[英]C++ Memory Error
When I compile my code, I repeatedly get the error 编译代码时,反复出现错误
free(): invalid next size (fast)
Yet the code only goes so far as to create references. 但是,该代码仅能创建引用。 Specifically, commenting out a specific line seems to fix the error; 具体来说,注释掉特定的一行似乎可以解决此错误; however, it's a very important line. 但是,这是一条非常重要的路线。
void neuron::updateWeights(layer &prevLayer) {
for(unsigned i = 0; i < prevLayer.size(); i++) {
double oldDeltaWeight = prevLayer[i].m_connections[m_index].m_deltaWeight;
double newDeltaWeight = eta * prevLayer[i].m_output * m_gradient + alpha * oldDeltaWeight;
prevLayer[i].m_connections[m_index].m_deltaWeight = newDeltaWeight; // THIS LINE
prevLayer[i].m_connections[m_index].m_weight += newDeltaWeight;
}
}
Any help would be very appreciated! 任何帮助将不胜感激!
EDIT: Additional code // Headers #include "../../Include/neuralNet.h" 编辑:附加代码//标头#include“ ../../Include/neuralNet.h”
// Libraries
#include <vector>
#include <iostream>
#include <cmath>
// Namespace
using namespace std;
// Class constructor
neuron::neuron(unsigned index, unsigned outputs) {
m_index = index;
for(unsigned i = 0; i < outputs; i++) {
m_connections.push_back(connection());
}
// Set default neuron output
setOutput(1.0);
}
double neuron::eta = 0.15; // overall net learning rate, [0.0..1.0]
double neuron::alpha = 0.5; // momentum, multiplier of last deltaWeight, [0.0..1.0]
// Definition of transfer function method
double neuron::transferFunction(double x) const {
return tanh(x); // -1 -> 1
}
// Transfer function derivation method
double neuron::transferFunctionDerivative(double x) const {
return 1 - x*x; // Derivative of tanh
}
// Set output value
void neuron::setOutput(double value) {
m_output = value;
}
// Forward propagate
void neuron::recalculate(layer &previousLayer) {
double sum = 0.0;
for(unsigned i = 0; i < previousLayer.size(); i++) {
sum += previousLayer[i].m_output * previousLayer[i].m_connections[m_index].m_weight;
}
setOutput(transferFunction(sum));
}
// Change weights based on target
void neuron::updateWeights(layer &prevLayer) {
for(unsigned i = 0; i < prevLayer.size(); i++) {
double oldDeltaWeight = prevLayer[i].m_connections[m_index].m_deltaWeight;
double newDeltaWeight = eta * prevLayer[i].m_output * m_gradient + alpha * oldDeltaWeight;
prevLayer[i].m_connections[m_index].m_deltaWeight = newDeltaWeight;
prevLayer[i].m_connections[m_index].m_weight += newDeltaWeight;
}
}
// Complex math stuff
void neuron::calculateOutputGradients(double target) {
double delta = target - m_output;
m_gradient = delta * transferFunctionDerivative(m_output);
}
double neuron::sumDOW(const layer &nextLayer) {
double sum = 0.0;
for(unsigned i = 1; i < nextLayer.size(); i++) {
sum += m_connections[i].m_weight * nextLayer[i].m_gradient;
}
return sum;
}
void neuron::calculateHiddenGradients(const layer &nextLayer) {
double dow = sumDOW(nextLayer);
m_gradient = dow * neuron::transferFunctionDerivative(m_output);
}
Also the line is called here 这行也叫这里
// Update weights
for(unsigned layerIndex = m_layers.size() - 1; layerIndex > 0; layerIndex--) {
layer ¤tLayer = m_layers[layerIndex];
layer &previousLayer = m_layers[layerIndex - 1];
for(unsigned i = 1; i < currentLayer.size(); i++) {
currentLayer[i].updateWeights(previousLayer);
}
}
Your constructor initialize N 'outputs' m_connections in the class. 您的构造函数在该类中初始化N个“输出” m_connections 。
But you have a lot of places calling: 但是您有很多地方在打电话:
m_connections[m_index]
What happens if m_index > outputs? 如果m_index>输出会怎样? Is this possible in your problem? 这可能是您的问题吗? Try including an assert ( http://www.cplusplus.com/reference/cassert/assert/ ) in the first line of the constructor: 尝试在构造函数的第一行中包含一个断言( http://www.cplusplus.com/reference/cassert/assert/ ):
assert(index < outputs)
You are probably having a bad pointer access somewhere. 您可能在某个地方访问了错误的指针。
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