jacobian

commit 5d68d1107c189807669ce821fa302aa41486e04f
parent 27782f158e1d58d64e02623f3880407ad5f54574
Author: David Freifeld <freifeld.david@gmail.com>
Date:   Tue, 16 Jun 2020 18:44:19 -0700

Also fixed header

Diffstat:
A
bpnn.hpp
 | 
56
++++++++++++++++++++++++++++++++++++++++++++++++++++++++

1 file changed, 56 insertions(+), 0 deletions(-)
diff --git a/bpnn.hpp b/bpnn.hpp
@@ -0,0 +1,56 @@
+#include "/Users/davidfreifeld/Downloads/eigen-3.3.7/Eigen/Dense"
+
+extern "C" {
+  #include "../mapreduce/mapreduce.h"
+}
+
+#include <vector>
+#include <array>
+#include <iostream>
+#include <string>
+#include <cstdio>
+#include <fstream>
+#include <random>
+#include <algorithm>
+
+class Layer {
+public:
+  Eigen::MatrixXd* contents;
+  Eigen::MatrixXd* weights;
+  Eigen::MatrixXd* bias;
+  Eigen::MatrixXd* dZ;
+
+  Layer(float* vals, int rows, int columns);
+  Layer(int rows, int columns);
+  void initWeights(Layer next);
+};
+
+class Network {
+public:
+  char* fpath;
+
+  std::vector<Layer> layers;
+  int length;
+
+  float learning_rate;
+  int batch_size;
+  int batches;
+  Eigen::MatrixXd* labels;
+
+  Network(char* path, int inputs, int hidden, int outputs, int neurons, int batch_sz, float rate);
+  void update_layer(float* vals, int datalen, int index);
+
+  Eigen::MatrixXd activate(Eigen::MatrixXd matrix);
+  Eigen::MatrixXd activate_deriv(Eigen::MatrixXd matrix);
+  void feedforward();
+  void list_net();
+
+  float cost();
+  float gradient(int mode, int layer, int node);
+  void backpropagate();
+  int next_batch();
+  void test(char* path);
+};
+
+void demo();
+int prep_file(char* path);