Layer library with forward propagation

layer.h

@@ -0,0 +1,48 @@
+#include "matrices.h"
+#include <cassert>
+#include <math.h>
+
+#define assertm(exp, msg) assert((void(msg), exp))
+
+class Layer {
+    private:
+    Matrix input;
+    Matrix weights;
+    Matrix raw_output;
+    Matrix activated_output;
+    Matrix biases;
+
+    float learning_rate = 0.1;
+
+    static inline float Sigmoid(float);
+    static inline float SigmoidPrime(float);
+
+    public:
+    inline Layer(int); // Number of neurons
+
+    inline void Forward(); // Forward Pass with sigmoid
+    inline void Forward(float (*activation)(float)); // Forward Pass with custom activation function
+};
+
+float Layer::Sigmoid(float x){
+    return 1 / (1 + exp(-x));
+}
+
+float Layer::SigmoidPrime(float x){
+    float buffer = Layer::Sigmoid(x);
+    return buffer * (1 - buffer);
+}
+
+void Layer::Forward(float (*activation)(float)){
+    // Multiply inputs by weights
+    // W x I + B = Z
+    this->raw_output = this->input.Multiply(&this->weights).Add(&this->biases);
+
+    // Now through activation function
+    // A = F(Z)
+    this->activated_output = this->raw_output.Function(activation);
+}
+
+void Layer::Forward(){
+    this->Forward(&Layer::Sigmoid);
+}