diff --git a/examples/workflow_by_code_gru.py b/examples/workflow_by_code_gru.py
index fdd0d9220..dece520d1 100644
--- a/examples/workflow_by_code_gru.py
+++ b/examples/workflow_by_code_gru.py
@@ -70,7 +70,7 @@ if __name__ == "__main__":
                 "lr": 1e-3,
                 "early_stop": 20,
                 "batch_size": 800,
-                "metric": "IC",
+                "metric": "loss",
                 "loss": "mse",
                 "seed": 0,
                 "GPU": 0,
diff --git a/qlib/contrib/model/pytorch_gru.py b/qlib/contrib/model/pytorch_gru.py
index 2dd8464e2..282ce72dd 100755
--- a/qlib/contrib/model/pytorch_gru.py
+++ b/qlib/contrib/model/pytorch_gru.py
@@ -46,7 +46,7 @@ class GRU(Model):
         dropout=0.0,
         n_epochs=200,
         lr=0.001,
-        metric="IC",
+        metric="",
         batch_size=2000,
         early_stop=20,
         loss="mse",
@@ -140,21 +140,17 @@ class GRU(Model):
     def metric_fn(self, pred, label):
 
         mask = torch.isfinite(label)
-        if self.metric == "IC":
-            return self.cal_ic(pred[mask], label[mask])
 
         if self.metric == "" or self.metric == "loss":  # use loss
             return -self.loss_fn(pred[mask], label[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)
 
-    def cal_ic(self, pred, label):
-        return torch.mean(pred * label)
 
     def train_epoch(self, x_train, y_train):
 
         x_train_values = x_train.values
-        y_train_values = np.squeeze(y_train.values) * 100
+        y_train_values = np.squeeze(y_train.values)
 
         self.gru_model.train()
 
@@ -193,7 +189,6 @@ class GRU(Model):
         losses = []
 
         indices = np.arange(len(x_values))
-        np.random.shuffle(indices)
 
         for i in range(len(indices))[:: self.batch_size]: