diff --git a/paddle/fluid/imperative/layer.cc b/paddle/fluid/imperative/layer.cc
index 15e237a0e8ec3e8560d51ca4d63f296cfbca805d..ef6d8f4016a057fd9256499067f72dd9d11c452f 100644
--- a/paddle/fluid/imperative/layer.cc
+++ b/paddle/fluid/imperative/layer.cc
@@ -115,7 +115,6 @@ framework::Variable* CreateVariable(const std::string& name,
     varname = string::Sprintf("%s@%d", varname, id);
   }
 
-  LOG(ERROR) << "creating var " << varname;
   VLOG(3) << "creating var " << varname;
   framework::Variable* var = scope->Var(varname);
   framework::LoDTensor* tensor = var->GetMutable<framework::LoDTensor>();
@@ -183,8 +182,6 @@ std::vector<Variable*> OpBase::ApplyGrad(framework::Scope* scope) {
         << framework::vectorize(var->Get<framework::LoDTensor>().dims()).size();
   }
 
-  LOG(ERROR) << "grad_op_desc_" << grad_op_desc_->Proto()->DebugString();
-
   for (const std::string& outvar : grad_op_desc_->OutputArgumentNames()) {
     VLOG(3) << "op grad output var " << outvar;
     block_->FindRecursiveOrCreateVar(outvar);
@@ -194,8 +191,6 @@ std::vector<Variable*> OpBase::ApplyGrad(framework::Scope* scope) {
       framework::VarDesc* var_desc = block_->FindVar(outvar);
       if (var_desc->GetType() == framework::proto::VarType::LOD_TENSOR) {
         var->GetMutable<framework::LoDTensor>();
-        // framework::Tensor* tensor = var->GetMutable<framework::LoDTensor>();
-        // tensor->mutable_data(platform::CPUPlace());
       } else {
         LOG(ERROR) << "tracer doesn't support yet";
       }
diff --git a/paddle/fluid/operators/cross_entropy_op.h b/paddle/fluid/operators/cross_entropy_op.h
index 2500c0443f5f276a85ee3e93a0ec08b4eea7a728..f123e11542d85c904a81fe2a87f59ab52511cc15 100644
--- a/paddle/fluid/operators/cross_entropy_op.h
+++ b/paddle/fluid/operators/cross_entropy_op.h
@@ -110,8 +110,6 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel<T> {
     auto* dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
     auto* label = ctx.Input<Tensor>("Label");
     auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
-    LOG(ERROR) << "CROSS ENTROPY GRAD DX: "
-               << ctx.op().Output(framework::GradVarName("X"));
     T* dx_data = dx->mutable_data<T>(ctx.GetPlace());
 
     // Following computation only depends on the last dimension size. So it's
diff --git a/python/paddle/fluid/framework.py b/python/paddle/fluid/framework.py
index dde08a79d229df7711abef674e992c984fde3022..3dc23bd06062b838283bea309c66774b9c4f9852 100644
--- a/python/paddle/fluid/framework.py
+++ b/python/paddle/fluid/framework.py
@@ -1281,8 +1281,6 @@ class Block(object):
         """
         op_desc = self.desc.append_op()
         op = Operator(block=self, desc=op_desc, *args, **kwargs)
-        print("append_op", kwargs.get("type"), kwargs.get("stop_gradient",
-                                                          False))
         if _in_imperative_mode():
             _imperative_tracer().trace(op.iop, [v._ivar for v in op.inputs],
                                        [v._ivar for v in op.outputs], self.desc,
@@ -1336,8 +1334,6 @@ class Block(object):
     def _prepend_op(self, *args, **kwargs):
         op_desc = self.desc._prepend_op()
         op = Operator(self, op_desc, *args, **kwargs)
-        print("prepend_op", kwargs.get("type"), kwargs.get("stop_gradient",
-                                                           False))
         if _in_imperative_mode():
             _imperative_tracer().trace(op.iop, [v._ivar for v in op.inputs],
                                        [v._ivar for v in op.outputs], self.desc,
diff --git a/python/paddle/fluid/tests/unittests/test_imperative_mnist.py b/python/paddle/fluid/tests/unittests/test_imperative_mnist.py
index 85b613bddca3e8b047b1acfdb4eb24f8658368f1..9d1e07999894bc4b2c08d4a096e30d5592fe0dbc 100644
--- a/python/paddle/fluid/tests/unittests/test_imperative_mnist.py
+++ b/python/paddle/fluid/tests/unittests/test_imperative_mnist.py
@@ -115,9 +115,7 @@ class TestImperativeMnist(unittest.TestCase):
             label._stop_gradient = True
 
             predict = mnist(img)
-            print(predict.shape, predict.dtype, label.shape, label.dtype)
             out = fluid.layers.cross_entropy(predict, label)
-            print(out.shape, out.dtype)
             out._backward()
             filter_grad = mnist._simple_img_conv_pool_1._conv2d._filter_param._gradient(
             )