diff --git a/paddle/fluid/operators/metrics/auc_op.cc b/paddle/fluid/operators/metrics/auc_op.cc
index 001d26936886f12efc6eaa0333bb12e4e7118d67..e0eebad08bb6b9a15d9c0f356215404884bee0e9 100644
--- a/paddle/fluid/operators/metrics/auc_op.cc
+++ b/paddle/fluid/operators/metrics/auc_op.cc
@@ -28,8 +28,9 @@ class AucOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasInput("Label"),
                    "Input of Label should not be null.");
     auto predict_width = ctx->GetInputDim("Predict")[1];
-    PADDLE_INFERSHAPE_ENFORCE_EQ(ctx, predict_width, 2,
-                                 "Only support binary classification");
+    PADDLE_INFERSHAPE_ENFORCE_LE(ctx, predict_width, 2,
+                                 "Only support binary classification,"
+                                 "prediction dims[1] should be 1 or 2");
     auto predict_height = ctx->GetInputDim("Predict")[0];
     auto label_height = ctx->GetInputDim("Label")[0];
 
diff --git a/paddle/fluid/operators/metrics/auc_op.h b/paddle/fluid/operators/metrics/auc_op.h
index 4ab5cfe53c67eeaa995d7e955eec63a065c5eec5..6fb4749b35a37dfbb18d322920b2744d7a0882d4 100644
--- a/paddle/fluid/operators/metrics/auc_op.h
+++ b/paddle/fluid/operators/metrics/auc_op.h
@@ -75,7 +75,10 @@ class AucKernel : public framework::OpKernel<T> {
     const auto *label_data = label->data<int64_t>();
 
     for (size_t i = 0; i < batch_size; i++) {
-      auto predict_data = inference_data[i * inference_width + 1];
+      // if predict_data[i] has dim of 2, then predict_data[i][1] is pos prob
+      // if predict_data[i] has dim of 1, then predict_data[i][0] is pos prob
+      auto predict_data =
+          inference_data[i * inference_width + (inference_width - 1)];
       PADDLE_ENFORCE_LE(predict_data, 1,
                         "The predict data must less or equal 1.");
       PADDLE_ENFORCE_GE(predict_data, 0,
diff --git a/python/paddle/fluid/tests/unittests/test_auc_single_pred_op.py b/python/paddle/fluid/tests/unittests/test_auc_single_pred_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d3e93fa57b081fa1ce0ec6309ee166335b05ec9
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/test_auc_single_pred_op.py
@@ -0,0 +1,64 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from op_test import OpTest
+from paddle.fluid import metrics
+
+
+class TestAucSinglePredOp(OpTest):
+    def setUp(self):
+        self.op_type = "auc"
+        pred = np.random.random((128, 2)).astype("float32")
+        pred0 = pred[:, 0].reshape(128, 1)
+        labels = np.random.randint(0, 2, (128, 1)).astype("int64")
+        num_thresholds = 200
+
+        stat_pos = np.zeros((num_thresholds + 1, )).astype("int64")
+        stat_neg = np.zeros((num_thresholds + 1, )).astype("int64")
+
+        self.inputs = {
+            'Predict': pred0,
+            'Label': labels,
+            "StatPos": stat_pos,
+            "StatNeg": stat_neg
+        }
+        self.attrs = {
+            'curve': 'ROC',
+            'num_thresholds': num_thresholds,
+            "slide_steps": 1
+        }
+
+        python_auc = metrics.Auc(name="auc",
+                                 curve='ROC',
+                                 num_thresholds=num_thresholds)
+        for i in range(128):
+            pred[i][1] = pred[i][0]
+        python_auc.update(pred, labels)
+
+        self.outputs = {
+            'AUC': np.array(python_auc.eval()),
+            'StatPosOut': np.array(python_auc._stat_pos),
+            'StatNegOut': np.array(python_auc._stat_neg)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == "__main__":
+    unittest.main()