import torch
from ._BaseOptimiser import BaseOptimiser
[docs]
class SGD(BaseOptimiser):
"""
Stochastic gradient descent optimiser with momentum. A first order method and therefore does not use information on second gradients, i.e. the hessian matrix. Hence, does not require a lot of memory.
Args:
learning_rate (float, optional): The learning rate of the optimiser. Must be positive. Defaults to 0.001.
momentum (float, optional): Determines how long the previous gradients affect the current direction. Must be in range [0, 1). Defaults to 0.9.
"""
def __init__(self, learning_rate=0.001, momentum=0.9):
if not isinstance(learning_rate, int | float) or learning_rate <= 0:
raise ValueError("learning_rate must be a positive real number.")
if not isinstance(momentum, int | float) or momentum < 0 or momentum >= 1:
raise ValueError("momentum must be in range [0, 1).")
self.learning_rate = learning_rate
self.momentum = momentum
[docs]
def initialise_parameters(self, model_parameters):
"""
Initialises the optimiser with the parameters that need to be optimised.
Args:
model_parameters (list[torch.Tensor]): The parameters that will be optimised. Must be a list or a tuple of torch tensors.
"""
if not isinstance(model_parameters, list | tuple):
raise TypeError("model_parameters must be a list or a tuple of torch tensors.")
self.model_parameters = model_parameters
self.changes = [torch.zeros_like(parameter) for parameter in self.model_parameters]
[docs]
def update_parameters(self):
"""
Takes a step towards the optimum for each parameter.
"""
for i, parameter in enumerate(self.model_parameters):
change = self.learning_rate * parameter.grad + self.momentum * self.changes[i]
parameter -= change
self.changes[i] = change