zysszy / grammarcnn Goto Github PK

I have run python3 run.py train ..., and when I run python3 run.py pre, it's slow to generate codes for a card.
It takes about 5-10 minutes for a card.

work begin
search level is 0
..... many times
1th card is finished

Is it normal? I'm assuming that if this can apply to real world applications, it should take less than 3 seconds.
Is it possible to make it faster?

单词和数字之间有什么特殊的关系吗？还是说就只是依次赋值的呀？

I encounter the following error while training the a model using
python3 run.py train [dev|test] [tree|var|func]

(py37) lab@master:~/GrammarCNN/model$ python3 run.py train [dev|test] [tree|var|func]
func]: command not found

Command 'test]' not found, did you mean:

command 'test' from deb coreutils (8.30-3ubuntu2)
command 'testr' from deb python3-testrepository (0.0.20-5)

Try: sudo apt install

Command 'var' not found, but there are 19 similar ones.

/home/lab/anaconda3/envs/py37/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/lab/anaconda3/envs/py37/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/lab/anaconda3/envs/py37/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/lab/anaconda3/envs/py37/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/lab/anaconda3/envs/py37/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/lab/anaconda3/envs/py37/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
Traceback (most recent call last):
File "run.py", line 215, in
main()
File "run.py", line 195, in main
os.system("tar -zxvf data" + sys.argv[3] + ".tar.gz")
IndexError: list index out of range
Exception ignored in: <_io.TextIOWrapper name='' mode='w' encoding='UTF-8'>
BrokenPipeError: [Errno 32] Broken pipe

Can you please suggest how I should debug the error.
Thank you

你好，请问训练中的rule.txt是怎么得到的呢？Java和python语言分别怎么处理的？

在论文里看到CNN for the Input那里有提到
For code generation of a card in HearthStone, the input is semi-structured data, contain- ing the card’s name, properties, and descriptions, illustrated in Figure 4a.
这里是token化后直接将他们拼接起来然后输入模型的么？

我看见里面有jar 包 ，python代码转ast是java转的？ 用了什么程序

请问您是在什么硬件配置下训练该模型的，训练模型大概花了多长时间，谢谢！

all I need to do is：
python3 run.py train test tree
python3 run.py train test var
python3 run.py train test func
python3 run.py test

or instead:
I need to python run.py 3*3=9 times？

I am surprised about the format of your input data which divide in a tree structure.
SO may you provide your code about how to preprocess the data?
I believe it is the most exquisite part in your work, others could draw on the experience of it.

同上

I have another question:
I find that in your code_generate_model.py
the tree-based CNN, tree-path CNN, pre-order CNN all call the same function def my_conv() in line 119-128?
so what's the difference? （except the input data）

Hello Dear Author,

This is a good implementation! Nowadays, there're not so many program generation works that make their codes open-sourced. At the same time, I see there are several AST-based baselines used in your publication, but I didn't find their official resources. It would be better if could play with some of them (Seq2Tree, SNM, and ASN) together with your GrammarCNN. Could I ask which Github resources (official or unofficial) for these AST-based baseline models are you used?