w4_2Multi-class Classification and Neural Networks编程解析

本文链接：https://blog.youkuaiyun.com/wangcong02345/article/details/79367383

w4_2Multi-class Classification and Neural Networks编程解析

w4_2Multi-class Classification and Neural Networks编程解析

1. ex3.m

1.1 ex3.m框架分析

把数据可视化, 在plotData.m中添加:

input_layer_size  = 400;  % 20x20 Input Images of Digits
num_labels = 10;          % 10 labels, from 1 to 10
                          % (note that we have mapped "0" to label 10)

# 第1步加载数据并显示,是里面有各种手写0-9的数字网格
load('ex3data1.mat'); % training data stored in arrays X, y
m = size(X, 1);
rand_indices = randperm(m);
sel = X(rand_indices(1:100), :);
displayData(sel);


#第2.1步 Vectorize Logistic Regression 
theta_t = [-2; -1; 1; 2];
X_t = [ones(5,1) reshape(1:15,5,3)/10];
y_t = ([1;0;1;0;1] >= 0.5);
lambda_t = 3;
[J grad] = lrCostFunction(theta_t, X_t, y_t, lambda_t);

# 第2.2 步 实现one-vs-All Training, 将手写的0-9图片区分出来
lambda = 0.1;
[all_theta] = oneVsAll(X, y, num_labels, lambda);

# 第3步进行预测
pred = predictOneVsAll(all_theta, X);

1.2 Regularized logistic regression 0 / 30

分两步,先计算 $J(\theta)$ 再计算 $\theta$

1.2.1 logistic regression Cost Function

带正则化项的logistic回归的损失函数:
$J\left( \theta \right)=\frac{1}{m}\sum\limits_{i=1}^{m}{\left[-{{y}^{(i)}}\log \left( {{h}_{\theta }}( {{x}^{(i)}}) \right)-\left( 1-{{y}^{(i)}} \right)\log \left( 1-{{h}_{\theta }}( {{x}^{(i)}}) \right)\right]}+\frac{\lambda }{2m}\sum\limits_{j=1}^{n}{\theta _{j}^{2}}$

h_theta = sigmoid(X*theta);
left = -(y'*log(h_theta));
right = (1-y)'*log(1-h_theta);
theta_line_num = length(theta);
reg = sum(theta(2:theta_line_num,:) .^ 2);
J = 1/m*(left-right)+lambda/(2*m)*reg;

第1行: 先计算 $h_\theta$ , 注意这儿的sigmoid函数己经写好了,在文件sigmoid.m中
第2行: 计算左半部分left= $\sum\limits_{i=1}^{m} -{{y}^{(i)}}\log \left( {{h}_{\theta }}( {{x}^{(i)}}) \right)$
第3行: 计算右半部分right= $\sum\limits_{i=1}^{m}\left( 1-{{y}^{(i)}} \right)\log \left( 1-{{h}_{\theta }}( {{x}^{(i)}}) \right)$
第4行: 计算列向量theta的长度: 因为 $\theta$ 是(28x1)的列向量, $\theta_0$ 不作为penalty项,所以要剔除
第5行: 从列向量theta的第2行到最后一行取平方,再求和,得到正则化项
第6行: 将上述的left right reg,代入到公式,即可求得 $J(\theta)$

1.2.2 Gradient for regularized LR

下面计算grad:

h_theta = sigmoid(X*theta);  --> 这个上面己经有了
left_grad = X' * (h_theta-y);
grad = 1/m*left_grad + lambda/m*theta;
grad(1) = 1/m*(X(:,1))'*(h_theta-y);

$\theta$ 的更新公式：
$\frac{\partial J\left( \theta \right)}{\partial {{\theta }_{j}}}= \left( \frac{1}{m}\sum\limits_{i=1}^{m}{({{h}_{\theta }}( {{x}^{(i)}} )-{{y}^{(i)}})x_{_{j}}^{(i)}} \right)+ \frac{\lambda}{m}\theta_j \quad for\ j \ge 1$
$\frac{\partial J\left( \theta \right)}{\partial {{\theta }_{j}}}= \left( \frac{1}{m}\sum\limits_{i=1}^{m}{({{h}_{\theta }}( {{x}^{(i)}} )-{{y}^{(i)}})x_{_{j}}^{(i)}} \right) \quad for \ j= 1$

第1行: 先计算 $h_\theta$ ,这个上面己经有了
第2,3行: 先计算左括号的部分,然后加上正则化项
第4行: 再单独计算 $\theta_0$ ,也就是代码里面的grad(1)

1.3 One-vs-all classifier training 0 / 20

实现 oneVsAll函数, 在oneVsAll.m中

initial_theta = zeros(n + 1, 1);
options = optimset('GradObj', 'on', 'MaxIter', 50);
for c=1:num_labels,
  theta = fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), initial_theta, options);
  all_theta(c,:) = theta';
end;

其中fmincg函数的实现是在 fmincg.m 中, 上面函数直接抄的提示中的实现.

1.4 One-vs-all classifier prediction 0 / 20

在 predictOneVsAll.m中实现

all_preds = all_theta * X';
[max_vals, max_ndxs] = max(all_preds);
p = max_ndxs';

2. ex3_nn.m

2.1 ex3_nn.m框架分析

%% Setup the parameters you will use for this exercise


#第1步加载数据并显示
load('ex3data1.mat');
m = size(X, 1);
sel = randperm(size(X, 1));
sel = sel(1:100);
displayData(X(sel, :));

input_layer_size  = 400;  % 20x20 Input Images of Digits
hidden_layer_size = 25;   % 25 hidden units
num_labels = 10;          % 10 labels, from 1 to 10   
                          % (note that we have mapped "0" to label 10)
# 第2步加载参数
load('ex3weights.mat');

# 第3步进行预测
pred = predict(Theta1, Theta2, X);

#第4步显示预测结果
rp = randperm(m);
for i = 1:m
    displayData(X(rp(i), :));
    pred = predict(Theta1, Theta2, X(rp(i),:));
    # 按q退出
    s = input('Paused - press enter to continue, q to exit:','s');
    if s == 'q'
      break
    end
end