FTML

class FTML(beta1=0.6, beta2=0.999, epsilon=1e-08, **kwargs)

The FTML optimizer.

This class implements the optimizer described in FTML - Follow the Moving Leader in Deep Learning, available at http://proceedings.mlr.press/v70/zheng17a/zheng17a.pdf.

Denote time step by t. The optimizer updates the weight by:

rescaled_grad = clip(grad * rescale_grad + wd * weight, clip_gradient)
v = beta2 * v + (1 - beta2) * square(rescaled_grad)
d_t = (1 - power(beta1, t)) / lr * square_root(v / (1 - power(beta2, t))) + epsilon)
z = beta1 * z + (1 - beta1) * rescaled_grad - (d_t - beta1 * d_(t-1)) * weight
weight = - z / d_t

For details of the update algorithm, see ftml_update.

This optimizer accepts the following parameters in addition to those accepted by Optimizer.

Parameters:

beta1 : float, optional

0 < beta1 < 1. Generally close to 0.5.

beta2 : float, optional

0 < beta2 < 1. Generally close to 1.

epsilon : float, optional

Small value to avoid division by 0.