def test_reasonet(device_id):
print("Device Id: {0}".format(device_id))
if device_id < 0:
pytest.skip('test only runs on GPU')
if sys.version_info[0] < 3:
pytest.skip('test only runs on Python 3.x')
import ReasoNet.reasonet as rsn
device.set_default_device(cntk_device(device_id))
data_path = os.path.join(module_path, "data/test.txt")
eval_path = os.path.join(module_path, "data/test.txt")
vocab_dim = 101100
entity_dim = 101
epoch_size = 1159400
eval_size = 1159400
hidden_dim = 300
max_rl_iter = 5
max_epochs = 1
embedding_dim = 100
params = rsn.model_params(vocab_dim=vocab_dim,
entity_dim=entity_dim,
hidden_dim=hidden_dim,
embedding_dim=embedding_dim,
embedding_init=None,
dropout_rate=0.2)
train_data = rsn.create_reader(data_path,
vocab_dim,
entity_dim,
True,
rand_size=epoch_size)
eval_data = rsn.create_reader(
eval_path, vocab_dim, entity_dim, False,
rand_size=eval_size) if eval_path is not None else None
embedding_init = None
#model = rsn.create_model(vocab_dim, entity_dim, hidden_dim, embedding_init=embedding_init, embedding_dim=embedding_dim, max_rl_iter=max_rl_iter, dropout_rate=0.2)
model = rsn.create_model(params)
learner = rsn.create_adam_learner(model.parameters)
(train_loss, train_acc,
eval_acc) = rsn.train(model,
params,
learner,
train_data,
max_epochs=max_epochs,
epoch_size=epoch_size,
save_model_flag=False,
model_name=os.path.basename(data_path),
eval_data=eval_data,
eval_size=eval_size,
check_point_freq=1,
minibatch_size=20000)
assert abs(train_loss - 0.0668) < 1e-2
assert abs(train_acc - 0.2179) < 1e-2
assert abs(eval_acc - 0.282) < 1e-2
def test_reasonet(device_id, is_1bit_sgd):
print("Device Id: {0}".format(device_id))
if device_id < 0:
pytest.skip('test only runs on GPU')
if is_1bit_sgd != 0:
pytest.skip('test doesn\'t support 1bit sgd')
import ReasoNet.reasonet as rsn
device.set_default_device(cntk_device(device_id))
data_path = os.path.join(module_path, "Data/fast_test.txt")
eval_path = os.path.join(module_path, "Data/fast_test.txt")
vocab_dim = 101100
entity_dim = 101
epoch_size = 1159400
eval_size = 1159400
hidden_dim = 256
max_rl_iter = 5
max_epochs = 1
embedding_dim = 300
att_dim = 384
params = rsn.model_params(vocab_dim=vocab_dim,
entity_dim=entity_dim,
hidden_dim=hidden_dim,
embedding_dim=embedding_dim,
embedding_init=None,
attention_dim=att_dim,
dropout_rate=0.2)
train_data = rsn.create_reader(data_path, vocab_dim, entity_dim, True)
eval_data = rsn.create_reader(eval_path, vocab_dim, entity_dim,
False) if eval_path is not None else None
embedding_init = None
model = rsn.create_model(params)
learner = rsn.create_adam_learner(model.parameters)
(train_loss, train_acc,
eval_acc) = rsn.train(model,
params,
learner,
train_data,
max_epochs=max_epochs,
epoch_size=epoch_size,
save_model_flag=False,
model_name=os.path.basename(data_path),
eval_data=eval_data,
eval_size=eval_size,
check_point_freq=1,
minibatch_size=5000)
assert abs(train_loss - 0.08067) < 1e-2
assert abs(train_acc - 0.21635) < 1e-2
if sys.version_info >= (3, ):
assert abs(eval_acc - 0.304) < 1e-2
else:
assert abs(eval_acc - 0.312) < 1e-2