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# Choosing variables for neural network used for image recognition

user4298
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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) # INPUTS 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))]; %else % accuracies = [mean(double(pred_training' == y))]; %end %last_cost = cost(length(cost)); %if length(costs) > 0 % costs = [costs; last_cost]; %else % costs = [last_cost]; %end %endfor % Testing samples fprintf('Loading prediction images'); [predictionData, images] = loadPredictionData(); [h2, pred] = predict(Theta1, Theta2, predictionData) for i = 1:length(pred) figure; displayData(predictionData(i, :)); title (strcat(translateIndexToTile(pred(i)), " Certainty:", num2str(max(h2(i, :))*100))); pause; endfor %y = provideAnswers(im_vector);  My questions are now: Are my numbers "off" in terms of a great difference between X and the rest? What should I do to improve this Neural Network? If I do feature normalization, do I need to multiply the numbers back to the 0-255 range again somewhere?
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