The tf.contrib module is depreciated so the data can't be loaded.
Also the link given to download the data is not working anymore.
Could you please check this?

请问这个模型应该怎么存，我想写一个单独的预测函数，在训练位置saver.save(sess, '../save_model/atttn_lstm_hierarchical/')
在预测的地方加载模型的时候使用

 with tf.Session()as sess:
        saver = tf.train.import_meta_graph('./save_model/atttn_lstm_hierarchical/-1000.meta')
        saver.restore(sess, tf.train.latest_checkpoint('./save_model/atttn_lstm_hierarchical'))

但是报错如下：

请问是什么问题呢

I am running your code without modifications on the dbpedia dataset and is receiving non-valid results as follows:

..
Validation accuracy: 0.000
Training finished, time consumed : 133.85788369178772 s
Start evaluating:
Test accuracy : 0.000000 %

Could you tell me what's wrong with "Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)" when I run cnn.py?

您好 我想问一下

   logits, self.cls_loss = cal_loss_logit(batch_embedded, self.keep_prob, reuse=False)
    embedding_perturbated = self._add_perturbation(batch_embedded, self.cls_loss)
    adv_logits, self.adv_loss = cal_loss_logit(embedding_perturbated, self.keep_prob, reuse=True)

这里为什么在不加扰动的时候，reuse=False，为什么在计算加上扰动求loss的时候reuse=True呢

谢谢~

I looked up again and again and the dimension of the output of tf.embedding_lookup should be [?,256,128], but you have written it as [?,256,100](in comments) either I couldn't understand the concept or there's something really wrong there. Please clarify.

Hi:
I have read the paper: Adversarial Training Methods For Semi-Supervised Text Classification.
Does the normalization of the input vectors need to be added if the input embeddings are not trainable ？ Because I know in the case of the trainable input embeddings, the normalization must exist.
What do you have in mind ？

这里马上要求出y的预测值了，而类别只有15个。而不是32个。
这里FC_W 的shape应该是：
[self.hidden_size, self.n_class]
而不是
[self.hidden_size, self.max_len]
吧？下面的FC_b 也是同样的问题。
老哥来看看是不是有问题，还是我理解错了~ @TobiasLee

Hi there, as I did see in your attn_bi_lstm.py (maybe also other approaches), used a random technique in embedding words. I did give it a try to use pre-trained embedding, however, I do not know how to set it up, also get an error with "must have rank at least 3" (so sorry, I am newer to Tensorflow). Thank you and much appreciate.

Word embedding

    embeddings_var = tf.Variable(tf.random_uniform([self.vocab_size, self.embedding_size], -1.0, 1.0),
                                 trainable=True)
    batch_embedded = tf.nn.embedding_lookup(embeddings_var, self.x)

    rnn_outputs, _ = bi_rnn(BasicLSTMCell(self.hidden_size),
                            BasicLSTMCell(self.hidden_size),
                            inputs=batch_embedded, dtype=tf.float32)

My trial

def generate_embedding(word_index, model_embedding,EMBEDDING_DIM):
  count6 = 0
   countNot6 = 0
   #embedding_matrix = np.zeros((len(word_index) + 1, EMBEDDING_DIM)) 
   embedding_matrix = np.asarray([np.random.uniform(-0.01,0.01,EMBEDDING_DIM) for _ in range((len(word_index) + 1))])
   list_oov = []
   for word, i in word_index.items():
       try:
           embedding_vector = model_embedding[word]
       except:
           list_oov.append(word)
           countNot6 +=1
           continue
       if embedding_vector is not None:
           count6 +=1
           embedding_matrix[i] = embedding_vector
   return embedding_matrix

batch_embedded = generate_embedding(word_index,word_embedding,EMBEDDING_DIM)
rnn_outputs, _ = bi_rnn(BasicLSTMCell(self.hidden_size),
                               BasicLSTMCell(self.hidden_size),
                               inputs=batch_embedded, dtype=tf.float32)

Note that, I got an error at inputs=batch_embedded

when I run the statement, it gives me an NameError
"NameError: name 'FLAGS' is not defined"
Could you please tell me what is 'FLAGS' referring to?

First of all thank you for your effort. My problem is that the implementation run correctly when I use dbpedia dataset, but when I try to my dataset I got an accuracy of 0. My dataset is in the Arabic language.

Epoch 19 start !
Train Epoch time: 108.163 s
validation accuracy: 0.932
Epoch 20 start !
Train Epoch time: 107.236 s
validation accuracy: 0.936
Training finished, time consumed : 2188.0112912654877 s
Start evaluating:

Test accuracy : 93.619048 %

This is the performance in attn_bi_lstm.py。 Why it’s lower than 98.23 % in readme?
Thanks!

i ran your model "adversarial_abblstm.py", but the compiler shows file /dbpedia_data/dbpedia_csv/train.csv does not exist, could you tell me where to download the train.csv and text.csv?

I am getting above error when I run bilstm attention program on DBpedia dataset

Traceback (most recent call last):
File "attn_bi_lstm.py", line 112, in
return_dict = run_train_step(classifier, sess, (x_batch, y_batch))
File "/home/kbk/Desktop/BudddiHealth/higher models/Text-Classification-master/models/utils/model_helper.py", line 26, in run_train_step
return sess.run(to_return, feed_dict)
File "/home/kbk/miniconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 877, in run
run_metadata_ptr)
File "/home/kbk/miniconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1076, in _run
str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (32, 15) for Tensor 'Placeholder_1:0', which has shape '(?,)'

> python attn_bi_lstm.py

InvalidArgumentError (see above for traceback): Received a label value of 
-2147483648 which is outside the valid range of [0, 15).  Label values: -2147483648
 -2147483648 -2147483648 -2147483648 -2147483648 -2147483648 
-2147483648 -2147483648 -2147483648 -2147483648 -2147483648
 -2147483648 -2147483648 -2147483648 -2147483648 -2147483648 
-2147483648 -2147483648 -2147483648 -2147483648 -2147483648 
-2147483648 -2147483648 -2147483648 -2147483648 -2147483648 
-2147483648 -2147483648 -2147483648 -2147483648 -2147483648 -2147483648
	 [[{{node SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits}}
 = SparseSoftmaxCrossEntropyWithLogits[T=DT_FLOAT, Tlabels=DT_INT32, 
_device="/job:localhost/replica:0/task:0/device:CPU:0"](xw_plus_b, _arg_Placeholder_1_0_1)]]

Printing y_train values:

5164    NaN
3458    NaN
3236    NaN
3118    NaN
1555    NaN
930     NaN
3188    NaN
2899    NaN
2918    NaN
1431    NaN
2373    NaN
1205    NaN
2734    NaN
2560    NaN
1495    NaN
5430    NaN
2912    NaN
2098    NaN
2410    NaN
4482    NaN
1045    1.0
2469    NaN
1703    NaN
250     NaN
5214    NaN
4767    NaN
849     NaN
976     NaN
5489    NaN
5545    NaN
5241    NaN
3128    NaN

Thanks

excuse me, the code in attn_bi_lstm.py, the graph of ABLSTM, I don't see you use "attention"。Maybe you just only use Bi-LSTM?

download it from Baidu Yun.