diff --git a/include/caffe/layers/cudnn_conv_layer.hpp b/include/caffe/layers/cudnn_conv_layer.hpp
index 1a0b5aac..b0ddd08a 100644
--- a/include/caffe/layers/cudnn_conv_layer.hpp
+++ b/include/caffe/layers/cudnn_conv_layer.hpp
@@ -30,7 +30,8 @@ template <typename Dtype>
 class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
  public:
   explicit CuDNNConvolutionLayer(const LayerParameter& param)
-      : ConvolutionLayer<Dtype>(param), handles_setup_(false) {}
+      : ConvolutionLayer<Dtype>(param), handles_setup_(false),
+        weight_initialized_{false} {}// Binary added: weight_initialized_
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
@@ -66,8 +67,9 @@ class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
   void **workspace;  // aliases into workspaceData
 
   // Binary net added
+  bool weight_initialized_;
   std::unique_ptr<Blob<Dtype>> weight_binary_;
-  void normalizeWeights(const bool truncateOriginalWeights);
+  void normalizeWeights();
   // Binary net end
 };
 #endif
diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp
index 7bedb12b..ba196595 100644
--- a/src/caffe/layers/cudnn_conv_layer.cpp
+++ b/src/caffe/layers/cudnn_conv_layer.cpp
@@ -229,19 +229,6 @@ void CuDNNConvolutionLayer<Dtype>::Reshape(
     cudnn::setTensor4dDesc<Dtype>(&bias_desc_,
         1, this->num_output_ / this->group_, 1, 1);
   }
-
-  // Binary added
-  if (this->layer_param_.convolution_param().binary())
-  {
-    CHECK_GE(this->blobs_.size(), 1);
-    CHECK_GT(this->blobs_[0]->shape().size(), 2u);
-    weight_binary_.reset(new Blob<Dtype>());
-    weight_binary_->Reshape(this->blobs_[0]->shape());
-    // Data to weightReal
-    const bool truncateOriginalWeights = true;
-    normalizeWeights(true);
-  }
-  // Binary added end
 }
 
 template <typename Dtype>
@@ -278,7 +265,7 @@ CuDNNConvolutionLayer<Dtype>::~CuDNNConvolutionLayer() {
 
 // Binary added
 template <typename Dtype>
-void CuDNNConvolutionLayer<Dtype>::normalizeWeights(const bool truncateOriginalWeights)
+void CuDNNConvolutionLayer<Dtype>::normalizeWeights()
 {
   // Data to weightReal
   auto* weightBinaryData = weight_binary_->mutable_cpu_data();
@@ -293,7 +280,8 @@ void CuDNNConvolutionLayer<Dtype>::normalizeWeights(const bool truncateOriginalW
     for (auto channel = 0 ; channel < weight_binary_->shape()[1] ; channel++)
     {
       const auto offset = offsetNum + channel * imageArea;
-      // XNOR-style
+
+      // // XNOR-style
       // // L1 norm
       // auto l1Norm = Dtype(0);
       // for (auto i = 0 ; i < imageArea ; i++)
@@ -307,17 +295,14 @@ void CuDNNConvolutionLayer<Dtype>::normalizeWeights(const bool truncateOriginalW
       // const auto sum = l1Norm / imageArea;
       // for (auto i = 0 ; i < imageArea ; i++)
       //   weightBinaryData[offset+i] = (weightRealData[offset+i] < 0 ? -sum : sum);
+
       // Old binary net style
       // truncate to +-1
       for (auto i = 0 ; i < imageArea ; i++)
         weightRealData[offset+i] = std::max(-Dtype(1), std::min(Dtype(1), weightRealData[offset+i]));
       // Binary approximation
-      if (truncateOriginalWeights)
-        for (auto i = 0 ; i < imageArea ; i++)
-          weightRealData[offset+i] = (weightRealData[offset+i] < 0 ? -Dtype(1) : Dtype(1));
-      else
-        for (auto i = 0 ; i < imageArea ; i++)
-          weightBinaryData[offset+i] = (weightRealData[offset+i] < 0 ? -Dtype(1) : Dtype(1));
+      for (auto i = 0 ; i < imageArea ; i++)
+        weightBinaryData[offset+i] = (weightRealData[offset+i] < 0 ? -Dtype(1) : Dtype(1));
     }
   }
 }
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index 495189d9..60647535 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -11,13 +11,31 @@ template <typename Dtype>
 void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   // Binary added
-  if (this->layer_param_.convolution_param().binary() && this->phase_ == TRAIN)
-    normalizeWeights(false);
+  if (this->layer_param_.convolution_param().binary())
+  {
+    // TRAIN
+    if (this->phase_ == TRAIN)
+      normalizeWeights();
+    // TEST + only first time
+    else if (!weight_initialized_)
+    {
+      weight_initialized_ = true;
+      CHECK_GE(this->blobs_.size(), 1);
+      CHECK_GT(this->blobs_[0]->shape().size(), 2u);
+      weight_binary_.reset(new Blob<Dtype>());
+      weight_binary_->Reshape(this->blobs_[0]->shape());
+      // Data to weightReal
+      normalizeWeights();
+    }
+  }
   // Binary added end
 
   // const Dtype* weight = this->blobs_[0]->gpu_data(); // Binary commented
   // Binary added
-  const Dtype* weight = (this->layer_param_.convolution_param().binary() && this->phase_ == TRAIN
+  // const Dtype* weight = weight_binary_->gpu_data();
+  // const Dtype* weight = (this->layer_param_.convolution_param().binary() && this->phase_ == TRAIN
+  //   ? weight_binary_->gpu_data() : this->blobs_[0]->gpu_data());
+  const Dtype* weight = (this->layer_param_.convolution_param().binary()
     ? weight_binary_->gpu_data() : this->blobs_[0]->gpu_data());
   // Binary added ended
   for (int i = 0; i < bottom.size(); ++i) {