Should i run training first?

[zezhishao]

I am a beginner of trajectory data mining, I notice that the embedding is written after training in README. I am a little confused. What I mean is that the embedding layer is a part of the encoder-decoder model so shouldn't i train this layer first? And then use it to the entire encoder-decoder model?
I really can not figure this out :( can you help me ?

[boathit]

Hi @zezhishao, the "embedding" in README means the process of encoding a trajectory into its vector representation, it is not the embedding layer as what we described in the paper. Sorry for making you feel confused, I had better change it to the term "encoding".

[zezhishao]

@boathit Thanks for your reply!

[zezhishao]

Hi~ @boathit I have some questions again~
The python program can not run correctly. The nn in pytorch 0.1.2 does not have the class KLDivLoss. And the KLDivLoss of latest version of pytorch is not a _WeightedLoss class, see at:

_Loss class
_WeightedLoss
latest KLDivLoss

So , in train.py, I can't pass this row:

criterion = nn.KLDivLoss(weight, size_average=False)

And I have another question for a comment in the batch_loss function in train.py :

In there, the comment is :
## (seq_len, generator_batch, hidden_size) => (seq_len*generator_batch, hidden_size).

After zip(output.split(generator_batch), target.split(generator_batch))
Should the o and t be # (generator_batch, args.batch_size, hidden_size)?

[boathit]

You are correct the size of o should be (generator_batch, batch_size, hidden_size). But PyTorch 0.1.12 does have KLDivLoss, please double check your PyTorch version. By the way, I plan to update the code to PyTorch 1.0+ within next week.

[boathit]

I made a typo in the requirement, it should be PyTorch 0.1.12 not 0.1.2. Sorry about that.

[zezhishao]

You are correct the size of o should be (generator_batch, batch_size, hidden_size). But PyTorch 0.1.12 does have KLDivLoss, please double check your PyTorch version. By the way, I plan to update the code to PyTorch 1.0+ within next week.

That's cool!