Merge pull request #135 from Oneflow-Inc/loss_reduce_mean_fp16

don't use reduce mean in fp16

Classification/cnns/of_cnn_train_val.py

@@ -86,7 +86,8 @@ def TrainNet():
     else:
         loss = flow.nn.sparse_softmax_cross_entropy_with_logits(labels, logits, name="softmax_loss")
 
-    loss = flow.math.reduce_mean(loss)
+    if not args.use_fp16:
+        loss = flow.math.reduce_mean(loss)
     flow.losses.add_loss(loss)
     predictions = flow.nn.softmax(logits)
     outputs = {"loss": loss, "predictions": predictions, "labels": labels}