import torch
from ._BaseOptimiser import BaseOptimiser
[docs]
class ADAGRAD(BaseOptimiser):
"""
The adaptive gradient optimiser. 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.
lr_decay (float, optional): Determines how fast the learning rate decreases. Must be positive. Defaults to 0.
weight_decay (float, optional): Determines if regularisation should be applied to the weights. Must be in range [0, 1). Defaults to 0.
"""
def __init__(self, learning_rate=0.001, lr_decay=0, weight_decay=0):
if not isinstance(learning_rate, int | float) or learning_rate <= 0:
raise ValueError("learning_rate must be a positive real number.")
if not isinstance(lr_decay, int | float) or lr_decay < 0:
raise ValueError("lr_decay must be positive.")
if not isinstance(weight_decay, int | float) or weight_decay < 0 or weight_decay >= 1:
raise ValueError("weight_decay must be in range [0, 1).")
self.learning_rate = learning_rate
self.lr_decay = lr_decay
self.weight_decay = weight_decay
[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.state_sum = [torch.zeros_like(param) for param in model_parameters]
self.t = 0
[docs]
def update_parameters(self):
"""
Takes a step towards the optimum for each parameter.
"""
self.t += 1
for i, parameter in enumerate(self.model_parameters):
learning_rate = self.learning_rate / (1 + (self.t - 1) * self.lr_decay)
if self.weight_decay > 0: parameter.grad += self.weight_decay * parameter
self.state_sum[i] += parameter.grad ** 2
parameter -= learning_rate * parameter.grad / (torch.sqrt(self.state_sum[i]) + 1e-10)