diff --git a/mlpp/convolutions/convolutions.cpp b/mlpp/convolutions/convolutions.cpp
index be6ddb1..443d7cd 100644
--- a/mlpp/convolutions/convolutions.cpp
+++ b/mlpp/convolutions/convolutions.cpp
@@ -161,7 +161,7 @@ Ref<MLPPTensor3> MLPPConvolutions::convolve_3d(const Ref<MLPPTensor3> &p_input,
 					}
 				}
 
-				feature_map->element_set(c,i, j,  convolving_input->dot(filter_flattened));
+				feature_map->element_set(c, i, j, convolving_input->dot(filter_flattened));
 			}
 		}
 	}
@@ -338,9 +338,9 @@ Ref<MLPPMatrix> MLPPConvolutions::dy(const Ref<MLPPMatrix> &input) {
 
 	for (int i = 0; i < input_size.y; i++) {
 		for (int j = 0; j < input_size.x; j++) {
-			if (j != 0 && j != input_size.y - 1) {
+			if (i != 0 && i != input_size.y - 1) {
 				deriv->element_set(i, j, input->element_get(i - 1, j) - input->element_get(i + 1, j));
-			} else if (j == 0) {
+			} else if (i == 0) {
 				deriv->element_set(i, j, -input->element_get(i + 1, j)); // 0 - E1 = Implicit zero-padding
 			} else {
 				deriv->element_set(i, j, input->element_get(i - 1, j)); // E0 - 0 =Implicit zero-padding
diff --git a/test/mlpp_tests.cpp b/test/mlpp_tests.cpp
index b96c559..c50bfa8 100644
--- a/test/mlpp_tests.cpp
+++ b/test/mlpp_tests.cpp
@@ -1477,19 +1477,23 @@ void MLPPTests::test_numerical_analysis() {
 	PLOG_MSG(conv.dx(A)->to_string());
 
 	/*
-	0 0 0 0
-	1 0 0 0
-	0 0 0 -1
-	0 0 0 0
+	[MLPPMatrix:
+	  [ -0 -0 -0 -0 ]
+	  [ 1 0 0 0 ]
+	  [ 0 0 0 -1 ]
+	  [ 0 0 0 0 ]
+	]
 	*/
 	PLOG_MSG("conv.dy(A)");
 	PLOG_MSG(conv.dy(A)->to_string());
 
 	/*
-	0 3.14159 0 0
-	1.5708 0 0 0
-	0 0 0 -1.5708
-	0 0 0 0
+	[MLPPMatrix:
+	  [ -0 -3.141593 -0 -3.141593 ]
+	  [ 1.570796 0 0 3.141593 ]
+	  [ 0 0 0 -1.570796 ]
+	  [ 0 0 0 3.141593 ]
+	]
 	*/
 	PLOG_MSG("conv.grad_orientation(A)");
 	PLOG_MSG(conv.grad_orientation(A)->to_string());
diff --git a/test/mlpp_tests_old.cpp b/test/mlpp_tests_old.cpp
index e5a5273..6fec46f 100644
--- a/test/mlpp_tests_old.cpp
+++ b/test/mlpp_tests_old.cpp
@@ -541,7 +541,6 @@ void MLPPTestsOld::test_numerical_analysis() {
 	// Checks for numerical analysis class.
 	MLPPNumericalAnalysisOld numAn;
 
-	/*
 	std::cout << numAn.quadraticApproximation(f_old, 0, 1) << std::endl;
 
 	std::cout << numAn.cubicApproximation(f_old, 0, 1.001) << std::endl;
@@ -550,12 +549,10 @@ void MLPPTestsOld::test_numerical_analysis() {
 
 	std::cout << numAn.quadraticApproximation(f_mv_old, { 0, 0, 0 }, { 1, 1, 1 }) << std::endl;
 
-
 	std::cout << numAn.numDiff(&f_old, 1) << std::endl;
 	std::cout << numAn.newtonRaphsonMethod(&f_old, 1, 1000) << std::endl;
 	std::cout << numAn.invQuadraticInterpolation(&f_old, { 100, 2, 1.5 }, 10) << std::endl;
 
-
 	std::cout << numAn.numDiff(&f_mv_old, { 1, 1 }, 1) << std::endl; // Derivative w.r.t. x.
 
 	alg.printVector(numAn.jacobian(&f_mv_old, { 1, 1 }));
@@ -567,8 +564,6 @@ void MLPPTestsOld::test_numerical_analysis() {
 	std::cout << numAn.numDiff_2(&f_mv_old, { 2, 2, 500 }, 2, 2) << std::endl;
 	std::cout << numAn.numDiff_3(&f_mv_old, { 2, 1000, 130 }, 0, 0, 0) << std::endl;
 
-
-
 	alg.printTensor(numAn.thirdOrderTensor(&f_mv_old, { 1, 1, 1 }));
 	std::cout << "Our Hessian." << std::endl;
 	alg.printMatrix(numAn.hessian(&f_mv_old, { 2, 2, 500 }));
@@ -582,12 +577,11 @@ void MLPPTestsOld::test_numerical_analysis() {
 	alg.printTensor(tensor);
 
 	alg.printMatrix(alg.tensor_vec_mult(tensor, { 1, 2 }));
-	
 
 	std::cout << numAn.cubicApproximation(f_mv_old, { 0, 0, 0 }, { 1, 1, 1 }) << std::endl;
 	std::cout << numAn.eulerianMethod(f_prime_old, { 1, 1 }, 1.5, 0.000001) << std::endl;
 	std::cout << numAn.eulerianMethod(f_prime_2var_old, { 2, 3 }, 2.5, 0.00000001) << std::endl;
-	*/
+
 	std::vector<std::vector<real_t>> A = {
 		{ 1, 0, 0, 0 },
 		{ 0, 0, 0, 0 },
@@ -595,9 +589,8 @@ void MLPPTestsOld::test_numerical_analysis() {
 		{ 0, 0, 0, 1 }
 	};
 
-	//alg.printMatrix(conv.dx(A));
-	//alg.printMatrix(conv.dy(A));
-
+	alg.printMatrix(conv.dx(A));
+	alg.printMatrix(conv.dy(A));
 
 	alg.printMatrix(conv.grad_orientation(A));