Mon Apr 17 16:00:00 GMT 2000

Neural_2H

/** * Title: Neural_2H<p> * Description: Two hidden layer back propagtion neural network model<p> * Copyright: Copyright (c) 2000 Mark Watson. Artisitic Open Source License<p> * @author Mark Watson * @version 1.2 */ import java.util.*; import java.io.*; class Neural_2H implements Serializable { protected int numInputs; protected int numHidden1; protected int numHidden2; protected int numOutputs; protected int numTraining; public float inputs[]; protected float hidden1[]; protected float hidden2[]; public float outputs[]; protected float W1[][]; protected float W2[][]; protected float W3[][]; protected float output_errors[]; protected float hidden1_errors[]; protected float hidden2_errors[]; transient protected Vector inputTraining = new Vector(); transient protected Vector outputTraining = new Vector(); Neural_2H(int num_in, int num_hidden1, int num_hidden2, int num_output) { numInputs = num_in; numHidden1 = num_hidden1; numHidden2 = num_hidden2; numOutputs = num_output; inputs = new float[numInputs]; hidden1 = new float[numHidden1]; hidden2 = new float[numHidden2]; outputs = new float[numOutputs]; W1 = new float[numInputs][numHidden1]; W2 = new float[numHidden1][numHidden2]; W3 = new float[numHidden2][numOutputs]; randomizeWeights(); output_errors = new float[numOutputs]; hidden1_errors = new float[numHidden1]; hidden2_errors = new float[numHidden2]; } public void addTrainingExample(float [] inputs, float [] outputs) { if (inputs.length != numInputs || outputs.length != numOutputs) { System.out.println("addTrainingExample(): array size is wrong"); return; } inputTraining.addElement(inputs); outputTraining.addElement(outputs); } public static Neural_2H Factory(String serialized_file_name) { Neural_2H nn = null; try { InputStream ins = ClassLoader.getSystemResourceAsStream(serialized_file_name); if (ins==null) { System.out.println("CachedExamples(): failed to open 'cache.dat' in JAR file"); System.exit(1); } else { ObjectInputStream p = new ObjectInputStream(ins); nn = (Neural_2H)p.readObject(); nn.inputTraining = new Vector(); nn.outputTraining = new Vector(); ins.close(); } } catch (Exception e) { e.printStackTrace(); return null; } return nn; } public void save(String file_name) { try { FileOutputStream ostream = new FileOutputStream(file_name); ObjectOutputStream p = new ObjectOutputStream(ostream); p.writeObject(this); p.flush(); ostream.close(); } catch (Exception e) { e.printStackTrace(); } } public void randomizeWeights() { // Randomize weights here: for (int ii=0; ii<numInputs; ii++) for (int hh=0; hh<numHidden1; hh++) W1[ii][hh] = 0.1f * (float)Math.random() - 0.05f; for (int ii=0; ii<numHidden1; ii++) for (int hh=0; hh<numHidden2; hh++) W2[ii][hh] = 0.1f * (float)Math.random() - 0.05f; for (int hh=0; hh<numHidden2; hh++) for (int oo=0; oo<numOutputs; oo++) W3[hh][oo] = 0.1f * (float)Math.random() - 0.05f; } public float [] recall(float [] in) { for (int i=0; i<numInputs; i++) inputs[i] = in[i]; forwardPass(); float [] ret = new float[numOutputs]; for (int i=0; i<numOutputs; i++) ret[i] = outputs[i]; return ret; } public void forwardPass() { int i, h, o; for (h=0; h<numHidden1; h++) { hidden1[h] = 0.0f; } for (h=0; h<numHidden2; h++) { hidden2[h] = 0.0f; } for (i=0; i<numInputs; i++) { for (h=0; h<numHidden1; h++) { hidden1[h] += inputs[i] * W1[i][h]; } } for (i=0; i<numHidden1; i++) { for (h=0; h<numHidden2; h++) { hidden2[h] += hidden1[i] * W2[i][h]; } } for (o=0; o<numOutputs; o++) outputs[o] = 0.0f; for (h=0; h<numHidden2; h++) { for (o=0; o<numOutputs; o++) { outputs[o] += sigmoid(hidden2[h]) * W3[h][o]; } } for (o=0; o<numOutputs; o++) outputs[o] = sigmoid(outputs[o]); } public float train() { return train(inputTraining, outputTraining); } private int current_example = 0; public float train(Vector ins, Vector v_outs) { int i, h, o; float error = 0.0f; int num_cases = ins.size(); //for (int example=0; example<num_cases; example++) { // zero out error arrays: for (h=0; h<numHidden1; h++) hidden1_errors[h] = 0.0f; for (h=0; h<numHidden2; h++) hidden2_errors[h] = 0.0f; for (o=0; o<numOutputs; o++) output_errors[o] = 0.0f; // copy the input values: for (i=0; i<numInputs; i++) { inputs[i] = ((float [])ins.elementAt(current_example))[i]; } // copy the output values: float [] outs = (float [])v_outs.elementAt(current_example); // perform a forward pass through the network: forwardPass(); for (o=0; o<numOutputs; o++) { output_errors[o] = (outs[o] - outputs[o]) *sigmoidP(outputs[o]); } for (h=0; h<numHidden2; h++) { hidden2_errors[h] = 0.0f; for (o=0; o<numOutputs; o++) { hidden2_errors[h] += output_errors[o]*W3[h][o]; } } for (h=0; h<numHidden1; h++) { hidden1_errors[h] = 0.0f; for (o=0; o<numHidden2; o++) { hidden1_errors[h] += hidden2_errors[o]*W2[h][o]; } } for (h=0; h<numHidden2; h++) { hidden2_errors[h] = hidden2_errors[h]*sigmoidP(hidden2[h]); } for (h=0; h<numHidden1; h++) { hidden1_errors[h] = hidden1_errors[h]*sigmoidP(hidden1[h]); } // update the hidden2 to output weights: for (o=0; o<numOutputs; o++) { for (h=0; h<numHidden2; h++) { W3[h][o] += 0.5 * output_errors[o] * hidden2[h]; } } // update the hidden1 to hidden2 weights: for (o=0; o<numHidden2; o++) { for (h=0; h<numHidden1; h++) { W2[h][o] += 0.5 * hidden2_errors[o] * hidden1[h]; } } // update the input to hidden1 weights: for (h=0; h<numHidden1; h++) { for (i=0; i<numInputs; i++) { W1[i][h] += 0.5 * hidden1_errors[h] * inputs[i]; } } for (o=0; o<numOutputs; o++) error += Math.abs(output_errors[o]); //} current_example++; if (current_example >= num_cases) current_example = 0; return error; } protected float sigmoid(float x) { return (float)((1.0f/(1.0f+Math.exp((double)(-x))))-0.5f); } protected float sigmoidP(float x) { double z = sigmoid(x) + 0.5f; return (float)(z * (1.0f - z)); } }


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