跑通test_trainer.py，联调结束，准备发布

fastNLP/models/base_model.py

@@ -20,11 +20,10 @@ class BaseModel(torch.nn.Module):
 class NaiveClassifier(BaseModel):
     def __init__(self, in_feature_dim, out_feature_dim):
         super(NaiveClassifier, self).__init__()
-        self.mlp = MLP([in_feature_dim, in_feature_dim, out_feature_dim, out_feature_dim])
-        self.softmax = torch.nn.Softmax(dim=0)
+        self.mlp = MLP([in_feature_dim, in_feature_dim, out_feature_dim])
 
     def forward(self, x):
-        return {"predict": self.softmax(self.mlp(x))}
+        return {"predict": torch.sigmoid(self.mlp(x))}
 
     def predict(self, x):
-        return {"predict": self.softmax(self.mlp(x))}
+        return {"predict": torch.sigmoid(self.mlp(x)) > 0.5}

test/core/test_trainer.py

@@ -13,11 +13,11 @@ from fastNLP.models.base_model import NaiveClassifier
 
 class TrainerTestGround(unittest.TestCase):
     def test_case(self):
-        mean = np.array([-5, -5])
+        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([5, 5])
+        mean = np.array([3, 3])
         cov = np.array([[1, 0], [0, 1]])
         class_B = np.random.multivariate_normal(mean, cov, size=(1000,))
 
@@ -39,7 +39,7 @@ class TrainerTestGround(unittest.TestCase):
                           print_every=10,
                           validate_every=-1,
                           dev_data=dev_set,
-                          optimizer=SGD(0.001),
+                          optimizer=SGD(0.1),
                           check_code_level=2
                           )
         trainer.train()