optimizer初始化调整参数顺序

fastNLP/core/__init__.py

@@ -2,9 +2,10 @@ from .batch import Batch
 from .dataset import DataSet
 from .fieldarray import FieldArray
 from .instance import Instance
-from .losses import LossFromTorch
-from .optimizer import Optimizer
+from .losses import LossFunc, CrossEntropyLoss, L1Loss, BCELoss, NLLLoss, LossInForward
+from .metrics import AccuracyMetric
+from .optimizer import Optimizer, SGD, Adam
 from .sampler import SequentialSampler, BucketSampler, RandomSampler, BaseSampler
 from .tester import Tester
 from .trainer import Trainer
-from .vocabulary import Vocabulary
+from .vocabulary import Vocabulary

fastNLP/core/optimizer.py

@@ -10,13 +10,15 @@ class Optimizer(object):
 
 
 class SGD(Optimizer):
-    def __init__(self, model_params=None, lr=0.01, momentum=0):
+    def __init__(self, lr=0.01, momentum=0, model_params=None):
         """
 
-        :param model_params: a generator. E.g. model.parameters() for PyTorch models.
         :param float lr: learning rate. Default: 0.01
         :param float momentum: momentum. Default: 0
+        :param model_params: a generator. E.g. model.parameters() for PyTorch models.
         """
+        if not isinstance(lr, float):
+            raise TypeError("learning rate has to be float.")
         super(SGD, self).__init__(model_params, lr=lr, momentum=momentum)
 
     def construct_from_pytorch(self, model_params):
@@ -28,13 +30,15 @@ class SGD(Optimizer):
 
 
 class Adam(Optimizer):
-    def __init__(self, model_params=None, lr=0.01, weight_decay=0):
+    def __init__(self, lr=0.01, weight_decay=0, model_params=None):
         """
 
-        :param model_params: a generator. E.g. model.parameters() for PyTorch models.
         :param float lr: learning rate
         :param float weight_decay:
+        :param model_params: a generator. E.g. model.parameters() for PyTorch models.
         """
+        if not isinstance(lr, float):
+            raise TypeError("learning rate has to be float.")
         super(Adam, self).__init__(model_params, lr=lr, weight_decay=weight_decay)
 
     def construct_from_pytorch(self, model_params):