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 run_training_on(dataset, working_directory):
"""Create a fresh trainer to train a model on the dataset.
The working directory is used to store intermediate model instances."""
model_trainer = MLPMeasurementModelTrainer(working_directory)
trained_model = model_trainer.train(dataset)
# Extract the ordered labels to map them to the outputs of the network
labels = dataset.ordered_labels()
# Convert the model to a string representation. It can be loaded later to apply it to new data
str_model = neon2iosmlp.model2string(model_trainer.model_path)
# Retrieve the layer configuration (number of nodes in each layer) to be able to reconstruct the network
layer_config = model_trainer.layers(dataset, trained_model)
return str_model, layer_config, labels
def run_training_on(dataset, working_directory):
"""Create a fresh trainer to train a model on the dataset.
The working directory is used to store intermediate model instances."""
model_trainer = MLPMeasurementModelTrainer(working_directory)
trained_model = model_trainer.train(dataset)
# Extract the ordered labels to map them to the outputs of the network
labels = dataset.ordered_labels()
# Convert the model to a string representation. It can be loaded later to apply it to new data
str_model = neon2iosmlp.model2string(model_trainer.model_path)
# Retrieve the layer configuration (number of nodes in each layer) to be able to reconstruct the network
layer_config = model_trainer.layers(dataset, trained_model)
return str_model, layer_config, labels
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