diff --git a/paddle/optimizer/CMakeLists.txt b/paddle/optimizer/CMakeLists.txt
index 06f6d83efe1155c8cbe76644d6b9efc91349b006..95d7ad720f3c6ab01ef3c8cf0987b710fa39c6ab 100644
--- a/paddle/optimizer/CMakeLists.txt
+++ b/paddle/optimizer/CMakeLists.txt
@@ -27,3 +27,4 @@ add_dependencies(optimizer gen_proto_cpp)
 
 add_simple_unittest(optimizer_test)
 add_simple_unittest(optimizer_factory_test)
+add_simple_unittest(Tensor_test)
diff --git a/paddle/optimizer/Tensor.h b/paddle/optimizer/Tensor.h
index d779bb50709153dee19dc2709608674875000006..fbfba4806a1c9dba2376ca81950974193f698059 100644
--- a/paddle/optimizer/Tensor.h
+++ b/paddle/optimizer/Tensor.h
@@ -5,34 +5,42 @@
  */
 
 #include <string.h>
-#include "paddle/math/BaseMatrix.h"
+#include "paddle/utils/Common.h"
+#include "paddle/utils/Logging.h"
 
 namespace paddle {
 namespace optimizer {
 
 template <class T>
-using TensorBase = BaseMatrixT<T>;
-
-template <class T>
-class TensorT : public TensorBase<T> {
+class TensorT {
 public:
-  TensorT(T* data, int size) : TensorBase<T>(1, size, 0, data, false, false) {}
+  TensorT(size_t h, size_t w, T* data) : height_(h), width_(w), data_(data_) {}
+  TensorT(T* data, int size) : height_(1), width_(size), data_(data) {}
   TensorT(const TensorT& t)
-      : TensorBase<T>(1, t.size(), 0, t.get_buffer(), false, false) {}
+      : TensorT(1, t.size(), 0, t.get_buffer(), false, false) {}
   TensorT& operator=(const TensorT& t) {
-    this->size_ = t.size();
+    this->width_ = t.size();
     this->data_ = t.get_buffer();
   }
   T* get_buffer() { return this->data_; }
   T& operator[](const int idx) {
     CHECK(idx >= 0 && idx < this->width_) << "out of index range";
-    return this->data_[idx];
+    return data_[idx];
+  }
+  T& operator[](const int idx) const {
+    CHECK(idx >= 0 && idx < this->width_) << "out of index range";
+    return data_[idx];
   }
   // TODO: replace with tensorshape
   size_t size() const { return this->width_; }
+
+protected:
+  size_t height_;
+  size_t width_;
+  T* data_;
 };
 
-// TODO(zhihong): design problem of dynamic datatype, need to fix
+// TODO(zhihong): design problem of dynamic datatype, need to fix it
 typedef TensorT<real> Tensor;
 
 }  // namespace optimizer
diff --git a/paddle/optimizer/Tensor_test.cpp b/paddle/optimizer/Tensor_test.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..4b7059f9943a9432fd36566141893216df5b1fca
--- /dev/null
+++ b/paddle/optimizer/Tensor_test.cpp
@@ -0,0 +1,21 @@
+#include "Tensor.h"
+#include <iostream>
+#include "gtest/gtest.h"
+
+using namespace paddle;
+using namespace paddle::optimizer;
+
+TEST(Tensor, indexer) {
+  real* ptr = new real[3];
+  Tensor t(ptr, 3);
+  for (auto i = 0; i < t.size(); ++i) {
+    t[i] = i;
+  }
+  ASSERT_EQ(t[2], 2);
+  ASSERT_EQ(t[1], 1);
+}
+
+int main(int argc, char** argv) {
+  testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}
diff --git a/paddle/optimizer/optimizer.cc b/paddle/optimizer/optimizer.cc
index fb2e543bf32570c9744296c50256663aee7da4ff..10b3339c2d5a6f57a45f3e93d7badd52bc08c42b 100644
--- a/paddle/optimizer/optimizer.cc
+++ b/paddle/optimizer/optimizer.cc
@@ -2,6 +2,7 @@
 #include <string>
 
 #include "parameter_optimizer.h"
+using namespace paddle;
 using namespace paddle::optimizer;
 
 template <paddle_element_type VALUE>
@@ -50,8 +51,8 @@ int paddle_update_parameter(paddle_optimizer* o,
                             const void* grad_buffer,
                             int num_bytes) {
   // TOOD(zhihong): datatype not work. need to add the runtime datatype
-  auto grad = reinterpret_cast<const real*>(grad_buffer);
-  Tensor gradient(const_cast<real*>(grad), num_bytes);
+  auto grad_type = reinterpret_cast<const real*>(grad_buffer);
+  Tensor* gradient = new Tensor(const_cast<real*>(grad_type), num_bytes);
   o->impl->update(gradient);
   return PADDLE_SUCCESS;
 }
diff --git a/paddle/optimizer/parameter_optimizer.cc b/paddle/optimizer/parameter_optimizer.cc
index 6d9fa5c8024013250e6e0b76707c9f0b467f5646..7e4aa42c4bf380fea846837254327f87cc899806 100644
--- a/paddle/optimizer/parameter_optimizer.cc
+++ b/paddle/optimizer/parameter_optimizer.cc
@@ -1,7 +1,7 @@
 #include <glog/logging.h>
-#include "adadelta_optimizer.h"
-#include "adagrad_optimizer.h"
-#include "adam_optimizer.h"
+// #include "adadelta_optimizer.h"
+// #include "adagrad_optimizer.h"
+// #include "adam_optimizer.h"
 #include "lr_policy.h"
 #include "sgd_optimizer.h"
 
@@ -36,20 +36,20 @@ ParameterOptimizer *ParameterOptimizer::create(
                               config.sgd().nesterov(),
                               lr);
     }
-    if (s == "Adadelta") {
-      return new AdagradOptimizer(
-          config.adagrad().epsilon(), config.adagrad().decay(), lr);
-    }
-    if (s == "Adagrad") {
-      return new AdagradOptimizer(
-          config.adagrad().epsilon(), config.adagrad().decay(), lr);
-    }
-    if (s == "Adam") {
-      return new AdadeltaOptimizer(config.adadelta().rho(),
-                                   config.adadelta().epsilon(),
-                                   config.adadelta().decay(),
-                                   lr);
-    }
+    // if (s == "Adadelta") {
+    //   return new AdagradOptimizer(
+    //       config.adagrad().epsilon(), config.adagrad().decay(), lr);
+    // }
+    // if (s == "Adagrad") {
+    //   return new AdagradOptimizer(
+    //       config.adagrad().epsilon(), config.adagrad().decay(), lr);
+    // }
+    // if (s == "Adam") {
+    //   return new AdadeltaOptimizer(config.adadelta().rho(),
+    //                                config.adadelta().epsilon(),
+    //                                config.adadelta().decay(),
+    //                                lr);
+    // }
     // default
     return new SGDOptimizer(config.sgd().momentum(),
                             config.sgd().decay(),
diff --git a/paddle/optimizer/sgd_optmizer.cc b/paddle/optimizer/sgd_optmizer.cc
index 03ddc81451715b458b33afdeb10d9bfd24f1b354..020867b93d5df96a292cb4e845703d6c7b1e62f9 100644
--- a/paddle/optimizer/sgd_optmizer.cc
+++ b/paddle/optimizer/sgd_optmizer.cc
@@ -16,7 +16,8 @@ void SGDOptimizer::set_weight(Tensor *p) {
 void SGDOptimizer::update(const Tensor &gradient) {
   num_sample_passed += 1;
   double learning_rate = lr_policy->get_learning_rate(num_sample_passed);
-  double velocity = 0.0;
+  real velocity = 0.0;
+  Tensor &param = *parameter_;
   for (size_t i = 0; i < parameter_->size(); ++i) {
     if (momentum == 0.0) {
       velocity =