Source code for espnet2.torch_utils.add_gradient_noise

import torch

[docs]def add_gradient_noise( model: torch.nn.Module, iteration: int, duration: float = 100, eta: float = 1.0, scale_factor: float = 0.55, ): """Adds noise from a standard normal distribution to the gradients. The standard deviation (`sigma`) is controlled by the three hyper-parameters below. `sigma` goes to zero (no noise) with more iterations. Args: model: Model. iteration: Number of iterations. duration: {100, 1000}: Number of durations to control the interval of the `sigma` change. eta: {0.01, 0.3, 1.0}: The magnitude of `sigma`. scale_factor: {0.55}: The scale of `sigma`. """ interval = (iteration // duration) + 1 sigma = eta / interval**scale_factor for param in model.parameters(): if param.grad is not None: _shape = param.grad.size() noise = sigma * torch.randn(_shape).to(param.device) param.grad += noise