jacobian

commit 596a70cc776ee327ad936880b664d0a48e37ce08
parent 047146227c9217d68329d2554b1fde9114589be5
Author: David Freifeld <freifeld.david@gmail.com>
Date:   Sat, 26 Sep 2020 19:10:10 -0700

GSL Expects/Ensures because apparently C++ contracts don't exist yet

Diffstat:
M
CMakeLists.txt
 | 
2
+-
M
src/bpnn.cpp
 | 
11
++++++++++-
M
src/bpnn.hpp
 | 
10
+++++++---

3 files changed, 18 insertions(+), 5 deletions(-)
diff --git a/CMakeLists.txt b/CMakeLists.txt
@@ -2,7 +2,7 @@ cmake_minimum_required(VERSION 3.10.0)
 
 set(CMAKE_CXX_COMPILER "g++")
 
-set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD 20)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 
 if (DEBUG)
diff --git a/src/bpnn.cpp b/src/bpnn.cpp
@@ -74,6 +74,9 @@ void Layer::init_weights(Layer next)
 Network::Network(char* path, int batch_sz, float learn_rate, float bias_rate, Regularization regularization, float l, float ratio, bool early_exit, float cutoff)
     :lambda(l), learning_rate(learn_rate), bias_lr(bias_rate), batch_size(batch_sz), reg_type(regularization), early_stop(early_exit), threshold(cutoff)
 {
+    Expects(batch_size > 0 && learning_rate > 0 &&
+            bias_rate > 0 && l >= 0 && ratio >= 0 && ratio <= 1);
+    printf("Hi\n");
     int total_instances = prep_file(path, SHUFFLED_PATH);
     val_instances = split_file(SHUFFLED_PATH, total_instances, ratio);
     prep(TRAIN_PATH, TRAIN_BIN_PATH);
@@ -81,11 +84,11 @@ Network::Network(char* path, int batch_sz, float learn_rate, float bias_rate, Re
     data = open(TRAIN_BIN_PATH, O_RDONLY | O_NONBLOCK);
     val_data = open(VAL_BIN_PATH, O_RDONLY | O_NONBLOCK);
     instances = total_instances - val_instances;
-    assert(batch_size > 0 || batch_size < instances);
     decay = [this]() -> void {};
     update = [this](std::vector<Eigen::MatrixXf> deltas, int i) {
         *layers[length-2-i].weights -= (learning_rate * deltas[i]);
     };
+    Ensures(batch_size < instances);
 }
 
 
@@ -128,6 +131,7 @@ void Network::init_decay(char* type, ...)
 
 void Network::add_prelu_layer(int nodes, float a)
 {
+    Expects(nodes > 0);
     length++;
     layers.emplace_back(batch_size, nodes, a);
     strcpy(layers[length-1].activation_str, "prelu");
@@ -145,6 +149,7 @@ void Network::add_prelu_layer(int nodes, float a)
 
 void Network::add_layer(int nodes, char* name, std::function<float(float)> activation, std::function<float(float)> activation_deriv)
 {
+    Expects(nodes > 0);
     length++;
     layers.emplace_back(batch_size, nodes);
     strcpy(layers[length-1].activation_str, name);
@@ -154,12 +159,14 @@ void Network::add_layer(int nodes, char* name, std::function<float(float)> activ
 
 void Network::initialize()
 {
+    Expects(length > 1);
     labels = new Eigen::MatrixXf (batch_size,layers[length-1].contents->cols());
     for (int i = 0; i < length-1; i++) layers[i].init_weights(layers[i+1]);
 }
 
 void Network::set_activation(int index, std::function<float(float)> custom, std::function<float(float)> custom_deriv)
 {
+    Expects(index >= 0 && index < length);
     layers[index].activation = custom;
     layers[index].activation_deriv = custom_deriv;
 }
@@ -214,6 +221,7 @@ void Network::feedforward()
 
 void Network::list_net()
 {
+    Expects(length > 1);
     std::cout << "-----------------------\nINPUT LAYER (LAYER 0)\n-----------------------\n\n\u001b[31mGENERAL INFO:\x1B[0;37m\nActivation Function: " << layers[0].activation_str << "\n\n\u001b[31mACTIVATIONS:\x1B[0;37m\n" << *layers[0].contents << "\n\n\u001b[31mWEIGHTS:\x1B[0;37m\n" << *layers[0].weights << "\n\n\u001b[31mBIASES:\x1B[0;37m\n" << *layers[0].bias << "\n\n\n";
     for (int i = 1; i < length-1; i++) {
         std::cout << "-----------------------\nLAYER " << i << "\n-----------------------\n\n\u001b[31mGENERAL INFO:\x1B[0;37m\nActivation Function: " << layers[i].activation_str;
@@ -334,6 +342,7 @@ 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];
 }
 
diff --git a/src/bpnn.hpp b/src/bpnn.hpp
@@ -6,18 +6,17 @@
 //#include "../../mapreduce/mapreduce.h"
 
 #include <vector>
-#include <array>
 #include <iostream>
 #include <string>
 #include <cstdio>
 #include <cmath>
-#include <fstream>
 #include <random>
 #include <algorithm>
 #include <sys/types.h>
 #include <sys/stat.h>
 #include <fcntl.h>
 #include <unistd.h>
+#include <gsl/gsl_assert>
 
 #define BUFFER_SIZE 600*1024
 enum Regularization {L1, L2};
@@ -76,7 +75,10 @@ public:
     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(char* path, int batch_sz, float learn_rate,
+            float bias_rate, Regularization regularization,
+            float l, float ratio, bool early_exit=true, float cutoff=0);
+    
     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, ...);
@@ -130,6 +132,8 @@ void prep(char* rname, char* wname);
 #define checknan(x, loc) if(x==INFINITY || x==NAN || x == -INFINITY) throw ValueError("Detected NaN in operation", loc)
 #else
 #define checknan(x, loc)
+#define Expects(cond) GSL_ASSUME(cond);
+#define Ensures(cond) GSL_ASSUME(cond);
 #endif
 
 #endif /* MODULE_H */