def learn_model_from_data(dataset_directory, working_directory):
dataset = AccelerationDataset(dataset_directory)
mlpmodel = MLPMeasurementModel(working_directory)
mlpmodel.train(dataset)
dataset.save_labels(os.path.join(working_directory, "labels.txt"))
neon2iosmlp.convert(mlpmodel.model_path, os.path.join(working_directory, "weights.raw"))
return mlpmodel.model_path
def learn_model_from_data(dataset, working_directory, model_name):
"""Use MLP to train the dataset and generate result in working_directory"""
model_trainer = MLPMeasurementModelTrainer(working_directory)
trained_model = model_trainer.train(dataset)
dataset.save_labels(os.path.join(working_directory, model_name + '_model.labels.txt'))
neon2iosmlp.convert(model_trainer.model_path, os.path.join(working_directory, model_name + '_model.weights.raw'))
layers = model_trainer.layers(dataset, trained_model)
neon2iosmlp.write_layers_to_file(layers, os.path.join(working_directory, model_name + '_model.layers.txt'))
return model_trainer, trained_model
def learn_model_from_data(dataset, working_directory, model_name):
"""Use MLP to train the dataset and generate result in working_directory"""
model_trainer = MLPMeasurementModelTrainer(working_directory)
trained_model = model_trainer.train(dataset)
dataset.save_labels(
os.path.join(working_directory, model_name + '_model.labels.txt'))
neon2iosmlp.convert(
model_trainer.model_path,
os.path.join(working_directory, model_name + '_model.weights.raw'))
layers = model_trainer.layers(dataset, trained_model)
neon2iosmlp.write_layers_to_file(
layers,
os.path.join(working_directory, model_name + '_model.layers.txt'))
return model_trainer, trained_model