BaseModel.py 文件源码

def _get_opt(self):
        # build the self.opt_op for training

        self.set_train_var()
        tvars = self.var_list
        self.print_trainable()
        with tf.name_scope("Optimizer"):
            opt = self._get_optx()
            grads = tf.gradients(self.loss+self.l2loss, tvars)
            grads = list(zip(grads, tvars))
            # Op to update all variables according to their gradient
            self.opt_op = opt.apply_gradients(grads_and_vars=grads,global_step = self.global_step)

            if self.flags.visualize and "grad" in self.flags.visualize:
                for grad, var in grads:
                    tf.summary.histogram(var.name + '/gradient', grad, collections=[tf.GraphKeys.GRADIENTS])