diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 68658086..aeb621aa 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -148,7 +148,9 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference)
 	cudnnDataType_t data_type = CUDNN_DATA_FLOAT;
 #endif
 	// Tensor Core uses CUDNN_TENSOR_OP_MATH instead of CUDNN_DEFAULT_MATH
+#if(CUDNN_MAJOR >= 7)
 	cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH);
+#endif
 
 	// INT8_CONFIG, INT8_EXT_CONFIG, INT8x4_CONFIG and INT8x4_EXT_CONFIG are only supported 
 	// on architectures with DP4A support (compute capability 6.1 and later).
diff --git a/src/http_stream.cpp b/src/http_stream.cpp
index 4392a278..abc99547 100644
--- a/src/http_stream.cpp
+++ b/src/http_stream.cpp
@@ -56,7 +56,7 @@ class MJPGWriter
 	int timeout; // master sock timeout, shutdown after timeout millis.
 	int quality; // jpeg compression [1..100]
 
-	int _write(int sock, char *s, int len)
+	int _write(int sock, char const*const s, int len)
 	{
 		if (len < 1) { len = strlen(s); }
 		return ::send(sock, s, len, 0);
@@ -128,7 +128,7 @@ public:
 		params.push_back(IMWRITE_JPEG_QUALITY);
 		params.push_back(quality);
 		cv::imencode(".jpg", frame, outbuf, params);
-		unsigned int outlen = outbuf.size();
+		size_t outlen = outbuf.size();
 
 #ifdef _WIN32 
 		for (unsigned i = 0; i<rread.fd_count; i++)
@@ -169,7 +169,7 @@ public:
 			else // existing client, just stream pix
 			{
 				char head[400];
-				sprintf(head, "--mjpegstream\r\nContent-Type: image/jpeg\r\nContent-Length: %lu\r\n\r\n", outlen);
+				sprintf(head, "--mjpegstream\r\nContent-Type: image/jpeg\r\nContent-Length: %zu\r\n\r\n", outlen);
 				_write(s, head, 0);
 				int n = _write(s, (char*)(&outbuf[0]), outlen);
 				//cerr << "known client " << s << " " << n << endl;
diff --git a/src/network.c b/src/network.c
index 10d1ead3..c906b585 100644
--- a/src/network.c
+++ b/src/network.c
@@ -356,7 +356,7 @@ int resize_network(network *net, int w, int h)
     //fflush(stderr);
     for (i = 0; i < net->n; ++i){
         layer l = net->layers[i];
-		printf(" %d: layer = %d,", i, l.type);
+		//printf(" %d: layer = %d,", i, l.type);
         if(l.type == CONVOLUTIONAL){
             resize_convolutional_layer(&l, w, h);
         }else if(l.type == CROP){