jacobian

commit 3dd39f3e4573c243d6328fe263fef5c1c1a05038
parent 24918d89d814946c10ce8578adc3dc0388a85a76
Author: David Freifeld <freifeld.david@gmail.com>
Date:   Thu, 11 Jun 2020 22:55:46 -0700

Full init, broken feedforward

Diffstat:
M
test.cpp
 | 
65
+++++++++++++++++++++++++++++++++++++++++++++++++++--------------

1 file changed, 51 insertions(+), 14 deletions(-)
diff --git a/test.cpp b/test.cpp
@@ -3,7 +3,6 @@
 #include <vector>
 #include <array>
 #include <iostream>
-#include <cstdlib>
 #include <cstdio>
 
 class Node;
@@ -45,7 +44,8 @@ public:
   Eigen::MatrixXd* weights;
 
   Layer(float* vals, int rows, int columns);
-  void initWeights(Layer next, int batch_sz);
+  Layer(int rows, int columns);
+  void initWeights(Layer next);
 };
 
 Layer::Layer(float* vals, int batch_sz, int nodes)
@@ -53,17 +53,30 @@ Layer::Layer(float* vals, int batch_sz, int nodes)
   contents = new Eigen::MatrixXd (batch_sz, nodes);
   int datalen = batch_sz*nodes;
   for (int i = 0; i < datalen; i++) {
-    (*contents)((int)i / 4,i%nodes) = vals[i];
+    (*contents)((int)i / nodes,i%nodes) = vals[i];
   }
-  std::cout << *contents << "\n";
+  std::cout << *contents << "\n\n";
 }
 
-void Layer::initWeights(Layer next, int batch_sz)
+Layer::Layer(int batch_sz, int nodes)
 {
-  weights = new Eigen::MatrixXd (contents->rows(), next.contents->cols());
+  contents = new Eigen::MatrixXd (batch_sz, nodes);
+  int datalen = batch_sz*nodes;
+  for (int i = 0; i < datalen; i++) {
+    (*contents)((int)i / nodes,i%nodes) = 0;
+  }
+  std::cout << *contents << "\n\n";
+}
+
+void Layer::initWeights(Layer next)
+{
+  weights = new Eigen::MatrixXd (contents->cols(), next.contents->cols());
+  printf("%i x %i\n", weights->rows(), weights->cols());
+  int nodes = weights->cols();
   for (int i = 0; i < (weights->rows()*weights->cols()); i++) {
-    *weights << rand();
+    (*weights)((int)i / nodes, i%nodes) = rand() / double(RAND_MAX);
   }
+  std::cout << *weights << "\n\n";
 }
 
 class Network {
@@ -72,10 +85,13 @@ public:
   int length;
 
   Network(char* path, int inputs, int hidden, int outputs, int neurons, int batch_sz);
+  void feedforward();
+  void activate(Eigen::MatrixXd matrix);
 };
 
 Network::Network(char* path, int inputs, int hidden, int outputs, int neurons, int batch_sz)
 {
+  length = hidden + 2;
   FILE* fptr = fopen(path, "r");
   int datalen = batch_sz*inputs;
   float batch[datalen];
@@ -85,18 +101,39 @@ Network::Network(char* path, int inputs, int hidden, int outputs, int neurons, i
     sscanf(line, "%f,%f,%f,%f,*f", &batch[0+(i*inputs)], &batch[1+(i*inputs)], &batch[2+(i*inputs)], &batch[3+(i*inputs)]);
   }
   float* batchptr = batch;
-  // for (int i = 0; i < datalen; i++) {
-  //   printf("%f (vs %f) at %x\n", batchptr[i], batch[i], batchptr);
-  // }
   layers.emplace_back(batchptr, batch_sz, inputs);
-  // for (int i = 0; i < hidden; i++) {
-  //   layers.emplace_back(neurons);
-  // }
-  // layers.emplace_back(outputs);
+  for (int i = 0; i < hidden; i++) {
+    layers.emplace_back(batch_sz, neurons);
+  }
+  layers.emplace_back(batch_sz, outputs);
+  for (int i = 0; i < hidden+1; i++) {
+    layers[i].initWeights(layers[i+1]);
+  }
+}
+
+void Network::activate(Eigen::MatrixXd matrix)
+{
+  int nodes = matrix.cols();
+  for (int i = 0; i < (matrix.rows()*matrix.cols()); i++) {
+    // (matrix)((int)i / nodes, i%nodes) = 
+  }
+}
+
+void Network::feedforward()
+{
+  for (int i = 0; i < length-1; i++) {
+    std::cout << "-----------\nRUN\n\n" << *layers[i].contents << "\n-\n\n";
+    Eigen::MatrixXd product = (*layers[i].contents) * (*layers[i].weights);
+    activate(product);
+    std::cout << "NEXT\n\n\n" << *layers[i+1].contents << "\n-\n\n";
+    layers[i+1].contents = &product;
+    std::cout << "UPDATE\n\n\n" << *layers[i+1].contents << "\n\n";
+  }
 }
 
 
 int main()
 {
   Network net ("./data_banknote_authentication.txt", 4, 2, 2, 5, 10);
+  net.feedforward();
 }