jacobian

commit b07e463e3f022e29f28f867c794bcd2b39dd64bd
parent e2e47b932dbe0e1450a56a0bd1c6fe77d674e2a6
Author: David Freifeld <freifeld.david@gmail.com>
Date:   Sun, 12 Jul 2020 11:20:40 -0700

Backprop doesn't segfault

Diffstat:
M
example.cpp
 | 
17
++++++++++-------
M
src/bpnn.cpp
 | 
24
++++++------------------

2 files changed, 16 insertions(+), 25 deletions(-)
diff --git a/example.cpp b/example.cpp
@@ -13,14 +13,17 @@ double bench(int batch_sz)
   net.init_decay("exp", 1, 10);
   net.initialize();
   //  checks(net);
-  net.next_batch();
-  net.feedforward();
-  std::cout << net.cost() << " " << net.accuracy() << "\n";
-  //for (int i = 0; i < 50; i++) {
-  //  net.train();
-  //}
+  for (int i = 0; i < 100; i++) {
+    net.next_batch();
+    net.feedforward();
+    net.backpropagate();
+    std::cout << net.cost() << " " << net.accuracy() << "\n";
+  }
+  // for (int i = 0; i < 1; i++) {
+  //   net.train();
+  // }
   auto end = std::chrono::high_resolution_clock::now();
-  net.list_net();
+  //net.list_net();
   return std::chrono::duration_cast<std::chrono::nanoseconds>(end - start).count() / pow(10,9);
 }
 
diff --git a/src/bpnn.cpp b/src/bpnn.cpp
@@ -222,29 +222,17 @@ float Network::accuracy()
   return (1.0/batch_size) * correct;
 }
 
-#define DELTA 1
 void Network::backpropagate()
 {
   std::vector<Eigen::MatrixXf> gradients;
   std::vector<Eigen::MatrixXf> deltas;
   Eigen::MatrixXf error (layers[length-1].contents->rows(), layers[length-1].contents->cols());
-  for (int i = 0; i < layers[length-1].contents->rows(); i++) {
-    for (int j = 0; j < layers[length-1].contents->cols(); j++) {
-      if (j == (*labels)(i,0)) {
-        float sum = 0;
-        for (int k = 0; k < layers[length-1].contents->cols(); k++) {
-          if (k == j) continue;
-          float intermediate = (*layers[length-1].contents)(i,j) - (*labels)(i,0);
-          if (intermediate > 0) sum+=1;
-        }
-        if (sum == 0) error(i,j) = 0; // IEEE floats are weird
-        else error(i,j) = -sum;
-      }
-      else {
-        float classloss = (*layers[length-1].contents)(i,j) - (*labels)(i,0) + DELTA;
-        if (classloss > 0) error(i, j) = 1;
-        else error(i, j) = 0;
-      }
+  for (int i = 0; i < error.rows(); i++) {
+    for (int j = 0; j < error.cols(); j++) {
+      float truth;
+      if (j==(*labels)(i,0)) truth = 1;
+      else truth = 0;
+      error(i,j) = truth - (*layers[length-1].contents)(i,j);
     }
   }
   //  std::cout << error << "\n\n";