C++內存錯誤

Question

當我編譯我的代碼，我多次得到錯誤

free(): invalid next size (fast) 

然而，代碼只是到目前爲止，以創建引用。具體來說，評論一條特定的線似乎可以解決錯誤;然而，這是一條非常重要的路線。

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; 
    } 
} 

任何幫助將不勝感激！

編輯： 附加代碼 //頭 的#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); 
} 

也行，這裏所謂的

// Update weights 
    for(unsigned layerIndex = m_layers.size() - 1; layerIndex > 0; layerIndex--) { 
     layer &currentLayer = m_layers[layerIndex]; 
     layer &previousLayer = m_layers[layerIndex - 1]; 

     for(unsigned i = 1; i < currentLayer.size(); i++) { 
      currentLayer[i].updateWeights(previousLayer); 
     } 
    }  

Answer 1

你構造函數初始化N'輸出'm_connections在課堂上。

但你有很多的地方致電：

m_connections[m_index] 

如果M_索引>輸出會發生什麼？這可能是你的問題嗎？ 嘗試，包括在構造函數中的第一行的斷言（http://www.cplusplus.com/reference/cassert/assert/）：

assert(index < outputs) 

你可能有一個糟糕的指針訪問的地方。