如何在MNIST上提高网络运行的准确性

Question

我遵循以下代码：https://github.com/HyTruongSon/Neural-Network-MNIST-CPP

这很容易理解。它的准确率为94%。我必须将其转换为具有更深层次(从5到10(的网络。为了让自己舒服，我只多加了一层。然而，无论我怎么训练，准确率都不会超过50%。我在每个隐藏层中添加了256个神经元。以下是我修改代码的方式：我添加了这样的额外层：

// From layer 1 to layer 2. Or: Input layer - Hidden layer
double *w1[n1 + 1], *delta1[n1 + 1], *out1;
// From layer 2 to layer 3. Or; Hidden layer - 2Hidden layer
double *w2[n2 + 1], *delta2[n2 + 1], *in2, *out2, *theta2;
// From layer 3 to layer 4. Or; Hidden layer - Output layer
double *w3[n3 + 1], *delta3[n3 + 1], *in3, *out3, *theta3;
// Layer 3 - Output layer
double *in4, *out4, *theta4;
double expected[n4 + 1];

前馈部分是这样修改的：

void perceptron() {
for (int i = 1; i <= n2; ++i) {
in2[i] = 0.0;
}
for (int i = 1; i <= n3; ++i) {
in3[i] = 0.0;
}
for (int i = 1; i <= n4; ++i) {
in4[i] = 0.0;
}
for (int i = 1; i <= n1; ++i) {
for (int j = 1; j <= n2; ++j) {
in2[j] += out1[i] * w1[i][j];
}
}
for (int i = 1; i <= n2; ++i) {
out2[i] = sigmoid(in2[i]);
}
/////
for (int i = 1; i <= n2; ++i) {
for (int j = 1; j <= n3; ++j) {
in3[j] += out2[i] * w2[i][j];
}
}
for (int i = 1; i <= 3; ++i) {
out3[i] = sigmoid(in3[i]);
}
////
for (int i = 1; i <= n3; ++i) {
for (int j = 1; j <= n4; ++j) {
in4[j] += out3[i] * w3[i][j];
}
}
for (int i = 1; i <= n4; ++i) {
out4[i] = sigmoid(in4[i]);
}
}

反向传播是这样改变的：

void back_propagation() {
double sum;
for (int i = 1; i <= n4; ++i) {
theta4[i] = out4[i] * (1 - out4[i]) * (expected[i] - out4[i]);
}
for (int i = 1; i <= n3; ++i) {
sum = 0.0;
for (int j = 1; j <= n4; ++j) {
sum += w3[i][j] * theta4[j];
}
theta3[i] = out3[i] * (1 - out3[i]) * sum;
}
for (int i = 1; i <= n3; ++i) {
for (int j = 1; j <= n4; ++j) {
delta3[i][j] = (learning_rate * theta4[j] * out3[i]) + (momentum * delta3[i][j]);
w3[i][j] += delta3[i][j];
}
}
/////////////
for (int i = 1; i <= n2; ++i) {
for (int j = 1; j <= n3; ++j) {
delta2[i][j] = (learning_rate * theta3[j] * out2[i]) + (momentum * delta2[i][j]);
w2[i][j] += delta2[i][j];
}
}
/////////////////
for (int i = 1; i <= n1; ++i) {
for (int j = 1 ; j <= n2 ; j++ ) {
delta1[i][j] = (learning_rate * theta2[j] * out1[i]) + (momentum * delta1[i][j]);
w1[i][j] += delta1[i][j];
}
}
}

我也发布了我的修改，因为我可能在这里的某个地方错了。一旦我将历元变量设置为1000，并让它训练24小时，仍然没有进展：(.我对此感到非常沮丧，我不知道我错在哪里了。

Answer 1

您忘记在从第3层到第2层的thetha2参数中添加反向传播了吗？

for (int i = 1; i <= n2; ++i) {
sum = 0.0;
for (int j = 1; j <= n3; ++j) {
sum += w2[i][j] * theta3[j];
}
theta2[i] = out2[i] * (1 - out2[i]) * sum;
}