diff --git a/README.md b/README.md
index 93a7574..3b16ec3 100644
--- a/README.md
+++ b/README.md
@@ -3,9 +3,10 @@ tkDNN is a Deep Neural Network library built with cuDNN primitives specifically
 The main scope is to do high performance inference on already trained models.
 
 this branch actually work on every NVIDIA GPU that support the dependencies:
-* CUDA 9
-* CUDNN 7.105
-* TENSORRT 4.02
+* CUDA 10.0
+* CUDNN 7.603
+* TENSORRT 6.01
+* OPENCV 4.1
 
 ## Workflow
 The recommended workflow follow these step:
diff --git a/include/tkDNN/Layer.h b/include/tkDNN/Layer.h
index c6bee42..ee73109 100644
--- a/include/tkDNN/Layer.h
+++ b/include/tkDNN/Layer.h
@@ -24,6 +24,8 @@ enum layerType_t {
     LAYER_YOLO
 };
 
+#define TKDNN_BN_MIN_EPSILON 1e-5
+
 /** 
     Simple layer Father class
 */
diff --git a/src/Conv2d.cpp b/src/Conv2d.cpp
index 3dfdce6..b275198 100644
--- a/src/Conv2d.cpp
+++ b/src/Conv2d.cpp
@@ -117,7 +117,7 @@ dnnType* Conv2d::infer(dataDim_t &dim, dnnType* srcData) {
             dstTensorDesc, dstData, dstTensorDesc, 
             dstData, biasTensorDesc, //same tensor descriptor as bias 
             scales_d, bias_d, mean_d, variance_d, 
-            CUDNN_BN_MIN_EPSILON);
+            TKDNN_BN_MIN_EPSILON);
     }
     //update data dimensions    
     dim = output_dim;
diff --git a/src/LayerWgs.cpp b/src/LayerWgs.cpp
index f27da0f..7851164 100644
--- a/src/LayerWgs.cpp
+++ b/src/LayerWgs.cpp
@@ -29,7 +29,7 @@ LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
         seek += outputs;
         readBinaryFile(weights_path.c_str(), outputs, &variance_h, &variance_d, seek);
 
-        float eps = CUDNN_BN_MIN_EPSILON;
+        float eps = TKDNN_BN_MIN_EPSILON;
 
         power_h = new dnnType[outputs];
         for(int i=0; i<outputs; i++) power_h[i] = 1.0f;
diff --git a/src/Network.cpp b/src/Network.cpp
index 491e32b..6dd559a 100644
--- a/src/Network.cpp
+++ b/src/Network.cpp
@@ -17,10 +17,6 @@ Network::Network(dataDim_t input_dim) {
              <<", CUDNN v"<<cu_ver<<")\n";
     dataType = CUDNN_DATA_FLOAT;
     tensorFormat = CUDNN_TENSOR_NCHW;
-
-    checkCUDNN( cudnnCreate(&cudnnHandle) );
-    checkERROR( cublasCreate(&cublasHandle) );
-
     num_layers = 0;
 
     fp16 = false;
@@ -38,6 +34,11 @@ Network::Network(dataDim_t input_dim) {
         std::cout<<COL_REDB<<"!! FP16 INERENCE ENABLED !!"<<COL_END<<"\n";
     if(dla)
         std::cout<<COL_GREENB<<"!! DLA INERENCE ENABLED !!"<<COL_END<<"\n";
+
+
+    checkCUDNN( cudnnCreate(&cudnnHandle) );
+    checkERROR( cublasCreate(&cublasHandle) );
+
 }
 
 Network::~Network() {