diff --git a/paddle/operators/gru_op.h b/paddle/operators/gru_op.h
index 2c9aa762428879232430e0e3e191b7b388454468..ba90ec9816c40a6a49065ac6efcee6b93dffce90 100644
--- a/paddle/operators/gru_op.h
+++ b/paddle/operators/gru_op.h
@@ -51,26 +51,16 @@ class GRUKernel : public framework::OpKernel<T> {
     auto* hidden = context.Output<LoDTensor>("Hidden");
     hidden->mutable_data<T>(context.GetPlace());
 
-    // context.ShareLoD("Input", "Gate");
-    // context.ShareLoD("Input", "ResetHiddenPrev");
     context.ShareLoD("Input", "Hidden");
 
-    // auto gate_dims = gate->dims();
     auto hidden_dims = hidden->dims();
 
-    // LoDTensor batch_gate, batch_reset_hidden_prev, batch_hidden;
-    // batch_gate.mutable_data<T>(gate_dims, context.GetPlace());
-    // batch_reset_hidden_prev.mutable_data<T>(hidden_dims, context.GetPlace());
-    // batch_hidden.mutable_data<T>(hidden_dims, context.GetPlace());
-
     bool is_reverse = context.Attr<bool>("is_reverse");
     math::LoDTensor2BatchFunctor<Place, T> to_batch;
-    // to_batch(context.device_context(), *input, batch_gate, is_reverse);
     to_batch(context.device_context(), *input, *batch_gate, true, is_reverse);
 
     int frame_size = hidden_dims[1];
     int batch_size = hidden_dims[0];
-    // auto g = EigenMatrix<T>::From(batch_gate);
     auto g = EigenMatrix<T>::From(*batch_gate);
     auto place = context.GetEigenDevice<Place>();
     if (bias) {
@@ -85,20 +75,13 @@ class GRUKernel : public framework::OpKernel<T> {
     gru_value.stateWeight =
         const_cast<T*>(weight_data + 2 * frame_size * frame_size);
     gru_value.prevOutValue = const_cast<T*>(h0_data);
-    // auto batch_starts = batch_gate.lod()[0];
     auto batch_starts = batch_gate->lod()[0];
-    // for (auto i = batch_gate->lod()[1].begin(); i !=
-    // batch_gate->lod()[1].end(); ++i)
-    // 	std::cout << static_cast<int>(*i) << ' ';
     size_t num_batch = batch_starts.size() - 1;
     for (size_t n = 0; n < num_batch; n++) {
       int bstart = static_cast<int>(batch_starts[n]);
       int bend = static_cast<int>(batch_starts[n + 1]);
       int cur_batch_size = bend - bstart;
 
-      // Tensor gate_t = batch_gate.Slice(bstart, bend);
-      // Tensor reset_hidden_prev_t = batch_reset_hidden_prev.Slice(bstart,
-      // bend);
       Tensor gate_t = batch_gate->Slice(bstart, bend);
       Tensor reset_hidden_prev_t = batch_reset_hidden_prev->Slice(bstart, bend);
       Tensor hidden_t = batch_hidden->Slice(bstart, bend);
@@ -113,13 +96,6 @@ class GRUKernel : public framework::OpKernel<T> {
     }
 
     math::Batch2LoDTensorFunctor<Place, T> to_seq;
-    // batch_gate.set_lod(batch_gate.lod());
-    // to_seq(context.device_context(), batch_gate, *gate);
-    // batch_reset_hidden_prev.set_lod(batch_gate.lod());
-    // to_seq(context.device_context(), batch_reset_hidden_prev,
-    //        *reset_hidden_prev);
-    // batch_hidden.set_lod(batch_gate.lod());
-    // to_seq(context.device_context(), batch_hidden, *hidden);
     batch_hidden->set_lod(batch_gate->lod());
     to_seq(context.device_context(), *batch_hidden, *hidden);
   }
@@ -167,11 +143,8 @@ class GRUGradKernel : public framework::OpKernel<T> {
     zero(context.device_context(), &batch_reset_hidden_prev_grad,
          static_cast<T>(0.0));
 
-    // batch_hidden.set_lod(batch_gate->lod());
     bool is_reverse = context.Attr<bool>("is_reverse");
     batch_hidden_grad.set_lod(batch_hidden->lod());
-    // context.ShareLoD(framework::GradVarName("Hidden"),
-    //                  framework::GradVarName("Input"));
     to_batch(context.device_context(), *hidden_grad, batch_hidden_grad, false,
              is_reverse);
 
diff --git a/python/paddle/v2/framework/tests/test_gru_op.py b/python/paddle/v2/framework/tests/test_gru_op.py
index 1c8bbabf12d9c38166c3d010a395b108532744ae..1848fb3491999a6a0d492cc93d1936a005bd7a31 100644
--- a/python/paddle/v2/framework/tests/test_gru_op.py
+++ b/python/paddle/v2/framework/tests/test_gru_op.py
@@ -62,7 +62,6 @@ class TestGRUOp(OpTest):
         return idx_in_seq_list
 
     def gru_step(self, x, h_p, w, b):
-        # print x.shape, h_p.shape, w.shape, b.shape
         batch_size = x.shape[0]
         frame_size = w.shape[0]
         g = x + np.tile(b, (batch_size, 1))
@@ -96,7 +95,6 @@ class TestGRUOp(OpTest):
         num_batch = len(idx_in_seq_list)
         end_idx = 0
         for batch_idx in range(num_batch):
-            # print idx_in_seq_list[batch_idx]
             x = input[idx_in_seq_list[batch_idx]]
             g, r_h_p, h = self.gru_step(x, h_p, w, b)
             if batch_idx < (num_batch - 1):
@@ -110,9 +108,8 @@ class TestGRUOp(OpTest):
         return batch_gate, batch_reset_hidden_prev, hidden
 
     def set_data(self):
-        lod = [[0, 2, 6, 9]]  #[[0, 1, 2, 3]]
+        lod = [[0, 2, 6, 9]]
         self.idx_in_seq_list = self.seq_to_batch(lod, self.is_reverse)
-        # print self.idx_in_seq_list
         batch_size = self.batch_size
         frame_size = self.frame_size
         input = np.random.rand(batch_size, frame_size * 3).astype('float64')