| /* |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you under the Apache License, Version 2.0 (the |
| * "License"); you may not use this file except in compliance |
| * with the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, |
| * software distributed under the License is distributed on an |
| * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| * KIND, either express or implied. See the License for the |
| * specific language governing permissions and limitations |
| * under the License. |
| */ |
| package opennlp.tools.dl; |
| |
| import java.io.BufferedWriter; |
| import java.io.File; |
| import java.io.FileWriter; |
| import java.io.IOException; |
| import java.util.Collections; |
| import java.util.Date; |
| import java.util.HashMap; |
| import java.util.HashSet; |
| import java.util.LinkedList; |
| import java.util.List; |
| import java.util.Map; |
| import java.util.Set; |
| |
| import org.apache.commons.math3.distribution.EnumeratedDistribution; |
| import org.apache.commons.math3.util.Pair; |
| import org.nd4j.linalg.api.iter.NdIndexIterator; |
| import org.nd4j.linalg.api.ndarray.INDArray; |
| import org.nd4j.linalg.api.ops.impl.transforms.SetRange; |
| import org.nd4j.linalg.api.ops.impl.transforms.SoftMax; |
| import org.nd4j.linalg.factory.Nd4j; |
| import org.nd4j.linalg.ops.transforms.Transforms; |
| |
| /** |
| * A min char/word-level vanilla RNN model, based on Andrej Karpathy's python code. |
| * See also: |
| * |
| * @see <a href="http://karpathy.github.io/2015/05/21/rnn-effectiveness">The Unreasonable Effectiveness of Recurrent Neural Networks</a> |
| * @see <a href="https://gist.github.com/karpathy/d4dee566867f8291f086">Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy</a> |
| */ |
| public class RNN { |
| |
| // hyperparameters |
| protected final float learningRate; // size of hidden layer of neurons |
| protected final int seqLength; // no. of steps to unroll the RNN for |
| protected final int hiddenLayerSize; |
| protected final int epochs; |
| protected final boolean useChars; |
| protected final int batch; |
| protected final int vocabSize; |
| protected final Map<String, Integer> charToIx; |
| protected final Map<Integer, String> ixToChar; |
| protected final List<String> data; |
| private final static double reg = 1e-8; |
| |
| // model parameters |
| private final INDArray wxh; // input to hidden |
| private final INDArray whh; // hidden to hidden |
| private final INDArray why; // hidden to output |
| private final INDArray bh; // hidden bias |
| private final INDArray by; // output bias |
| |
| private INDArray hPrev = null; // memory state |
| |
| public RNN(float learningRate, int seqLength, int hiddenLayerSize, int epochs, String text) { |
| this(learningRate, seqLength, hiddenLayerSize, epochs, text, 1, true); |
| } |
| |
| public RNN(float learningRate, int seqLength, int hiddenLayerSize, int epochs, String text, int batch, boolean useChars) { |
| this.learningRate = learningRate; |
| this.seqLength = seqLength; |
| this.hiddenLayerSize = hiddenLayerSize; |
| this.epochs = epochs; |
| this.batch = batch; |
| this.useChars = useChars; |
| |
| String[] textTokens = useChars ? toStrings(text.toCharArray()) : text.split(" "); |
| data = new LinkedList<>(); |
| Collections.addAll(data, textTokens); |
| Set<String> tokens = new HashSet<>(data); |
| vocabSize = tokens.size(); |
| |
| System.out.printf("data has %d tokens, %d unique.\n", data.size(), vocabSize); |
| charToIx = new HashMap<>(); |
| ixToChar = new HashMap<>(); |
| int i = 0; |
| for (String c : tokens) { |
| charToIx.put(c, i); |
| ixToChar.put(i, c); |
| i++; |
| } |
| |
| wxh = Nd4j.randn(hiddenLayerSize, vocabSize).mul(0.01); |
| whh = Nd4j.randn(hiddenLayerSize, hiddenLayerSize).mul(0.01); |
| why = Nd4j.randn(vocabSize, hiddenLayerSize).mul(0.01); |
| bh = Nd4j.zeros(hiddenLayerSize, 1); |
| by = Nd4j.zeros(vocabSize, 1); |
| } |
| |
| private String[] toStrings(char[] chars) { |
| String[] strings = new String[chars.length]; |
| for (int i = 0; i < chars.length; i++) { |
| strings[i] = String.valueOf(chars[i]); |
| } |
| return strings; |
| } |
| |
| public void learn() { |
| |
| int currentEpoch = 0; |
| |
| int n = 0; |
| int p = 0; |
| |
| // memory variables for Adagrad |
| INDArray mWxh = Nd4j.zerosLike(wxh); |
| INDArray mWhh = Nd4j.zerosLike(whh); |
| INDArray mWhy = Nd4j.zerosLike(why); |
| |
| INDArray mbh = Nd4j.zerosLike(bh); |
| INDArray mby = Nd4j.zerosLike(by); |
| |
| // loss at iteration 0 |
| double smoothLoss = -Math.log(1.0 / vocabSize) * seqLength; |
| |
| while (true) { |
| // prepare inputs (we're sweeping from left to right in steps seqLength long) |
| if (p + seqLength + 1 >= data.size() || n == 0) { |
| hPrev = Nd4j.zeros(hiddenLayerSize, 1); // reset RNN memory |
| p = 0; // go from start of data |
| currentEpoch++; |
| if (currentEpoch == epochs) { |
| System.out.println("training finished: e:" + epochs + ", l: " + smoothLoss + ", h:(" + learningRate + ", " + seqLength + ", " + hiddenLayerSize + ")"); |
| break; |
| } |
| } |
| |
| INDArray inputs = getSequence(p); |
| INDArray targets = getSequence(p + 1); |
| |
| // sample from the model every now and then |
| if (n % 1000 == 0 && n > 0) { |
| String txt = sample(inputs.getInt(0)); |
| System.out.printf("\n---\n %s \n----\n", txt); |
| } |
| |
| INDArray dWxh = Nd4j.zerosLike(wxh); |
| INDArray dWhh = Nd4j.zerosLike(whh); |
| INDArray dWhy = Nd4j.zerosLike(why); |
| |
| INDArray dbh = Nd4j.zerosLike(bh); |
| INDArray dby = Nd4j.zerosLike(by); |
| |
| // forward seqLength characters through the net and fetch gradient |
| double loss = lossFun(inputs, targets, dWxh, dWhh, dWhy, dbh, dby); |
| smoothLoss = smoothLoss * 0.999 + loss * 0.001; |
| if (Double.isNaN(smoothLoss)) { |
| System.out.println("loss is NaN (over/underflow occured, try adjusting hyperparameters)"); |
| break; |
| } |
| if (n % 100 == 0) { |
| System.out.printf("iter %d, loss: %f\n", n, smoothLoss); // print progress |
| } |
| |
| if (n% batch == 0) { |
| |
| // perform parameter update with Adagrad |
| mWxh.addi(dWxh.mul(dWxh)); |
| wxh.subi((dWxh.mul(learningRate)).div(Transforms.sqrt(mWxh).add(reg))); |
| |
| mWhh.addi(dWhh.mul(dWhh)); |
| whh.subi(dWhh.mul(learningRate).div(Transforms.sqrt(mWhh).add(reg))); |
| |
| mWhy.addi(dWhy.mul(dWhy)); |
| why.subi(dWhy.mul(learningRate).div(Transforms.sqrt(mWhy).add(reg))); |
| |
| mbh.addi(dbh.mul(dbh)); |
| bh.subi(dbh.mul(learningRate).div(Transforms.sqrt(mbh).add(reg))); |
| |
| mby.addi(dby.mul(dby)); |
| by.subi(dby.mul(learningRate).div(Transforms.sqrt(mby).add(reg))); |
| } |
| |
| p += seqLength; // move data pointer |
| n++; // iteration counter |
| } |
| } |
| |
| protected INDArray getSequence(int p) { |
| INDArray inputs = Nd4j.create(seqLength); |
| int c = 0; |
| for (String ch : data.subList(p, p + seqLength)) { |
| Integer ix = charToIx.get(ch); |
| inputs.putScalar(c, ix); |
| c++; |
| } |
| return inputs; |
| } |
| |
| /** |
| * inputs, targets are both list of integers |
| * hprev is Hx1 array of initial hidden state |
| * returns the modified loss, gradients on model parameters |
| */ |
| private double lossFun(INDArray inputs, INDArray targets, INDArray dWxh, INDArray dWhh, INDArray dWhy, INDArray dbh, |
| INDArray dby) { |
| |
| INDArray xs = Nd4j.zeros(inputs.length(), vocabSize); |
| INDArray hs = null; |
| INDArray ys = null; |
| INDArray ps = null; |
| |
| INDArray hs1 = Nd4j.create(hPrev.shape()); |
| Nd4j.copy(hPrev, hs1); |
| |
| double loss = 0; |
| |
| // forward pass |
| for (int t = 0; t < inputs.length(); t++) { |
| int tIndex = inputs.getScalar(t).getInt(0); |
| xs.putScalar(t, tIndex, 1); // encode in 1-of-k representation |
| INDArray hsRow = t == 0 ? hs1 : hs.getRow(t - 1); |
| INDArray hst = Transforms.tanh(wxh.mmul(xs.getRow(t).transpose()).add(whh.mmul(hsRow)).add(bh)); // hidden state |
| if (hs == null) { |
| hs = init(inputs.length(), hst.shape()); |
| } |
| hs.putRow(t, hst); |
| |
| INDArray yst = (why.mmul(hst)).add(by); // unnormalized log probabilities for next chars |
| if (ys == null) { |
| ys = init(inputs.length(), yst.shape()); |
| } |
| ys.putRow(t, yst); |
| INDArray pst = Nd4j.getExecutioner().execAndReturn(new SoftMax(yst)); // probabilities for next chars |
| if (ps == null) { |
| ps = init(inputs.length(), pst.shape()); |
| } |
| ps.putRow(t, pst); |
| loss += -Math.log(pst.getDouble(targets.getInt(t))); // softmax (cross-entropy loss) |
| } |
| |
| // backward pass: compute gradients going backwards |
| INDArray dhNext = Nd4j.zerosLike(hPrev); |
| for (int t = inputs.length() - 1; t >= 0; t--) { |
| INDArray dy = ps.getRow(t); |
| dy.putRow(targets.getInt(t), dy.getRow(targets.getInt(t)).sub(1)); // backprop into y |
| INDArray hst = hs.getRow(t); |
| dWhy.addi(dy.mmul(hst.transpose())); // derivative of hy layer |
| dby.addi(dy); |
| INDArray dh = why.transpose().mmul(dy).add(dhNext); // backprop into h |
| INDArray dhraw = (Nd4j.ones(hst.shape()).sub(hst.mul(hst))).mul(dh); // backprop through tanh nonlinearity |
| dbh.addi(dhraw); |
| dWxh.addi(dhraw.mmul(xs.getRow(t))); |
| INDArray hsRow = t == 0 ? hs1 : hs.getRow(t - 1); |
| dWhh.addi(dhraw.mmul(hsRow.transpose())); |
| dhNext = whh.transpose().mmul(dhraw); |
| } |
| |
| this.hPrev = hs.getRow(inputs.length() - 1); |
| |
| return loss; |
| } |
| |
| protected INDArray init(int t, int[] aShape) { |
| INDArray as; |
| int[] shape = new int[1 + aShape.length]; |
| shape[0] = t; |
| System.arraycopy(aShape, 0, shape, 1, aShape.length); |
| as = Nd4j.create(shape); |
| return as; |
| } |
| |
| /** |
| * sample a sequence of integers from the model, using current (hPrev) memory state, seedIx is seed letter for first time step |
| */ |
| public String sample(int seedIx) { |
| |
| INDArray x = Nd4j.zeros(vocabSize, 1); |
| x.putScalar(seedIx, 1); |
| int sampleSize = 2 * seqLength; |
| INDArray ixes = Nd4j.create(sampleSize); |
| |
| INDArray h = hPrev.dup(); |
| |
| for (int t = 0; t < sampleSize; t++) { |
| h = Transforms.tanh(wxh.mmul(x).add(whh.mmul(h)).add(bh)); |
| INDArray y = (why.mmul(h)).add(by); |
| INDArray pm = Nd4j.getExecutioner().execAndReturn(new SoftMax(y)).ravel(); |
| |
| List<Pair<Integer, Double>> d = new LinkedList<>(); |
| for (int pi = 0; pi < vocabSize; pi++) { |
| d.add(new Pair<>(pi, pm.getDouble(0, pi))); |
| } |
| EnumeratedDistribution<Integer> distribution = new EnumeratedDistribution<>(d); |
| |
| int ix = distribution.sample(); |
| |
| x = Nd4j.zeros(vocabSize, 1); |
| x.putScalar(ix, 1); |
| ixes.putScalar(t, ix); |
| } |
| |
| return getSampleString(ixes); |
| } |
| |
| protected String getSampleString(INDArray ixes) { |
| StringBuilder txt = new StringBuilder(); |
| |
| NdIndexIterator ndIndexIterator = new NdIndexIterator(ixes.shape()); |
| while (ndIndexIterator.hasNext()) { |
| int[] next = ndIndexIterator.next(); |
| if (!useChars && txt.length() > 0) { |
| txt.append(' '); |
| } |
| txt.append(ixToChar.get(ixes.getInt(next))); |
| } |
| return txt.toString(); |
| } |
| |
| public int getVocabSize() { |
| return vocabSize; |
| } |
| |
| @Override |
| public String toString() { |
| return getClass().getName() + "{" + |
| "learningRate=" + learningRate + |
| ", seqLength=" + seqLength + |
| ", hiddenLayerSize=" + hiddenLayerSize + |
| ", epochs=" + epochs + |
| ", vocabSize=" + vocabSize + |
| ", useChars=" + useChars + |
| ", batch=" + batch + |
| '}'; |
| } |
| |
| public void serialize(String prefix) throws IOException { |
| BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(new File(prefix + new Date().toString() + ".txt"))); |
| bufferedWriter.write("wxh"); |
| bufferedWriter.write(wxh.toString()); |
| bufferedWriter.write("whh"); |
| bufferedWriter.write(whh.toString()); |
| bufferedWriter.write("why"); |
| bufferedWriter.write(why.toString()); |
| bufferedWriter.write("bh"); |
| bufferedWriter.write(bh.toString()); |
| bufferedWriter.write("by"); |
| bufferedWriter.write(by.toString()); |
| bufferedWriter.flush(); |
| bufferedWriter.close(); |
| } |
| } |