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""" |
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Summary: |
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Functions to save and store a model. The current keras |
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function to do this does not work in python3. Therefore, we |
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implemented our own functions until the keras functionality has matured. |
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Example function calls in 'Tutorial mcfly on PAMAP2.ipynb' |
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""" |
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from keras.models import model_from_json |
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import json |
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import pickle |
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import numpy as np |
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def savemodel(model,filepath,modelname): |
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""" Save model + weights + params TO json + npy + pkl file, respectively |
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Input: |
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- model (Keras object) |
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- filepath: directory where the data will be stored |
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- modelname: name of the model to be used in the filename |
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""" |
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json_string = model.to_json() # save architecture to json string |
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with open(filepath + modelname + '_architecture.json', 'w') as outfile: |
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json.dump(json_string, outfile, sort_keys = True, indent = 4, ensure_ascii=False) |
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wweights = model.get_weights() #get weight from model |
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np.save(filepath+modelname+'_weights',wweights) #save weights in npy file |
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return None |
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def loadmodel(filepath,modelname): |
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""" Load model + weights FROM json + npy file, respectively |
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Input: |
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- filepath: directory where the data will be stored |
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- modelname: name of the model to be used in the filename |
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""" |
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with open(filepath + modelname + '_architecture.json', 'r') as outfile: |
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json_string_loaded = json.load(outfile) |
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model_repro = model_from_json(json_string_loaded) |
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wweights2 = model_repro.get_weights() # extracting the weights would give us the untrained/default weights |
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wweights_recovered =np.load(filepath+modelname+'_weights.npy') #load the original weights |
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model_repro.set_weights(wweights_recovered) # now set the weights |
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return model_repro |
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# If we would use standard Keras function, which stores model and weights |
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# in HDF5 format it would look like code below. However, we did not use this |
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# because |
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# https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model |
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# it is not compatible with default Keras version in python3. |
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# from keras.models import load_model |
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# import h5py |
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# modelh5=models[0] |
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# modelh5.save(resultpath+'mymodel.h5') |
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# del modelh5 |
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# modelh5 = load_model(resultpath+'mymodel.h5') |
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NLeSC /
mcfly
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