Module: pennylane

class RMSPropOptimizer(stepsize=0.01, decay=0.9, eps=1e-08)[source]

Root mean squared propagation optimizer.

The root mean square progation optimizer is a modified Adagrad optimizer, with a decay of learning rate adaptation.

Extensions of the Adagrad optimization method generally start the sum \(a\) over past gradients in the denominator of the learning rate at a finite \(t'\) with \(0 < t' < t\), or decay past gradients to avoid an ever-decreasing learning rate.

Root Mean Square propagation is such an adaptation, where

\[a_i^{(t+1)} = \gamma a_i^{(t)} + (1-\gamma) (\partial_{x_i} f(x^{(t)}))^2.\]
  • stepsize (float) – the user-defined hyperparameter \(\eta\) used in the Adagrad optmization
  • decay (float) – the learning rate decay \(\gamma\)
  • eps (float) – offset \(\epsilon\) added for numerical stability (see Adagrad)
apply_grad(grad, x)[source]

Update the variables x to take a single optimization step. Flattens and unflattens the inputs to maintain nested iterables as the parameters of the optimization.

  • grad (array) – The gradient of the objective function at point \(x^{(t)}\): \(\nabla f(x^{(t)})\)
  • x (array) – the current value of the variables \(x^{(t)}\)

the new values \(x^{(t+1)}\)

Return type: