diff --git a/python/paddle/nn/functional/loss.py b/python/paddle/nn/functional/loss.py
index c1800a781d4ba08e7d479801f79fc87db77f5513..4d09f1d5c38fc8cfc0be3cd0ebc16fc3e89edbc5 100755
--- a/python/paddle/nn/functional/loss.py
+++ b/python/paddle/nn/functional/loss.py
@@ -1703,11 +1703,16 @@ def cross_entropy(input,
                         "when weight is provided" \
                             .format(input.shape[axis], weight.shape[-1]))
 
-                valid_label = paddle.where(label == ignore_index,
-                                           paddle.zeros_like(label), label)
+                ignore_weight_mask = (
+                    label != ignore_index)  # ignored position will be False
+
+                valid_label = paddle.cast(
+                    ignore_weight_mask,
+                    dtype=label.dtype) * label  # ignored position will be 0
+
+                ignore_weight_mask = paddle.cast(
+                    ignore_weight_mask, out.dtype)  # convert from 0 to 0.0
 
-                ignore_weight_mask = paddle.cast((label != ignore_index),
-                                                 out.dtype)
                 if ignore_weight_mask.ndim > 1 and ignore_weight_mask.shape[
                         axis] == 1:
                     # TODO: Temporarily use squeeze instead of squeeze_
@@ -1821,10 +1826,16 @@ def cross_entropy(input,
                                  "when weight is provided" \
                                  .format(input.shape[axis], weight.shape[-1]))
 
-            valid_label = paddle.where(label == ignore_index,
-                                       paddle.zeros_like(label), label)
-            ignore_weight_mask = paddle.cast((label != ignore_index),
-                                             input.dtype)
+            ignore_weight_mask = (
+                label != ignore_index)  # ignored position will be False
+
+            valid_label = paddle.cast(
+                ignore_weight_mask,
+                dtype=label.dtype) * label  # ignored position will be 0
+
+            ignore_weight_mask = paddle.cast(ignore_weight_mask,
+                                             out.dtype)  # convert from 0 to 0.0
+
             if ignore_weight_mask.ndim > 1 and ignore_weight_mask.shape[
                     axis] == 1:
                 ignore_weight_mask = paddle.squeeze(ignore_weight_mask, axis)