tf_tree_lstm.py 文件源码

def add_training_op(self):
        loss=self.total_loss
        opt1=tf.train.AdagradOptimizer(self.config.lr)
        opt2=tf.train.AdagradOptimizer(self.config.emb_lr)

        ts=tf.trainable_variables()
        gs=tf.gradients(loss,ts)
        gs_ts=zip(gs,ts)

        gt_emb,gt_nn=[],[]
        for g,t in gs_ts:
            #print t.name,g.name
            if "Embed/embedding:0" in t.name:
                #g=tf.Print(g,[g.get_shape(),t.get_shape()])
                gt_emb.append((g,t))
                #print t.name
            else:
                gt_nn.append((g,t))
                #print t.name

        train_op1=opt1.apply_gradients(gt_nn)
        train_op2=opt2.apply_gradients(gt_emb)
        train_op=[train_op1,train_op2]

        return train_op