thomasmesnard / deepmind-teaching-machines-to-read-and-comprehend Goto Github PK

Hi,
I found that the variable n_entities is not be initially assigned. There will be a error when the class QADataset is constructed.

I got it ... so sorry asking such a stupid question

Hey Guys,

Firstly great work! Are you able share the Test file or script to produce predictions from the model?

Cheers

It failed when writing model to folder model_params after finished epoch 0 first 1000 steps. It's not a big issue but wasted several hours since I was not aware of that. Please add check (and create that folder in case of missing) in train.py. Thanks!

I found that when representing the context word (concatenating the forward and backward rnn), the backward rnn is not reversed to match the sequence order of the forward rnn.

Hi,
How can I check the performance of the trained model on the test data?

First of all, awesome work on this project!

I have trained a model. How can I get predictions from the trained model?

Thanks,

Raffi

Hi,

I want to make my own ale models for DQN Reinforce Learning using my video file input.

In video images, there are class labels in the corner for supervised training.
How can I make my own ALE model?

Thank you in advances.

Can I get the pre trained weights file for this model?

rzai@rzai00:/prj/DeepMind-Teaching-Machines-to-Read-and-Comprehend$ export DATAPATH=/prj/DeepMind-Teaching-Machines-to-Read-and-Comprehend/

rzai@rzai00:/prj/DeepMind-Teaching-Machines-to-Read-and-Comprehend$ python train.py deepmind_deep_lstm
Using gpu device 0: GeForce GTX 1080 (CNMeM is disabled, cuDNN 5005)
No plotting extension available.
Traceback (most recent call last):
File "train.py", line 46, in
ds, train_stream = data.setup_datastream(path, vocab_path, config)
File "/home/rzai/prj/DeepMind-Teaching-Machines-to-Read-and-Comprehend/data.py", line 137, in setup_datastream
ds = QADataset(path, vocab_file, config.n_entities, need_sep_token=config.concat_ctx_and_question)
AttributeError: 'module' object has no attribute 'n_entities'
rzai@rzai00:/prj/DeepMind-Teaching-Machines-to-Read-and-Comprehend$

rzai@rzai00:/prj/DeepMind-Teaching-Machines-to-Read-and-Comprehend$
rzai@rzai00:/prj/DeepMind-Teaching-Machines-to-Read-and-Comprehend$ CUDA_VISIBLE_DEVICES=1 python train.py deepmind_deep_lstm
Using gpu device 0: GeForce GTX 1080 (CNMeM is disabled)
No plotting extension available.
<module 'config.deepmind_deep_lstm' from '/home/rzai/prj/DeepMind-Teaching-Machines-to-Read-and-Comprehend/config/deepmind_deep_lstm.pyc'>
Traceback (most recent call last):
File "train.py", line 53, in
m = config.Model(config, ds.vocab_size)
File "/home/rzai/prj/DeepMind-Teaching-Machines-to-Read-and-Comprehend/model/deep_lstm.py", line 74, in init
cost = Softmax().categorical_cross_entropy(answer, probs).mean()
File "/usr/local/lib/python2.7/dist-packages/blocks/bricks/base.py", line 362, in call
return self.application.apply(self, *args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/blocks/bricks/base.py", line 297, in apply
outputs = self.application_function(brick, *args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/blocks/bricks/simple.py", line 390, in categorical_cross_entropy
x.copy(name='log_probabilities'))
TypeError: copy() got an unexpected keyword argument 'name'
rzai@rzai00:~/prj/DeepMind-Teaching-Machines-to-Read-and-Comprehend$

Firstly, thank you for open sourcing this project.

How did you get the vocab.txt file? There are 29,406 words in the file.

However, I counted all the unique words in the CNN dataset and there are 119,567 unique words.