神经网络作业归纳(作业3) - zLulus/My_Note GitHub Wiki

问题：识别0~9的手写数字

多级分类方案

1.加载数据，20*20像素的数字图片与数字结果（0~9），其中0在计算中表示为10
并进行可视化
2.确定方程：cost function和 ∂J/∂theta (=gradient)
1
2
3.使用fmincg函数进行数据训练，得到训练结果all_theta
4.使用all_theta去计算数据，得到预测结果

神经网络方案

1.该作业中是一个已经训练好的神经网络，需要写调用的方法
2.按照之前教的，一层层往下递推
3

function p = predict(Theta1, Theta2, X)
%PREDICT Predict the label of an input given a trained neural network
%   p = PREDICT(Theta1, Theta2, X) outputs the predicted label of X given the
%   trained weights of a neural network (Theta1, Theta2)

% Useful values
m = size(X, 1);
num_labels = size(Theta2, 1);

% You need to return the following variables correctly 
p = zeros(size(X, 1), 1);

% ====================== YOUR CODE HERE ======================
% Instructions: Complete the following code to make predictions using
%               your learned neural network. You should set p to a 
%               vector containing labels between 1 to num_labels.
%
% Hint: The max function might come in useful. In particular, the max
%       function can also return the index of the max element, for more
%       information see 'help max'. If your examples are in rows, then, you
%       can use max(A, [], 2) to obtain the max for each row.
%

% Add ones to the X data matrix  -jin
X = [ones(m, 1) X];

a2 = sigmoid(X * Theta1');   % 第二层激活函数输出
a2 = [ones(m, 1) a2];        % 第二层加入b  第一列加入1
a3 = sigmoid(a2 * Theta2');  
[aa,p] = max(a3,[],2);               % 返回每行最大值的索引位置，也就是预测的数字

% =========================================================================


end