Comments (6)
Have you generated the dataset using the first command ? If not, first generate the dataset, then launch the second command. Otherwise, can you check that your datasets/carracing folder is not empty (beware, there's an s at the end of dataset, if you generated in dataset/carracing, this might be the cause of your issue) ?
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I generated the dataset with the same command structure, but used 500 rollouts and 64 threads. And yes, I meant datasets with "s". So the path to my *.npz is "world-models/datasets/carracing/thread_x/rollout_y.npz", where x goes from 0 to 63, and y goes from 0 to 7.
Should I run the first command with 1000 rollouts?
Thanks!
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Ok, this is probably the problem, you need to generate at least 601 rollouts ... That's a bit hardcoded, but basically we are using the first 600 rollout files to train and the rest to test. If you generate at least 800 rollouts (to have a decent test set) you should be fine.
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Alright, I will try to get >601 and report back!
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Small correction, we use the 600 lasts as test set, and the rest as train set.
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The VAE is finally training, thanks!
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Related Issues (20)
- Shouldn't this be outside the for loop? HOT 3
- Data generation script: No module named 'utils' HOT 3
- Different transform in trainvae.py & trainmdrnn.py HOT 2
- sleep(0.1) leads to infty loops HOT 3
- inconsisent MDRNN / MDRNNCell behavoir HOT 3
- Error training MD-rnn HOT 3
- Splitting of Train and Validation / Test set HOT 1
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- Possible error when predicting next action (class RolloutGenerator) HOT 7
- a multi-process problem in the controller
- Training the controller and getting stuck in local minima HOT 5
- The definition of GMM linear layer may wrong? Or I have missed something? HOT 4
- Multiprocessing very slow HOT 2
- issue about gmm_loss HOT 2
- Controller Input HOT 1
- the train_controller always break off when trainning about 15min
- MDRNN losses extremely low due to numerical instability?
- problem about training VAE
- MDRNN doesn't train properly on carracing?
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