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我試圖用反向傳播實現一個非常簡單的神經網絡。我試圖用邏輯運算符AND來訓練網絡。但預測它對我沒有好處。 :(在Swift中反向傳播的簡單神經網絡
public class ActivationFunction {
class func sigmoid(x: Float) -> Float {
return 1.0/(1.0 + exp(-x))
}
class func dSigmoid(x: Float) -> Float {
return x * (1 - x)
}
}
public class NeuralNetConstants {
public static let learningRate: Float = 0.3
public static let momentum: Float = 0.6
public static let iterations: Int = 100000
}
public class Layer {
private var output: [Float]
private var input: [Float]
private var weights: [Float]
private var dWeights: [Float]
init(inputSize: Int, outputSize: Int) {
self.output = [Float](repeating: 0, count: outputSize)
self.input = [Float](repeating: 0, count: inputSize + 1)
self.weights = [Float](repeating: (-2.0...2.0).random(), count: (1 + inputSize) * outputSize)
self.dWeights = [Float](repeating: 0, count: weights.count)
}
public func run(inputArray: [Float]) -> [Float] {
input = inputArray
input[input.count-1] = 1
var offSet = 0
for i in 0..<output.count {
for j in 0..<input.count {
output[i] += weights[offSet+j] * input[j]
}
output[i] = ActivationFunction.sigmoid(x: output[i])
offSet += input.count
}
return output
}
public func train(error: [Float], learningRate: Float, momentum: Float) -> [Float] {
var offset = 0
var nextError = [Float](repeating: 0, count: input.count)
for i in 0..<output.count {
let delta = error[i] * ActivationFunction.dSigmoid(x: output[i])
for j in 0..<input.count {
let weightIndex = offset + j
nextError[j] = nextError[j] + weights[weightIndex] * delta
let dw = input[j] * delta * learningRate
weights[weightIndex] += dWeights[weightIndex] * momentum + dw
dWeights[weightIndex] = dw
}
offset += input.count
}
return nextError
}
}
public class BackpropNeuralNetwork {
private var layers: [Layer] = []
public init(inputSize: Int, hiddenSize: Int, outputSize: Int) {
self.layers.append(Layer(inputSize: inputSize, outputSize: hiddenSize))
self.layers.append(Layer(inputSize: hiddenSize, outputSize: outputSize))
}
public func getLayer(index: Int) -> Layer {
return layers[index]
}
public func run(input: [Float]) -> [Float] {
var activations = input
for i in 0..<layers.count {
activations = layers[i].run(inputArray: activations)
}
return activations
}
public func train(input: [Float], targetOutput: [Float], learningRate: Float, momentum: Float) {
let calculatedOutput = run(input: input)
var error = [Float](repeating: 0, count: calculatedOutput.count)
for i in 0..<error.count {
error[i] = targetOutput[i] - calculatedOutput[i]
}
for i in (0...layers.count-1).reversed() {
error = layers[i].train(error: error, learningRate: learningRate, momentum: momentum)
}
}
}
extension ClosedRange where Bound: FloatingPoint {
public func random() -> Bound {
let range = self.upperBound - self.lowerBound
let randomValue = (Bound(arc4random_uniform(UINT32_MAX))/Bound(UINT32_MAX)) * range + self.lowerBound
return randomValue
}
}
這是我的訓練數據我只是想，我的網絡學習簡單AND邏輯運算符
我的輸入數據：
let traningData: [[Float]] = [ [0,0], [0,1], [1,0], [1,1] ]
let traningResults: [[Float]] = [ [0], [0], [0], [1] ]
let backProb = BackpropNeuralNetwork(inputSize: 2, hiddenSize: 3, outputSize: 1)
for iterations in 0..<NeuralNetConstants.iterations {
for i in 0..<traningResults.count {
backProb.train(input: traningData[i], targetOutput: traningResults[i], learningRate: NeuralNetConstants.learningRate, momentum: NeuralNetConstants.momentum)
}
for i in 0..<traningResults.count {
var t = traningData[i]
print("\(t[0]), \(t[1]) -- \(backProb.run(input: t)[0])")
}
}
這是我整個的代碼爲神經網絡代碼並不真的很靈敏，但我認爲理解神經網絡的理論首先更重要，那麼代碼將更加靈活。問題是我的結果完全錯誤。這是我得到
0.0, 0.0 -- 0.246135
0.0, 1.0 -- 0.251307
1.0, 0.0 -- 0.24325
1.0, 1.0 -- 0.240923
這就是我想要得到
0,0, 0,0 -- 0,000
0,0, 1,0 -- 0,005
1,0, 0,0 -- 0,005
1,0, 1,0 -- 0,992
好比較的Java實現正常工作..
public class ActivationFunction {
public static float sigmoid(float x) {
return (float) (1/(1 + Math.exp(-x)));
}
public static float dSigmoid(float x) {
return x*(1-x); // because the output is the sigmoid(x) !!! we dont have to apply it twice
}
}
public class NeuralNetConstants {
private NeuralNetConstants() {
}
public static final float LEARNING_RATE = 0.3f;
public static final float MOMENTUM = 0.6f;
public static final int ITERATIONS = 100000;
}
public class Layer {
private float[] output;
private float[] input;
private float[] weights;
private float[] dWeights;
private Random random;
public Layer(int inputSize, int outputSize) {
output = new float[outputSize];
input = new float[inputSize + 1];
weights = new float[(1 + inputSize) * outputSize];
dWeights = new float[weights.length];
this.random = new Random();
initWeights();
}
public void initWeights() {
for (int i = 0; i < weights.length; i++) {
weights[i] = (random.nextFloat() - 0.5f) * 4f;
}
}
public float[] run(float[] inputArray) {
System.arraycopy(inputArray, 0, input, 0, inputArray.length);
input[input.length - 1] = 1; // bias
int offset = 0;
for (int i = 0; i < output.length; i++) {
for (int j = 0; j < input.length; j++) {
output[i] += weights[offset + j] * input[j];
}
output[i] = ActivationFunction.sigmoid(output[i]);
offset += input.length;
}
return Arrays.copyOf(output, output.length);
}
public float[] train(float[] error, float learningRate, float momentum) {
int offset = 0;
float[] nextError = new float[input.length];
for (int i = 0; i < output.length; i++) {
float delta = error[i] * ActivationFunction.dSigmoid(output[i]);
for (int j = 0; j < input.length; j++) {
int previousWeightIndex = offset + j;
nextError[j] = nextError[j] + weights[previousWeightIndex] * delta;
float dw = input[j] * delta * learningRate;
weights[previousWeightIndex] += dWeights[previousWeightIndex] * momentum + dw;
dWeights[previousWeightIndex] = dw;
}
offset += input.length;
}
return nextError;
}
}
public class BackpropNeuralNetwork {
private Layer[] layers;
public BackpropNeuralNetwork(int inputSize, int hiddenSize, int outputSize) {
layers = new Layer[2];
layers[0] = new Layer(inputSize, hiddenSize);
layers[1] = new Layer(hiddenSize, outputSize);
}
public Layer getLayer(int index) {
return layers[index];
}
public float[] run(float[] input) {
float[] inputActivation = input;
for (int i = 0; i < layers.length; i++) {
inputActivation = layers[i].run(inputActivation);
}
return inputActivation;
}
public void train(float[] input, float[] targetOutput, float learningRate, float momentum) {
float[] calculatedOutput = run(input);
float[] error = new float[calculatedOutput.length];
for (int i = 0; i < error.length; i++) {
error[i] = targetOutput[i] - calculatedOutput[i];
}
for (int i = layers.length - 1; i >= 0; i--) {
error = layers[i].train(error, learningRate, momentum);
}
}
}
public class NeuralNetwork {
/**
* @param args the command line arguments
*/
public static void main(String[] args) {
float[][] trainingData = new float[][] {
new float[] { 0, 0 },
new float[] { 0, 1 },
new float[] { 1, 0 },
new float[] { 1, 1 }
};
float[][] trainingResults = new float[][] {
new float[] { 0 },
new float[] { 0 },
new float[] { 0 },
new float[] { 1 }
};
BackpropNeuralNetwork backpropagationNeuralNetworks = new BackpropNeuralNetwork(2, 3,1);
for (int iterations = 0; iterations < NeuralNetConstants.ITERATIONS; iterations++) {
for (int i = 0; i < trainingResults.length; i++) {
backpropagationNeuralNetworks.train(trainingData[i], trainingResults[i],
NeuralNetConstants.LEARNING_RATE, NeuralNetConstants.MOMENTUM);
}
System.out.println();
for (int i = 0; i < trainingResults.length; i++) {
float[] t = trainingData[i];
System.out.printf("%d epoch\n", iterations + 1);
System.out.printf("%.1f, %.1f --> %.3f\n", t[0], t[1], backpropagationNeuralNetworks.run(t)[0]);
}
}
}
}
請加你有你所期望的網絡預測，什麼什麼問題，細節。 –
@MatiasValdenegro回覆你的回覆我更新了我的問題 – BilalReffas