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Need clarifications regarding a couple of points in code
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In 'lets_start.py' you are evaluating a model after every 'evaluation_period' number of epochs. But you are also running the operation 'step' during evaluation (line 44 in lets_start.py) which will also update the model weights rather than just using the learned weights to evaluate. Why is this done?. Please clarify if I am missing something.
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In the file 'model.py' at line 15, you are creating the discount factor array (self.df) and using it only for loss type 'naive' in the 'loss_func' (line 89) and not for loss type 'oi'. This is different from the paper as you mention in equation 9 in your paper that you are using it. Is there any reason for not using it in the code for 'oi' loss type?. Please clarify.
Thanks for your questions.
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This is just training time evaluation. For test time, see
eval.py. Actually, note that our eval samples and test samples are generated randomly from a distribution, which means the probability of seeing a previously tested sample is 0. Therefore is does not matter whether we apply gradient descent on the weight. -
The actual loss function we are applying is in equation (4) of our paper. The experimental details actually shows a previous version of our loss function. We apologize for the mistake.
@all, Please I am trying to run the code but am having this error about "save_path": "./ckpt/8_dim_quad/h128_s50_srnn/", that the file is missing. Kindly help me on way forward, thanks in advance
raceback (most recent call last):
File "lets_start.py", line 59, in
@GbengaOdesanmi Please create the directory ckpt\xxxx manually at the project root, and change the saving_path in config.json to xxxx