-
关键字：
数据维度，词向量 -
问题描述：在使用
fluid.layers.dynamic_lstm建立一个长短期记忆网络时，出现数据维度或者权重不一致的错误。 -
报错信息：
<ipython-input-3-3c2a355f1576> in stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num)
6
7 # fc1 = fluid.layers.fc(input=emb, size=hid_dim)
----> 8 lstm1, cell1 = fluid.layers.dynamic_lstm(input=emb, size=hid_dim)
9
10 inputs = [fc1, lstm1]
/opt/conda/envs/py35-paddle1.0.0/lib/python3.5/site-packages/paddle/fluid/layers/nn.py in dynamic_lstm(input, size, h_0, c_0, param_attr, bias_attr, use_peepholes, is_reverse, gate_activation, cell_activation, candidate_activation, dtype, name)
434 'gate_activation': gate_activation,
435 'cell_activation': cell_activation,
--> 436 'candidate_activation': candidate_activation
437 })
438 return hidden, cell
/opt/conda/envs/py35-paddle1.0.0/lib/python3.5/site-packages/paddle/fluid/layer_helper.py in append_op(self, *args, **kwargs)
48
49 def append_op(self, *args, **kwargs):
---> 50 return self.main_program.current_block().append_op(*args, **kwargs)
51
52 def multiple_input(self, input_param_name='input'):
/opt/conda/envs/py35-paddle1.0.0/lib/python3.5/site-packages/paddle/fluid/framework.py in append_op(self, *args, **kwargs)
1205 """
1206 op_desc = self.desc.append_op()
-> 1207 op = Operator(block=self, desc=op_desc, *args, **kwargs)
1208 self.ops.append(op)
1209 return op
/opt/conda/envs/py35-paddle1.0.0/lib/python3.5/site-packages/paddle/fluid/framework.py in __init__(***failed resolving arguments***)
654 if self._has_kernel(type):
655 self.desc.infer_var_type(self.block.desc)
--> 656 self.desc.infer_shape(self.block.desc)
657
658 def _has_kernel(self, op_type):
EnforceNotMet: Enforce failed. Expected w_dims[0] == frame_size, but received w_dims[0]:128 != frame_size:32.
The first dimension of Input(Weight) should be 32. at [/paddle/paddle/fluid/operators/lstm_op.cc:63]
PaddlePaddle Call Stacks:
- 问题复现：使用
fluid.layers.embedding接口把输入的转换成词向量，然后使用这些词向量传入到fluid.layers.dynamic_lstm接口中，计划使用fluid.layers.dynamic_lstm接口创建一个长短期记忆网络。但是在执行训练时就报以上的错误，错误代码如下：
emb = fluid.layers.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
lstm1, cell1 = fluid.layers.dynamic_lstm(input=emb, size=hid_dim)
- 解决问题：上面的错误是因为使用
fluid.layers.embedding创建的词向量和fluid.layers.dynamic_lstm所需的输入的维度不一致，为了解决这个问题，可以在中间加一个全连接层统一大小。正确代码如下：
emb = fluid.layers.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
fc1 = fluid.layers.fc(input=emb, size=hid_dim)
lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)