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Could you please provide the full hyper-parameters settings and the data processs for TaxiBJ and other datasets in your experiment? I want to reproduce your results but I am not very clear when I read the papers.

And another question is that, why the TaxiBJ experiment results are so different in this work and the other work of your team(Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics). One of the metric is RMSE while the other is MSE, but the square of RMSE is also not equal to MSE.(see the PredRnn result of this two work)

It is really an interesting work. could you list the experiment environment (e.g the operation system, graphics card...)? when I run the Moving MNIST experiment (follow the hyper-parameters setting in the paper) on a 2x 1080Ti, I found that the train time is so long (about 20 days). is it normal？

Hi @xzr12, thanks for great work. I have a few questions:

1. What is the scheduled sampling used in PredCNN codebase? Why use it?

2. Why in the code you are trying to divide a single image into 16 blocks via the hparam called patch_size, which is set to 4 by default? Will that destroy global shape information?

Appreciate if you could provide some answers.

@xzr12
Hi Ziru,

Thanks for your paper and code.
After skimming your paper, I have a question: Are you comparing to "VPN baseline" or "VPN"?

Best,
Pharrell

Hello,thank you for the implementation. I've tried to run the code and I'm receiving this error:
ValueError: slice index 0 of dimension 1 out of bounds. for 'predcnn/strided_slice_10' (op: 'StridedSlice') with input shapes: [32,0,16,16,16], [2], [2], [2] and with computed input tensors: input[1] = <0 0>, input[2] = <0 1>, input[3] = <1 1>.

Am I missing something?