Coursera machine learning week 5 assignment answers – Andrew Ng

function g = sigmoidGradient(z)
  %SIGMOIDGRADIENT returns the gradient of the sigmoid function
  %evaluated at z
  %   g = SIGMOIDGRADIENT(z) computes the gradient of the sigmoid function
  %   evaluated at z. This should work regardless if z is a matrix or a
  %   vector. In particular, if z is a vector or matrix, you should return
  %   the gradient for each element.
  g = zeros(size(z));
  % ====================== YOUR CODE HERE ======================
  % Instructions: Compute the gradient of the sigmoid function evaluated at
  %               each value of z (z can be a matrix, vector or scalar).
  g = sigmoid(z).*(1-sigmoid(z));
  % =============================================================
end

function W = randInitializeWeights(L_in, L_out)
  %RANDINITIALIZEWEIGHTS Randomly initialize the weights of a layer with L_in
  %incoming connections and L_out outgoing connections
  %   W = RANDINITIALIZEWEIGHTS(L_in, L_out) randomly initializes the weights
  %   of a layer with L_in incoming connections and L_out outgoing
  %   connections.
  %
  %   Note that W should be set to a matrix of size(L_out, 1 + L_in) as
  %   the first column of W handles the "bias" terms
  %
  % You need to return the following variables correctly
  W = zeros(L_out, 1 + L_in);
  % ====================== YOUR CODE HERE ======================
  % Instructions: Initialize W randomly so that we break the symmetry while
  %               training the neural network.
  %
  % Note: The first column of W corresponds to the parameters for the bias unit
  %
  % epsilon_init = 0.12;
  epsilon_init = sqrt(6)/(sqrt(L_in)+sqrt(L_out));
  W = - epsilon_init + rand(L_out, 1 + L_in) * 2 * epsilon_init ;
  % =========================================================================
end

function [J, grad] = nnCostFunction(nn_params, ...
      input_layer_size, ...
      hidden_layer_size, ...
      num_labels, ...
      X, y, lambda)
  %NNCOSTFUNCTION Implements the neural network cost function for a two layer
  %neural network which performs classification
  %   [J grad] = NNCOSTFUNCTON(nn_params, hidden_layer_size, num_labels, ...
  %   X, y, lambda) computes the cost and gradient of the neural network. The
  %   parameters for the neural network are "unrolled" into the vector
  %   nn_params and need to be converted back into the weight matrices.
  %
  %   The returned parameter grad should be a "unrolled" vector of the
  %   partial derivatives of the neural network.
  %
  % Reshape nn_params back into the parameters Theta1 and Theta2, the weight matrices
  % for our 2 layer neural network
  % DIMENSIONS:
  % Theta1 = 25 x 401
  % Theta2 = 10 x 26
  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));
  % Setup some useful variables
  m = size(X, 1);
  % You need to return the following variables correctly
  J = 0;
  Theta1_grad = zeros(size(Theta1)); %25 x401
  Theta2_grad = zeros(size(Theta2)); %10 x 26
  % ====================== YOUR CODE HERE ======================
  % Instructions: You should complete the code by working through the
  %               following parts.
  %
  % Part 1: Feedforward the neural network and return the cost in the
  %         variable J. After implementing Part 1, you can verify that your
  %         cost function computation is correct by verifying the cost
  %         computed in ex4.m
  %
  % Part 2: Implement the backpropagation algorithm to compute the gradients
  %         Theta1_grad and Theta2_grad. You should return the partial derivatives of
  %         the cost function with respect to Theta1 and Theta2 in Theta1_grad and
  %         Theta2_grad, respectively. After implementing Part 2, you can check
  %         that your implementation is correct by running checkNNGradients
  %
  %         Note: The vector y passed into the function is a vector of labels
  %               containing values from 1..K. You need to map this vector into a
  %               binary vector of 1's and 0's to be used with the neural network
  %               cost function.
  %
  %         Hint: We recommend implementing backpropagation using a for-loop
  %               over the training examples if you are implementing it for the
  %               first time.
  %
  % Part 3: Implement regularization with the cost function and gradients.
  %
  %         Hint: You can implement this around the code for
  %               backpropagation. That is, you can compute the gradients for
  %               the regularization separately and then add them to Theta1_grad
  %               and Theta2_grad from Part 2.
  %
  %%%%%%%%%%% Part 1: Calculating J w/o Regularization %%%%%%%%%%%%%%%
  X = [ones(m,1), X];  % Adding 1 as first column in X
  a1 = X; % 5000 x 401
  z2 = a1 * Theta1';  % m x hidden_layer_size == 5000 x 25
  a2 = sigmoid(z2); % m x hidden_layer_size == 5000 x 25
  a2 = [ones(size(a2,1),1), a2]; % Adding 1 as first column in z = (Adding bias unit) % m x (hidden_layer_size + 1) == 5000 x 26
  z3 = a2 * Theta2';  % m x num_labels == 5000 x 10
  a3 = sigmoid(z3); % m x num_labels == 5000 x 10
  h_x = a3; % m x num_labels == 5000 x 10
  %Converting y into vector of 0's and 1's for multi-class classification
  %%%%% WORKING %%%%%
  % y_Vec = zeros(m,num_labels);
  % for i = 1:m
  %     y_Vec(i,y(i)) = 1;
  % end
  %%%%%%%%%%%%%%%%%%%
  y_Vec = (1:num_labels)==y; % m x num_labels == 5000 x 10
  %Costfunction Without regularization
  J = (1/m) * sum(sum((-y_Vec.*log(h_x))-((1-y_Vec).*log(1-h_x))));  %scalar
  %%%%%%%%%%% Part 2: Implementing Backpropogation for Theta_gra w/o Regularization %%%%%%%%%%%%%
  %%%%%%% WORKING: Backpropogation using for loop %%%%%%%
  % for t=1:m
  %     % Here X is including 1 column at begining
  %
  %     % for layer-1
  %     a1 = X(t,:)'; % (n+1) x 1 == 401 x 1
  %
  %     % for layer-2
  %     z2 = Theta1 * a1;  % hidden_layer_size x 1 == 25 x 1
  %     a2 = [1; sigmoid(z2)]; % (hidden_layer_size+1) x 1 == 26 x 1
  %
  %     % for layer-3
  %     z3 = Theta2 * a2; % num_labels x 1 == 10 x 1
  %     a3 = sigmoid(z3); % num_labels x 1 == 10 x 1
  %
  %     yVector = (1:num_labels)'==y(t); % num_labels x 1 == 10 x 1
  %
  %     %calculating delta values
  %     delta3 = a3 - yVector; % num_labels x 1 == 10 x 1
  %
  %     delta2 = (Theta2' * delta3) .* [1; sigmoidGradient(z2)]; % (hidden_layer_size+1) x 1 == 26 x 1
  %
  %     delta2 = delta2(2:end); % hidden_layer_size x 1 == 25 x 1 %Removing delta2 for bias node
  %
  %     % delta_1 is not calculated because we do not associate error with the input
  %
  %     % CAPITAL delta update
  %     Theta1_grad = Theta1_grad + (delta2 * a1'); % 25 x 401
  %     Theta2_grad = Theta2_grad + (delta3 * a2'); % 10 x 26
  %
  % end
  %
  % Theta1_grad = (1/m) * Theta1_grad; % 25 x 401
  % Theta2_grad = (1/m) * Theta2_grad; % 10 x 26
  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  %%%%%% WORKING: Backpropogation (Vectorized Implementation) %%%%%%%
  % Here X is including 1 column at begining
  A1 = X; % 5000 x 401
  Z2 = A1 * Theta1';  % m x hidden_layer_size == 5000 x 25
  A2 = sigmoid(Z2); % m x hidden_layer_size == 5000 x 25
  A2 = [ones(size(A2,1),1), A2]; % Adding 1 as first column in z = (Adding bias unit) % m x (hidden_layer_size + 1) == 5000 x 26
  Z3 = A2 * Theta2';  % m x num_labels == 5000 x 10
  A3 = sigmoid(Z3); % m x num_labels == 5000 x 10
  % h_x = a3; % m x num_labels == 5000 x 10
  y_Vec = (1:num_labels)==y; % m x num_labels == 5000 x 10
  DELTA3 = A3 - y_Vec; % 5000 x 10
  DELTA2 = (DELTA3 * Theta2) .* [ones(size(Z2,1),1) sigmoidGradient(Z2)]; % 5000 x 26
  DELTA2 = DELTA2(:,2:end); % 5000 x 25 %Removing delta2 for bias node
  Theta1_grad = (1/m) * (DELTA2' * A1); % 25 x 401
  Theta2_grad = (1/m) * (DELTA3' * A2); % 10 x 26
  %%%%%%%%%%%% WORKING: DIRECT CALCULATION OF THETA GRADIENT WITH REGULARISATION %%%%%%%%%%%
  % %Regularization term is later added in Part 3
  % Theta1_grad = (1/m) * Theta1_grad + (lambda/m) * [zeros(size(Theta1, 1), 1) Theta1(:,2:end)]; % 25 x 401
  % Theta2_grad = (1/m) * Theta2_grad + (lambda/m) * [zeros(size(Theta2, 1), 1) Theta2(:,2:end)]; % 10 x 26
  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  %%%%%%%%%%%% Part 3: Adding Regularisation term in J and Theta_grad %%%%%%%%%%%%%
  reg_term = (lambda/(2*m)) * (sum(sum(Theta1(:,2:end).^2)) + sum(sum(Theta2(:,2:end).^2))); %scalar
  %Costfunction With regularization
  J = J + reg_term; %scalar
  %Calculating gradients for the regularization
  Theta1_grad_reg_term = (lambda/m) * [zeros(size(Theta1, 1), 1) Theta1(:,2:end)]; % 25 x 401
  Theta2_grad_reg_term = (lambda/m) * [zeros(size(Theta2, 1), 1) Theta2(:,2:end)]; % 10 x 26
  %Adding regularization term to earlier calculated Theta_grad
  Theta1_grad = Theta1_grad + Theta1_grad_reg_term;
  Theta2_grad = Theta2_grad + Theta2_grad_reg_term;
  % -------------------------------------------------------------
  % =========================================================================
  % Unroll gradients
  grad = [Theta1_grad(:) ; Theta2_grad(:)];
end