<s> import theano <EOL> import theano . tensor as T <EOL> import numpy as np <EOL> from lasagne . updates import get_or_compute_grads <EOL> from collections import OrderedDict <EOL> def graves_rmsprop ( loss_or_grads , params , learning_rate = <NUM_LIT> , chi = <NUM_LIT> , alpha = <NUM_LIT> , epsilon = <NUM_LIT> ) : <EOL> r"""<STR_LIT>""" <EOL> grads = get_or_compute_grads ( loss_or_grads , params ) <EOL> updates = OrderedDict ( ) <EOL> for param , grad in zip ( params , grads ) : <EOL> value = param . get_value ( borrow = True ) <EOL> n = theano . shared ( np . zeros ( value . shape , dtype = value . dtype ) , <EOL> broadcastable = param . broadcastable ) <EOL> g = theano . shared ( np . zeros ( value . shape , dtype = value . dtype ) , <EOL> broadcastable = param . broadcastable ) <EOL> delta = theano . shared ( np . zeros ( value . shape , dtype = value . dtype ) , <EOL> broadcastable = param . broadcastable ) <EOL> n_ip1 = chi * n + ( <NUM_LIT:1.> - chi ) * grad ** <NUM_LIT:2> <EOL> g_ip1 = chi * g + ( <NUM_LIT:1.> - chi ) * grad <EOL> delta_ip1 = alpha * delta - learning_rate * grad / T . sqrt ( n_ip1 + g_ip1 ** <NUM_LIT:2> + epsilon ) <EOL> updates [ n ] = n_ip1 <EOL> updates [ g ] = g_ip1 <EOL> updates [ delta ] = delta_ip1 <EOL> updates [ param ] = param + delta_ip1 <EOL> return updates </s>
