我正在使用FANN的C ++包装器,并且已经训练了时间序列输入的预测器。现在我想查看将网络输出作为输入反馈的结果。
我最初尝试过这个:
fann_type *previousOutput = net.run(previousOutput);
结果是:
Program received signal SIGSEGV, Segmentation fault.
0x00007ffff7bc51fb in fann_run () from /usr/local/lib/libfann.so.2
(gdb) bt
#0 0x00007ffff7bc51fb in fann_run () from /usr/local/lib/libfann.so.2
#1 0x000000000040161f in FANN::neural_net::run (this=0x7fffffffe420,
input=0x7fffffffe5b8) at /usr/local/include/fann_cpp.h:1107
#2 0x000000000040118d in run () at generate_dream.cpp:34
#3 0x000000000040133b in main (argc=1, argv=0x7fffffffe5a8)
at generate_dream.cpp:55
我也尝试过:
fann_type *tmpOutput = net.run(previousOutput);
previousOutput = *tmpOutput; // feedback loop.
导致相同的错误。
那么这样做的正确方法是什么?似乎问题是run()返回一个指针,而不是实际的数据。
另外,因为我的输入在我的训练数据中是离散的(0和1缩放到-1和1),我可能需要在反馈之前离散网络输出。这将涉及循环网络输出并构造一个新的离散化值的fann_type数组,但由于run()想要一个指针,我不知道如何做到这一点。
感谢。
#include "floatfann.h"
#include "fann_cpp.h"
#include <ios>
#include <iostream>
#include <fstream>
#include <sstream>
#include <sys/time.h>
using namespace std;
// Test function that demonstrates usage of the fann C++ wrapper
void run()
{
// Load file previously trained (for example by learn_sequence)
FANN::neural_net net;
net.create_from_file("learn_sequential.net");
// load datafile
FANN::training_data data;
if (data.read_train_from_file("../data/backgroundState_FANN.data")) {
data.scale_train_data(-1, 1);
// Seed with last pattern from dataset.
fann_type *previousOutput = data.get_input()[data.length_train_data()];
// Length of dream
for (unsigned int i = 0; i < 10000; i++)
{
fann_type *tmpOutput;
tmpOutput = net.run(previousOutput);
previousOutput = tmpOutput; // feedback loop.
// print out each
for (unsigned int j = 0; j < data.num_input_train_data(); j++) {
cout << "RESULT " << i << " " << j << " " << previousOutput[j] <<endl;
}
}
} else
cout << "Data file could not be loaded" << endl;
}
/* Startup function. Syncronizes C and C++ output, calls the test function
and reports any exceptions */
int main(int argc, char **argv)
{
try
{
std::ios::sync_with_stdio(); // Syncronize cout and printf output
run();
}
catch (...)
{
cerr << endl << "Abnormal exception." << endl;
}
return 0;
}
答案 0 :(得分:0)
如果我单独处理数组元素,那么它按照我的预期工作:
fann_type *tmpOutput = net.run(previousOutput);
// copy element by element from output to new array.
fann_type newInput[data.num_input_train_data()];
for (unsigned int j = 0; j < data.num_input_train_data(); j++) {
newInput[j] = tmpOutput[j];
}
previousOutput = newInput; // feedback loop.