深度学习中的优化算法之Adadelta
** **之前在https://blog.csdn.net/fengbingchun/article/details/124766283 介绍过深度学习中的优化算法AdaGrad，这里介绍下深度学习的另一种优化算法Adadelta。论文名字为：《ADADELTA: AN ADAPTIVE LEARNING RATE METHOD》，论文地址：https://arxiv.org/pdf/1212.5701.pdf
** Adadelta一种自适应学习率方法，是AdaGrad的扩展，建立在AdaGrad****的基础上，旨在减少其过激的、单调递减的学习率**。Adadelta不是积累所有过去的平方梯度，而是将积累的过去梯度的窗口限制为某个固定大小。如下图所示，截图来自：https://arxiv.org/pdf/1609.04747.pdf
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使用Adadelta，我们甚至不需要设置默认学习率这一超参数，因为它已从更新规则中消除，它使用参数本身的变化率来调整学习率。
** Adadelta可被认为是梯度下降的进一步扩展，它建立在AdaGrad和RMSProp的基础上，并改变了自定义步长的计算(changes the calculation of the custom step size)****，进而不再需要初始学习率超参数**。
** **Adadelta旨在加速优化过程，例如减少达到最优值所需的迭代次数，或提高优化算法的能力，例如获得更好的最终结果。
** **最好将Adadelta理解为AdaGrad和RMSProp算法的扩展。Adadelta是RMSProp的进一步扩展，旨在提高算法的收敛性并消除对手动指定初始学习率的需要。
** **与RMSProp一样，Adadelta为每个参数计算平方偏导数的衰减移动平均值。关键区别在于使用delta的衰减平均值或参数变化来计算参数的步长。(The decaying moving average of the squared partial derivative is calculated for each parameter, as with RMSProp. The key difference is in the calculation of the step size for a parameter that uses the decaying average of the delta or change in parameter.)
** **以上内容主要参考：https://machinelearningmastery.com
** **以下是与AdaGrad不同的代码片段：
** **1.在原有枚举类Optimizaiton的基础上新增Adadelta：
enum class Optimization {
BGD, // Batch Gradient Descent
SGD, // Stochastic Gradient Descent
MBGD, // Mini-batch Gradient Descent
SGD_Momentum, // SGD with Momentum
AdaGrad, // Adaptive Gradient
RMSProp, // Root Mean Square Propagation
Adadelta // an adaptive learning rate method
};** **2.calculate_gradient_descent函数：
void LogisticRegression2::calculate_gradient_descent(int start, int end)
{
switch (optim_) {
case Optimization::Adadelta: {
int len = end - start;
std::vector<float> g(feature_length_, 0.), p(feature_length_, 0.);
std::vector<float> z(len, 0.), dz(len, 0.);
for (int i = start, x = 0; i < end; ++i, ++x) {
z[x] = calculate_z(data_->samples[random_shuffle_[i]]);
dz[x] = calculate_loss_function_derivative(calculate_activation_function(z[x]), data_->labels[random_shuffle_[i]]);
for (int j = 0; j < feature_length_; ++j) {
float dw = data_->samples[random_shuffle_[i]][j] * dz[x];
g[j] = mu_ * g[j] + (1. - mu_) * (dw * dw); // formula 10
float alpha = (eps_ + std::sqrt(p[j])) / (eps_ + std::sqrt(g[j]));
float change = alpha * dw;
p[j] = mu_ * p[j] + (1. - mu_) * (change * change); // formula 15
w_[j] = w_[j] - change;
}
b_ -= (eps_ * dz[x]);
}
}
break;
case Optimization::RMSProp: {
int len = end - start;
std::vector<float> g(feature_length_, 0.);
std::vector<float> z(len, 0), dz(len, 0);
for (int i = start, x = 0; i < end; ++i, ++x) {
z[x] = calculate_z(data_->samples[random_shuffle_[i]]);
dz[x] = calculate_loss_function_derivative(calculate_activation_function(z[x]), data_->labels[random_shuffle_[i]]);
for (int j = 0; j < feature_length_; ++j) {
float dw = data_->samples[random_shuffle_[i]][j] * dz[x];
g[j] = mu_ * g[j] + (1. - mu_) * (dw * dw); // formula 18
w_[j] = w_[j] - alpha_ * dw / (std::sqrt(g[j]) + eps_);
}
b_ -= (alpha_ * dz[x]);
}
}
break;
case Optimization::AdaGrad: {
int len = end - start;
std::vector<float> g(feature_length_, 0.);
std::vector<float> z(len, 0), dz(len, 0);
for (int i = start, x = 0; i < end; ++i, ++x) {
z[x] = calculate_z(data_->samples[random_shuffle_[i]]);
dz[x] = calculate_loss_function_derivative(calculate_activation_function(z[x]), data_->labels[random_shuffle_[i]]);
for (int j = 0; j < feature_length_; ++j) {
float dw = data_->samples[random_shuffle_[i]][j] * dz[x];
g[j] += dw * dw;
w_[j] = w_[j] - alpha_ * dw / (std::sqrt(g[j]) + eps_);
}
b_ -= (alpha_ * dz[x]);
}
}
break;
case Optimization::SGD_Momentum: {
int len = end - start;
std::vector<float> change(feature_length_, 0.);
std::vector<float> z(len, 0), dz(len, 0);
for (int i = start, x = 0; i < end; ++i, ++x) {
z[x] = calculate_z(data_->samples[random_shuffle_[i]]);
dz[x] = calculate_loss_function_derivative(calculate_activation_function(z[x]), data_->labels[random_shuffle_[i]]);
for (int j = 0; j < feature_length_; ++j) {
float new_change = mu_ * change[j] - alpha_ * (data_->samples[random_shuffle_[i]][j] * dz[x]);
w_[j] += new_change;
change[j] = new_change;
}
b_ -= (alpha_ * dz[x]);
}
}
break;
case Optimization::SGD:
case Optimization::MBGD: {
int len = end - start;
std::vector<float> z(len, 0), dz(len, 0);
for (int i = start, x = 0; i < end; ++i, ++x) {
z[x] = calculate_z(data_->samples[random_shuffle_[i]]);
dz[x] = calculate_loss_function_derivative(calculate_activation_function(z[x]), data_->labels[random_shuffle_[i]]);
for (int j = 0; j < feature_length_; ++j) {
w_[j] = w_[j] - alpha_ * (data_->samples[random_shuffle_[i]][j] * dz[x]);
}
b_ -= (alpha_ * dz[x]);
}
}
break;
case Optimization::BGD:
default: // BGD
std::vector<float> z(m_, 0), dz(m_, 0);
float db = 0.;
std::vector<float> dw(feature_length_, 0.);
for (int i = 0; i < m_; ++i) {
z[i] = calculate_z(data_->samples[i]);
o_[i] = calculate_activation_function(z[i]);
dz[i] = calculate_loss_function_derivative(o_[i], data_->labels[i]);
for (int j = 0; j < feature_length_; ++j) {
dw[j] += data_->samples[i][j] * dz[i]; // dw(i)+=x(i)(j)*dz(i)
}
db += dz[i]; // db+=dz(i)
}
for (int j = 0; j < feature_length_; ++j) {
dw[j] /= m_;
w_[j] -= alpha_ * dw[j];
}
b_ -= alpha_*(db/m_);
}
}** **执行结果如下图所示：测试函数为test_logistic_regression2_gradient_descent，多次执行每种配置，最终结果都相同。图像集使用MNIST，其中训练图像总共10000张，0和1各5000张，均来自于训练集；预测图像总共1800张，0和1各900张，均来自于测试集。在AdaGrad中设置学习率为0.01，eps为1e-8及其它配置参数相同的情况下，AdaGrad耗时为17秒；在Adadelta中设置eps为1e-3时，Adadelta耗时为26秒；它们的识别率均为100%。
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