%% Machine Learning Online Class - Exercise  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  = ;  % 20x20 Input Images of Digits

hidden_layer_size = ;   %  hidden units

num_labels = ;          %  labels, from  to

                          % (note that we have mapped "" to label )

%% =========== Part : 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, );

% Randomly select  data points to display

sel = randperm(size(X, ));

sel = sel(:);

displayData(X(sel, :));

fprintf('Program paused. Press enter to continue.\n');

pause;

%% ================ Part : 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(25x401) and Theta2(10x26)

load('ex4weights.mat');

% Unroll parameters

nn_params = [Theta1(:) ; Theta2(:)];

%% ================ Part : 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 , you

%  will get to implement the regularized cost.

%

fprintf('\nFeedforward Using Neural Network ...\n')

% Weight regularization parameter (we set this to  here).

lambda = ;

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 : 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  here).

lambda = ;

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 : 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([- -0.5  0.5 ]);

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 : 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 : 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 : 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 = ;

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 = %f): %f ' ...

         '\n(for lambda = 3, this value should be about 0.576051)\n\n'], lambda, debug_J);

fprintf('Program paused. Press enter to continue.\n');

pause;

%% =================== Part : 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', );

%  You should also try different values of lambda

lambda = ;

% 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(:hidden_layer_size * (input_layer_size + )), ...

                 hidden_layer_size, (input_layer_size + ));

Theta2 = reshape(nn_params(( + (hidden_layer_size * (input_layer_size + ))):end), ...

                 num_labels, (hidden_layer_size + ));

fprintf('Program paused. Press enter to continue.\n');

pause;

%% ================= Part : 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(:, :end));

fprintf('\nProgram paused. Press enter to continue.\n');

pause;

%% ================= Part : 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)) * );