OpenDeep

Hey! Yep, the network configuration get saved in the outputs/model_name/config.pkl file (unless you specified a different path), and parameters are normally saved in outputs/model_name/trained_epoch_xx.pkl files. You can use the model.load_params('/path/to/param_file.pkl') after you initialize the model to load existing weights. To create a new model with the same initialization arguments, just load that config.pkl file and use Python's operator on the loaded dictionary (i.e. model = Model(config_dict)). After you initialize and load the model parameters, you can call model.run(data) to produce outputs.

I have an example character-level NER tagger here if you needed more reference: https://github.com/mbeissinger/ner/blob/master/experiment.py

Let me know if you have any more questions!

It appears that the markdown parser made the python '**' operator bold. The section that was affected is:

and use Python's ** operator on the loaded dictionary (i.e. model = Model(**config_dict))

Thank you for answering me. The example was helpful to me. I've tried several ways to implement including Caffe, Torch, or using Theano directly. Opendeep seems to be the simplest one, and the live plotting is what exactly I've been finding!

Thanks! Let me know if you have any more feedback, always trying to make it better :)

markus
I have a question on this
I tried to resue the trained model and I coded like this

dae = DenoisingAutoencoder()
result = dae.load_params('outputs/dae/bee/trained_epoch_1000.pkl')
print(result)

result is True when I run
Is this correct ? or Can I use pickle to dump it and load it later

markus
I tried to dump the dae but I got this error

cPickle.PicklingError: Can't pickle <type 'instancemethod'>: attribute lookup builtin.instancemethod failed

1) Yes that is the correct way to reuse model parameters. The load_params(filename) function returns a success boolean; the model you use will have loaded the params if it returns True. dae in your case should be using the trained_epoch_1000.pkl params.
2) Are you on the master branch? I just tried the code initializing a DenoisingAutoencoder and calling save_param('test_filename.pkl') on the model and it saved fine.

*save_params('test_filename.pkl')