jacobian

commit e9d03fd360944cd588d5f1b0d6da5ae86e3fd32f
parent 3c5fa282b5ec8210a8b9f9ba11beff8965c2cce3
Author: David Freifeld <freifeld.david@gmail.com>
Date:   Sun,  6 Sep 2020 20:56:20 -0700

Beginning RNN implementation

Diffstat:
A
src/rnn.cpp
 | 
43
+++++++++++++++++++++++++++++++++++++++++++

1 file changed, 43 insertions(+), 0 deletions(-)
diff --git a/src/rnn.cpp b/src/rnn.cpp
@@ -0,0 +1,43 @@
+#include "bpnn.hpp"
+
+class RecurrentLayer : public Layer {
+public:
+    Eigen::MatrixXf* rec_weights;
+    void init_weights(RecurrentLayer next);
+    RecurrentLayer(int rows, int columns, float a=0);
+};
+
+RecurrentLayer::RecurrentLayer(int rows, int columns, float a)
+    :Layer(rows, columns, a)
+{
+    rec_weights = new Eigen::MatrixXf(contents->cols(), contents->cols());
+    for (int i = 0; i < (rec_weights->rows()*rec_weights->cols()); i++) {
+        std::random_device rd;
+        std::mt19937 gen(rd()); 
+        (*rec_weights)(static_cast<int>(i / columns), i%columns) = d(gen);
+    }
+}
+
+void init_weights(RecurrentLayer next)
+{
+    v = new Eigen::MatrixXf (contents->cols(), next.contents->cols());
+    m = new Eigen::MatrixXf (contents->cols(), next.contents->cols());
+    weights = new Eigen::MatrixXf (contents->cols(), next.contents->cols());
+    int nodes = weights->cols();
+    int n = contents->cols() + next.contents->cols();
+    std::normal_distribution<float> d(0,sqrt(1.0/n));
+    for (int i = 0; i < (weights->rows()*weights->cols()); i++) {
+        std::random_device rd;
+        std::mt19937 gen(rd()); 
+        (*weights)(static_cast<int>(i / nodes), i%nodes) = d(gen);
+        (*v)(static_cast<int>(i / nodes), i%nodes) = 0;
+        (*m)(static_cast<int>(i / nodes), i%nodes) = 0;
+    }
+}
+    
+class RNN : public Network {
+public:
+    void feedforward();
+    void backpropagate();
+    RNN();
+};