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    <p>Here is a very simple java implementation with tests in the main method :</p> <pre><code>import java.util.Arrays; import java.util.Random; public class MLP { public static class MLPLayer { float[] output; float[] input; float[] weights; float[] dweights; boolean isSigmoid = true; public MLPLayer(int inputSize, int outputSize, Random r) { output = new float[outputSize]; input = new float[inputSize + 1]; weights = new float[(1 + inputSize) * outputSize]; dweights = new float[weights.length]; initWeights(r); } public void setIsSigmoid(boolean isSigmoid) { this.isSigmoid = isSigmoid; } public void initWeights(Random r) { for (int i = 0; i &lt; weights.length; i++) { weights[i] = (r.nextFloat() - 0.5f) * 4f; } } public float[] run(float[] in) { System.arraycopy(in, 0, input, 0, in.length); input[input.length - 1] = 1; int offs = 0; Arrays.fill(output, 0); for (int i = 0; i &lt; output.length; i++) { for (int j = 0; j &lt; input.length; j++) { output[i] += weights[offs + j] * input[j]; } if (isSigmoid) { output[i] = (float) (1 / (1 + Math.exp(-output[i]))); } offs += input.length; } return Arrays.copyOf(output, output.length); } public float[] train(float[] error, float learningRate, float momentum) { int offs = 0; float[] nextError = new float[input.length]; for (int i = 0; i &lt; output.length; i++) { float d = error[i]; if (isSigmoid) { d *= output[i] * (1 - output[i]); } for (int j = 0; j &lt; input.length; j++) { int idx = offs + j; nextError[j] += weights[idx] * d; float dw = input[j] * d * learningRate; weights[idx] += dweights[idx] * momentum + dw; dweights[idx] = dw; } offs += input.length; } return nextError; } } MLPLayer[] layers; public MLP(int inputSize, int[] layersSize) { layers = new MLPLayer[layersSize.length]; Random r = new Random(1234); for (int i = 0; i &lt; layersSize.length; i++) { int inSize = i == 0 ? inputSize : layersSize[i - 1]; layers[i] = new MLPLayer(inSize, layersSize[i], r); } } public MLPLayer getLayer(int idx) { return layers[idx]; } public float[] run(float[] input) { float[] actIn = input; for (int i = 0; i &lt; layers.length; i++) { actIn = layers[i].run(actIn); } return actIn; } public void train(float[] input, float[] targetOutput, float learningRate, float momentum) { float[] calcOut = run(input); float[] error = new float[calcOut.length]; for (int i = 0; i &lt; error.length; i++) { error[i] = targetOutput[i] - calcOut[i]; // negative error } for (int i = layers.length - 1; i &gt;= 0; i--) { error = layers[i].train(error, learningRate, momentum); } } public static void main(String[] args) throws Exception { float[][] train = new float[][]{new float[]{0, 0}, new float[]{0, 1}, new float[]{1, 0}, new float[]{1, 1}}; float[][] res = new float[][]{new float[]{0}, new float[]{1}, new float[]{1}, new float[]{0}}; MLP mlp = new MLP(2, new int[]{2, 1}); mlp.getLayer(1).setIsSigmoid(false); Random r = new Random(); int en = 500; for (int e = 0; e &lt; en; e++) { for (int i = 0; i &lt; res.length; i++) { int idx = r.nextInt(res.length); mlp.train(train[idx], res[idx], 0.3f, 0.6f); } if ((e + 1) % 100 == 0) { System.out.println(); for (int i = 0; i &lt; res.length; i++) { float[] t = train[i]; System.out.printf("%d epoch\n", e + 1); System.out.printf("%.1f, %.1f --&gt; %.3f\n", t[0], t[1], mlp.run(t)[0]); } } } } } </code></pre>
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