Created by: wxl1351641822

这是我想要实现的代码：

`import paddlehub as hub
max_seq_len=128
# dataset = MyDataset()
module = hub.Module(name="bert_chinese_L-12_H-768_A-12")
inputs, outputs, program = module.context(
trainable=True, max_seq_len=max_seq_len)
print(outputs)
# Use "pooled_output" for classification tasks on an entire sentence.
pooled_output = outputs["pooled_output"]
seq_output=outputs['sequence_output']
print(seq_output)
hidden_units=[128]
cls_feats = fluid.layers.dropout(
            x=seq_output,
            dropout_prob=0,
            dropout_implementation="upscale_in_train")
print(cls_feats)
if hidden_units is not None:
    for n_hidden in hidden_units:
        # cls_feats = fluid.layers.fc(
        #     input=cls_feats, size=n_hidden, act=None)
        # print(cls_feats)
        cls_feats = fluid.layers.fc(input=cls_feats, size=n_hidden,
                    act=None, bias_attr=None)
        print(cls_feats)
        lstm_h, c = fluid.layers.dynamic_lstm(
            input=cls_feats, size=n_hidden, is_reverse=False)
        print(lstm_h)
        # # 最大池化
        lstm_max = fluid.layers.sequence_pool(input=lstm_h, pool_type='max')
        print(lstm_max)
        # # 激活函数
        cls_feats = fluid.layers.tanh(lstm_max)
        print(cls_feats)

logits = fluid.layers.fc(
    input=cls_feats,
    size=3,
    param_attr=fluid.ParamAttr(
        name="cls_out_w",
        initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
    bias_attr=fluid.ParamAttr(
        name="cls_out_b", initializer=fluid.initializer.Constant(0.)),
    act="softmax")

ret_infers = fluid.layers.reshape(
    x=fluid.layers.argmax(logits, axis=1), shape=[-1, 1])
----------------------
Error Message Summary:
----------------------
Error: Size of target LoD should be greater than 1.
  [Hint: Expected level0.size() > 1UL, but received level0.size():1 <= 1UL:1.] at (/paddle/paddle/fluid/operators/lod_reset_op.h:60)
  [operator < lod_reset > error]