diff --git a/demo/detection/detection.cpp b/demo/detection/detection.cpp
index 838cf88..216ec31 100644
--- a/demo/detection/detection.cpp
+++ b/demo/detection/detection.cpp
@@ -10,8 +10,8 @@
 const char *reg_bias = "../tests/yolo/layers/g31.bin";
 
 int prob_sort(const void *pa, const void *pb) {
-    tkDNN::box a = *(tkDNN::box *)pa;
-    tkDNN::box b = *(tkDNN::box *)pb;
+    tk::dnn::box a = *(tk::dnn::box *)pa;
+    tk::dnn::box b = *(tk::dnn::box *)pb;
     float diff = a.prob - b.prob;
     if(diff < 0) return 1;
     else if(diff > 0) return -1;
@@ -48,7 +48,7 @@ cv::Mat GetSquareImage(const cv::Mat& img, int target_width) {
 
 //return inference time
 double compute_image( cv::Mat imageORIG, 
-                    tkDNN::NetworkRT *netRT, tkDNN::RegionInterpret *rI,
+                    tk::dnn::NetworkRT *netRT, tk::dnn::RegionInterpret *rI,
                     dnnType *input, dnnType *output) {
 
     //Resize with padding and convert to float
@@ -136,8 +136,8 @@ int main(int argc, char *argv[]) {
     if(!fileExist(tensor_path))
         FatalError("unable to read serialRT file");
     //convert network to tensorRT
-    tkDNN::NetworkRT netRT(NULL, tensor_path);
-    tkDNN::RegionInterpret rI(netRT.input_dim, netRT.output_dim, 80, 4, 5, thresh, reg_bias);
+    tk::dnn::NetworkRT netRT(NULL, tensor_path);
+    tk::dnn::RegionInterpret rI(netRT.input_dim, netRT.output_dim, 80, 4, 5, thresh, reg_bias);
 
     dnnType *input = new float[netRT.input_dim.tot()];
     dnnType *output = new float[netRT.output_dim.tot()];
@@ -172,9 +172,9 @@ int main(int argc, char *argv[]) {
             FatalError("could not read labels");
 
 
-        qsort(rI.res_boxes, rI.res_boxes_n, sizeof(tkDNN::box), prob_sort);
+        qsort(rI.res_boxes, rI.res_boxes_n, sizeof(tk::dnn::box), prob_sort);
         for(int i=0; i<rI.res_boxes_n; i++) {
-            tkDNN::box bx = rI.res_boxes[i];
+            tk::dnn::box bx = rI.res_boxes[i];
             std::cout<<" ("<<int(bx.prob*100)<<"%) "<<bx.cl
                      <<": "<<bx.x<<" "<<bx.y<<" "<<bx.w<<" "<<bx.h<<"\n";
 
@@ -184,7 +184,7 @@ int main(int argc, char *argv[]) {
         }
 
         std::cout<<"GROUND TRUTH\n";
-        tkDNN::box gt[256];
+        tk::dnn::box gt[256];
         int gt_n = 0; 
         int cl;
         float x, y, w, h;
@@ -212,7 +212,7 @@ int main(int argc, char *argv[]) {
             int prec = 0;
             for(int j=0; j<i; j++) {            //for each detected in sub group
                 for(int z=0; z<gt_n; z++) {     //control each ground truth
-                    float iou = tkDNN::RegionInterpret::box_iou(rI.res_boxes[j], gt[z]);
+                    float iou = tk::dnn::RegionInterpret::box_iou(rI.res_boxes[j], gt[z]);
                     if(iou > 0.6f && rI.res_boxes[j].cl == gt[z].cl) {
                         prec++;
                         break;
diff --git a/demo/live/live.cpp b/demo/live/live.cpp
index b502081..e04d605 100644
--- a/demo/live/live.cpp
+++ b/demo/live/live.cpp
@@ -18,8 +18,8 @@ const char *reg_bias = "../tests/yolo/layers/g31.bin";
 #endif
 
 int prob_sort(const void *pa, const void *pb) {
-    tkDNN::box a = *(tkDNN::box *)pa;
-    tkDNN::box b = *(tkDNN::box *)pb;
+    tk::dnn::box a = *(tk::dnn::box *)pa;
+    tk::dnn::box b = *(tk::dnn::box *)pb;
     float diff = a.prob - b.prob;
     if(diff < 0) return 1;
     else if(diff > 0) return -1;
@@ -56,7 +56,7 @@ cv::Mat GetSquareImage(const cv::Mat& img, int target_width) {
 
 //return inference time
 double compute_image( cv::Mat imageORIG, 
-                    tkDNN::NetworkRT *netRT, tkDNN::RegionInterpret *rI,
+                    tk::dnn::NetworkRT *netRT, tk::dnn::RegionInterpret *rI,
                     dnnType *input, dnnType *output) {
     TIMER_START
 
@@ -148,8 +148,8 @@ int main(int argc, char *argv[]) {
         FatalError("unable to read serialRT file");
 
     //convert network to tensorRT
-    tkDNN::NetworkRT netRT(NULL, tensor_path);
-    tkDNN::RegionInterpret rI(netRT.input_dim, netRT.output_dim, CLASS, 4, 5, thresh, reg_bias);
+    tk::dnn::NetworkRT netRT(NULL, tensor_path);
+    tk::dnn::RegionInterpret rI(netRT.input_dim, netRT.output_dim, CLASS, 4, 5, thresh, reg_bias);
 
     dnnType *input = new float[netRT.input_dim.tot()];
     dnnType *output = new float[netRT.output_dim.tot()];
@@ -170,9 +170,9 @@ int main(int argc, char *argv[]) {
 
         mTime += compute_image(img, &netRT, &rI, input, output); 
 
-        qsort(rI.res_boxes, rI.res_boxes_n, sizeof(tkDNN::box), prob_sort);
+        qsort(rI.res_boxes, rI.res_boxes_n, sizeof(tk::dnn::box), prob_sort);
         for(int i=0; i<rI.res_boxes_n; i++) {
-            tkDNN::box bx = rI.res_boxes[i];
+            tk::dnn::box bx = rI.res_boxes[i];
             std::cout<<" ("<<int(bx.prob*100)<<"%) "<<bx.cl
                      <<": "<<bx.x<<" "<<bx.y<<" "<<bx.w<<" "<<bx.h<<"\n";
 
diff --git a/include/Layer.h b/include/Layer.h
index 22c1ea7..3898471 100644
--- a/include/Layer.h
+++ b/include/Layer.h
@@ -5,7 +5,7 @@
 #include "utils.h"
 #include "Network.h"
 
-namespace tkDNN {
+namespace tk { namespace dnn {
 
 enum layerType_t {
     LAYER_DENSE,
@@ -340,5 +340,5 @@ public:
     static float box_iou(box a, box b);
 };
 
-}
+}}
 #endif //LAYER_H
diff --git a/include/Network.h b/include/Network.h
index 1442d9b..958129c 100644
--- a/include/Network.h
+++ b/include/Network.h
@@ -3,7 +3,7 @@
 
 #include "utils.h"
 
-namespace tkDNN {
+namespace tk { namespace dnn {
 
 /**
     Data rapresentation beetween layers
@@ -62,5 +62,5 @@ public:
     bool fp16;
 };
 
-}
+}}
 #endif //NETWORK_H
diff --git a/include/NetworkRT.h b/include/NetworkRT.h
index 73515da..b8e2e04 100644
--- a/include/NetworkRT.h
+++ b/include/NetworkRT.h
@@ -6,7 +6,7 @@
 #include "Layer.h"
 #include "NvInfer.h"
 
-namespace tkDNN {
+namespace tk { namespace dnn {
 
 class NetworkRT {
 
@@ -62,5 +62,5 @@ template<typename T> T readBUF(const char*& buffer)
     return val;
 }
 
-}
+}}
 #endif //NETWORKRT_H
diff --git a/src/Activation.cpp b/src/Activation.cpp
index cc91a82..5ef62d5 100644
--- a/src/Activation.cpp
+++ b/src/Activation.cpp
@@ -3,7 +3,7 @@
 #include "Layer.h"
 #include "kernels.h"
 
-namespace tkDNN {
+namespace tk { namespace dnn {
 
 Activation::Activation(Network *net, int act_mode) : 
     Layer(net) {
@@ -63,4 +63,4 @@ dnnType* Activation::infer(dataDim_t &dim, dnnType* srcData) {
     return dstData;
 }
 
-}
+}}
diff --git a/src/Conv2d.cpp b/src/Conv2d.cpp
index f1d9a5d..bfb8316 100644
--- a/src/Conv2d.cpp
+++ b/src/Conv2d.cpp
@@ -2,7 +2,7 @@
 
 #include "Layer.h"
 
-namespace tkDNN {
+namespace tk { namespace dnn {
 
 Conv2d::Conv2d( Network *net, int out_ch, int kernelH, int kernelW, 
                 int strideH, int strideW, int paddingH, int paddingW,
@@ -125,4 +125,4 @@ dnnType* Conv2d::infer(dataDim_t &dim, dnnType* srcData) {
     return dstData;
 }
 
-}
+}}
diff --git a/src/Dense.cpp b/src/Dense.cpp
index 237dfc8..f86aa4a 100644
--- a/src/Dense.cpp
+++ b/src/Dense.cpp
@@ -2,7 +2,7 @@
 
 #include "Layer.h"
 
-namespace tkDNN {
+namespace tk { namespace dnn {
 
 Dense::Dense(Network *net, int out_ch, const char* fname_weights) : 
     LayerWgs(net, net->getOutputDim().tot(), out_ch, 1, 1, 1, fname_weights) {
@@ -55,4 +55,4 @@ dnnType* Dense::infer(dataDim_t &dim, dnnType* srcData) {
     return dstData;
 }
 
-}
+}}
diff --git a/src/Flatten.cpp b/src/Flatten.cpp
index 732d941..8906c88 100644
--- a/src/Flatten.cpp
+++ b/src/Flatten.cpp
@@ -3,7 +3,7 @@
 #include "Layer.h"
 #include "kernels.h"
 
-namespace tkDNN {
+namespace tk { namespace dnn {
 
 Flatten::Flatten(Network *net) : Layer(net) {
 
@@ -33,4 +33,4 @@ dnnType* Flatten::infer(dataDim_t &dim, dnnType* srcData) {
     return dstData;
 }
 
-}
\ No newline at end of file
+}}
\ No newline at end of file
diff --git a/src/Layer.cpp b/src/Layer.cpp
index 4f585f7..2d49f77 100644
--- a/src/Layer.cpp
+++ b/src/Layer.cpp
@@ -2,7 +2,7 @@
 
 #include "Layer.h"
 
-namespace tkDNN {
+namespace tk { namespace dnn {
 
 Layer::Layer(Network *net) {
 
@@ -23,4 +23,4 @@ Layer::~Layer() {
     checkCUDNN( cudnnDestroyTensorDescriptor(dstTensorDesc) );
 }
 
-}
\ No newline at end of file
+}}
\ No newline at end of file
diff --git a/src/LayerWgs.cpp b/src/LayerWgs.cpp
index d6469a0..d8bb881 100644
--- a/src/LayerWgs.cpp
+++ b/src/LayerWgs.cpp
@@ -4,7 +4,7 @@
 #include "Layer.h"
 #include "kernels.h"
 
-namespace tkDNN {
+namespace tk { namespace dnn {
 
 LayerWgs::LayerWgs(Network *net, int inputs, int outputs, 
                    int kh, int kw, int kl, 
@@ -114,4 +114,4 @@ LayerWgs::~LayerWgs() {
     }
 }
 
-}
+}}
diff --git a/src/MulAdd.cpp b/src/MulAdd.cpp
index 2e4a824..0c2a962 100644
--- a/src/MulAdd.cpp
+++ b/src/MulAdd.cpp
@@ -3,7 +3,7 @@
 #include "Layer.h"
 #include "kernels.h"
 
-namespace tkDNN {
+namespace tk { namespace dnn {
 
 MulAdd::MulAdd(Network *net, dnnType mul, dnnType add) : Layer(net) {
 
@@ -41,4 +41,4 @@ dnnType* MulAdd::infer(dataDim_t &dim, dnnType* srcData) {
     return dstData;
 }
 
-}
\ No newline at end of file
+}}
\ No newline at end of file
diff --git a/src/Network.cpp b/src/Network.cpp
index 3cc342d..cade05f 100644
--- a/src/Network.cpp
+++ b/src/Network.cpp
@@ -5,7 +5,7 @@
 #include "Network.h"
 #include "Layer.h"
 
-namespace tkDNN {
+namespace tk { namespace dnn {
 
 Network::Network(dataDim_t input_dim) {
     this->input_dim = input_dim;
@@ -99,4 +99,4 @@ void Network::print() {
 }
 
 
-}
+}}
diff --git a/src/NetworkRT.cpp b/src/NetworkRT.cpp
index ebc2206..d35a230 100644
--- a/src/NetworkRT.cpp
+++ b/src/NetworkRT.cpp
@@ -25,7 +25,7 @@ class Logger : public ILogger {
     }
 } loggerRT;
 
-namespace tkDNN {
+namespace tk { namespace dnn {
 
 std::map<Layer*, nvinfer1::ITensor*>tensors; 
 
@@ -399,4 +399,4 @@ bool NetworkRT::deserialize(const char *filename) {
     return true;
 }
 
-}
+}}
diff --git a/src/Pooling.cpp b/src/Pooling.cpp
index c66829e..6c2afc8 100644
--- a/src/Pooling.cpp
+++ b/src/Pooling.cpp
@@ -3,7 +3,7 @@
 #include "Layer.h"
 #include "kernels.h"
 
-namespace tkDNN {
+namespace tk { namespace dnn {
 
 Pooling::Pooling( Network *net, int winH, int winW, int strideH, int strideW,
                   tkdnnPoolingMode_t pool_mode) : 
@@ -107,4 +107,4 @@ dnnType* Pooling::infer(dataDim_t &dim, dnnType* srcData) {
     return dstData;
 }
 
-}
+}}
diff --git a/src/Region.cpp b/src/Region.cpp
index 8a4a2fa..6407eba 100644
--- a/src/Region.cpp
+++ b/src/Region.cpp
@@ -9,7 +9,7 @@
 #include "Layer.h"
 #include "kernels.h"
 
-namespace tkDNN {
+namespace tk { namespace dnn {
 
 Region::Region(Network *net, int classes, int coords, int num) : 
     Layer(net) {
@@ -339,4 +339,4 @@ void RegionInterpret::showImageResult(dnnType *input_h) {
 #endif
 }
 
-}
+}}
diff --git a/src/Reorg.cpp b/src/Reorg.cpp
index c13c37d..52862b0 100644
--- a/src/Reorg.cpp
+++ b/src/Reorg.cpp
@@ -3,7 +3,7 @@
 #include "Layer.h"
 #include "kernels.h"
 
-namespace tkDNN {
+namespace tk { namespace dnn {
 
 Reorg::Reorg(Network *net, int stride) : Layer(net) {
 
@@ -31,4 +31,4 @@ dnnType* Reorg::infer(dataDim_t &dim, dnnType* srcData) {
     return dstData;
 }
 
-}
+}}
diff --git a/src/Route.cpp b/src/Route.cpp
index 8e7d456..77a5f24 100644
--- a/src/Route.cpp
+++ b/src/Route.cpp
@@ -3,7 +3,7 @@
 #include "Layer.h"
 #include "kernels.h"
 
-namespace tkDNN {
+namespace tk { namespace dnn {
 
 Route::Route(Network *net, Layer **layers, int layers_n) : Layer(net) {
 
@@ -53,4 +53,4 @@ dnnType* Route::infer(dataDim_t &dim, dnnType* srcData) {
     return dstData;
 }
 
-}
\ No newline at end of file
+}}
\ No newline at end of file
diff --git a/src/Softmax.cpp b/src/Softmax.cpp
index 076b723..af08f7f 100644
--- a/src/Softmax.cpp
+++ b/src/Softmax.cpp
@@ -3,7 +3,7 @@
 #include "Layer.h"
 #include "kernels.h"
 
-namespace tkDNN {
+namespace tk { namespace dnn {
 
 Softmax::Softmax(Network *net) : Layer(net) {
 
@@ -44,4 +44,4 @@ dnnType* Softmax::infer(dataDim_t &dim, dnnType* srcData) {
     return dstData;
 }
 
-}
+}}
diff --git a/src/pluginsRT/ActivationLeakyRT.cpp b/src/pluginsRT/ActivationLeakyRT.cpp
index 7376857..4609158 100644
--- a/src/pluginsRT/ActivationLeakyRT.cpp
+++ b/src/pluginsRT/ActivationLeakyRT.cpp
@@ -53,7 +53,7 @@ public:
 
 	virtual void serialize(void* buffer) override {
 		char *buf = reinterpret_cast<char*>(buffer);
-		tkDNN::writeBUF(buf, size);
+		tk::dnn::writeBUF(buf, size);
 	}
 
 	int size;
diff --git a/src/pluginsRT/Int8Calibrator.cpp b/src/pluginsRT/Int8Calibrator.cpp
index e33d7db..ad759bf 100644
--- a/src/pluginsRT/Int8Calibrator.cpp
+++ b/src/pluginsRT/Int8Calibrator.cpp
@@ -8,7 +8,7 @@
 class BatchStream
 {
 public:
-	BatchStream(tkDNN::dataDim_t dim, int batchSize, int maxBatches)
+	BatchStream(tk::dnn::dataDim_t dim, int batchSize, int maxBatches)
 	{
         mBatchSize = batchSize;
         mMaxBatches = maxBatches;
diff --git a/src/pluginsRT/RegionRT.cpp b/src/pluginsRT/RegionRT.cpp
index f1e7e9e..bcfb4c7 100644
--- a/src/pluginsRT/RegionRT.cpp
+++ b/src/pluginsRT/RegionRT.cpp
@@ -74,12 +74,12 @@ public:
 
 	virtual void serialize(void* buffer) override {
 		char *buf = reinterpret_cast<char*>(buffer);
-		tkDNN::writeBUF(buf, classes);
-		tkDNN::writeBUF(buf, coords);
-		tkDNN::writeBUF(buf, num);
-		tkDNN::writeBUF(buf, c);
-		tkDNN::writeBUF(buf, h);
-		tkDNN::writeBUF(buf, w);
+		tk::dnn::writeBUF(buf, classes);
+		tk::dnn::writeBUF(buf, coords);
+		tk::dnn::writeBUF(buf, num);
+		tk::dnn::writeBUF(buf, c);
+		tk::dnn::writeBUF(buf, h);
+		tk::dnn::writeBUF(buf, w);
 	}
 
 	int c, h, w;
diff --git a/src/pluginsRT/ReorgRT.cpp b/src/pluginsRT/ReorgRT.cpp
index d85b366..25bfbde 100644
--- a/src/pluginsRT/ReorgRT.cpp
+++ b/src/pluginsRT/ReorgRT.cpp
@@ -53,10 +53,10 @@ public:
 
 	virtual void serialize(void* buffer) override {
 		char *buf = reinterpret_cast<char*>(buffer);
-		tkDNN::writeBUF(buf, stride);
-		tkDNN::writeBUF(buf, c);
-		tkDNN::writeBUF(buf, h);
-		tkDNN::writeBUF(buf, w);
+		tk::dnn::writeBUF(buf, stride);
+		tk::dnn::writeBUF(buf, c);
+		tk::dnn::writeBUF(buf, h);
+		tk::dnn::writeBUF(buf, w);
 	}
 
 	int c, h, w, stride;
diff --git a/tests/mnist/test_mnist.cpp b/tests/mnist/test_mnist.cpp
index 49c74a4..e2d4dc0 100644
--- a/tests/mnist/test_mnist.cpp
+++ b/tests/mnist/test_mnist.cpp
@@ -11,18 +11,18 @@ const char *output_bin   = "../tests/mnist/output.bin";
 int main() {
 
     // Network layout
-    tkDNN::dataDim_t dim(1, 1, 28, 28, 1);
-    tkDNN::Network net(dim);
-    tkDNN::Conv2d     l0(&net, 20, 5, 5, 1, 1, 0, 0, c0_bin);
-    tkDNN::Pooling    l1(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
-    tkDNN::Conv2d     l2(&net, 50, 5, 5, 1, 1, 0, 0, c1_bin);
-    tkDNN::Pooling    l3(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
-    tkDNN::Dense      l4(&net, 500, d2_bin);
-    tkDNN::Activation l5(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Dense      l6(&net, 10, d3_bin);
-    tkDNN::Softmax    l7(&net);
+    tk::dnn::dataDim_t dim(1, 1, 28, 28, 1);
+    tk::dnn::Network net(dim);
+    tk::dnn::Conv2d     l0(&net, 20, 5, 5, 1, 1, 0, 0, c0_bin);
+    tk::dnn::Pooling    l1(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
+    tk::dnn::Conv2d     l2(&net, 50, 5, 5, 1, 1, 0, 0, c1_bin);
+    tk::dnn::Pooling    l3(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
+    tk::dnn::Dense      l4(&net, 500, d2_bin);
+    tk::dnn::Activation l5(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Dense      l6(&net, 10, d3_bin);
+    tk::dnn::Softmax    l7(&net);
 
-    tkDNN::NetworkRT netRT(&net, "mnist.rt");
+    tk::dnn::NetworkRT netRT(&net, "mnist.rt");
 
     // Load input
     dnnType *data;
@@ -43,7 +43,7 @@ int main() {
     //std::cout<<"\n======= CUDNN RESULT =======\n";
     //printDeviceVector(10, out_data);
  
-    tkDNN::dataDim_t dim2(1, 1, 28, 28, 1);
+    tk::dnn::dataDim_t dim2(1, 1, 28, 28, 1);
 
     std::cout<<"TENSORRT inference:\n"; {
         dim2.print();
diff --git a/tests/mnist/test_mnistRT.cpp b/tests/mnist/test_mnistRT.cpp
index a0814c6..c337097 100644
--- a/tests/mnist/test_mnistRT.cpp
+++ b/tests/mnist/test_mnistRT.cpp
@@ -27,16 +27,16 @@ int main() {
 
     std::cout<<"\n==== CUDNN ====\n";
     // Network layout
-    tkDNN::dataDim_t dim(1, 1, 28, 28, 1);
-	tkDNN::Network net(dim);
-    tkDNN::Conv2d     l0(&net, 20, 5, 5, 1, 1, 0, 0, c0_bin);
-    tkDNN::Pooling    l1(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
-    tkDNN::Conv2d     l2(&net, 50, 5, 5, 1, 1, 0, 0, c1_bin);
-    tkDNN::Pooling    l3(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
-    tkDNN::Dense      l4(&net, 500, d2_bin);
-    tkDNN::Activation l5(&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Dense      l6(&net, 10, d3_bin);
-    tkDNN::Softmax    l7(&net);
+    tk::dnn::dataDim_t dim(1, 1, 28, 28, 1);
+	tk::dnn::Network net(dim);
+    tk::dnn::Conv2d     l0(&net, 20, 5, 5, 1, 1, 0, 0, c0_bin);
+    tk::dnn::Pooling    l1(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
+    tk::dnn::Conv2d     l2(&net, 50, 5, 5, 1, 1, 0, 0, c1_bin);
+    tk::dnn::Pooling    l3(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
+    tk::dnn::Dense      l4(&net, 500, d2_bin);
+    tk::dnn::Activation l5(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Dense      l6(&net, 10, d3_bin);
+    tk::dnn::Softmax    l7(&net);
  
     // Load input
     dnnType *data;
@@ -71,7 +71,7 @@ int main() {
 	auto input = network->addInput("data", dt, DimsCHW{ 1, 28, 28});
 	assert(input != nullptr);
 
-    tkDNN::Conv2d *c0 = &l0; 
+    tk::dnn::Conv2d *c0 = &l0; 
     Weights w { dt, c0->data_h, c0->inputs*c0->outputs*c0->kernelH*c0->kernelW};
     Weights b { dt, c0->bias_h, c0->outputs};
 	// Add a convolution layer with 20 outputs and a 5x5 filter.
@@ -84,7 +84,7 @@ int main() {
 	assert(pool1 != nullptr);
 	pool1->setStride(DimsHW{2, 2});
 
-    tkDNN::Conv2d *c1 = &l2; 
+    tk::dnn::Conv2d *c1 = &l2; 
     Weights w1 { dt, c1->data_h, c1->inputs*c1->outputs*c1->kernelH*c1->kernelW};
     Weights b1 { dt, c1->bias_h, c1->outputs};
 	// Add a second convolution layer with 50 outputs and a 5x5 filter.
@@ -97,7 +97,7 @@ int main() {
 	assert(pool2 != nullptr);
 	pool2->setStride(DimsHW{2, 2});
 
-    tkDNN::Dense *d2 = &l4; 
+    tk::dnn::Dense *d2 = &l4; 
     Weights w2 { dt, d2->data_h, d2->inputs*d2->outputs};
     Weights b2 { dt, d2->bias_h, d2->outputs};
 	// Add a fully connected layer with 500 outputs.
@@ -108,7 +108,7 @@ int main() {
 	auto relu1 = network->addActivation(*ip1->getOutput(0), ActivationType::kRELU);
 	assert(relu1 != nullptr);
 
-    tkDNN::Dense *d3 = &l6; 
+    tk::dnn::Dense *d3 = &l6; 
     Weights w3 { dt, d3->data_h, d3->inputs*d3->outputs};
     Weights b3 { dt, d3->bias_h, d3->outputs};
 	// Add a second fully connected layer with 20 outputs.
diff --git a/tests/simple/test_simple.cpp b/tests/simple/test_simple.cpp
index e0a47d6..a7628ea 100644
--- a/tests/simple/test_simple.cpp
+++ b/tests/simple/test_simple.cpp
@@ -10,15 +10,15 @@ const char *output_bin  = "../tests/simple/output.bin";
 int main() {
 
     // Network layout
-    tkDNN::dataDim_t dim(1, 1, 10, 10, 1);
-    tkDNN::Network net(dim);
-    tkDNN::Conv2d     l0(&net, 2, 4, 4, 2, 2, 0, 0, c0_bin);
-    tkDNN::Activation l1(&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Conv2d     l2(&net, 4, 2, 2, 1, 1, 0, 0, c1_bin);
-    tkDNN::Activation l3(&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Flatten    l4(&net);
-    tkDNN::Dense      l5(&net, 4, d2_bin);
-    tkDNN::Activation l6(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::dataDim_t dim(1, 1, 10, 10, 1);
+    tk::dnn::Network net(dim);
+    tk::dnn::Conv2d     l0(&net, 2, 4, 4, 2, 2, 0, 0, c0_bin);
+    tk::dnn::Activation l1(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     l2(&net, 4, 2, 2, 1, 1, 0, 0, c1_bin);
+    tk::dnn::Activation l3(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Flatten    l4(&net);
+    tk::dnn::Dense      l5(&net, 4, d2_bin);
+    tk::dnn::Activation l6(&net, CUDNN_ACTIVATION_RELU);
 
     // Load input
     dnnType *data;
diff --git a/tests/test_rtinference/rtinference.cpp b/tests/test_rtinference/rtinference.cpp
index bd8e6ff..f99d003 100644
--- a/tests/test_rtinference/rtinference.cpp
+++ b/tests/test_rtinference/rtinference.cpp
@@ -11,7 +11,7 @@ int main(int argc, char *argv[]) {
     srand (0); 
 
     //convert network to tensorRT
-    tkDNN::NetworkRT netRT(NULL, argv[1]);
+    tk::dnn::NetworkRT netRT(NULL, argv[1]);
 
     dnnType *input = new float[netRT.input_dim.tot()];
     dnnType *output = new float[netRT.input_dim.tot()];
diff --git a/tests/yolo/yolo.cpp b/tests/yolo/yolo.cpp
index a485542..05404b0 100644
--- a/tests/yolo/yolo.cpp
+++ b/tests/yolo/yolo.cpp
@@ -31,75 +31,75 @@ const char *output_bin = "../tests/yolo/layers/output.bin";
 int main() {
 
     // Network layout
-    tkDNN::dataDim_t dim(1, 3, 608, 608, 1);
-    tkDNN::Network net(dim);
+    tk::dnn::dataDim_t dim(1, 3, 608, 608, 1);
+    tk::dnn::Network net(dim);
 
-    tkDNN::Conv2d     c0 (&net, 32, 3, 3, 1, 1, 1, 1,   c0_bin, true);
-    tkDNN::Activation a0 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p1 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c0 (&net, 32, 3, 3, 1, 1, 1, 1,   c0_bin, true);
+    tk::dnn::Activation a0 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p1 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c2 (&net, 64, 3, 3, 1, 1, 1, 1,   c2_bin, true);
-    tkDNN::Activation a2 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p3 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c2 (&net, 64, 3, 3, 1, 1, 1, 1,   c2_bin, true);
+    tk::dnn::Activation a2 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p3 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c4 (&net, 128, 3, 3, 1, 1, 1, 1,  c4_bin, true);
-    tkDNN::Activation a4 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c5 (&net, 64, 1, 1, 1, 1, 0, 0,   c5_bin, true);
-    tkDNN::Activation a5 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c6 (&net, 128, 3, 3, 1, 1, 1, 1,  c6_bin, true);
-    tkDNN::Activation a6 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p7 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c4 (&net, 128, 3, 3, 1, 1, 1, 1,  c4_bin, true);
+    tk::dnn::Activation a4 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c5 (&net, 64, 1, 1, 1, 1, 0, 0,   c5_bin, true);
+    tk::dnn::Activation a5 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c6 (&net, 128, 3, 3, 1, 1, 1, 1,  c6_bin, true);
+    tk::dnn::Activation a6 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p7 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c8 (&net, 256, 3, 3, 1, 1, 1, 1,  c8_bin, true);
-    tkDNN::Activation a8 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c9 (&net, 128, 1, 1, 1, 1, 0, 0,  c9_bin, true);
-    tkDNN::Activation a9 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c10(&net, 256, 3, 3, 1, 1, 1, 1,  c10_bin, true);
-    tkDNN::Activation a10(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p11(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c8 (&net, 256, 3, 3, 1, 1, 1, 1,  c8_bin, true);
+    tk::dnn::Activation a8 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c9 (&net, 128, 1, 1, 1, 1, 0, 0,  c9_bin, true);
+    tk::dnn::Activation a9 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c10(&net, 256, 3, 3, 1, 1, 1, 1,  c10_bin, true);
+    tk::dnn::Activation a10(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p11(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c12(&net, 512, 3, 3, 1, 1, 1, 1,  c12_bin, true);
-    tkDNN::Activation a12(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c13(&net, 256, 1, 1, 1, 1, 0, 0,  c13_bin, true);
-    tkDNN::Activation a13(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c14(&net, 512, 3, 3, 1, 1, 1, 1,  c14_bin, true);
-    tkDNN::Activation a14(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c15(&net, 256, 1, 1, 1, 1, 0, 0,  c15_bin, true);
-    tkDNN::Activation a15(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c16(&net, 512, 3, 3, 1, 1, 1, 1,  c16_bin, true); 
-    tkDNN::Activation a16(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p17(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c12(&net, 512, 3, 3, 1, 1, 1, 1,  c12_bin, true);
+    tk::dnn::Activation a12(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c13(&net, 256, 1, 1, 1, 1, 0, 0,  c13_bin, true);
+    tk::dnn::Activation a13(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c14(&net, 512, 3, 3, 1, 1, 1, 1,  c14_bin, true);
+    tk::dnn::Activation a14(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c15(&net, 256, 1, 1, 1, 1, 0, 0,  c15_bin, true);
+    tk::dnn::Activation a15(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c16(&net, 512, 3, 3, 1, 1, 1, 1,  c16_bin, true); 
+    tk::dnn::Activation a16(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p17(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c18(&net, 1024, 3, 3, 1, 1, 1, 1, c18_bin, true);
-    tkDNN::Activation a18(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c19(&net, 512, 1, 1, 1, 1, 0, 0,  c19_bin, true);
-    tkDNN::Activation a19(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c20(&net, 1024, 3, 3, 1, 1, 1, 1, c20_bin, true);
-    tkDNN::Activation a20(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c21(&net, 512, 1, 1, 1, 1, 0, 0,  c21_bin, true);
-    tkDNN::Activation a21(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c22(&net, 1024, 3, 3, 1, 1, 1, 1, c22_bin, true);
-    tkDNN::Activation a22(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c23(&net, 1024, 3, 3, 1, 1, 1, 1, c23_bin, true);
-    tkDNN::Activation a23(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c24(&net, 1024, 3, 3, 1, 1, 1, 1, c24_bin, true);
-    tkDNN::Activation a24(&net, tkDNN::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c18(&net, 1024, 3, 3, 1, 1, 1, 1, c18_bin, true);
+    tk::dnn::Activation a18(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c19(&net, 512, 1, 1, 1, 1, 0, 0,  c19_bin, true);
+    tk::dnn::Activation a19(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c20(&net, 1024, 3, 3, 1, 1, 1, 1, c20_bin, true);
+    tk::dnn::Activation a20(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c21(&net, 512, 1, 1, 1, 1, 0, 0,  c21_bin, true);
+    tk::dnn::Activation a21(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c22(&net, 1024, 3, 3, 1, 1, 1, 1, c22_bin, true);
+    tk::dnn::Activation a22(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c23(&net, 1024, 3, 3, 1, 1, 1, 1, c23_bin, true);
+    tk::dnn::Activation a23(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c24(&net, 1024, 3, 3, 1, 1, 1, 1, c24_bin, true);
+    tk::dnn::Activation a24(&net, tk::dnn::ACTIVATION_LEAKY);
 
-    tkDNN::Layer *m25_layers[1] = { &a16 };
-    tkDNN::Route      m25(&net, m25_layers, 1);
-    tkDNN::Conv2d     c26(&net, 64, 1, 1, 1, 1, 0, 0,   c26_bin, true);
-    tkDNN::Activation a26(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Reorg      r27(&net, 2);
+    tk::dnn::Layer *m25_layers[1] = { &a16 };
+    tk::dnn::Route      m25(&net, m25_layers, 1);
+    tk::dnn::Conv2d     c26(&net, 64, 1, 1, 1, 1, 0, 0,   c26_bin, true);
+    tk::dnn::Activation a26(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Reorg      r27(&net, 2);
 
-    tkDNN::Layer *m28_layers[2] = { &r27, &a24 };
-    tkDNN::Route      m28(&net, m28_layers, 2);
+    tk::dnn::Layer *m28_layers[2] = { &r27, &a24 };
+    tk::dnn::Route      m28(&net, m28_layers, 2);
 
-    tkDNN::Conv2d     c29(&net, 1024, 3, 3, 1, 1, 1, 1, c29_bin, true);
-    tkDNN::Activation a29(&net, tkDNN::ACTIVATION_LEAKY);   
-    tkDNN::Conv2d     c30(&net, 425, 1, 1, 1, 1, 0, 0,  c30_bin, false);
-    tkDNN::Region     g31(&net, 80, 4, 5);
+    tk::dnn::Conv2d     c29(&net, 1024, 3, 3, 1, 1, 1, 1, c29_bin, true);
+    tk::dnn::Activation a29(&net, tk::dnn::ACTIVATION_LEAKY);   
+    tk::dnn::Conv2d     c30(&net, 425, 1, 1, 1, 1, 0, 0,  c30_bin, false);
+    tk::dnn::Region     g31(&net, 80, 4, 5);
     
-    tkDNN::RegionInterpret rI(dim, g31.output_dim, 80, 4, 5, 0.6f, g31_bin);
+    tk::dnn::RegionInterpret rI(dim, g31.output_dim, 80, 4, 5, 0.6f, g31_bin);
 
     // Load input
     dnnType *data;
@@ -110,11 +110,11 @@ int main() {
     net.print();
 
     //convert network to tensorRT
-    tkDNN::NetworkRT netRT(&net, "yolo.rt");
+    tk::dnn::NetworkRT netRT(&net, "yolo.rt");
 
     dnnType *out_data, *out_data2; // cudnn output, tensorRT output
 
-    tkDNN::dataDim_t dim1 = dim; //input dim
+    tk::dnn::dataDim_t dim1 = dim; //input dim
     printCenteredTitle(" CUDNN inference ", '=', 30); {
         dim1.print();
         TIMER_START
@@ -123,7 +123,7 @@ int main() {
         dim1.print();   
     }
     
-    tkDNN::dataDim_t dim2 = dim;
+    tk::dnn::dataDim_t dim2 = dim;
     printCenteredTitle(" TENSORRT inference ", '=', 30); {
         dim2.print();
         TIMER_START
diff --git a/tests/yolo_224/yolo_224.cpp b/tests/yolo_224/yolo_224.cpp
index 16adb4c..9d6d28a 100644
--- a/tests/yolo_224/yolo_224.cpp
+++ b/tests/yolo_224/yolo_224.cpp
@@ -31,75 +31,75 @@ const char *output_bin = "../tests/yolo_224/layers/output.bin";
 int main() {
 
     // Network layout
-    tkDNN::dataDim_t dim(1, 3, 224, 224, 1);
-    tkDNN::Network net(dim);
+    tk::dnn::dataDim_t dim(1, 3, 224, 224, 1);
+    tk::dnn::Network net(dim);
 
-    tkDNN::Conv2d     c0 (&net, 32, 3, 3, 1, 1, 1, 1,   c0_bin, true);
-    tkDNN::Activation a0 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p1 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c0 (&net, 32, 3, 3, 1, 1, 1, 1,   c0_bin, true);
+    tk::dnn::Activation a0 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p1 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c2 (&net, 64, 3, 3, 1, 1, 1, 1,   c2_bin, true);
-    tkDNN::Activation a2 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p3 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c2 (&net, 64, 3, 3, 1, 1, 1, 1,   c2_bin, true);
+    tk::dnn::Activation a2 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p3 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c4 (&net, 128, 3, 3, 1, 1, 1, 1,  c4_bin, true);
-    tkDNN::Activation a4 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c5 (&net, 64, 1, 1, 1, 1, 0, 0,   c5_bin, true);
-    tkDNN::Activation a5 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c6 (&net, 128, 3, 3, 1, 1, 1, 1,  c6_bin, true);
-    tkDNN::Activation a6 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p7 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c4 (&net, 128, 3, 3, 1, 1, 1, 1,  c4_bin, true);
+    tk::dnn::Activation a4 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c5 (&net, 64, 1, 1, 1, 1, 0, 0,   c5_bin, true);
+    tk::dnn::Activation a5 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c6 (&net, 128, 3, 3, 1, 1, 1, 1,  c6_bin, true);
+    tk::dnn::Activation a6 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p7 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c8 (&net, 256, 3, 3, 1, 1, 1, 1,  c8_bin, true);
-    tkDNN::Activation a8 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c9 (&net, 128, 1, 1, 1, 1, 0, 0,  c9_bin, true);
-    tkDNN::Activation a9 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c10(&net, 256, 3, 3, 1, 1, 1, 1,  c10_bin, true);
-    tkDNN::Activation a10(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p11(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c8 (&net, 256, 3, 3, 1, 1, 1, 1,  c8_bin, true);
+    tk::dnn::Activation a8 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c9 (&net, 128, 1, 1, 1, 1, 0, 0,  c9_bin, true);
+    tk::dnn::Activation a9 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c10(&net, 256, 3, 3, 1, 1, 1, 1,  c10_bin, true);
+    tk::dnn::Activation a10(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p11(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c12(&net, 512, 3, 3, 1, 1, 1, 1,  c12_bin, true);
-    tkDNN::Activation a12(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c13(&net, 256, 1, 1, 1, 1, 0, 0,  c13_bin, true);
-    tkDNN::Activation a13(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c14(&net, 512, 3, 3, 1, 1, 1, 1,  c14_bin, true);
-    tkDNN::Activation a14(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c15(&net, 256, 1, 1, 1, 1, 0, 0,  c15_bin, true);
-    tkDNN::Activation a15(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c16(&net, 512, 3, 3, 1, 1, 1, 1,  c16_bin, true); 
-    tkDNN::Activation a16(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p17(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c12(&net, 512, 3, 3, 1, 1, 1, 1,  c12_bin, true);
+    tk::dnn::Activation a12(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c13(&net, 256, 1, 1, 1, 1, 0, 0,  c13_bin, true);
+    tk::dnn::Activation a13(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c14(&net, 512, 3, 3, 1, 1, 1, 1,  c14_bin, true);
+    tk::dnn::Activation a14(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c15(&net, 256, 1, 1, 1, 1, 0, 0,  c15_bin, true);
+    tk::dnn::Activation a15(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c16(&net, 512, 3, 3, 1, 1, 1, 1,  c16_bin, true); 
+    tk::dnn::Activation a16(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p17(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c18(&net, 1024, 3, 3, 1, 1, 1, 1, c18_bin, true);
-    tkDNN::Activation a18(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c19(&net, 512, 1, 1, 1, 1, 0, 0,  c19_bin, true);
-    tkDNN::Activation a19(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c20(&net, 1024, 3, 3, 1, 1, 1, 1, c20_bin, true);
-    tkDNN::Activation a20(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c21(&net, 512, 1, 1, 1, 1, 0, 0,  c21_bin, true);
-    tkDNN::Activation a21(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c22(&net, 1024, 3, 3, 1, 1, 1, 1, c22_bin, true);
-    tkDNN::Activation a22(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c23(&net, 1024, 3, 3, 1, 1, 1, 1, c23_bin, true);
-    tkDNN::Activation a23(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c24(&net, 1024, 3, 3, 1, 1, 1, 1, c24_bin, true);
-    tkDNN::Activation a24(&net, tkDNN::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c18(&net, 1024, 3, 3, 1, 1, 1, 1, c18_bin, true);
+    tk::dnn::Activation a18(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c19(&net, 512, 1, 1, 1, 1, 0, 0,  c19_bin, true);
+    tk::dnn::Activation a19(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c20(&net, 1024, 3, 3, 1, 1, 1, 1, c20_bin, true);
+    tk::dnn::Activation a20(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c21(&net, 512, 1, 1, 1, 1, 0, 0,  c21_bin, true);
+    tk::dnn::Activation a21(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c22(&net, 1024, 3, 3, 1, 1, 1, 1, c22_bin, true);
+    tk::dnn::Activation a22(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c23(&net, 1024, 3, 3, 1, 1, 1, 1, c23_bin, true);
+    tk::dnn::Activation a23(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c24(&net, 1024, 3, 3, 1, 1, 1, 1, c24_bin, true);
+    tk::dnn::Activation a24(&net, tk::dnn::ACTIVATION_LEAKY);
 
-    tkDNN::Layer *m25_layers[1] = { &a16 };
-    tkDNN::Route      m25(&net, m25_layers, 1);
-    tkDNN::Conv2d     c26(&net, 64, 1, 1, 1, 1, 0, 0,   c26_bin, true);
-    tkDNN::Activation a26(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Reorg      r27(&net, 2);
+    tk::dnn::Layer *m25_layers[1] = { &a16 };
+    tk::dnn::Route      m25(&net, m25_layers, 1);
+    tk::dnn::Conv2d     c26(&net, 64, 1, 1, 1, 1, 0, 0,   c26_bin, true);
+    tk::dnn::Activation a26(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Reorg      r27(&net, 2);
 
-    tkDNN::Layer *m28_layers[2] = { &r27, &a24 };
-    tkDNN::Route      m28(&net, m28_layers, 2);
+    tk::dnn::Layer *m28_layers[2] = { &r27, &a24 };
+    tk::dnn::Route      m28(&net, m28_layers, 2);
 
-    tkDNN::Conv2d     c29(&net, 1024, 3, 3, 1, 1, 1, 1, c29_bin, true);
-    tkDNN::Activation a29(&net, tkDNN::ACTIVATION_LEAKY);   
-    tkDNN::Conv2d     c30(&net, 425, 1, 1, 1, 1, 0, 0,  c30_bin, false);
-    tkDNN::Region     g31(&net, 80, 4, 5);
+    tk::dnn::Conv2d     c29(&net, 1024, 3, 3, 1, 1, 1, 1, c29_bin, true);
+    tk::dnn::Activation a29(&net, tk::dnn::ACTIVATION_LEAKY);   
+    tk::dnn::Conv2d     c30(&net, 425, 1, 1, 1, 1, 0, 0,  c30_bin, false);
+    tk::dnn::Region     g31(&net, 80, 4, 5);
     
-    tkDNN::RegionInterpret rI(dim, g31.output_dim, 80, 4, 5, 0.6f, g31_bin);
+    tk::dnn::RegionInterpret rI(dim, g31.output_dim, 80, 4, 5, 0.6f, g31_bin);
 
     // Load input
     dnnType *data;
@@ -110,11 +110,11 @@ int main() {
     net.print();
 
     //convert network to tensorRT
-    tkDNN::NetworkRT netRT(&net, "yolo_224.rt");
+    tk::dnn::NetworkRT netRT(&net, "yolo_224.rt");
 
     dnnType *out_data, *out_data2; // cudnn output, tensorRT output
 
-    tkDNN::dataDim_t dim1 = dim; //input dim
+    tk::dnn::dataDim_t dim1 = dim; //input dim
     printCenteredTitle(" CUDNN inference ", '=', 30); {
         dim1.print();
         TIMER_START
@@ -123,7 +123,7 @@ int main() {
         dim1.print();   
     }
     
-    tkDNN::dataDim_t dim2 = dim;
+    tk::dnn::dataDim_t dim2 = dim;
     printCenteredTitle(" TENSORRT inference ", '=', 30); {
         dim2.print();
         TIMER_START
diff --git a/tests/yolo_relu/yolo_relu.cpp b/tests/yolo_relu/yolo_relu.cpp
index be1294a..ddb0802 100644
--- a/tests/yolo_relu/yolo_relu.cpp
+++ b/tests/yolo_relu/yolo_relu.cpp
@@ -31,75 +31,75 @@ const char *output_bin = "../tests/yolo_relu/layers/output.bin";
 int main() {
 
     // Network layout
-    tkDNN::dataDim_t dim(1, 3, 608, 608, 1);
-    tkDNN::Network net(dim);
+    tk::dnn::dataDim_t dim(1, 3, 608, 608, 1);
+    tk::dnn::Network net(dim);
 
-    tkDNN::Conv2d     c0 (&net, 32, 3, 3, 1, 1, 1, 1,   c0_bin, true);
-    tkDNN::Activation a0 (&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Pooling    p1 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c0 (&net, 32, 3, 3, 1, 1, 1, 1,   c0_bin, true);
+    tk::dnn::Activation a0 (&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Pooling    p1 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c2 (&net, 64, 3, 3, 1, 1, 1, 1,   c2_bin, true);
-    tkDNN::Activation a2 (&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Pooling    p3 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c2 (&net, 64, 3, 3, 1, 1, 1, 1,   c2_bin, true);
+    tk::dnn::Activation a2 (&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Pooling    p3 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c4 (&net, 128, 3, 3, 1, 1, 1, 1,  c4_bin, true);
-    tkDNN::Activation a4 (&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Conv2d     c5 (&net, 64, 1, 1, 1, 1, 0, 0,   c5_bin, true);
-    tkDNN::Activation a5 (&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Conv2d     c6 (&net, 128, 3, 3, 1, 1, 1, 1,  c6_bin, true);
-    tkDNN::Activation a6 (&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Pooling    p7 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c4 (&net, 128, 3, 3, 1, 1, 1, 1,  c4_bin, true);
+    tk::dnn::Activation a4 (&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     c5 (&net, 64, 1, 1, 1, 1, 0, 0,   c5_bin, true);
+    tk::dnn::Activation a5 (&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     c6 (&net, 128, 3, 3, 1, 1, 1, 1,  c6_bin, true);
+    tk::dnn::Activation a6 (&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Pooling    p7 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c8 (&net, 256, 3, 3, 1, 1, 1, 1,  c8_bin, true);
-    tkDNN::Activation a8 (&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Conv2d     c9 (&net, 128, 1, 1, 1, 1, 0, 0,  c9_bin, true);
-    tkDNN::Activation a9 (&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Conv2d     c10(&net, 256, 3, 3, 1, 1, 1, 1,  c10_bin, true);
-    tkDNN::Activation a10(&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Pooling    p11(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c8 (&net, 256, 3, 3, 1, 1, 1, 1,  c8_bin, true);
+    tk::dnn::Activation a8 (&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     c9 (&net, 128, 1, 1, 1, 1, 0, 0,  c9_bin, true);
+    tk::dnn::Activation a9 (&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     c10(&net, 256, 3, 3, 1, 1, 1, 1,  c10_bin, true);
+    tk::dnn::Activation a10(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Pooling    p11(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c12(&net, 512, 3, 3, 1, 1, 1, 1,  c12_bin, true);
-    tkDNN::Activation a12(&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Conv2d     c13(&net, 256, 1, 1, 1, 1, 0, 0,  c13_bin, true);
-    tkDNN::Activation a13(&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Conv2d     c14(&net, 512, 3, 3, 1, 1, 1, 1,  c14_bin, true);
-    tkDNN::Activation a14(&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Conv2d     c15(&net, 256, 1, 1, 1, 1, 0, 0,  c15_bin, true);
-    tkDNN::Activation a15(&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Conv2d     c16(&net, 512, 3, 3, 1, 1, 1, 1,  c16_bin, true); 
-    tkDNN::Activation a16(&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Pooling    p17(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c12(&net, 512, 3, 3, 1, 1, 1, 1,  c12_bin, true);
+    tk::dnn::Activation a12(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     c13(&net, 256, 1, 1, 1, 1, 0, 0,  c13_bin, true);
+    tk::dnn::Activation a13(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     c14(&net, 512, 3, 3, 1, 1, 1, 1,  c14_bin, true);
+    tk::dnn::Activation a14(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     c15(&net, 256, 1, 1, 1, 1, 0, 0,  c15_bin, true);
+    tk::dnn::Activation a15(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     c16(&net, 512, 3, 3, 1, 1, 1, 1,  c16_bin, true); 
+    tk::dnn::Activation a16(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Pooling    p17(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c18(&net, 1024, 3, 3, 1, 1, 1, 1, c18_bin, true);
-    tkDNN::Activation a18(&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Conv2d     c19(&net, 512, 1, 1, 1, 1, 0, 0,  c19_bin, true);
-    tkDNN::Activation a19(&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Conv2d     c20(&net, 1024, 3, 3, 1, 1, 1, 1, c20_bin, true);
-    tkDNN::Activation a20(&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Conv2d     c21(&net, 512, 1, 1, 1, 1, 0, 0,  c21_bin, true);
-    tkDNN::Activation a21(&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Conv2d     c22(&net, 1024, 3, 3, 1, 1, 1, 1, c22_bin, true);
-    tkDNN::Activation a22(&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Conv2d     c23(&net, 1024, 3, 3, 1, 1, 1, 1, c23_bin, true);
-    tkDNN::Activation a23(&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Conv2d     c24(&net, 1024, 3, 3, 1, 1, 1, 1, c24_bin, true);
-    tkDNN::Activation a24(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     c18(&net, 1024, 3, 3, 1, 1, 1, 1, c18_bin, true);
+    tk::dnn::Activation a18(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     c19(&net, 512, 1, 1, 1, 1, 0, 0,  c19_bin, true);
+    tk::dnn::Activation a19(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     c20(&net, 1024, 3, 3, 1, 1, 1, 1, c20_bin, true);
+    tk::dnn::Activation a20(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     c21(&net, 512, 1, 1, 1, 1, 0, 0,  c21_bin, true);
+    tk::dnn::Activation a21(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     c22(&net, 1024, 3, 3, 1, 1, 1, 1, c22_bin, true);
+    tk::dnn::Activation a22(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     c23(&net, 1024, 3, 3, 1, 1, 1, 1, c23_bin, true);
+    tk::dnn::Activation a23(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Conv2d     c24(&net, 1024, 3, 3, 1, 1, 1, 1, c24_bin, true);
+    tk::dnn::Activation a24(&net, CUDNN_ACTIVATION_RELU);
 
-    tkDNN::Layer *m25_layers[1] = { &a16 };
-    tkDNN::Route      m25(&net, m25_layers, 1);
-    tkDNN::Conv2d     c26(&net, 64, 1, 1, 1, 1, 0, 0,   c26_bin, true);
-    tkDNN::Activation a26(&net, CUDNN_ACTIVATION_RELU);
-    tkDNN::Reorg      r27(&net, 2);
+    tk::dnn::Layer *m25_layers[1] = { &a16 };
+    tk::dnn::Route      m25(&net, m25_layers, 1);
+    tk::dnn::Conv2d     c26(&net, 64, 1, 1, 1, 1, 0, 0,   c26_bin, true);
+    tk::dnn::Activation a26(&net, CUDNN_ACTIVATION_RELU);
+    tk::dnn::Reorg      r27(&net, 2);
 
-    tkDNN::Layer *m28_layers[2] = { &r27, &a24 };
-    tkDNN::Route      m28(&net, m28_layers, 2);
+    tk::dnn::Layer *m28_layers[2] = { &r27, &a24 };
+    tk::dnn::Route      m28(&net, m28_layers, 2);
 
-    tkDNN::Conv2d     c29(&net, 1024, 3, 3, 1, 1, 1, 1, c29_bin, true);
-    tkDNN::Activation a29(&net, CUDNN_ACTIVATION_RELU);   
-    tkDNN::Conv2d     c30(&net, 425, 1, 1, 1, 1, 0, 0,  c30_bin, false);
-    tkDNN::Region     g31(&net, 80, 4, 5);
+    tk::dnn::Conv2d     c29(&net, 1024, 3, 3, 1, 1, 1, 1, c29_bin, true);
+    tk::dnn::Activation a29(&net, CUDNN_ACTIVATION_RELU);   
+    tk::dnn::Conv2d     c30(&net, 425, 1, 1, 1, 1, 0, 0,  c30_bin, false);
+    tk::dnn::Region     g31(&net, 80, 4, 5);
     
-    tkDNN::RegionInterpret rI(dim, g31.output_dim, 80, 4, 5, 0.3f, g31_bin);
+    tk::dnn::RegionInterpret rI(dim, g31.output_dim, 80, 4, 5, 0.3f, g31_bin);
 
     // Load input
     dnnType *data;
@@ -110,11 +110,11 @@ int main() {
     net.print();
 
     //convert network to tensorRT
-    tkDNN::NetworkRT netRT(&net, "yolo_relu.rt");
+    tk::dnn::NetworkRT netRT(&net, "yolo_relu.rt");
 
     dnnType *out_data, *out_data2; // cudnn output, tensorRT output
 
-    tkDNN::dataDim_t dim1 = dim; //input dim
+    tk::dnn::dataDim_t dim1 = dim; //input dim
     printCenteredTitle(" CUDNN inference ", '=', 30); {
         dim1.print();
         TIMER_START
@@ -123,7 +123,7 @@ int main() {
         dim1.print();   
     }
     
-    tkDNN::dataDim_t dim2 = dim;
+    tk::dnn::dataDim_t dim2 = dim;
     printCenteredTitle(" TENSORRT inference ", '=', 30); {
         dim2.print();
         TIMER_START
diff --git a/tests/yolo_tiny/yolo_tiny.cpp b/tests/yolo_tiny/yolo_tiny.cpp
index fc2703d..0255108 100644
--- a/tests/yolo_tiny/yolo_tiny.cpp
+++ b/tests/yolo_tiny/yolo_tiny.cpp
@@ -18,38 +18,38 @@ const char *output_bin = "../tests/yolo_tiny/layers/output.bin";
 int main() {
 
     // Network layout
-    tkDNN::dataDim_t dim(1, 3, 416, 416, 1);
-    tkDNN::Network net(dim);
+    tk::dnn::dataDim_t dim(1, 3, 416, 416, 1);
+    tk::dnn::Network net(dim);
 
-    tkDNN::Conv2d     c0 (&net, 16, 3, 3, 1, 1, 1, 1,   c0_bin, true);
-    tkDNN::Activation a0 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p1 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c0 (&net, 16, 3, 3, 1, 1, 1, 1,   c0_bin, true);
+    tk::dnn::Activation a0 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p1 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c2 (&net, 32, 3, 3, 1, 1, 1, 1,   c2_bin, true);
-    tkDNN::Activation a2 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p3 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c2 (&net, 32, 3, 3, 1, 1, 1, 1,   c2_bin, true);
+    tk::dnn::Activation a2 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p3 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c4 (&net, 64, 3, 3, 1, 1, 1, 1,  c4_bin, true);
-    tkDNN::Activation a4 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p5 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c4 (&net, 64, 3, 3, 1, 1, 1, 1,  c4_bin, true);
+    tk::dnn::Activation a4 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p5 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c6 (&net, 128, 3, 3, 1, 1, 1, 1,  c6_bin, true);
-    tkDNN::Activation a6 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p7(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c6 (&net, 128, 3, 3, 1, 1, 1, 1,  c6_bin, true);
+    tk::dnn::Activation a6 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p7(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c8(&net, 256, 3, 3, 1, 1, 1, 1,  c8_bin, true);
-    tkDNN::Activation a8(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p9(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c8(&net, 256, 3, 3, 1, 1, 1, 1,  c8_bin, true);
+    tk::dnn::Activation a8(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p9(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c10(&net, 512, 3, 3, 1, 1, 1, 1, c10_bin, true);
-    tkDNN::Activation a10(&net, tkDNN::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c10(&net, 512, 3, 3, 1, 1, 1, 1, c10_bin, true);
+    tk::dnn::Activation a10(&net, tk::dnn::ACTIVATION_LEAKY);
 
-    tkDNN::Conv2d     c11(&net, 1024, 3, 3, 1, 1, 1, 1, c11_bin, true);
-    tkDNN::Activation a11(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c12(&net, 512, 3, 3, 1, 1, 1, 1, c12_bin, true);
-    tkDNN::Activation a12(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c13(&net, 425, 1, 1, 1, 1, 0, 0, c13_bin, false);
-    tkDNN::Region     g14(&net, 80, 4, 5);
+    tk::dnn::Conv2d     c11(&net, 1024, 3, 3, 1, 1, 1, 1, c11_bin, true);
+    tk::dnn::Activation a11(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c12(&net, 512, 3, 3, 1, 1, 1, 1, c12_bin, true);
+    tk::dnn::Activation a12(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c13(&net, 425, 1, 1, 1, 1, 0, 0, c13_bin, false);
+    tk::dnn::Region     g14(&net, 80, 4, 5);
 
     // Load input
     dnnType *data;
@@ -60,11 +60,11 @@ int main() {
     net.print();
 
     //convert network to tensorRT
-    tkDNN::NetworkRT netRT(&net, "yolo_tiny.rt");
+    tk::dnn::NetworkRT netRT(&net, "yolo_tiny.rt");
 
     dnnType *out_data, *out_data2; // cudnn output, tensorRT output
 
-    tkDNN::dataDim_t dim1 = dim; //input dim
+    tk::dnn::dataDim_t dim1 = dim; //input dim
     printCenteredTitle(" CUDNN inference ", '=', 30); {
         dim1.print();
         TIMER_START
@@ -73,7 +73,7 @@ int main() {
         dim1.print();   
     }
  
-    tkDNN::dataDim_t dim2 = dim;
+    tk::dnn::dataDim_t dim2 = dim;
     printCenteredTitle(" TENSORRT inference ", '=', 30); {
         dim2.print();
         TIMER_START
diff --git a/tests/yolo_voc/yolo_voc.cpp b/tests/yolo_voc/yolo_voc.cpp
index 843c981..d0b9456 100644
--- a/tests/yolo_voc/yolo_voc.cpp
+++ b/tests/yolo_voc/yolo_voc.cpp
@@ -31,75 +31,75 @@ const char *output_bin = "../tests/yolo_voc/layers/output.bin";
 int main() {
 
     // Network layout
-    tkDNN::dataDim_t dim(1, 3, 416, 416, 1);
-    tkDNN::Network net(dim);
+    tk::dnn::dataDim_t dim(1, 3, 416, 416, 1);
+    tk::dnn::Network net(dim);
 
-    tkDNN::Conv2d     c0 (&net, 32, 3, 3, 1, 1, 1, 1,   c0_bin, true);
-    tkDNN::Activation a0 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p1 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c0 (&net, 32, 3, 3, 1, 1, 1, 1,   c0_bin, true);
+    tk::dnn::Activation a0 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p1 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c2 (&net, 64, 3, 3, 1, 1, 1, 1,   c2_bin, true);
-    tkDNN::Activation a2 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p3 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c2 (&net, 64, 3, 3, 1, 1, 1, 1,   c2_bin, true);
+    tk::dnn::Activation a2 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p3 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c4 (&net, 128, 3, 3, 1, 1, 1, 1,  c4_bin, true);
-    tkDNN::Activation a4 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c5 (&net, 64, 1, 1, 1, 1, 0, 0,   c5_bin, true);
-    tkDNN::Activation a5 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c6 (&net, 128, 3, 3, 1, 1, 1, 1,  c6_bin, true);
-    tkDNN::Activation a6 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p7 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c4 (&net, 128, 3, 3, 1, 1, 1, 1,  c4_bin, true);
+    tk::dnn::Activation a4 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c5 (&net, 64, 1, 1, 1, 1, 0, 0,   c5_bin, true);
+    tk::dnn::Activation a5 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c6 (&net, 128, 3, 3, 1, 1, 1, 1,  c6_bin, true);
+    tk::dnn::Activation a6 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p7 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c8 (&net, 256, 3, 3, 1, 1, 1, 1,  c8_bin, true);
-    tkDNN::Activation a8 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c9 (&net, 128, 1, 1, 1, 1, 0, 0,  c9_bin, true);
-    tkDNN::Activation a9 (&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c10(&net, 256, 3, 3, 1, 1, 1, 1,  c10_bin, true);
-    tkDNN::Activation a10(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p11(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c8 (&net, 256, 3, 3, 1, 1, 1, 1,  c8_bin, true);
+    tk::dnn::Activation a8 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c9 (&net, 128, 1, 1, 1, 1, 0, 0,  c9_bin, true);
+    tk::dnn::Activation a9 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c10(&net, 256, 3, 3, 1, 1, 1, 1,  c10_bin, true);
+    tk::dnn::Activation a10(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p11(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c12(&net, 512, 3, 3, 1, 1, 1, 1,  c12_bin, true);
-    tkDNN::Activation a12(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c13(&net, 256, 1, 1, 1, 1, 0, 0,  c13_bin, true);
-    tkDNN::Activation a13(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c14(&net, 512, 3, 3, 1, 1, 1, 1,  c14_bin, true);
-    tkDNN::Activation a14(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c15(&net, 256, 1, 1, 1, 1, 0, 0,  c15_bin, true);
-    tkDNN::Activation a15(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c16(&net, 512, 3, 3, 1, 1, 1, 1,  c16_bin, true); 
-    tkDNN::Activation a16(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Pooling    p17(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    tk::dnn::Conv2d     c12(&net, 512, 3, 3, 1, 1, 1, 1,  c12_bin, true);
+    tk::dnn::Activation a12(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c13(&net, 256, 1, 1, 1, 1, 0, 0,  c13_bin, true);
+    tk::dnn::Activation a13(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c14(&net, 512, 3, 3, 1, 1, 1, 1,  c14_bin, true);
+    tk::dnn::Activation a14(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c15(&net, 256, 1, 1, 1, 1, 0, 0,  c15_bin, true);
+    tk::dnn::Activation a15(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c16(&net, 512, 3, 3, 1, 1, 1, 1,  c16_bin, true); 
+    tk::dnn::Activation a16(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p17(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
 
-    tkDNN::Conv2d     c18(&net, 1024, 3, 3, 1, 1, 1, 1, c18_bin, true);
-    tkDNN::Activation a18(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c19(&net, 512, 1, 1, 1, 1, 0, 0,  c19_bin, true);
-    tkDNN::Activation a19(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c20(&net, 1024, 3, 3, 1, 1, 1, 1, c20_bin, true);
-    tkDNN::Activation a20(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c21(&net, 512, 1, 1, 1, 1, 0, 0,  c21_bin, true);
-    tkDNN::Activation a21(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c22(&net, 1024, 3, 3, 1, 1, 1, 1, c22_bin, true);
-    tkDNN::Activation a22(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c23(&net, 1024, 3, 3, 1, 1, 1, 1, c23_bin, true);
-    tkDNN::Activation a23(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Conv2d     c24(&net, 1024, 3, 3, 1, 1, 1, 1, c24_bin, true);
-    tkDNN::Activation a24(&net, tkDNN::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c18(&net, 1024, 3, 3, 1, 1, 1, 1, c18_bin, true);
+    tk::dnn::Activation a18(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c19(&net, 512, 1, 1, 1, 1, 0, 0,  c19_bin, true);
+    tk::dnn::Activation a19(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c20(&net, 1024, 3, 3, 1, 1, 1, 1, c20_bin, true);
+    tk::dnn::Activation a20(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c21(&net, 512, 1, 1, 1, 1, 0, 0,  c21_bin, true);
+    tk::dnn::Activation a21(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c22(&net, 1024, 3, 3, 1, 1, 1, 1, c22_bin, true);
+    tk::dnn::Activation a22(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c23(&net, 1024, 3, 3, 1, 1, 1, 1, c23_bin, true);
+    tk::dnn::Activation a23(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c24(&net, 1024, 3, 3, 1, 1, 1, 1, c24_bin, true);
+    tk::dnn::Activation a24(&net, tk::dnn::ACTIVATION_LEAKY);
 
-    tkDNN::Layer *m25_layers[1] = { &a16 };
-    tkDNN::Route      m25(&net, m25_layers, 1);
-    tkDNN::Conv2d     c26(&net, 64, 1, 1, 1, 1, 0, 0,   c26_bin, true);
-    tkDNN::Activation a26(&net, tkDNN::ACTIVATION_LEAKY);
-    tkDNN::Reorg      r27(&net, 2);
+    tk::dnn::Layer *m25_layers[1] = { &a16 };
+    tk::dnn::Route      m25(&net, m25_layers, 1);
+    tk::dnn::Conv2d     c26(&net, 64, 1, 1, 1, 1, 0, 0,   c26_bin, true);
+    tk::dnn::Activation a26(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Reorg      r27(&net, 2);
 
-    tkDNN::Layer *m28_layers[2] = { &r27, &a24 };
-    tkDNN::Route      m28(&net, m28_layers, 2);
+    tk::dnn::Layer *m28_layers[2] = { &r27, &a24 };
+    tk::dnn::Route      m28(&net, m28_layers, 2);
 
-    tkDNN::Conv2d     c29(&net, 1024, 3, 3, 1, 1, 1, 1, c29_bin, true);
-    tkDNN::Activation a29(&net, tkDNN::ACTIVATION_LEAKY);   
-    tkDNN::Conv2d     c30(&net, 125, 1, 1, 1, 1, 0, 0,  c30_bin, false);
-    tkDNN::Region     g31(&net, 20, 4, 5);
+    tk::dnn::Conv2d     c29(&net, 1024, 3, 3, 1, 1, 1, 1, c29_bin, true);
+    tk::dnn::Activation a29(&net, tk::dnn::ACTIVATION_LEAKY);   
+    tk::dnn::Conv2d     c30(&net, 125, 1, 1, 1, 1, 0, 0,  c30_bin, false);
+    tk::dnn::Region     g31(&net, 20, 4, 5);
     
-    tkDNN::RegionInterpret rI(dim, g31.output_dim, 20, 4, 5, 0.6f, g31_bin);
+    tk::dnn::RegionInterpret rI(dim, g31.output_dim, 20, 4, 5, 0.6f, g31_bin);
 
     // Load input
     dnnType *data;
@@ -110,11 +110,11 @@ int main() {
     net.print();
 
     //convert network to tensorRT
-    tkDNN::NetworkRT netRT(&net, "yolo_voc.rt");
+    tk::dnn::NetworkRT netRT(&net, "yolo_voc.rt");
 
     dnnType *out_data, *out_data2; // cudnn output, tensorRT output
 
-    tkDNN::dataDim_t dim1 = dim; //input dim
+    tk::dnn::dataDim_t dim1 = dim; //input dim
     printCenteredTitle(" CUDNN inference ", '=', 30); {
         dim1.print();
         TIMER_START
@@ -123,7 +123,7 @@ int main() {
         dim1.print();   
     }
     
-    tkDNN::dataDim_t dim2 = dim;
+    tk::dnn::dataDim_t dim2 = dim;
     printCenteredTitle(" TENSORRT inference ", '=', 30); {
         dim2.print();
         TIMER_START