update_function.py 文件源码

def fit(self, weights, o_error, tpo):
        updates = []
        gradients = theano.grad(o_error, weights)


        for c, w, g in zip(self.t_cache, weights, gradients):
            new_cache = tpo["decay_rate"] * c + ( 1- tpo["decay_rate"]) * T.sqr(g)
            new_weights = w - (g * tpo["learn_rate"]) / T.sqrt(new_cache + 0.1**8)
            updates.append((w, new_weights))
            updates.append((c, new_cache))

        return updates


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