Created by: hyo009

多个全联接层作为序列输入lstm 网络结构至fully connected layers部分都能跑通，但加入lstm后，出现如下错误：

F0306 06:04:09.414822  1037 LstmLayer.cpp:155] Check failed: input.sequenceStartPositions 
*** Check failure stack trace: ***
    @     0x7f5e44ff8a5d  google::LogMessage::Fail()
    @     0x7f5e44ffc50c  google::LogMessage::SendToLog()
    @     0x7f5e44ff8583  google::LogMessage::Flush()
    @     0x7f5e44ffda1e  google::LogMessageFatal::~LogMessageFatal()
    @     0x7f5e44d49882  paddle::LstmLayer::forward()
    @     0x7f5e44bd7e3d  paddle::NeuralNetwork::forward()
    @     0x7f5e44bd8a93  paddle::GradientMachine::forwardBackward()
    @     0x7f5e44fd4764  GradientMachine::forwardBackward()
    @     0x7f5e44b70fc9  _wrap_GradientMachine_forwardBackward
    @           0x4c30ce  PyEval_EvalFrameEx
    @           0x4b9ab6  PyEval_EvalCodeEx
    @           0x4c1e6f  PyEval_EvalFrameEx
    @           0x4b9ab6  PyEval_EvalCodeEx
    @           0x4c16e7  PyEval_EvalFrameEx
    @           0x4b9ab6  PyEval_EvalCodeEx
    @           0x4c16e7  PyEval_EvalFrameEx
    @           0x4b9ab6  PyEval_EvalCodeEx
    @           0x4c1e6f  PyEval_EvalFrameEx
    @           0x4b9ab6  PyEval_EvalCodeEx
    @           0x4eb30f  (unknown)
    @           0x4e5422  PyRun_FileExFlags
    @           0x4e3cd6  PyRun_SimpleFileExFlags
    @           0x493ae2  Py_Main
    @     0x7f5e61cde830  __libc_start_main
    @           0x4933e9  _start
    @              (nil)  (unknown)
Aborted (core dumped)

加入lstm的模型结构如下：

...

fc_layer_1_dict = {}
for iter in range(13):
	fc_1 = paddle.layer.fc(input=pool_layer_4_dict[iter], size=10*4, act=paddle.activation.Relu())
	fc_layer_1_dict[iter] = fc_1

lstm_layer_1_dict = {}
lstm = paddle.layer.lstmemory(input=fc_layer_1_dict[0],bias_attr=paddle.attr.Param(initial_std=0., l2_rate=0.))
lstm_layer_1_dict[0] = lstm

inputs = [fc_layer_1_dict[0],lstm]
for iter in range(1,13):
	fc = paddle.layer.fc(input=[fc_layer_1_dict[iter],lstm],
							size=10*4,
							act=paddle.activation.Linear())
        lstm_layer_1_dict[iter] = paddle.layer.lstmemory(input=fc,bias_attr=paddle.attr.Param(initial_std=0., l2_rate=0.))
	inputs = [fc, lstm_layer_1_dict[iter]]

fc_last = paddle.layer.pooling(input=inputs[0], pooling_type=paddle.pooling.Max())
lstm_last = paddle.layer.pooling(input=inputs[1], pooling_type=paddle.pooling.Max())