Unable to open largetext weights. about textgenrnn HOT 8 OPEN

When I ran the example above and got garbage, I had trained another model in the meantime, which presumably overwrote textgenrnn_vocab.json and textgenrnn_config.json. I guess the .save() strategy always worked for regular shorttext training b/c _vocab.json and _config.json weren't changing. Probably if I went back and tried to load a shorttext model after training a longtext model, it wouldn't work either.

Solution: if I want to save a largetext model to come back to later, it looks like the strategy is to manually copy textgenrnn_weights.hdf5, textgenrnn_vocab.json and textgenrnn_config.json to a new folder before running another model.

I confirmed that this works by training a largetext model:
recipes_len20.train_from_largetext_file('datasets/recipes.txt', new_model=True, num_epochs=1, max_length=20)

Manually copying the textgenrnn_vocab.json, textgenrnn_config.json, and textgenrnn_weights.hdf5 to another folder, Training another model, And then loading a new model from the original saved weights, vocab, config:
recipes_len20_reload2 = textgenrnn(weights_path='weights/recipes_len20/textgenrnn_weights.hdf5', vocab_path='weights/recipes_len20/textgenrnn_vocab.json', config_path='weights/recipes_len20/textgenrnn_config.json')
Then I was able to sample from my saved model:

>>>recipes_len20_reload2.generate()
canned with a deveined tomatoes and crushed
salt.  Add the sausage and cook on high speed and sugar to the shrimp.  Seal the casserole in the tomato sauce, pat
each additional mixed
1  water
1  egg
1 tablespoon lemon juice
1 tablespoon curry powder
1/2 teaspoon crumbled cheddar cheese, shredded
1  c

It would be nice if .save() did all the copying-to-a-new-folder for me, but at least this seems to work.

from textgenrnn.

If you are using a custom model, you need to load with the config.json and the vocab.json that were also generated.

from textgenrnn.

When I saved my model, it looks like it only saved a .hdf5 file, not the .json files. The command I used was something like this:

recipes_old.save('weights/recipes.hdf5')

Then when I do this, after having trained some other models in the meantime:

recipes = textgenrnn(weights_path='weights/recipes.hdf5', vocab_path='textgenrnn_vocab.json', config_path='textgenrnn_config
.json')

I don't get an error message, but the generated text looks like garbage.

recipes.generate()
) o D T wT f 99 95 u5555555555" "5''80g00'..t'' '-w...ssr0r0Bswoc3c- - 9- ""T0 0A5 p5-5F9G 5T '.'qr0 l - 0 00 J - 0 - 0 5 0 - 0 0 - 0 0 - 0 F C - 6 F -

Was I supposed to save using a different method?

from textgenrnn.

The the saved weight has shape (106, 100). part of the error message implies that a new model was trained (the 106 corresponds to the size of the vocabulary). Those files should have been generated before the model started training.

Each training epoch saves the weights; there shouldn't be a reason to explicitly call .save() and I should probably consider depreciating that.

I double checked the code path of train_from_file and this order-of-operations should be working correctly, although I'm not sure why garbage got generated.

from textgenrnn.

Saving to a new folder w/ the 3 files is a good use case to avoid depreciation. I'll look into it.

Thanks for the very detailed workflow! :)

from textgenrnn.

Make some experiments when got similar error.. I found, that new_model=False work great but
new_model=True created broken weights (if weights=1,2 MB. then weights is broken). So if you need train new model, my advice is just do not specify this parameter. In that case I tested few times and library generated each time correct weights (with 1,9 MB.)
Currently using new_model=True generated broken weights each time for me.
Hope that can helps anyone!

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Adding this in case it helps people, because it's still possible to end up with a shape error with the workflow above.

textgenrnn_vocab.json and textgenrnn_config.json only update if training is run with "new_model=true"

So, if you initialize a model with my_model = textgenrnn() and then train, the training may still work but the textgenrnn_vocab.json and textgenrnn_config.json won't match your model (so you won't be able to go back to these results later) unless you also run it with "new_model=true".

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Thanks for following up, Janelle!

I admit I haven't polished the transfer learning workflow; I'll take a look at some point.

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