diff --git a/paddle/fluid/operators/cvm_op.cc b/paddle/fluid/operators/cvm_op.cc
index a1a8744c323ca1cd783e0adb83cc260ffe8ce978..be7d4780f83ae5f3dbc1442353e95e85666d77b9 100644
--- a/paddle/fluid/operators/cvm_op.cc
+++ b/paddle/fluid/operators/cvm_op.cc
@@ -30,8 +30,10 @@ class CVMOp : public framework::OperatorWithKernel {
     OP_INOUT_CHECK(ctx->HasOutput("Y"), "Output", "Y", "CVM");
 
     auto x_dims = ctx->GetInputDim("X");
-    PADDLE_ENFORCE_EQ(x_dims.size(), 2UL, platform::errors::InvalidArgument(
-                                              "Input(X)'s rank should be 2."));
+    PADDLE_ENFORCE_EQ(
+        x_dims.size(), 2UL,
+        platform::errors::InvalidArgument(
+            "Input(X)'s rank should be 2, but got %d", x_dims.size()));
 
     if (ctx->Attrs().Get<bool>("use_cvm")) {
       ctx->SetOutputDim("Y", {x_dims[0], x_dims[1]});
@@ -68,26 +70,31 @@ class CVMGradientOp : public framework::OperatorWithKernel {
     auto x_dims = ctx->GetInputDim("X");
     auto cvm_dims = ctx->GetInputDim("CVM");
     auto dy_dims = ctx->GetInputDim(framework::GradVarName("Y"));
-    PADDLE_ENFORCE_EQ(x_dims.size(), 2, platform::errors::InvalidArgument(
-                                            "Input(X)'s rank should be 2."));
+    PADDLE_ENFORCE_EQ(
+        x_dims.size(), 2,
+        platform::errors::InvalidArgument(
+            "Expect Input(X)'s rank == 2, but got %d", x_dims.size()));
     PADDLE_ENFORCE_EQ(
         dy_dims.size(), 2,
-        platform::errors::InvalidArgument("Input(Y@Grad)'s rank should be 2."));
+        platform::errors::InvalidArgument(
+            "Expect Input(X)'s rank == 2, but got %d", dy_dims.size()));
     PADDLE_ENFORCE_EQ(
         cvm_dims.size(), 2,
-        platform::errors::InvalidArgument("Input(CVM)'s rank should be 2."));
+        platform::errors::InvalidArgument(
+            "Expect Input(X)'s rank == 2, but got %d", cvm_dims.size()));
 
     PADDLE_ENFORCE_EQ(
         x_dims[0], dy_dims[0],
         platform::errors::InvalidArgument(
             "The 1st dimension of Input(X) and Input(Y@Grad) should "
-            "be equal."));
+            "be equal, X is %d, Y@Grad is %d",
+            x_dims[0], dy_dims[0]));
 
     PADDLE_ENFORCE_EQ(
         cvm_dims[1], 2,
         platform::errors::InvalidArgument(
             "When Attr(soft_label) == false, the 2nd dimension of "
-            "Input(CVM) should be 2."));
+            "Input(CVM) should be 2, but got %d cvm_dims[1]"));
     ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
     ctx->ShareLoD("X", framework::GradVarName("X"));
   }
diff --git a/paddle/fluid/operators/optimizers/ftrl_op.cc b/paddle/fluid/operators/optimizers/ftrl_op.cc
index 3bdafbb96d5c40be651d6cad68806e14a214a28d..a75be6e580dcd7b1f39c313382a9759986e3a1da 100644
--- a/paddle/fluid/operators/optimizers/ftrl_op.cc
+++ b/paddle/fluid/operators/optimizers/ftrl_op.cc
@@ -42,7 +42,9 @@ class FTRLOp : public framework::OperatorWithKernel {
     auto param_dim = ctx->GetInputDim("Param");
     PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("Grad"),
                       platform::errors::InvalidArgument(
-                          "Two input of FTRL Op's dimension must be same."));
+                          "Two input of FTRL Op's dimension must be same, but "
+                          "param_dim is %d, Grad is %d",
+                          param_dim, ctx->GetInputDim("Grad")));
 
     auto lr_dim = ctx->GetInputDim("LearningRate");
     PADDLE_ENFORCE_NE(framework::product(lr_dim), 0,
@@ -51,9 +53,10 @@ class FTRLOp : public framework::OperatorWithKernel {
                           "been initialized. You may need to confirm "
                           "if you put exe.run(startup_program) "
                           "after optimizer.minimize function."));
-    PADDLE_ENFORCE_EQ(
-        framework::product(lr_dim), 1,
-        platform::errors::InvalidArgument("Learning Rate should be a scalar."));
+    PADDLE_ENFORCE_EQ(framework::product(lr_dim), 1,
+                      platform::errors::InvalidArgument(
+                          "Learning Rate should be a scalar, but got %d",
+                          framework::product(lr_dim)));
 
     ctx->SetOutputDim("ParamOut", param_dim);
     ctx->SetOutputDim("SquaredAccumOut", param_dim);