jacobian

commit eb0c8873135d1ce14b1096d371ff52d196188f52
parent bfbdc5aa33576145d4e7e47e154339bab4589c5f
Author: David Freifeld <freifeld.david@gmail.com>
Date:   Sun, 11 Oct 2020 20:23:52 -0700

Tweaks

Diffstat:
M
src/bpnn.cpp
 | 
20
++++++++------------
M
src/bpnn.hpp
 | 
40
++++++++++++++--------------------------

2 files changed, 22 insertions(+), 38 deletions(-)
diff --git a/src/bpnn.cpp b/src/bpnn.cpp
@@ -93,6 +93,11 @@ Network::Network(char* path, int batch_sz, float learn_rate, float bias_rate, Re
     Ensures(batch_size < instances && data > 0 && val_data > 0);
 }
 
+Network::~Network()
+{
+    close(data);
+    close(val_data);
+}
 
 void Network::init_decay(char* type, ...)
 {
@@ -104,22 +109,19 @@ void Network::init_decay(char* type, ...)
         decay = [this, a_0, k]() -> void {
             learning_rate = a_0 * learning_rate/k;
         };
-    }
-    else if (strcmp(type, "exp") == 0) {
+    } else if (strcmp(type, "exp") == 0) {
         float a_0 = va_arg(args, double);
         float k = va_arg(args, double);
         decay = [this, a_0, k]() -> void {
             learning_rate = a_0 * exp(-k * epochs);
         };
-    }
-    else if (strcmp(type, "frac") == 0) {
+    } else if (strcmp(type, "frac") == 0) {
         float a_0 = va_arg(args, double);
         float k = va_arg(args, double);
         decay = [this, a_0, k]() -> void {
             learning_rate = a_0 / (1+(k * epochs));
         };
-    }
-    else if (strcmp(type, "linear") == 0) {
+    } else if (strcmp(type, "linear") == 0) {
         int max_ep = va_arg(args, double);
         decay = [this, max_ep]() -> void {
             learning_rate = 1 - epochs/max_ep;
@@ -342,12 +344,6 @@ void Network::backpropagate()
     }
 }
 
-void Network::update_layer(float* vals, int datalen, int index)
-{
-    Expects(datalen > 0);
-    for (int i = 0; i < datalen; i++) (*layers[index].contents)(static_cast<int>(i / layers[index].contents->cols()), i%layers[index].contents->cols()) = vals[i];
-}
-
 #include "data.cpp"
 
 float Network::validate(char* path)
diff --git a/src/bpnn.hpp b/src/bpnn.hpp
@@ -44,66 +44,54 @@ public:
 };
 
 class Network {
-public:
-    int data;
-    int val_data;
     int instances;
     char buf[BUFFER_SIZE];
     char* p;
-    int val_instances;
-    int test_instances;
-    Eigen::MatrixXf numerical_grad(int i, float epsilon);
-    void update_layer(float* vals, int datalen, int index);
-  
-    std::vector<Layer> layers;
-    int length = 0;
-
     float epoch_acc;
     float epoch_cost;
     float val_acc;
     float val_cost;
-  
+    std::function<void(void)> decay;
+    std::function<void(std::vector<Eigen::MatrixXf>, int, int)> grad_calc;
+    std::function<void(std::vector<Eigen::MatrixXf>, int)> update;
+public:
+    int data;
+    int val_data;
+    int val_instances;
+    int test_instances;
+    std::vector<Layer> layers;
+    int length = 0;
     float learning_rate;
     float bias_lr;
     float lambda;
+    bool early_stop;
+    float threshold;
     Regularization reg_type;
     int batch_size;
-
     bool silenced = false;
     int epochs = 0;
     int batches = 0;
     Eigen::MatrixXf* labels;
-
-    std::function<void(void)> decay;
-    std::function<void(std::vector<Eigen::MatrixXf>, int, int)> grad_calc;
-    std::function<void(std::vector<Eigen::MatrixXf>, int)> update;
-
+    
     Network(char* path, int batch_sz, float learn_rate,
             float bias_rate, Regularization regularization,
             float l, float ratio, bool early_exit=true, float cutoff=0);
-    
+    ~Network();
     void add_layer(int nodes, char* name, std::function<float(float)> activation, std::function<float(float)> activation_deriv);
     void add_prelu_layer(int nodes, float a);
     void init_decay(char* type, ...);
     void init_optimizer(char* name, ...);
     void initialize();
-    void grad_check();
     void set_activation(int index, std::function<float(float)> custom, std::function<float(float)> custom_deriv);
-  
     void feedforward();
     void softmax();
     void list_net();
-
-    bool early_stop;
-    float threshold;
-
     float cost();
     float accuracy();
     void backpropagate();
     int next_batch(int fd);
     float validate(char* path);
     void train();
-
     float get_acc() {return epoch_acc;}
     float get_val_acc() {return val_acc;}
     float get_cost() {return epoch_cost;}