commit 1a8160e23035713197f4e350e49bf567519b7ca7
parent 6899010c1d774d2e50be0a8ac47f11988ce13f3c
Author: David Freifeld <freifeld.david@gmail.com>
Date: Mon, 3 Aug 2020 11:56:02 -0700
Attempts to use old mapreduce code
Diffstat:
4 files changed, 87 insertions(+), 126 deletions(-)
diff --git a/mapreduce.a b/mapreduce.a
Binary files differ.
diff --git a/src/bpnn.hpp b/src/bpnn.hpp
@@ -3,7 +3,7 @@
#include <Eigen/Dense>
-#include "../../mapreduce/mapreduce.hpp"
+#include "../../mapreduce/mapreduce.h"
#include <vector>
#include <array>
diff --git a/src/mr_bpnn_2.cpp b/src/mr_bpnn_2.cpp
@@ -3,135 +3,61 @@
// Jacobian
//
// Created by David Freifeld
-// Copyright © 2020 David Freifeld. All rights reserved.
//
-
-#include <pybind11/pybind11.h>
-#include <pybind11/functional.h>
#include "bpnn.hpp"
-namespace py = pybind11;
-
-// struct pair* map (struct pair input_pair)
-// {
-// char* path = new char[100];
-// strcpy(path, (char*)input_pair.key);
-// strcat(path, "_shuf");
-// printf("%s and %s\n", path, (char*)input_pair.key);
-// int linecount = prep_file((char*)input_pair.key, path);
-// Network* net = new Network (path, 4, 2, 1, 5, 10, 2);
-// auto begin = std::chrono::high_resolution_clock::now();
-// // std::cout << "\n\n\n";
-// float epoch_cost = 1000;
-// float epoch_accuracy = -1;
-// int epochs = 0;
-// int total_epochs = 50;
-// net->batches= 0;
-// // net.feedforward();
-// // net.backpropagate();
-// // std::cout << net.cost() << "\n";
-
-// printf("Beginning train on %i instances for %i epochs...\n", linecount, 50);
-// while (epochs < total_epochs) {
-// auto ep_begin = std::chrono::high_resolution_clock::now();
-// // int linecount = prep_file("./data_banknote_authentication.txt");
-// float cost_sum = 0;
-// float acc_sum = 0;
-// double times[5] = {0};
-// for (int i = 0; i <= linecount-net->batch_size; i+=net->batch_size) {
-// // auto feed_begin = std::chrono::high_resolution_clock::now();
-// net->feedforward();
-// // auto back_begin = std::chrono::high_resolution_clock::now();
-// net->backpropagate();
-// // auto cost_begin = std::chrono::high_resolution_clock::now();
-// cost_sum += net->cost();
-// // std::cout << acc_sum << " "<< net.accuracy() << " " << net.batch_size << "\n";
-// // auto acc_begin = std::chrono::high_resolution_clock::now();
-// acc_sum += net->accuracy();
-// // std::cout << net.cost() << " as it is " << net.labels[0] << " vs " << *net.layers[net.length-1].contents << "\n";
-// // auto batch_begin = std::chrono::high_resolution_clock::now();
-
-// if (i != linecount-net->batch_size) { // Don't try to advance batch on final batch.
-// net->next_batch();
-// }
-// net->batches++;
-// // auto loop_end = std::chrono::high_resolution_clock::now();
-// // times[0] += std::chrono::duration_cast<std::chrono::nanoseconds>(back_begin -feed_begin).count() / pow(10,9);
-// // times[1] += std::chrono::duration_cast<std::chrono::nanoseconds>(cost_begin - back_begin).count() / pow(10,9);
-// // times[2] += std::chrono::duration_cast<std::chrono::nanoseconds>(acc_begin - cost_begin).count() / pow(10,9);
-// // times[3] += std::chrono::duration_cast<std::chrono::nanoseconds>(batch_begin - acc_begin).count() / pow(10,9);
-// // times[4] += std::chrono::duration_cast<std::chrono::nanoseconds>(loop_end - batch_begin).count() / pow(10,9);
-// }
-// epoch_accuracy = 1.0/((float) linecount/net->batch_size) * acc_sum;
-// epoch_cost = 1.0/((float) linecount/net->batch_size) * cost_sum;
-// auto ep_end = std::chrono::high_resolution_clock::now();
-// double epochtime = (double) std::chrono::duration_cast<std::chrono::nanoseconds>(ep_end-ep_begin).count() / pow(10,9);
-// printf("Epoch %i/%i - time %f - cost %f - acc %f\n", epochs+1, total_epochs, epochtime, epoch_cost, epoch_accuracy);
-// // printf("Avg time spent across %i batches: %lf on feedforward, %lf on backprop, %lf on cost, %lf on acc, %lf on next batch.\n", net.batches, times[0]/net.batches, times[1]/net.batches, times[2]/net.batches, times[3]/net.batches, times[4]/net.batches);
-// // printf("Time spent across epoch: %lf on feedforward, %lf on backprop, %lf on cost, %lf on acc, %lf on next batch, %lf other.\n", times[0], times[1], times[2], times[3], times[4], epochtime-times[0]-times[1]-times[2]-times[3]-times[4]);
-// net->batches=1;
-// epochs++;
-// }
-// struct pair* output = new struct pair;
-// char* key = new char[100];
-// strcpy(key, path);
-// output[0].key = key;
-// output[0].value = net;
-// return output;
-// }
+struct pair* map (struct pair input_pair)
+{
+ char* path = new char[100];
+ strcpy(path, (char*)input_pair.key);
+ strcat(path, "_shuf");
+ printf("%s and %s\n", path, (char*)input_pair.key);
+ int linecount = prep_file((char*)input_pair.key, path);
+ Network* net = new Network(path, 16, 0.0155, 0.03, 2, 0, 0.9);
+ for (int i = 0; i < 50; i++) {
+ net->train();
+ }
+ struct pair* output = new struct pair;
+ char* key = new char[100];
+ strcpy(key, path);
+ output[0].key = key;
+ output[0].value = net;
+ return output;
+}
-// struct pair* reduce (struct pair* input_pairs)
-// {
-// struct pair* output = new struct pair[6];
-// for (int i = 0; input_pairs[i].key != 0x0; i++) {
-// float* acc = new float;
-// *acc = ((Network*)input_pairs[i].value)->test("./test.txt");
-// output[i].key = input_pairs[i].key;
-// output[i].value = acc;
-// }
-// return output;
-// }
+struct pair* reduce (struct pair* input_pairs)
+{
+ struct pair* output = new struct pair[6];
+ for (int i = 0; input_pairs[i].key != 0x0; i++) {
+ float* cost = new float;
+ *cost = ((Network*)input_pairs[i].value)->get_val_cost();
+ output[i].key = input_pairs[i].key;
+ output[i].value = cost;
+ }
+ return output;
+}
-// void translate(char* path)
-// {
-// FILE* rptr = fopen(path, "r");
-// FILE* wptr = fopen("./translated", "w");
-// char* line = new char[MAXLINE];
-// char* newline = new char[MAXLINE];
-// while (fgets(line, MAXLINE, rptr) != NULL) {
-// void* addr1;
-// void* addr2;
-// sscanf(line, "%p %p", &addr1, &addr2);
-// sprintf(newline, "%s %f", (char*)addr1, *(float*)addr2);
-// int batch_num = strtol((char*)addr1, NULL, 10);
-// fprintf(wptr, "%s %f\n",(char*)addr1, *(float*)addr2);
-// }
-// fclose(rptr);
-// fclose(wptr);
-// free(newline);
-// free(line);
-// }
+void translate(char* path)
+{
+ FILE* rptr = fopen(path, "r");
+ FILE* wptr = fopen("./translated", "w");
+ char* line = new char[MAXLINE];
+ char* newline = new char[MAXLINE];
+ while (fgets(line, MAXLINE, rptr) != NULL) {
+ void* addr1;
+ void* addr2;
+ sscanf(line, "%p %p", &addr1, &addr2);
+ sprintf(newline, "%s %f", (char*)addr1, *(float*)addr2);
+ int batch_num = strtol((char*)addr1, NULL, 10);
+ fprintf(wptr, "%s %f\n",(char*)addr1, *(float*)addr2);
+ }
+ fclose(rptr);
+ fclose(wptr);
+ free(newline);
+ free(line);
+}
-PYBIND11_MODULE(mrbpnn, m) {
- m.doc() = "Fast machine learning in C++"; // optional module docstring
-
- py::class_<Network>(m, "Network")
- .def(py::init<char*, int, float, float, int, float, float>())
- .def("add_layer", &Network::add_layer, py::arg("nodes"), py::arg("activation"))
- .def("add_prelu_layer", &Network::add_prelu_layer, py::arg("nodes"), py::arg("a"))
- .def("initialize", &Network::initialize)
- .def("init_decay", &Network::init_decay, py::arg("type"), py::arg("a_0"), py::arg("k"))
- .def("set_activation", &Network::set_activation)
- .def("feedforward", &Network::feedforward)
- .def("backpropagate", &Network::backpropagate)
- .def("list_net", &Network::list_net)
- .def("cost", &Network::cost)
- .def("accuracy", &Network::accuracy)
- .def("update_layer", &Network::update_layer, py::arg("vals"), py::arg("len"), py::arg("index"))
- .def("next_batch", &Network::next_batch)
- .def("train", &Network::train)
- .def("get_acc", &Network::get_acc)
- .def("get_cost", &Network::get_cost)
- .def("get_val_acc", &Network::get_val_acc)
- .def("get_val_cost", &Network::get_val_cost);
+int main()
+{
+ begin("./data_banknote_authentication.txt", map, reduce, translate, 1, 2, "108.169.4.115", 1);
}
diff --git a/src/pybind.cpp b/src/pybind.cpp
@@ -0,0 +1,35 @@
+//
+// pybind.cpp
+// Jacobian
+//
+// Created by David Freifeld
+//
+
+#include <pybind11/pybind11.h>
+#include <pybind11/functional.h>
+#include "bpnn.hpp"
+namespace py = pybind11;
+
+PYBIND11_MODULE(mrbpnn, m) {
+ m.doc() = "Fast machine learning in C++"; // optional module docstring
+
+ py::class_<Network>(m, "Network")
+ .def(py::init<char*, int, float, float, int, float, float>())
+ .def("add_layer", &Network::add_layer, py::arg("nodes"), py::arg("activation"))
+ .def("add_prelu_layer", &Network::add_prelu_layer, py::arg("nodes"), py::arg("a"))
+ .def("initialize", &Network::initialize)
+ .def("init_decay", &Network::init_decay, py::arg("type"), py::arg("a_0"), py::arg("k"))
+ .def("set_activation", &Network::set_activation)
+ .def("feedforward", &Network::feedforward)
+ .def("backpropagate", &Network::backpropagate)
+ .def("list_net", &Network::list_net)
+ .def("cost", &Network::cost)
+ .def("accuracy", &Network::accuracy)
+ .def("update_layer", &Network::update_layer, py::arg("vals"), py::arg("len"), py::arg("index"))
+ .def("next_batch", &Network::next_batch)
+ .def("train", &Network::train)
+ .def("get_acc", &Network::get_acc)
+ .def("get_cost", &Network::get_cost)
+ .def("get_val_acc", &Network::get_val_acc)
+ .def("get_val_cost", &Network::get_val_cost);
+}
