diff --git a/paddle/operators/reshape_op.cc b/paddle/operators/reshape_op.cc
index 306dfa8069bce1009455d8720155331fe296610e..164f3104eb69caf4f93542de39c3d089694ffe03 100644
--- a/paddle/operators/reshape_op.cc
+++ b/paddle/operators/reshape_op.cc
@@ -42,19 +42,23 @@ class ReshapeOp : public framework::OperatorWithKernel {
       if (shape[i] == -1) {
         neg_dims_idx.push_back(i);
         PADDLE_ENFORCE(neg_dims_idx.size() <= 1,
-                     "Only one dimension of Attr(shape) can be -1.");
+                       "Only one dimension of Attr(shape) can be unknown.");
       }
     }
 
-    // capacity check
     int64_t capacity =
         std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
     int64_t in_size = framework::product(x_dims);
     if (neg_dims_idx.size() == 1) {
-       shape[neg_dims_idx[0]] = in_size / (-capacity);
-       PADDLE_ENFORCE(shape[neg_dims_idx[0]] > 0,
-                      "The size of Input(X) mismatches with Attr(shape).");
+      // dim infer
+      shape[neg_dims_idx[0]] = in_size / (-capacity);
+      // recalculate capacity
+      capacity = std::accumulate(shape.begin(), shape.end(), 1,
+                                 std::multiplies<int>());
     }
+    // capacity check
+    PADDLE_ENFORCE(capacity == in_size,
+                   "The size of Input(X) mismatches with Attr(shape).");
     // resize output
     std::vector<int64_t> shape_int64(shape.size(), 0);
     std::transform(shape.begin(), shape.end(), shape_int64.begin(),
diff --git a/python/paddle/v2/fluid/tests/test_reshape_op.py b/python/paddle/v2/fluid/tests/test_reshape_op.py
index 16bb6bb2af67f7d32a2fafc1cb37412084ec0829..18ee3aece656276fec9671df9baf298b7fd3c9b1 100644
--- a/python/paddle/v2/fluid/tests/test_reshape_op.py
+++ b/python/paddle/v2/fluid/tests/test_reshape_op.py
@@ -17,5 +17,19 @@ class TestReshapeOp(OpTest):
         self.check_grad(["X"], "Out")
 
 
+class TestReshapeOpDimInfer(OpTest):
+    def setUp(self):
+        self.op_type = "reshape"
+        self.inputs = {'X': np.random.random((10, 20)).astype("float32")}
+        self.attrs = {'shape': [4, -1, 5]}
+        self.outputs = {'Out': self.inputs['X'].reshape(self.attrs['shape'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(["X"], "Out")
+
+
 if __name__ == '__main__':
     unittest.main()