diff --git a/MLPP/ANN/ANN.cpp b/MLPP/ANN/ANN.cpp
index 7410957..06eae81 100644
--- a/MLPP/ANN/ANN.cpp
+++ b/MLPP/ANN/ANN.cpp
@@ -70,7 +70,7 @@ namespace MLPP {
             outputLayer->bias -= learning_rate * alg.sum_elements(outputLayer->delta) / n;
 
             auto hiddenLayerAvn = network[network.size() - 1].activation_map[network[network.size() - 1].activation];
-            network[network.size() - 1].delta = alg.hadamard_product(alg.vecmult(outputLayer->delta, outputLayer->weights), (avn.*hiddenLayerAvn)(network[network.size() - 1].z, 1));
+            network[network.size() - 1].delta = alg.hadamard_product(alg.outerProduct(outputLayer->delta, outputLayer->weights), (avn.*hiddenLayerAvn)(network[network.size() - 1].z, 1));
             std::vector<std::vector<double>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[network.size() - 1].input), network[network.size() - 1].delta);
             
             network[network.size() - 1].weights = alg.subtraction(network[network.size() - 1].weights, alg.scalarMultiply(learning_rate/n, hiddenLayerWGrad));
diff --git a/MLPP/AutoEncoder/AutoEncoder.cpp b/MLPP/AutoEncoder/AutoEncoder.cpp
index 423c0eb..24c9284 100644
--- a/MLPP/AutoEncoder/AutoEncoder.cpp
+++ b/MLPP/AutoEncoder/AutoEncoder.cpp
@@ -107,7 +107,7 @@ namespace MLPP {
             std::vector<double> error = alg.subtraction(y_hat, inputSet[outputIndex]);
             
             // Weight updation for layer 2
-            std::vector<std::vector<double>> D2_1 = alg.vecmult(error, a2);
+            std::vector<std::vector<double>> D2_1 = alg.outerProduct(error, a2);
             weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate, alg.transpose(D2_1)));
 
             // Bias updation for layer 2
@@ -116,7 +116,7 @@ namespace MLPP {
             // Weight updation for layer 1
              std::vector<double> D1_1 = alg.mat_vec_mult(weights2, error);
              std::vector<double> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
-             std::vector<std::vector<double>> D1_3 = alg.vecmult(inputSet[outputIndex], D1_2);
+             std::vector<std::vector<double>> D1_3 = alg.outerProduct(inputSet[outputIndex], D1_2);
 
             weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate, D1_3));
             // Bias updation for layer 1
diff --git a/MLPP/LinAlg/LinAlg.cpp b/MLPP/LinAlg/LinAlg.cpp
index c2c4085..cdf5af8 100644
--- a/MLPP/LinAlg/LinAlg.cpp
+++ b/MLPP/LinAlg/LinAlg.cpp
@@ -539,7 +539,7 @@ namespace MLPP{
         }
     }
 
-    std::vector<std::vector<double>> LinAlg::vecmult(std::vector<double> a, std::vector<double> b){
+    std::vector<std::vector<double>> LinAlg::outerProduct(std::vector<double> a, std::vector<double> b){
         std::vector<std::vector<double>> C;
         C.resize(a.size());
         for(int i = 0; i < C.size(); i++){
@@ -671,6 +671,14 @@ namespace MLPP{
         return full(n, 1);
     }
 
+    std::vector<std::vector<double>> LinAlg::diag(std::vector<double> a){
+        std::vector<std::vector<double>> B = zeromat(a.size(), a.size());
+        for(int i = 0; i < B.size(); i++){
+            B[i][i] = a[i];
+        }
+        return B;
+    }
+
     std::vector<double> LinAlg::full(int n, int k){
         std::vector<double> full; 
         full.resize(n);
diff --git a/MLPP/LinAlg/LinAlg.hpp b/MLPP/LinAlg/LinAlg.hpp
index 3ea493a..effadb3 100644
--- a/MLPP/LinAlg/LinAlg.hpp
+++ b/MLPP/LinAlg/LinAlg.hpp
@@ -80,7 +80,7 @@ namespace MLPP{
         
         // VECTOR FUNCTIONS
 
-        std::vector<std::vector<double>> vecmult(std::vector<double> a, std::vector<double> b); // This multiplies a, bT 
+        std::vector<std::vector<double>> outerProduct(std::vector<double> a, std::vector<double> b); // This multiplies a, bT 
         
         std::vector<double> hadamard_product(std::vector<double> a, std::vector<double> b);
 
@@ -112,6 +112,8 @@ namespace MLPP{
 
         std::vector<double> onevec(int n);
 
+        std::vector<std::vector<double>> diag(std::vector<double> a);
+
         std::vector<double> full(int n, int k);
 
         double max(std::vector<double> a);
diff --git a/MLPP/MLP/MLP.cpp b/MLPP/MLP/MLP.cpp
index 028829b..bd251a3 100644
--- a/MLPP/MLP/MLP.cpp
+++ b/MLPP/MLP/MLP.cpp
@@ -65,7 +65,7 @@ namespace MLPP {
             std::vector<std::vector<double>> D1_1;
             D1_1.resize(n);
 
-            D1_1 = alg.vecmult(error, weights2);
+            D1_1 = alg.outerProduct(error, weights2);
 
             std::vector<std::vector<double>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
 
@@ -125,7 +125,7 @@ namespace MLPP {
             // Weight updation for layer 1
             std::vector<double> D1_1 = alg.scalarMultiply(error, weights2);
             std::vector<double> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
-            std::vector<std::vector<double>> D1_3 = alg.vecmult(inputSet[outputIndex], D1_2);
+            std::vector<std::vector<double>> D1_3 = alg.outerProduct(inputSet[outputIndex], D1_2);
 
             weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate, D1_3));
             weights1 = regularization.regWeights(weights1, lambda, alpha, reg);
@@ -204,7 +204,7 @@ namespace MLPP {
 
                 //Calculating the weight/bias for layer 1
 
-                std::vector<std::vector<double>> D1_1 = alg.vecmult(error, weights2);
+                std::vector<std::vector<double>> D1_1 = alg.outerProduct(error, weights2);
 
                 std::vector<std::vector<double>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
 
diff --git a/MLPP/SoftmaxNet/SoftmaxNet.cpp b/MLPP/SoftmaxNet/SoftmaxNet.cpp
index 2b847c1..06bbb3a 100644
--- a/MLPP/SoftmaxNet/SoftmaxNet.cpp
+++ b/MLPP/SoftmaxNet/SoftmaxNet.cpp
@@ -113,7 +113,7 @@ namespace MLPP{
             std::vector<double> error = alg.subtraction(y_hat, outputSet[outputIndex]);
             
             // Weight updation for layer 2
-            std::vector<std::vector<double>> D2_1 = alg.vecmult(error, a2);
+            std::vector<std::vector<double>> D2_1 = alg.outerProduct(error, a2);
             weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate, alg.transpose(D2_1)));
             weights2 = regularization.regWeights(weights2, lambda, alpha, reg);
 
@@ -123,7 +123,7 @@ namespace MLPP{
             // Weight updation for layer 1
             std::vector<double> D1_1 = alg.mat_vec_mult(weights2, error);
             std::vector<double> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
-            std::vector<std::vector<double>> D1_3 = alg.vecmult(inputSet[outputIndex], D1_2);
+            std::vector<std::vector<double>> D1_3 = alg.outerProduct(inputSet[outputIndex], D1_2);
 
             weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate, D1_3));
             weights1 = regularization.regWeights(weights1, lambda, alpha, reg);
diff --git a/MLPP/SoftmaxReg/SoftmaxReg.cpp b/MLPP/SoftmaxReg/SoftmaxReg.cpp
index fb8b34e..30a0edb 100644
--- a/MLPP/SoftmaxReg/SoftmaxReg.cpp
+++ b/MLPP/SoftmaxReg/SoftmaxReg.cpp
@@ -88,7 +88,7 @@ namespace MLPP{
             cost_prev = Cost({y_hat}, {outputSet[outputIndex]});
                 
             // Calculating the weight gradients            
-            std::vector<std::vector<double>> w_gradient = alg.vecmult(inputSet[outputIndex], alg.subtraction(y_hat, outputSet[outputIndex]));
+            std::vector<std::vector<double>> w_gradient = alg.outerProduct(inputSet[outputIndex], alg.subtraction(y_hat, outputSet[outputIndex]));
 
             // Weight Updation
             weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, w_gradient));
diff --git a/SharedLib/MLPP.so b/SharedLib/MLPP.so
index ab67ef8..20bcd6f 100755
Binary files a/SharedLib/MLPP.so and b/SharedLib/MLPP.so differ
diff --git a/main.cpp b/main.cpp
index 0bc6fe2..0789275 100644
--- a/main.cpp
+++ b/main.cpp
@@ -363,6 +363,7 @@ int main() {
 
     // std::cout << alg.trace({{1,2}, {3,4}}) << std::endl;
     // alg.printMatrix(alg.pinverse({{1,2}, {3,4}}));
+    // alg.printMatrix(alg.diag({1,2,3,4,5}));
 
     return 0;
 }
\ No newline at end of file