In line 26 in the Cnn.py file, did you mean to set trainable=True (its the default setting default).

If yes, then is there a theoretical justification for it or did you empirically find this to give the best performance?

W = tf.Variable(tf.random_uniform([62, 5],
minval=-math.sqrt(6/(3position_size+3embedding_size)),
maxval=math.sqrt(6/(3position_size+3embedding_size))),
name="W")
这几行代码的功能是为了干什么啊？

我的邮箱[email protected].

I get this error when running train.py:

FileNotFoundError: [Errno 2] No such file or directory: '../data/vector1.txt'

Where can I get vector1.txt?

您好，我想问一下，我在使用你的模型时，在进行预测的时候，模型的给出的预测值，总是一个固定值，没有变化，我想请问一下，这个是什么问题。

Originally posted by @Mariobai in #5 (comment)

你好，我想问一下你这预测是在哪里预测的，为什么我这找不到呢？

训练是报以下错误：
FileNotFoundError: [Errno 2] No such file or directory: '../data/vector1.txt'
请问之前data目录下有vector1.txt这个文件吗？有的话可以提供吗？谢谢！

The test data should be collected for each entity pair in the map, self.bags_test (map<str, list>); however, the keys are set in a different way when a key is checked(L96). This bug effects test.py and test scores.

CURRENT : if entity1+"\t"+entity2 not in self.bags_test:
SUGGESTED : if entity1+" "+entity2 not in self.bags_test:

您好，我跑了train.py，得到如下报错，谷歌了好多没有解决，希望您有空能回复下我，报错如下：
/home/lijing/Envs/information_ex/bin/python /home/lijing/Desktop/resre/ResCNN_RelationExtraction-master/ResidualCNN9/train.py

Parameters:
ALLOW_SOFT_PLACEMENT=True
BATCH_SIZE=64
CHECKPOINT_EVERY=100
DROPOUT_KEEP_PROB=0.5
EMBEDDING_DIM=50
EVALUATE_EVERY=1000
FILTER_SIZES=3
L2_REG_LAMBDA=0.0
LOG_DEVICE_PLACEMENT=False
NUM_EPOCHS=200
NUM_FILTERS=128
SEQUENCE_LENGTH=100

WordTotal= 114044
Word dimension= 1
RelationTotal: 53
Start loading training data.

Start loading testing data.

570088 96678
2017-10-09 11:13:25.942678: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-09 11:13:25.943045: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-09 11:13:25.943053: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-10-09 11:13:25.943059: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-09 11:13:25.943065: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
Finish randomize data
Start Training
Writing to /home/lijing/Desktop/resre/ResCNN_RelationExtraction-master/ResidualCNN9/runs/1507518810

Initialize variables.
Batch data
Traceback (most recent call last):
File "/home/lijing/Desktop/resre/ResCNN_RelationExtraction-master/ResidualCNN9/train.py", line 152, in
loss = train_step(x_batch, y_batch, p1_batch, p2_batch)
File "/home/lijing/Desktop/resre/ResCNN_RelationExtraction-master/ResidualCNN9/train.py", line 110, in train_step
feed_dict)
File "/home/lijing/Envs/information_ex/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 895, in run
run_metadata_ptr)
File "/home/lijing/Envs/information_ex/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1100, in _run
% (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (64, 100, 1) for Tensor 'input_x:0', which has shape '(?, 100, 50)'

Process finished with exit code 1

我的环境如下：
bleach (1.5.0)
html5lib (0.9999999)
jieba (0.39)
Markdown (2.6.9)
network (0.1)
nltk (3.2.5)
numpy (1.13.3)
pip (9.0.1)
protobuf (3.4.0)
pudb (2017.1.1)
Pygments (1.3)
scikit-learn (0.19.0)
setuptools (36.5.0)
six (1.11.0)
tensorflow (1.3.0)
tensorflow-tensorboard (0.1.7)
Werkzeug (0.12.2)
wheel (0.30.0)

how you evaluate the pr?
i couldn't understand your code about this part.