Hi I would like to propose a better implementation for 'test_indices':

We can remove the unneeded np.array casting:

Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))

Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))

I tried some RNN regression learning based on the code in the "PyTorch-Tutorial/tutorial-contents/403_RNN_regressor.py" file, which did not work for me at all.

According to an accepted answer on stack-overflow (https://stackoverflow.com/questions/52857213/recurrent-network-rnn-wont-learn-a-very-simple-function-plots-shown-in-the-q?noredirect=1#comment92916825_52857213), it turns out that the li

when i run the example which using timit to build a lstm+ctc system,i meet a mistake,the error as follows：

can you give a suggest how to deal with it,thank very much.

I add a function(according the code in the tutorial4) to calculate the bleu score, but i get the vey low score(0.09), could you tell me why?
This is code to calculate bleu:

def translate_sentence(sentence, src_field, trg_field, model, device, max_len = 50):
    
    model.eval()
        
    if isinstance(sentence, str):
        nlp = spacy.load('de')
        tokens = [token.te

I looked up the documentation for sequence_loss_by_example and it doesn't seem to be taking vocab_size as argument. I'd really appreciate it if you could help me understand what this argument is doing. Thanks a lot!
` loss = seq2seq.sequence_loss_by_example([self.logits],
[tf.reshape(self.targets, [-1])],
[tf.ones([args.batch_size * args.seq_length])],

e.g. some short methods may contain a description in the return tag, but not a description of the method itself. (to avoid redundancy).

Doing this would extract more methods, but they may be of lower quality if incorrectly parsed or automatically generated. I'd expect a description such as @return bool STARTDESCRIPTION true if this is a float to be extracted (I'm not familiar with how the dat

1. This comment should be a part of README or another document;
2. The working dump_rnn.py should be in training/, not just some link.
3. Either denoise_training should use fout again instead of stdout or fout should be removed.
4. Generated header uses float-format numbers like #define VAD_GRU_SIZE 24.0.
5. There s

大佬您好，我参考了您得assignment1中得word2vec.py得实现。但是在运行过程中梯度检测报错了。
==== Gradient check for skip-gram ====
Gradient check failed.
First gradient error found at index (0, 0)
Your gradient: -0.087147 Numerical gradient: 1254.567123
我是用py3实现的，之前的所有代码几乎一致，也都正确通过了，唯独这里通过不了。之后我将您的代码直接全部拷贝下来运行，同样报以上错误，请问您知道怎么回事嘛，您当时运行通过了吗？