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the kwyk models should be converted to tensorflow 2 and tensorflow-probability. this will make transfer learning / hacking easier for more users.
this works with tensorflow 1.13.1 and uses the saved models in https://github.com/patrick-mcclure/nobrainer.
the spike and slab model uses variables p in concrete dropout, whereas the other two models do not.
p
import numpy as np import tensorflow as tf # Load saved model. export_dir = 'saved_models/all_50_bvwn_multi_prior/1556816070/' predictor = tf.contrib.predictor.from_saved_model(export_dir) # Get pre-trained weights. ps = predictor.session.run(predictor.graph.get_collection('ps')) ms = predictor.session.run(predictor.graph.get_collection('ms')) sigmas = predictor.session.run(predictor.graph.get_collection('sigmas')) # Save to file. np.savez_compressed('spike-slab.npz', ms=ms, sigmas=sigmas, ps=ps)
related to neuronets/trained-models#3
The text was updated successfully, but these errors were encountered:
closed by neuronets/nobrainer#80
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the kwyk models should be converted to tensorflow 2 and tensorflow-probability. this will make transfer learning / hacking easier for more users.
instructions to extract the pre-trained weights from saved models
this works with tensorflow 1.13.1 and uses the saved models in https://github.com/patrick-mcclure/nobrainer.
the spike and slab model uses variables
p
in concrete dropout, whereas the other two models do not.related to neuronets/trained-models#3
The text was updated successfully, but these errors were encountered: