74 lines
1.9 KiB
Python
Executable File
74 lines
1.9 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright (c) 2017 Mycroft AI Inc.
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from prettyparse import create_parser
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from precise.model import create_model
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from precise.params import inject_params, save_params
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from precise.train_data import TrainData
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usage = '''
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Train a new model on a dataset
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:model str
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Keras model file (.net) to load from and save to
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:-e --epochs int 10
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Number of epochs to train model for
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:-sb --save-best
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Only save the model each epoch if its stats improve
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:-nv --no-validation
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Disable accuracy and validation calculation
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to improve speed during training
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:-mm --metric-monitor str loss
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Metric used to determine when to save
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:-em --extra-metrics
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Add extra metrics during training
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...
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'''
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def main():
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args = TrainData.parse_args(create_parser(usage))
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inject_params(args.model)
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save_params(args.model)
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data = TrainData.from_both(args.db_file, args.db_folder, args.data_dir)
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print('Data:', data)
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(inputs, outputs), test_data = data.load(True, not args.no_validation)
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print('Inputs shape:', inputs.shape)
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print('Outputs shape:', outputs.shape)
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if test_data:
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print('Test inputs shape:', test_data[0].shape)
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print('Test outputs shape:', test_data[1].shape)
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if 0 in inputs.shape or 0 in outputs.shape:
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print('Not enough data to train')
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exit(1)
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model = create_model(args.model, args.no_validation, args.extra_metrics)
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model.summary()
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from keras.callbacks import ModelCheckpoint
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checkpoint = ModelCheckpoint(args.model, monitor=args.metric_monitor,
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save_best_only=args.save_best)
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try:
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model.fit(inputs, outputs, 5000, args.epochs, validation_data=test_data,
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callbacks=[checkpoint])
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except KeyboardInterrupt:
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print()
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finally:
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model.save(args.model)
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if __name__ == '__main__':
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main()
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