diff --git a/paddle/operators/reshape_op.cc b/paddle/operators/reshape_op.cc
index 39bf2118d603881531bf583ae468e8dc9b8bd181..306dfa8069bce1009455d8720155331fe296610e 100644
--- a/paddle/operators/reshape_op.cc
+++ b/paddle/operators/reshape_op.cc
@@ -34,21 +34,27 @@ class ReshapeOp : public framework::OperatorWithKernel {
     auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
     PADDLE_ENFORCE(shape.size() > 0, "Attr(shape) shouldn't be empty.");
     auto x_dims = ctx->GetInputDim("X");
-    // TODO(qiao) change batch_size
-    for (size_t i = 1; i < shape.size(); ++i) {
-      PADDLE_ENFORCE(shape[i] > 0,
-                     "Each dimension of Attr(shape) "
-                     "must be positive except the first one.");
-    }
-    if (shape[0] < 0) {
-      shape[0] = x_dims[0];
+
+    std::vector<size_t> neg_dims_idx;
+    for (size_t i = 0; i < shape.size(); ++i) {
+      PADDLE_ENFORCE(shape[i] > 0 || shape[i] == -1,
+                     "Each dimension of Attr(shape) must be positive or -1.");
+      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.");
+      }
     }
+
     // capacity check
     int64_t capacity =
         std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
     int64_t in_size = framework::product(x_dims);
-    PADDLE_ENFORCE_EQ(capacity, in_size,
+    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).");
+    }
     // resize output
     std::vector<int64_t> shape_int64(shape.size(), 0);
     std::transform(shape.begin(), shape.end(), shape_int64.begin(),
@@ -88,6 +94,9 @@ the tensor X into a 1-D tensor:
 
     [1, 2, 3, 4]
 
+One dimension in the target shape can be set -1, and the real dimension 
+will be infered from the original shape of Input(X) and other
+dimensions in the target shape.
 )DOC");
   }
 };