tf-keras-skeleton.py 文件源码

def get_updates(self, params, constraints, loss):
        grads = self.get_gradients(loss, params)
        self.updates = [K.update_add(self.iterations, 1)]

        lr = self.lr
        if self.initial_decay > 0:
          lr *= (1. / (1. + self.decay * self.iterations))

        t = self.iterations + 1
        lr_t = lr / (1. - K.pow(self.beta_1, t))

        shapes = [K.int_shape(p) for p in params]
        # zero init of 1st moment
        ms = [K.zeros(shape) for shape in shapes]
        # zero init of exponentially weighted infinity norm
        us = [K.zeros(shape) for shape in shapes]
        self.weights = [self.iterations] + ms + us

        for p, g, m, u in zip(params, grads, ms, us):

          m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
          u_t = K.maximum(self.beta_2 * u, K.abs(g))
          p_t = p - lr_t * m_t / (u_t + self.epsilon)

          self.updates.append(K.update(m, m_t))
          self.updates.append(K.update(u, u_t))

          new_p = p_t
          # apply constraints
          if p in constraints:
            c = constraints[p]
            new_p = c(new_p)
          self.updates.append(K.update(p, new_p))
        return self.updates