fixed LeakyReLU, Optimizer

mindspore/nn/layer/activation.py

@@ -250,7 +250,7 @@ class LeakyReLU(Cell):
 
     def construct(self, x):
         alpha = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))
-        if self.alpha <= 1:
+        if alpha <= 1:
             out = P.Maximum()(alpha * x, x)
         else:
             out = P.Minimum()(alpha * x, x)

mindspore/nn/layer/embedding.py

@@ -45,8 +45,8 @@ class Embedding(Cell):
 
     Inputs:
         - **input** (Tensor) - Tensor of shape :math:`(\text{batch_size}, \text{input_length})`. The element of
-        the Tensor should be integer and not larger than vocab_size. else the corresponding embedding vector is zero
-        if larger than vocab_size.
+            the Tensor should be integer and not larger than vocab_size. else the corresponding embedding vector is zero
+            if larger than vocab_size.
     Outputs:
         Tensor of shape :math:`(\text{batch_size}, \text{input_length}, \text{embedding_size})`.
 

mindspore/nn/optim/optimizer.py

@@ -93,13 +93,13 @@ class Optimizer(Cell):
 
         if isinstance(loss_scale, int):
             loss_scale = float(loss_scale)
-        validator.check_value_type("loss_scale", loss_scale, [float], None)
-        validator.check_number_range("loss_scale", loss_scale, 0.0, float("inf"), Rel.INC_NEITHER, None)
+        validator.check_value_type("loss_scale", loss_scale, [float], self.cls_name)
+        validator.check_number_range("loss_scale", loss_scale, 0.0, float("inf"), Rel.INC_NEITHER, self.cls_name)
 
         if isinstance(weight_decay, int):
             weight_decay = float(weight_decay)
-        validator.check_value_type("weight_decay", weight_decay, [float], None)
-        validator.check_number_range("weight_decay", weight_decay, 0.0, float("inf"), Rel.INC_LEFT, None)
+        validator.check_value_type("weight_decay", weight_decay, [float], self.cls_name)
+        validator.check_number_range("weight_decay", weight_decay, 0.0, float("inf"), Rel.INC_LEFT, self.cls_name)
 
         self.is_group = False
         self.is_group_lr = False