def main(argv):
if len(argv) < 3:
usage = 'Usage: %s RESULT_FILE.json DATASET_FILE'
print(usage % argv[0], file=sys.stderr)
return 1
result_file = argv[1]
dataset_file = argv[2]
with open(result_file, 'r') as file:
result = json.loads(file.read())
network = NeuralNetwork(result['lambda'], result['structure'])
dataset = Dataset(parsing.parse_dataset_file(dataset_file), normalize=True)
batches = dataset.random_folds(NUM_BATCHES)
counter = 1
print('Iterations,J_t')
for j_t in network.train(batches, **result['training'], skip=SKIP):
print('%s,%s' % (counter * SKIP, j_t))
counter += 1
if counter >= MAX_SAMPLES: break
else:
print('%s,%s' % (counter * SKIP, j_t))
def test_random_folds():
dataset = Dataset([
([0.], [1., 0.]),
([1.], [1., 0.]),
([2.], [1., 0.]),
([3.], [1., 0.]),
([4.], [0., 1.]),
([5.], [0., 1.]),
([6.], [0., 1.]),
([7.], [0., 1.]),
])
folds = dataset.random_folds(4)
assert len(folds) == 4
for fold in folds:
assert isinstance(fold, Dataset)
assert len(fold) == 2
# cada fold deve ter uma instância de cada classe
assert fold.expectations[0].tolist() != fold.expectations[1].tolist()