

def updates(self, learning_rate):
    updates = []
    for (p, g, a, d) in zip(self.parameters, self.grads, self.a, self.d):
        new_a = ((self.rho * a) + ((1 - self.rho) * T.square(g)))
        updates.append((a, new_a))
        update = ((g * T.sqrt((d + self.epsilon))) / T.sqrt((new_a + self.epsilon)))
        new_p = (p - (learning_rate * update))
        updates.append((p, new_p))
        new_d_a = ((self.rho * d) + ((1 - self.rho) * T.square(update)))
        updates.append((d, new_d_a))
    return updates

dataset/ETHPy150Open sharadmv/deepx/deepx/optimize/adadelta.py Adadelta.updates/original