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- input_layer_size = 3;
- hidden_layer_size = 5;
- num_labels = 1;
- X = load('X.csv');
- y = load('y.csv');
- 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(:)];
- options = optimset('MaxIter', 350);
- lambda = 0.5;
- costFunction = @(p) nnCostFunction(p, ...
- input_layer_size, ...
- hidden_layer_size, ...
- num_labels, X, y, lambda);
- [nn_params, cost] = fmincg(costFunction, initial_nn_params, options);
- 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));
- pred = predict(Theta1, Theta2, X);
- fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);
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