 2a Dataset

Searching the best data augmentation on BCIC IV 2a Dataset

Searching the best data augmentation on BCIC IV 2a Dataset

Self-supervised learning on EEG with relative positioning

Self-supervised learning on EEG with relative positioning

Fingers flexion decoding on BCIC IV 4 ECoG Dataset

Fingers flexion decoding on BCIC IV 4 ECoG Dataset

Sleep staging on the Sleep Physionet dataset using Chambon2018 network

Sleep staging on the Sleep Physionet dataset using Chambon2018 network

Sleep staging on the Sleep Physionet dataset using Eldele2021

Sleep staging on the Sleep Physionet dataset using Eldele2021

Sleep staging on the Sleep Physionet dataset using U-Sleep network

Sleep staging on the Sleep Physionet dataset using U-Sleep network