Errors come in the network.py

    # CalculateMean cross-entropy loss
    with tf.name_scope("loss"):
        log_likelihood, self.transition_params = tf.contrib.crf.crf_log_likelihood(
            self.logits, self.input_y, self.sequence_lengths)
        self.loss = tf.reduce_mean(-log_likelihood,name="loss")

==================================================
the parametere "self.transition_params" is nan returned by function tf.contrib.crf.crf_log_likelihood

error details ::
InvalidArgumentError (see above for traceback): Nan in summary histogram for: transitions_0/grad/hist
[[Node: transitions_0/grad/hist = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](transitions_0/grad/hist/tag, gradients/AddN_4)]]
......

When I run the model on my dataset. The Dev Dataset is a little larger than the conll2003. So in the Evaluation, this error

2018-12-28 12:14:05.446349: W tensorflow/core/framework/allocator.cc:122] Allocation of 8827056000 exceeds 10% of system memory.

came up. Maybe the Evaluation should also be fed batch by batch.

tf version: 1.1.0
python version: 3.4.5

2018-03-15T11:38:40.251583: step 1, loss 68.8194
Traceback (most recent call last):
File "/dhome/jayhsu/miniconda2/envs/ENV/lib/python3.4/site-packages/tensorflow/python/client/session.py", line 1039, in _do_call
return fn(*args)
File "/dhome/jayhsu/miniconda2/envs/ENV/lib/python3.4/site-packages/tensorflow/python/client/session.py", line 1021, in _run_fn
status, run_metadata)
File "/dhome/jayhsu/miniconda2/envs/ENV/lib/python3.4/contextlib.py", line 66, in exit
next(self.gen)
File "/dhome/jayhsu/miniconda2/envs/ENV/lib/python3.4/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Nan in summary histogram for: transitions_0/grad/hist
[[Node: transitions_0/grad/hist = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](transitions_0/grad/hist/tag, gradients/AddN_4)]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "BasicTextPreprocessing_CNN_CRF.py", line 298, in
embedd_table,char_batch,char_embedd_table)
File "BasicTextPreprocessing_CNN_CRF.py", line 284, in train_step
feed_dict)
File "/dhome/jayhsu/miniconda2/envs/ENV/lib/python3.4/site-packages/tensorflow/python/client/session.py", line 778, in run
run_metadata_ptr)
File "/dhome/jayhsu/miniconda2/envs/ENV/lib/python3.4/site-packages/tensorflow/python/client/session.py", line 982, in _run
feed_dict_string, options, run_metadata)
File "/dhome/jayhsu/miniconda2/envs/ENV/lib/python3.4/site-packages/tensorflow/python/client/session.py", line 1032, in _do_run
target_list, options, run_metadata)
File "/dhome/jayhsu/miniconda2/envs/ENV/lib/python3.4/site-packages/tensorflow/python/client/session.py", line 1052, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Nan in summary histogram for: transitions_0/grad/hist
[[Node: transitions_0/grad/hist = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](transitions_0/grad/hist/tag, gradients/AddN_4)]]

Caused by op 'transitions_0/grad/hist', defined at:
File "BasicTextPreprocessing_CNN_CRF.py", line 226, in
grad_hist_summary = tf.summary.histogram("{}/grad/hist".format(v.name), g)
File "/dhome/jayhsu/miniconda2/envs/ENV/lib/python3.4/site-packages/tensorflow/python/summary/summary.py", line 209, in histogram
tag=scope.rstrip('/'), values=values, name=scope)
File "/dhome/jayhsu/miniconda2/envs/ENV/lib/python3.4/site-packages/tensorflow/python/ops/gen_logging_ops.py", line 139, in _histogram_summary
name=name)
File "/dhome/jayhsu/miniconda2/envs/ENV/lib/python3.4/site-packages/tensorflow/python/framework/op_def_library.py", line 768, in apply_op
op_def=op_def)
File "/dhome/jayhsu/miniconda2/envs/ENV/lib/python3.4/site-packages/tensorflow/python/framework/ops.py", line 2336, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/dhome/jayhsu/miniconda2/envs/ENV/lib/python3.4/site-packages/tensorflow/python/framework/ops.py", line 1228, in init
self._traceback = _extract_stack()

InvalidArgumentError (see above for traceback): Nan in summary histogram for: transitions_0/grad/hist
[[Node: transitions_0/grad/hist = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](transitions_0/grad/hist/tag, gradients/AddN_4)]]

Exception ignored in: <bound method Session.del of <tensorflow.python.client.session.Session object at 0x7ff256b115f8>>
Traceback (most recent call last):
File "/dhome/jayhsu/miniconda2/envs/ENV/lib/python3.4/site-packages/tensorflow/python/client/session.py", line 587, in del
AttributeError: 'NoneType' object has no attribute 'TF_NewStatus'

Hi, @LopezGG , I run your original code by using the sample data, however, it always has an error "InvalidArgumentError (see above for traceback): Nan in summary histogram for: transitions_0/grad/hist
[[Node: transitions_0/grad/hist = HistogramSummary[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](transitions_0/grad/hist/tag, gradients/AddN_4)]]"

Can you help me ? I am looking forward to your reply. Thank you very much!

In the test_NER.py file, you forget to add the last ''' to the string.

Hi,
I have a problem with the tagging scheme. In your sample data(https://github.com/LopezGG/NN_NER_tensorFlow/tree/master/Sample_Data), the scheme is the BIOES, but the data I find(https://github.com/glample/tagger/tree/master/dataset) is BIO-1 scheme. Where can I find the BIOES version of CoNLL-2003 dataset? Thanks~

你好，感谢你的分享，让我有机会下载研究你的代码~
我在运行过程中遇到了问题，梯度值总是nan，只有第一次迭代可以正常显示loss，调整参数也没有解决问题，不知道你是否遇到，或能给我一些建议
另外我没有在项目中找到用于测试的数据，希望能分享下，感谢！

pooled = tf.nn.max_pool(
h_expand,
ksize=[1,sequence_length * max_char_per_word,1, 1],
strides=[1, max_char_per_word, 1, 1],
padding='SAME',
name="pooled")

I'm confused about the second dimension of ksize, which covers all characters in one sentence . According to the paper, as what I have understand, the filter of max pooling should cover characters in one word once rather than in the whole sentence. So I think it should be changed to "ksize=[1, max_char_per_word, 1, 1]" and I think the original code would generate same character representation for each word in a sentence (I have tested this part with small examples). I'm not sure if I misunderstand something here, what do you think?

Thank you very much for your code！

I run the Eval.py on the “sampleData/test_predictions_41000.txt”

But the result does not seem to be very high

How is the result (F1: 91.21 )in the paper obtained?

Thank you very much!!
Best Regards!

Thanks for your contribution. I am running the code successfully. I wonder whether the reimplementation of the program is approximate to the original paper result F1=91.21% or not.
[https://github.com/XuezheMax/LasagneNLP]. The Notes.pdf shows four named entity (P,R,F1), not the whole P,R,F1. Look forward to your reply. Many thanks.