carpedm20 / memn2n-tensorflow Goto Github PK

ptb data is not the QA task, so it makes me confused.
Thank you @carpedm20

Hello @carpedm20 , thanks for this project, it is helping me get better insight into the paper :)
Quick question: during re-implementation of the code, I am getting the following error:

Aout = tf.matmul(self.hid3dim, Ain, adjoint_b=True) TypeError: matmul() got an unexpected keyword argument 'adjoint_b'

Any idea what the cause could be?

Thanks a ton :)

Can you pls add a requirements.txt ?

Hi, thanks for the code.

I'm getting the following error when running main

Traceback (most recent call last):
  File "main.py", line 52, in <module>
    tf.app.run()
  File "/home/eders/anaconda/lib/python2.7/site-packages/tensorflow/python/platform/default/_app.py", line 30, in run
    sys.exit(main(sys.argv))
Traceback (most recent call last):
  File "main.py", line 52, in <module>
    tf.app.run()
  File "/home/eders/anaconda/lib/python2.7/site-packages/tensorflow/python/platform/default/_app.py", line 30, in run
    sys.exit(main(sys.argv))
  File "main.py", line 43, in main
    model = MemN2N(FLAGS, sess)
  File "/home/eders/python/MemN2N-tensorflow/model.py", line 26, in __init__
    raise Exception(" [!] Directory %s not found" % self.checkpoint_dir)
Exception:  [!] Directory checkpoints not found

I tried to use --data_dir but that didn't work either.

I am a student of Yunnan University in China and learning the code ,could you help me get the 'past.builtins'? Thangks

I don't know how to use this model.
I need a code to answer questions.

ex)
context{ Sam walks into the kitchen
Sam picks up an apple
Sam walks into the bedroom
Sam drops the apple
}

     Q: Where is the apple?
     A: Bedroom

please exam code

In the code of this repo, context matrix of shape [batch_size, mem_size] is chosen randomly as below
m = random.randrange(self.mem_size, len(data)) target[b][data[m]] = 1 context[b] = data[m - self.mem_size:m]
My quesiton (I am sorry it is not actually an 'issue' but my personal quesion) is what approaches I can take to get better result rather than just random?
Any kind of material that is helpful is welcomed :)

https://nlp.stanford.edu/blog/a-new-multi-turn-multi-domain-task-oriented-dialogue-dataset/

how to solve this?

envy@ub1404:/media/envy/data1t/os_prj/github/MemN2N-tensorflow$ PYTHONPATH=~/os_prj/github/tensorflow/_python_build python main.py --nhop 6 --mem_size 100
Read 929589 words from data/ptb.train.txt
Read 73760 words from data/ptb.valid.txt
Read 82430 words from data/ptb.test.txt
{'batch_size': 128,
'checkpoint_dir': 'checkpoints',
'data_dir': 'data',
'data_name': 'ptb',
'edim': 150,
'init_hid': 0.1,
'init_lr': 0.01,
'init_std': 0.05,
'is_test': False,
'lindim': 75,
'max_grad_norm': 50,
'mem_size': 100,
'nepoch': 100,
'nhop': 6,
'nwords': 10000,
'show': False}
Traceback (most recent call last):
File "main.py", line 52, in
tf.app.run()
File "/home/envy/os_pri/github/tensorflow/_python_build/tensorflow/python/platform/default/_app.py", line 30, in run
sys.exit(main(sys.argv))
File "main.py", line 43, in main
model = MemN2N(FLAGS, sess)
File "/media/envy/data1t/os_prj/github/MemN2N-tensorflow/model.py", line 26, in init
raise Exception(" [!] Directory %s not found" % self.checkpoint_dir)
Exception: [!] Directory checkpoints not found
envy@ub1404:/media/envy/data1t/os_prj/github/MemN2N-tensorflow$

Hi,

Thanks for the code. I notice that in the original code, the weights of C are shared, as in https://github.com/facebook/MemNN/blob/946c4784b59dcf053bbbbb9637d6814bc152c276/MemN2N-lang-model/model.lua#L67-L74

However, I cannot find the weight sharing part in this code. Did I miss something? Thank you.

I found your code measuring norms and clipping gradients for each parameter separately. But in the paper, the authors said "the l2 norm of the whole gradient of all parameters..." and your method was used in QA tasks.