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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 keras |
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import json |
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import numpy as np |
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import os |
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import uuid |
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from collections import namedtuple |
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TrainedModel = namedtuple( |
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'TrainedModel', ['history', 'model']) |
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def savemodel(model, filepath, modelname): |
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""" Save model to json file and weights to npy file |
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Parameters |
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---------- |
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model : Keras object |
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model to save |
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filepath : str |
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directory where the data will be stored |
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modelname : str |
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name of the model to be used in the filename |
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Returns |
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---------- |
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json_path : str |
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Path to json file with architecture |
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numpy_path : str |
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Path to npy file with weights |
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""" |
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json_string = model.to_json() # save architecture to json string |
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json_path = os.path.join(filepath, modelname + '_architecture.json') |
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with open(json_path, 'w') as outfile: |
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json.dump(json_string, outfile, sort_keys=True, indent=4, |
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ensure_ascii=False) |
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wweights = model.get_weights() # get weight from model |
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numpy_path = os.path.join(filepath, modelname + '_weights') |
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np.save(numpy_path, |
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wweights) # save weights in npy file |
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return json_path, numpy_path |
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def loadmodel(filepath, modelname): |
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""" Load model + weights from json + npy file, respectively |
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Parameters |
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---------- |
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filepath : str |
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directory where the data will be stored |
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modelname : str |
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name of the model to be used in the filename |
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Returns |
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---------- |
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model_repro : Keras object |
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reproduced model |
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""" |
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with open(os.path.join(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() |
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# extracting the weights would give us the untrained/default weights |
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wweights_recovered = np.load( |
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os.path.join(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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1 |
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try: |
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import noodles |
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from noodles.serial.numpy import arrays_to_string |
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from noodles.serial.namedtuple import SerNamedTuple |
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from pathlib import Path |
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class SerModel(noodles.serial.Serialiser): |
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def __init__(self, tmpdir): |
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self.tmpdir = Path(tmpdir) |
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super(SerModel, self).__init__(keras.models.Model) |
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def encode(self, obj, make_rec): |
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random_filename = self.tmpdir / (str(uuid.uuid4()) + '.hdf5') |
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obj.save(str(random_filename)) |
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return make_rec({'filename': str(random_filename)}, |
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files=[str(random_filename)], ref=True) |
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def decode(self, cls, data): |
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return keras.models.load_model(data['filename']) |
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112
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113
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def serial_registry(tmpdir='.'): |
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return noodles.serial.Registry( |
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# parent=noodles.serial.pickle() + |
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parent=noodles.serial.base() + arrays_to_string(), |
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types={ |
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keras.models.Model: SerModel(tmpdir), |
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TrainedModel: SerNamedTuple(TrainedModel) |
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} |
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) |
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1 |
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except ImportError: |
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pass |
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NLeSC /
mcfly
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