我正在尝试使用 paddle-paddle （https://github.com/baidu/Paddle）来训练（编码器 - 解码器）序列以进行POS标记的序列模型 .
但是我没有使用单词indices的单热嵌入作为输入，而是使用我用 numpy 创建的虚构单词向量 . 我在 dataprovider.py 的 hook() 函数中添加了单词vectors到 settings 变量：
def hook(settings, src_dict, trg_dict, file_list, **kwargs):
# job_mode = 1: training mode
# job_mode = 0: generating mode
settings.job_mode = trg_dict is not None
settings.src_dict = src_dict
settings.logger.info("src dict len : %d" % (len(settings.src_dict)))
settings.sample_count = 0
settings.thematrix = np.random.rand(len(src_dict), len(trg_dict))
if settings.job_mode:
settings.trg_dict = trg_dict
settings.slots = [
#integer_value_sequence(len(settings.src_dict)),
dense_vector_sequence(len(settings.src_dict)),
integer_value_sequence(len(settings.trg_dict)),
integer_value_sequence(len(settings.trg_dict)),
]
settings.logger.info("trg dict len : %d" % (len(settings.trg_dict)))
else:
settings.slots = [
integer_value_sequence(len(settings.src_dict)),
integer_value_sequence(len(open(file_list[0], "r").readlines()))
]
当迭代句子及其POS标签时，我已经在https://github.com/alvations/rowrow/blob/master/dataprovider.py#L66处产生了这些虚构的向量而不是单词indices .
在序列模型的序列中，由于输入（也就是 data_layer() ）不是使用完全连接的层将矢量输入压缩到编码器大小，即https://github.com/alvations/rowrow/blob/master/seqToseq_net.py#L49：
src_word_id = data_layer(name='source_language_word', size=source_dict_dim)
src_embedding = fc_layer(input=src_word_id, size=word_vector_dim)
src_forward = simple_gru(input=src_embedding, size=encoder_size)
src_backward = simple_gru(input=src_embedding, size=encoder_size, reverse=True)
encoded_vector = concat_layer(input=[src_forward, src_backward])
with mixed_layer(size=decoder_size) as encoded_proj:
encoded_proj += full_matrix_projection(input=encoded_vector)
通常，嵌入层将类似于：
src_embedding = embedding_layer(
input=src_word_id,
size=word_vector_dim,
param_attr=ParamAttr(name='_source_language_embedding'))
神经网络计算图似乎是正确的，因为它在运行 train.sh 时没有抛出任何与网络相关的错误 .
但是在获取下一批时它会引发错误：
~/Paddle/demo/rowrow$ bash train.sh
I1104 18:59:42.636052 18632 Util.cpp:151] commandline: /home/ltan/Paddle/binary/bin/../opt/paddle/bin/paddle_trainer --config=train.conf --save_dir=/home/ltan/Paddle/demo/rowrow/model --use_gpu=true --num_passes=100 --show_parameter_stats_period=1000 --trainer_count=4 --log_period=10 --dot_period=5
I1104 18:59:46.503566 18632 Util.cpp:126] Calling runInitFunctions
I1104 18:59:46.503810 18632 Util.cpp:139] Call runInitFunctions done.
[WARNING 2016-11-04 18:59:46,847 default_decorators.py:40] please use keyword arguments in paddle config.
[INFO 2016-11-04 18:59:46,856 networks.py:1125] The input order is [source_language_word, target_language_word, target_language_next_word]
[INFO 2016-11-04 18:59:46,857 networks.py:1132] The output order is [__cost_0__]
I1104 18:59:46.871026 18632 Trainer.cpp:170] trainer mode: Normal
I1104 18:59:46.871906 18632 MultiGradientMachine.cpp:108] numLogicalDevices=1 numThreads=4 numDevices=4
I1104 18:59:46.988584 18632 PyDataProvider2.cpp:247] loading dataprovider dataprovider::process
[INFO 2016-11-04 18:59:46,990 dataprovider.py:15] src dict len : 45661
[INFO 2016-11-04 18:59:47,316 dataprovider.py:26] trg dict len : 422
I1104 18:59:47.347944 18632 PyDataProvider2.cpp:247] loading dataprovider dataprovider::process
[INFO 2016-11-04 18:59:47,348 dataprovider.py:15] src dict len : 45661
[INFO 2016-11-04 18:59:47,657 dataprovider.py:26] trg dict len : 422
I1104 18:59:47.658279 18632 GradientMachine.cpp:134] Initing parameters..
I1104 18:59:49.244287 18632 GradientMachine.cpp:141] Init parameters done.
F1104 18:59:50.485621 18632 PythonUtil.h:213] Check failed: PySequence_Check(seq_)
*** Check failure stack trace: ***
@ 0x7f71f521adaa (unknown)
@ 0x7f71f521ace4 (unknown)
@ 0x7f71f521a6e6 (unknown)
@ 0x7f71f521d687 (unknown)
@ 0x54dac9 paddle::DenseScanner::fill()
@ 0x54f1d1 paddle::SequenceScanner::fill()
@ 0x5543cc paddle::PyDataProvider2::getNextBatchInternal()
@ 0x5779b2 paddle::DataProvider::getNextBatch()
@ 0x6a01f7 paddle::Trainer::trainOnePass()
@ 0x6a3b57 paddle::Trainer::train()
@ 0x53a2b3 main
@ 0x7f71f4426f45 (unknown)
@ 0x545ae5 (unknown)
@ (nil) (unknown)
/home/ltan/Paddle/binary/bin/paddle: line 81: 18632 Aborted (core dumped) ${DEBUGGER} $MYDIR/../opt/paddle/bin/paddle_trainer ${@:2}
我've tried asking on Paddle' s gitter.im但是没有回应 .
有人知道吗：
-
what does the error mean?
-
how to feed a dense vector sequence into a seqToseq model in Paddle?
-
Why is Paddle throwing this error when feeding in a dense_vector_sequence to a SeqToseq model?