def run1(self): # test distributed training print('local rank', get_local_rank()) set_rng_seed(100) data_set = prepare_fake_dataset() data_set.set_input("x", flag=True) data_set.set_target("y", flag=True) model = NaiveClassifier(2, 2) trainer = DistTrainer( model=model, train_data=data_set, optimizer=SGD(lr=0.1), loss=CrossEntropyLoss(pred="predict", target="y"), batch_size_per_gpu=8, n_epochs=3, print_every=50, save_path=self.save_path, ) trainer.train() """ # 应该正确运行 """ if trainer.is_master and os.path.exists(self.save_path): shutil.rmtree(self.save_path)
def run4(self): set_rng_seed(100) data_set, model = prepare_env() train_set, dev_set = data_set.split(0.3) model = NaiveClassifier(2, 1) trainer = DistTrainer( train_set, model, optimizer=SGD(lr=0.1), loss=BCELoss(pred="predict", target="y"), batch_size_per_gpu=32, n_epochs=3, print_every=50, dev_data=dev_set, metrics=AccuracyMetric(pred="predict", target="y"), validate_every=-1, save_path=self.save_path, ) trainer.train() """ # 应该正确运行 """ if trainer.is_master and os.path.exists(self.save_path): shutil.rmtree(self.save_path)
def test_case(self): def prepare_fake_dataset(): mean = np.array([-3, -3]) cov = np.array([[1, 0], [0, 1]]) class_A = np.random.multivariate_normal(mean, cov, size=(1000,)) mean = np.array([3, 3]) cov = np.array([[1, 0], [0, 1]]) class_B = np.random.multivariate_normal(mean, cov, size=(1000,)) data_set = DataSet([Instance(x=[float(item[0]), float(item[1])], y=[0.0]) for item in class_A] + [Instance(x=[float(item[0]), float(item[1])], y=[1.0]) for item in class_B]) return data_set data_set = prepare_fake_dataset() data_set.set_input("x") data_set.set_target("y") model = NaiveClassifier(2, 1) trainer = Trainer(data_set, model, loss=BCELoss(pred="predict", target="y"), n_epochs=1, batch_size=32, print_every=50, optimizer=SGD(lr=0.1), check_code_level=2, use_tqdm=False, callbacks=[EchoCallback()]) trainer.train()
def test_save_path(self): data_set = prepare_fake_dataset() data_set.set_input("x", flag=True) data_set.set_target("y", flag=True) train_set, dev_set = data_set.split(0.3) model = NaiveClassifier(2, 1) save_path = 'test_save_models' trainer = Trainer(train_set, model, optimizer=SGD(lr=0.1), loss=BCELoss(pred="predict", target="y"), batch_size=32, n_epochs=10, print_every=50, dev_data=dev_set, metrics=AccuracyMetric(pred="predict", target="y"), validate_every=-1, save_path=save_path, use_tqdm=True, check_code_level=2) trainer.train()
def prepare_env(): mean = np.array([-3, -3]) cov = np.array([[1, 0], [0, 1]]) class_A = np.random.multivariate_normal(mean, cov, size=(1000,)) mean = np.array([3, 3]) cov = np.array([[1, 0], [0, 1]]) class_B = np.random.multivariate_normal(mean, cov, size=(1000,)) data_set = DataSet([Instance(x=[float(item[0]), float(item[1])], y=[0.0]) for item in class_A] + [Instance(x=[float(item[0]), float(item[1])], y=[1.0]) for item in class_B]) data_set.set_input("x") data_set.set_target("y") model = NaiveClassifier(2, 1) return data_set, model