多分類例題 - 手寫數字識別
阿新 • • 發佈:2020-09-18
多分類例題 - 手寫數字識別
提供的資料集包括5000張手寫數字0~9圖片及對應的正確數字值。其中,每一張圖片已被預處理成20 * 20 畫素的灰白圖片,並轉化成灰度存入到矩陣中。要求利用OVA演算法進行手寫數字識別。
繪製訓練集
本題的圖片繪製涉及灰度的一些內容,我並不瞭解。這裡使用coursera的程式碼進行繪製(因此也沒必要展示了),簡單觀察一下各個圖片。
設計思路
- 由於輸出有0~9十種狀態,因此需要擬合10個目標函式,分別估計給定數字為0~9的可能性。最終取可能性最大的數字作為最終預測答案。
- 由於每一個數據都是20*20畫素的灰白圖片,因此可以將其轉化1*400的向量。即每一個輸入值有400個屬性。
- 由於matlab下標從0開始,因此在for迴圈中求0的目標函式不太方便。因此coursera提供的資料集中所有的y=0均用y=10代替。(其實沒必要這麼做,繞來繞去更加麻煩。還不如直接mod10)
訓練
首先寫出代價函式的計算函式,方便後期呼叫
function [J, grad] = lrCostFunction(theta, X, y, lambda) %LRCOSTFUNCTION Compute cost and gradient for logistic regression with %regularization % J = LRCOSTFUNCTION(theta, X, y, lambda) computes the cost of using % theta as the parameter for regularized logistic regression and the % gradient of the cost w.r.t. to the parameters. % Initialize some useful values m = length(y); % number of training examples % You need to return the following variables correctly J = 0; grad = zeros(size(theta)); % ====================== YOUR CODE HERE ====================== % Instructions: Compute the cost of a particular choice of theta. % You should set J to the cost. % Compute the partial derivatives and set grad to the partial % derivatives of the cost w.r.t. each parameter in theta % % Hint: The computation of the cost function and gradients can be % efficiently vectorized. For example, consider the computation % % sigmoid(X * theta) % % Each row of the resulting matrix will contain the value of the % prediction for that example. You can make use of this to vectorize % the cost function and gradient computations. % % Hint: When computing the gradient of the regularized cost function, % there're many possible vectorized solutions, but one solution % looks like: % grad = (unregularized gradient for logistic regression) % temp = theta; % temp(1) = 0; % because we don't add anything for j = 0 % grad = grad + YOUR_CODE_HERE (using the temp variable) % J = -1/m * sum(y .* log(sigmoid(X*theta))+(1-y) .* log(1-sigmoid(X*theta))) + lambda / (2*m) * sum(theta(2:end,:) .* theta(2:end,:)); temp = theta; temp(1) = 0; grad = 1/m * X' * (sigmoid(X*theta)-y) + lambda/m * temp; % ============================================================= grad = grad(:); end
然後在開始利用OVA訓練10個目標函式。利用for迴圈分別對1至10(還記得麼,數字0用10表示)求解目標函式。
function [all_theta] = oneVsAll(X, y, num_labels, lambda) %ONEVSALL trains multiple logistic regression classifiers and returns all %the classifiers in a matrix all_theta, where the i-th row of all_theta %corresponds to the classifier for label i % [all_theta] = ONEVSALL(X, y, num_labels, lambda) trains num_labels % logistic regression classifiers and returns each of these classifiers % in a matrix all_theta, where the i-th row of all_theta corresponds % to the classifier for label i % Some useful variables m = size(X, 1); n = size(X, 2); % You need to return the following variables correctly all_theta = zeros(num_labels, n + 1); % Add ones to the X data matrix X = [ones(m, 1) X]; % ====================== YOUR CODE HERE ====================== % Instructions: You should complete the following code to train num_labels % logistic regression classifiers with regularization % parameter lambda. % % Hint: theta(:) will return a column vector. % % Hint: You can use y == c to obtain a vector of 1's and 0's that tell you % whether the ground truth is true/false for this class. % % Note: For this assignment, we recommend using fmincg to optimize the cost % function. It is okay to use a for-loop (for c = 1:num_labels) to % loop over the different classes. % % fmincg works similarly to fminunc, but is more efficient when we % are dealing with large number of parameters. % % Example Code for fmincg: % % % Set Initial theta % initial_theta = zeros(n + 1, 1); % % % Set options for fminunc % options = optimset('GradObj', 'on', 'MaxIter', 50); % % % Run fmincg to obtain the optimal theta % % This function will return theta and the cost % [theta] = ... % fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), ... % initial_theta, options); % for idx=1:10 Init_theta = zeros(size(X,2),1); options = optimset('GradObj','on','MaxIter',50); [theta,~,~]= fmincg(@(t)lrCostFunction(t,X,(y==idx),lambda),Init_theta,options); theta = theta'; all_theta(idx,:) = theta; end % ========================================================================= end
呼叫方法:
fprintf('\nTraining One-vs-All Logistic Regression...\n')
lambda = 0.1;
[all_theta] = oneVsAll(X, y, num_labels, lambda);
fprintf('Program paused. Press enter to continue.\n');
pause;
結果判定
下面這段程式碼來自coursera,它只能判斷經驗誤差,而不能判斷泛化誤差。
pred = predictOneVsAll(all_theta, X);
fprintf('\nTrraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);