diff --git a/python/paddle/nn/functional/loss.py b/python/paddle/nn/functional/loss.py
index 4e538fed64547b65b75c0d1a556e7fdff68486a9..a3115cff6a3b3ba4196a83b0715bfc203e67bcf4 100755
--- a/python/paddle/nn/functional/loss.py
+++ b/python/paddle/nn/functional/loss.py
@@ -1650,6 +1650,24 @@ def cross_entropy(input,
     if input_dims - 1 == label_dims:
         label = paddle.unsqueeze(label, axis=axis)
     if in_dygraph_mode():
+		if soft_label == False:
+            valid_label = paddle.where(
+                label == ignore_index,
+                paddle.zeros_like(label),
+                label)
+            if len(paddle.nonzero(valid_label < 0)) > 0:
+                invalid_label = paddle.gather_nd(
+                    valid_label, paddle.nonzero(valid_label < 0))
+                raise ValueError(
+                    "Target({}) is out of class_dimension's lower bound({})".
+                    format(invalid_label[0], 0))
+            if len(paddle.nonzero(valid_label >= input.shape[-1])) > 0:
+                invalid_label = paddle.gather_nd(
+                    valid_label, paddle.nonzero(valid_label >= input.shape[-1]))
+                raise ValueError(
+                    "Target({}) is out of class_dimension's upper bound({})".
+                    format(invalid_label[0], input.shape[-1] - 1))
+
         _, out = _C_ops.softmax_with_cross_entropy(
             input, label, 'soft_label', soft_label, 'ignore_index',
             ignore_index, 'numeric_stable_mode', True, 'axis', axis,
@@ -1681,27 +1699,6 @@ def cross_entropy(input,
                         weight's class_dimension({}) \
                             when weight is provided"
                         .format(input.shape[-1], weight.shape[-1]))
-                valid_label = paddle.where(
-                    label == ignore_index,
-                    paddle.to_tensor(
-                        0, dtype=label.dtype),
-                    label)
-
-                if (len(paddle.nonzero(valid_label < 0)) > 0) or (
-                        len(paddle.nonzero(valid_label >= input.shape[-1])) > 0
-                ):
-                    invalid_label = paddle.gather_nd(
-                        input, paddle.nonzero(valid_label < 0))
-                    if invalid_label.numel() > 0:
-                        raise ValueError(
-                            "Target({}) is out of class_dimension's lower bound({})".
-                            format(invalid_label[0], 0))
-                    invalid_label = paddle.gather_nd(
-                        input, paddle.nonzero(valid_label >= input.shape[-1]))
-                    if invalid_label.numel() > 0:
-                        raise ValueError(
-                            "Target({}) is out of class_dimension's upper bound({})".
-                            format(invalid_label[0], input.shape[-1]))
 
                 ignore_weight_mask = paddle.cast((label != ignore_index),
                                                  out.dtype)
@@ -1709,7 +1706,7 @@ def cross_entropy(input,
                         -1] == 1:
                     ignore_weight_mask.squeeze_(-1)
                 weight_gather = _C_ops.gather_nd(
-                    weight, valid_label)  # ignore的位置暂时用label0的权重代替
+                    weight, valid_label)
                 weight_gather = _C_ops.elementwise_mul(weight_gather,
                                                        ignore_weight_mask)
                 input_shape = list(label.shape)
@@ -1804,6 +1801,12 @@ def cross_entropy(input,
             weight_gather_reshape = reshape(weight_gather, shape=out_shape)
             out = paddle.cast(out, weight_gather_reshape.dtype)
         else:
+			if input.shape[-1] != weight.shape[-1]:
+                raise ValueError("input's class_dimension({}) must equal to "\
+                        "weight's class_dimension({}) "\
+                            "when weight is provided"
+                                 .format(input.shape[-1], weight.shape[-1]))
+
             weight_gather = paddle.gather_nd(
                 weight, label)  #trans weight from class to sample, shape:N
             input_shape = list(label.shape)