Reference trained models.
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I saved the model/checkpoint I used for comparison with humans on munctional0:
/home/ardila/ConvNet__2013-11-22_15.25.16
@yamins81 is adding it to the database
This has been done. I've put this model in the "convnet_checkpoint_db" database, in the gridfs filesystem named "reference_models".
E.g. you can get this fs by running
import gpumodel
fs = gpumodel.get_checkpoint_fs(host, port, 'convnet_checkpoint_db', 'reference_models')
I inserted the model with the following experiment_data key:
{'experiment_id': 'imagenet_training_reference_0'}
where host/port is where you have your database connection (forwarding to dicarlo5.mit.edu:27017)
@yamins81 If I want to further train a reference model, what is the correct way to do this? I imagine that if I follow what is done in the examples, new checkpoints will be saved to the same database.
You follow the existing example; but you'll want to change the "experiment_id" value in the experiment_data dictionary. You'll also want to change the filesystem collection in this case; you don't want to save random models into the "reference_models" collection, but instead into some other collection (e.g., the default one, "convnet_checkpoint_fs").
I understand the changes you think should be made, and I can try them, but I don't understand why what you are saying doesn't load an untrained model. What about the call to convnet.py is telling it to load the trained model?
--load-query is used