%% Machine Learning Online Class - Exercise 4 Neural Network Learning % Instructions % ------------ % % This file contains code that helps you get started on the % linear exercise. You will need to complete the following functions % in this exericse: % % sigmoidGradient.m % randInitializeWeights.m % nnCostFunction.m % % For this exercise, you will not need to change any code in this file, % or any other files other than those mentioned above. % %% Initialization clear ; close all; clc
%% Setup the parameters you will use for this exercise 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) %% =========== Part 1: Loading and Visualizing Data ============= % We start the exercise by first loading and visualizing the dataset. % You will be working with a dataset that contains handwritten digits. % % Load Training Data fprintf(‘Loading and Visualizing Data ...\n‘) load(‘ex4data1.mat‘); m = size(X, 1); % Randomly select 100 data points to display sel = randperm(size(X, 1)); sel = sel(1:100);
sel(1:100); 1 2 3 4 5 ...100
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a = X(sel, :);
X(sel, :); 1....... 2....... 3....... 4....... 5....... . . . 100......
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displayData(X(sel, :)); fprintf(‘Program paused. Press enter to continue.\n‘); pause; %% ================ Part 2: Loading Parameters ================ % In this part of the exercise, we load some pre-initialized % neural network parameters. fprintf(‘\nLoading Saved Neural Network Parameters ...\n‘) % Load the weights into variables Theta1 and Theta2 load(‘ex4weights.mat‘); % Unroll parameters nn_params = [Theta1(:) ; Theta2(:)];
https://www.cnblogs.com/liu-wang/p/9466123.html
解释
%% ================ Part 3: Compute Cost (Feedforward) ================ % To the neural network, you should first start by implementing the % feedforward part of the neural network that returns the cost only. You % should complete the code in nnCostFunction.m to return cost. After % implementing the feedforward to compute the cost, you can verify that % your implementation is correct by verifying that you get the same cost % as us for the fixed debugging parameters. % % We suggest implementing the feedforward cost *without* regularization % first so that it will be easier for you to debug. Later, in part 4, you % will get to implement the regularized cost. % fprintf(‘\nFeedforward Using Neural Network ...\n‘) % Weight regularization parameter (we set this to 0 here). lambda = 0; J = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, ... num_labels, X, y, lambda); fprintf([‘Cost at parameters (loaded from ex4weights): %f ‘... ‘\n(this value should be about 0.287629)\n‘], J); fprintf(‘\nProgram paused. Press enter to continue.\n‘); pause; %% =============== Part 4: Implement Regularization =============== % Once your cost function implementation is correct, you should now % continue to implement the regularization with the cost. % fprintf(‘\nChecking Cost Function (w/ Regularization) ... \n‘) % Weight regularization parameter (we set this to 1 here). lambda = 1; J = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, ... num_labels, X, y, lambda); fprintf([‘Cost at parameters (loaded from ex4weights): %f ‘... ‘\n(this value should be about 0.383770)\n‘], J); fprintf(‘Program paused. Press enter to continue.\n‘); pause; %% ================ Part 5: Sigmoid Gradient ================ % Before you start implementing the neural network, you will first % implement the gradient for the sigmoid function. You should complete the % code in the sigmoidGradient.m file. % fprintf(‘\nEvaluating sigmoid gradient...\n‘) g = sigmoidGradient([1 -0.5 0 0.5 1]); fprintf(‘Sigmoid gradient evaluated at [1 -0.5 0 0.5 1]:\n ‘); fprintf(‘%f ‘, g); fprintf(‘\n\n‘); fprintf(‘Program paused. Press enter to continue.\n‘); pause; %% ================ Part 6: Initializing Pameters ================ % In this part of the exercise, you will be starting to implment a two % layer neural network that classifies digits. You will start by % implementing a function to initialize the weights of the neural network % (randInitializeWeights.m) fprintf(‘\nInitializing Neural Network Parameters ...\n‘) initial_Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size); initial_Theta2 = randInitializeWeights(hidden_layer_size, num_labels); % Unroll parameters initial_nn_params = [initial_Theta1(:) ; initial_Theta2(:)]; %% =============== Part 7: Implement Backpropagation =============== % Once your cost matches up with ours, you should proceed to implement the % backpropagation algorithm for the neural network. You should add to the % code you‘ve written in nnCostFunction.m to return the partial % derivatives of the parameters. % fprintf(‘\nChecking Backpropagation... \n‘); % Check gradients by running checkNNGradients checkNNGradients; fprintf(‘\nProgram paused. Press enter to continue.\n‘); pause; %% =============== Part 8: Implement Regularization =============== % Once your backpropagation implementation is correct, you should now % continue to implement the regularization with the cost and gradient. % fprintf(‘\nChecking Backpropagation (w/ Regularization) ... \n‘) % Check gradients by running checkNNGradients lambda = 3; checkNNGradients(lambda); % Also output the costFunction debugging values debug_J = nnCostFunction(nn_params, input_layer_size, ... hidden_layer_size, num_labels, X, y, lambda); fprintf([‘\n\nCost at (fixed) debugging parameters (w/ lambda = 10): %f ‘ ... ‘\n(this value should be about 0.576051)\n\n‘], debug_J); fprintf(‘Program paused. Press enter to continue.\n‘); pause; %% =================== Part 8: Training NN =================== % You have now implemented all the code necessary to train a neural % network. To train your neural network, we will now use "fmincg", which % is a function which works similarly to "fminunc". Recall that these % advanced optimizers are able to train our cost functions efficiently as % long as we provide them with the gradient computations. % fprintf(‘\nTraining Neural Network... \n‘) % After you have completed the assignment, change the MaxIter to a larger % value to see how more training helps. options = optimset(‘MaxIter‘, 50); % You should also try different values of lambda lambda = 1; % Create "short hand" for the cost function to be minimized costFunction = @(p) nnCostFunction(p, ... input_layer_size, ... hidden_layer_size, ... num_labels, X, y, lambda); % Now, costFunction is a function that takes in only one argument (the % neural network parameters) [nn_params, cost] = fmincg(costFunction, initial_nn_params, options); % Obtain Theta1 and Theta2 back from nn_params Theta1 = reshape(nn_params(1:hidden_layer_size * (input_layer_size + 1)), ... hidden_layer_size, (input_layer_size + 1)); Theta2 = reshape(nn_params((1 + (hidden_layer_size * (input_layer_size + 1))):end), ... num_labels, (hidden_layer_size + 1)); fprintf(‘Program paused. Press enter to continue.\n‘); pause; %% ================= Part 9: Visualize Weights ================= % You can now "visualize" what the neural network is learning by % displaying the hidden units to see what features they are capturing in % the data. fprintf(‘\nVisualizing Neural Network... \n‘) displayData(Theta1(:, 2:end)); fprintf(‘\nProgram paused. Press enter to continue.\n‘); pause; %% ================= Part 10: Implement Predict ================= % After training the neural network, we would like to use it to predict % the labels. You will now implement the "predict" function to use the % neural network to predict the labels of the training set. This lets % you compute the training set accuracy. pred = predict(Theta1, Theta2, X); fprintf(‘\nTraining Set Accuracy: %f\n‘, mean(double(pred == y)) * 100);
原文地址：https://www.cnblogs.com/liu-wang/p/9466132.html
时间： 2024-11-04 08:46:46