def main(argv):
try:
dataset_file = argv[1]
except IndexError:
print('Usage: %s DATASET_FILE' % argv[0], file=sys.stderr)
return 1
instances = parsing.parse_dataset_file(dataset_file)
dataset = Dataset(instances, normalize=True)
structures = ([dataset.num_inputs()] + hidden + [dataset.num_outputs()]
for hidden in HIDDEN_LAYERS)
for structure in structures:
for lambda_ in LAMBDAS:
network = NeuralNetwork(lambda_, structure)
results = cross_validation(network, dataset, NUM_FOLDS, **PARAMS)
save_results(RESULTS_DIR, results)
def main(argv):
try:
dataset_file = argv[1]
except IndexError:
print('Usage: %s DATASET_FILE' % argv[0], file=sys.stderr)
return 1
instances = parsing.parse_dataset_file(dataset_file)
dataset = Dataset(instances, normalize=True)
n_inputs = dataset.num_inputs()
n_outputs = dataset.num_outputs()
structures = ([n_inputs] + hidden_layers + [n_outputs]
for hidden_layers in hidden_layer_variations(
NUMS_HIDDEN_LAYERS, NUMS_NEURONS_PER_LAYER))
for network in network_variations(LAMBDAS, structures):
results = cross_validation(network, dataset, NUM_FOLDS, **PARAMS)
save_results(RESULTS_DIR, results)