optimizers.py 文件源码

def _apply_dense(self, grad, var):
        lr_t = tf.cast(self._lr_t, var.dtype.base_dtype)
        beta1_t = tf.cast(self._beta1_t, var.dtype.base_dtype)
        beta2_t = tf.cast(self._beta2_t, var.dtype.base_dtype)
        if var.dtype.base_dtype == tf.float16:
            # Can't use 1e-8 due to underflow
            eps = 1e-7
        else:
            eps = 1e-8

        v = self.get_slot(var, "v")
        v_t = v.assign(beta1_t * v + (1. - beta1_t) * grad)
        m = self.get_slot(var, "m")
        m_t = m.assign(tf.maximum(beta2_t * m + eps, tf.abs(grad)))
        g_t = v_t / m_t

        var_update = tf.assign_sub(var, lr_t * g_t)
        return tf.group(*[var_update, m_t, v_t])