Source code for pennylane.optimize.momentum

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.

# Licensed under the Apache License, Version 2.0 (the "License");
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"""Momentum optimizer"""
from .gradient_descent import GradientDescentOptimizer

[docs]class MomentumOptimizer(GradientDescentOptimizer): r"""Gradient-descent optimizer with momentum. The momentum optimizer adds a "momentum" term to gradient descent which considers the past gradients: .. math:: x^{(t+1)} = x^{(t)} - a^{(t+1)}. The accumulator term :math:`a` is updated as follows: .. math:: a^{(t+1)} = m a^{(t)} + \eta \nabla f(x^{(t)}), with user defined parameters: * :math:`\eta`: the step size * :math:`m`: the momentum Args: stepsize (float): user-defined hyperparameter :math:`\eta` momentum (float): user-defined hyperparameter :math:`m` """ def __init__(self, stepsize=0.01, momentum=0.9): super().__init__(stepsize) self.momentum = momentum self.accumulation = None
[docs] def apply_grad(self, grad, args): r"""Update the trainable args to take a single optimization step. Flattens and unflattens the inputs to maintain nested iterables as the parameters of the optimization. Args: grad (tuple [array]): the gradient of the objective function at point :math:`x^{(t)}`: :math:`\nabla f(x^{(t)})`. args (tuple): the current value of the variables :math:`x^{(t)}`. Returns: list [array]: the new values :math:`x^{(t+1)}`. """ args_new = list(args) if self.accumulation is None: self.accumulation = [0.0] * len(args) trained_index = 0 for index, arg in enumerate(args): if getattr(arg, "requires_grad", False): self._update_accumulation(index, grad[trained_index]) args_new[index] = arg - self.accumulation[index] trained_index += 1 return args_new
def _update_accumulation(self, index, grad): r"""Update the accumulation. Args: index (int): index of argument to update. grad (ndarray): gradient at index """ self.accumulation[index] = self.momentum * self.accumulation[index] + self.stepsize * grad
[docs] def reset(self): """Reset optimizer by erasing memory of past steps.""" self.accumulation = None