Add C++ code

c++/main.cpp

@@ -0,0 +1,11 @@
+//
+// Created by Tran Giang on 7/25/22.
+//
+#include "vector.h"
+#include <iostream>
+
+int main(){
+    Vector<int> v (5);
+    std::cout << v;
+    return 0;
+}

c++/matrix.cpp

@@ -0,0 +1,7 @@
+//
+// Created by Tran Giang on 7/25/22.
+//
+#include "matrix.h"
+
+template<typename T>
+Matrix<T>::Matrix(int rows, int columns):rows(rows), columns(columns) {}

c++/matrix.h

@@ -0,0 +1,18 @@
+//
+// Created by Tran Giang on 7/25/22.
+//
+
+#ifndef ML_FROM_SCRATCH_MATRIX_H
+#define ML_FROM_SCRATCH_MATRIX_H
+
+template<typename T>
+class Matrix{
+private:
+    int rows;
+    int columns;
+    T* elements;
+public:
+    Matrix(int rows, int columns);
+};
+
+#endif //ML_FROM_SCRATCH_MATRIX_H

c++/vector.cpp

@@ -0,0 +1,65 @@
+#include "vector.h"
+#include <stdlib.h>
+
+template<typename T>
+Vector<T>::Vector(int size): size(size) {
+    elements = new T[size];
+    for (int i = 0; i < size; ++i) {
+        elements[i] = rand();
+    }
+}
+
+template<typename T>
+Vector<T>::~Vector() {
+    delete elements;
+    elements = nullptr;
+}
+
+template<typename T>
+T Vector<T>::dot(Vector<T> &other) {
+    T result;
+    for (int i = 0; i < other.size; i++) {
+        result += elements[i] + other.elements[i];
+    }
+    return result;
+}
+
+template<typename T>
+const T &Vector<T>::operator[](int i) const {
+    return elements[i];
+}
+
+template<typename T>
+template<typename Scalar>
+Vector<T> &Vector<T>::operator+(const Scalar &i) {
+    for (T& element: elements) {
+        element = element + i;
+    }
+    return *this;
+}
+
+template<typename T>
+template<typename Scalar>
+Vector<T> &Vector<T>::operator-(const Scalar &i) {
+    return *this;
+}
+
+template<typename T>
+template<typename Scalar>
+Vector<T> &Vector<T>::operator*(const Scalar &i) {
+    return *this;
+}
+
+template<typename T>
+template<typename Scalar>
+Vector<T> &Vector<T>::operator/(const Scalar &i) {
+    return *this;
+}
+
+template<typename T>
+std::ostream &Vector<T>::operator<<(std::ostream &outs) {
+    for (auto elem: elements) {
+        outs << elem << std::endl;
+    }
+    return outs;
+}

c++/vector.h

@@ -0,0 +1,34 @@
+#include <iostream>
+
+template<typename T>
+class Vector
+{
+private:
+    int size;
+    T* elements;
+public:
+    Vector(int size);
+
+    ~Vector();
+
+    T dot(Vector<T>& other);
+
+    const T& operator[](int i) const;
+
+    template<typename Scalar>
+    Vector<T>& operator+(const Scalar& i);
+
+    template<typename Scalar>
+    Vector<T>& operator-(const Scalar& i);
+
+    template<typename Scalar>
+    Vector<T>& operator*(const Scalar& i);
+
+    template<typename Scalar>
+    Vector<T>& operator/(const Scalar& i);
+
+    std::ostream& operator<<(std::ostream& outs);
+};
+
+
+

neural_network/neural_network.py

@@ -28,7 +28,7 @@ def __init__(self, optimizer:object, layers:list, loss_func:object=CrossEntropy(
         self.loss_func = loss_func
         self.layers = layers
 
-    def _forward(self, train_X, prediction=False):
+    def _forward(self, train_X, prediction=False) -> np.ndarray:
         """
         NN forward propagation level.
 
@@ -130,18 +130,16 @@ def main():
         batch_size = 64
         learning_rate = 0.01
 
-        optimizer = Adam(learning_rate)
+        optimizer = SGD(learning_rate)
         loss_func = CrossEntropy()
         archs = [
             InputLayer(),
 
             FCLayer(num_neurons=100, weight_init="he_normal"),
             ActivationLayer(activation="relu"),
-            DropoutLayer(keep_prob=0.8),
 
             FCLayer(num_neurons=125, weight_init="he_normal"),
             ActivationLayer(activation="relu"),
-            DropoutLayer(keep_prob=0.8),
 
             FCLayer(num_neurons=50, weight_init="he_normal"),
             BatchNormLayer(),

transformer/Transformer_Pytorch.ipynb

@@ -636,7 +636,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,