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Choosing variables for neural network used for image recognition

user4298 Published in May 26, 2018, 11:40 pm

I have a training set of 89 images of 6 different domino tiles plus one "control" group of a baby - all divided over 7 groups. The output y is thus 7. Each image is 100x100 and is black and white, resulting in an X of 100.000.

I am using the 1 hidden layer neural network-code from Andrew Ng's coursera course using Octave. It has been slightly modified.

I first tried this with 3 different groups (two domino tiles, one baby) and it managed to get a near 100% accuracy. I have now increased it to 7 different image groups. The accuracy has dropped WAY down and it is hardly getting anything right but the baby photos (which differ highly from the domino tiles).

I have tried 10 different lambda values, 10 different neuron numbers between 5-20 as well as trying different amount of iterations and plotted it against cost and accuracy in order to find the best fit.

I also tried feature normalization (commented out in the code below) but it didn't help.

This is the code I am using:

% Initialization
clear ; close all; clc; more off;
pkg load image;

fprintf('Running Domino Identifier ... \n');

%iteration_vector = [100, 300, 1000, 3000, 10000, 30000];
%accuracies = [];
%costs = [];

%for iterations_i = 1:length(iteration_vector)

  input_layer_size  = 10000;  % 100x100 Input Images of Digits
  hidden_layer_size = 50;   % Hidden units
  num_labels = 7;          % Number of different outputs
  iterations = 100000; % Number of iterations during training
  lambda = 0.13;
  %hidden_layer_size = hidden_layers(hidden_layers_i);
  %lambda = lambdas(lambda_i)
  %iterations = %iteration_vector(iterations_i)

  [X,y] = loadTrainingData(num_labels);
  %[X_norm, mu, sigma] = featureNormalize(X_unnormed);
  %X = X_norm;

  initial_Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size);
  initial_Theta2 = randInitializeWeights(hidden_layer_size, num_labels);
  initial_nn_params = [initial_Theta1(:) ; initial_Theta2(:)];

  [J grad] = nnCostFunction(initial_nn_params, input_layer_size, hidden_layer_size, num_labels, X, y, lambda);

  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', iterations);

  % 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));

  displayData(Theta1(:, 2:end));
  [predictionData, images] = loadTrainingData(num_labels);
  [h2_training, pred_training] = predict(Theta1, Theta2, predictionData);
  fprintf('\nTraining Accuracy: %f\n', mean(double(pred_training' == y)) * 100);

  %if length(accuracies) > 0
  %  accuracies = [accuracies; mean(double(pred_training' == y))];
  % accuracies = [mean(double(pred_training' == y))];

  %last_cost = cost(length(cost));
  %if length(costs) > 0
  %  costs = [costs; last_cost];
  % costs = [last_cost];
%endfor % Testing samples

fprintf('Loading prediction images');
[predictionData, images] = loadPredictionData();
[h2, pred] = predict(Theta1, Theta2, predictionData)

for i = 1:length(pred)  
  displayData(predictionData(i, :));
  title (strcat(translateIndexToTile(pred(i)), " Certainty:", num2str(max(h2(i, :))*100))); 
%y = provideAnswers(im_vector);

My questions are now:

  1. Are my numbers "off" in terms of a great difference between X and the rest?

  2. What should I do to improve this Neural Network?

  3. If I do feature normalization, do I need to multiply the numbers back to the 0-255 range again somewhere?

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