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Keras documentation only appears to support HDF5 for storing weights:
https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model
However I see that QuantConnect implemented an ObjectStore feature here:
https://www.quantconnect.com/docs/algorithm-reference/machine-learning#Machine-Learning-Storing-Trained-Models
But when I look at the Keras example here, nothing is being stored or loaded:
https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/KerasNeuralNetworkAlgorithm.py
How can I store trained keras models with weights in QuantConnect?
Thank You!
Adam W
According toÂ
https://www.quantconnect.com/docs/key-concepts/supported-librariesThe package h5py is supported on QC, so this should be able to be put into an ObjectStore feature. Haven't played around with the feature yet, but something like might work for now:
Â
class MyAlgo(QCAlgorithm): def Initialize(self): # Store weights self.weights = None self.Schedule.On(*args, self.TrainModel) def TrainModel(self): # Some NN architecture # Some NN layer layer = Dense(5) # Weights for the layer in Numpy array layer_weights = layer.get_weights() self.weights = # Loop over all layers and store in listThen when you are making predictions, set the layer weights with layer.set_weights().
Pierre Pohly
With the Object Store u can Load and Save the weights for ur keras model.Â
Save Weights:Â
w = model.get_weights() encodedWeights = json.dumps(w, cls=NumpyArrayEncoder) self.ObjectStore.Save("model_weights", str(encodedWeights))Â
Load Weights:Â
decodedWeights = json.loads(self.ObjectStore.Read("model_weights")) w = np.asarray(decodedWeights) model.set_weights(w)Â
Jonathan Ganucheau
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