3533912016c64f1f03516657c1d93f02a11e5c67,keras/optimizers.py,Adam,get_updates,#Adam#Any#Any#Any#,320

Before Change

            p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)

            self.updates.append((m, m_t))
            self.updates.append((v, v_t))

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

    def get_config(self):

After Change

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

        t = self.iterations + 1
        lr_t = self.lr * K.sqrt(1. - K.pow(self.beta_2, t)) / (1. - K.pow(self.beta_1, t))

        ms = [K.variable(np.zeros(K.get_value(p).shape)) for p in params]
        vs = [K.variable(np.zeros(K.get_value(p).shape)) for p in params]
        self.weights = ms + vs

        for p, g, m, v in zip(params, grads, ms, vs):
            m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
            v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
            p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)

            self.updates.append(K.update(m, m_t))
            self.updates.append(K.update(v, v_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

    def get_config(self):

Instances