diff --git a/Makefile b/Makefile
index 4cfaf0d..087652a 100644
--- a/Makefile
+++ b/Makefile
@@ -1,5 +1,5 @@
-CC=g++ -std=c++17 -g -Ofast -Wall -Wextra -Iincludes -D_GLIBCXX_DEBUG -fsanitize=address
-# CC=g++ -std=c++17 -Ofast -Wall -Wextra -Iincludes -DNDEBUG
+# CC=clang++ -std=c++20 -g -Ofast -Wall -Wextra -Iincludes -D_GLIBCXX_DEBUG -fsanitize=address
+CC=clang++ -std=c++20 -Ofast -Wall -Wextra -Wpedantic -Iincludes -DNDEBUG
 
 DATASET=$(addprefix dataset/,t10k-labels-idx1-ubyte train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte)
 BINARY=$(addprefix bin/,k-NN dnn tnn)
diff --git a/includes/nn.h b/includes/nn.h
index c0a4b5f..a3f4f12 100644
--- a/includes/nn.h
+++ b/includes/nn.h
@@ -18,7 +18,7 @@ struct LayerLinear : Layer {
 	float *W, *A;
 
 	LayerLinear(size_t I, size_t O);
-	~LayerLinear();
+	~LayerLinear() override;
 
 	LayerLinear(std::ifstream&);
 	virtual void save(std::ofstream&) override;
@@ -53,7 +53,7 @@ struct LayerConvolutional : Layer {
 	float *W, *A;
 
 	LayerConvolutional(size_t, size_t, std::array<size_t, 2>, std::array<size_t, 2>);
-	~LayerConvolutional();
+	~LayerConvolutional() override;
 
 	LayerConvolutional(std::ifstream&);
 	virtual void save(std::ofstream&) override;
diff --git a/src/dataset.cpp b/src/dataset.cpp
index 5e76987..64b71df 100644
--- a/src/dataset.cpp
+++ b/src/dataset.cpp
@@ -2,6 +2,8 @@
 #include <fstream>
 #include <array>
 #include <vector>
+
+#include <dataset.h>
 using namespace std;
 
 vector<uint8_t> train_labels;
diff --git a/src/nn.cpp b/src/nn.cpp
index 1965cf0..4e945d6 100644
--- a/src/nn.cpp
+++ b/src/nn.cpp
@@ -3,16 +3,15 @@
 #include <vector>
 #include <fstream>
 
-// #include <nn.h>
-#include "../includes/nn.h"
+#include <nn.h>
 using namespace std;
 
 Layer* Layer::fromFile(int idx, ifstream& fin) {
 	switch (idx) {
-		case 0: puts("LayerLinear"); return new LayerLinear(fin);
-		case 1: puts("LayerSigmoid"); return new LayerSigmoid;
-		case 2: puts("LayerAveragePooling"); return new LayerAveragePooling(fin);
-		case 3: puts("LayerConvolutional"); return new LayerConvolutional(fin);
+		case 0: return new LayerLinear(fin);
+		case 1: return new LayerSigmoid;
+		case 2: return new LayerAveragePooling(fin);
+		case 3: return new LayerConvolutional(fin);
 		default: assert(false); return nullptr;
 	}
 }