/**
* Gibbs sampling step: hidden ---> visible ---> hidden
*
* @param h the hidden input
* @return the expected values and samples of both the visible samples given the hidden and the
* new hidden input and expected values
*/
public Pair<Pair<INDArray, INDArray>, Pair<INDArray, INDArray>> gibbhVh(INDArray h) {
Pair<INDArray, INDArray> v1MeanAndSample = sampleVisibleGivenHidden(h);
INDArray vSample = v1MeanAndSample.getSecond();
Pair<INDArray, INDArray> h1MeanAndSample = sampleHiddenGivenVisible(vSample);
return new Pair<>(v1MeanAndSample, h1MeanAndSample);
}
public static void main(String[] args) throws Exception {
int iterations = 100;
Nd4j.dtype = DataBuffer.Type.DOUBLE;
Nd4j.factory().setDType(DataBuffer.Type.DOUBLE);
List<String> cacheList = new ArrayList<>();
log.info("Load & Vectorize data....");
File wordFile = new ClassPathResource("words.txt").getFile();
Pair<InMemoryLookupTable, VocabCache> vectors = WordVectorSerializer.loadTxt(wordFile);
VocabCache cache = vectors.getSecond();
INDArray weights = vectors.getFirst().getSyn0();
for (int i = 0; i < cache.numWords(); i++) cacheList.add(cache.wordAtIndex(i));
log.info("Build model....");
BarnesHutTsne tsne =
new BarnesHutTsne.Builder()
.setMaxIter(iterations)
.theta(0.5)
.normalize(false)
.learningRate(500)
.useAdaGrad(false)
.usePca(false)
.build();
log.info("Store TSNE Coordinates for Plotting....");
String outputFile = "target/archive-tmp/tsne-standard-coords.csv";
(new File(outputFile)).getParentFile().mkdirs();
tsne.plot(weights, 2, cacheList, outputFile);
}
@Test
public void testSubSampleLayerNoneBackprop() throws Exception {
Layer layer = getCNNConfig(nChannelsIn, depth, kernelSize, stride, padding);
Pair<Gradient, INDArray> out = layer.backpropGradient(epsilon);
assertEquals(epsilon.shape().length, out.getSecond().shape().length);
assertEquals(nExamples, out.getSecond().size(1)); // depth retained
}
/**
* An individual iteration
*
* @param p the probabilities that certain points are near each other
* @param i the iteration (primarily for debugging purposes)
*/
public void step(INDArray p, int i) {
Pair<Double, INDArray> costGradient = gradient(p);
INDArray yIncs = costGradient.getSecond();
log.info("Cost at iteration " + i + " was " + costGradient.getFirst());
y.addi(yIncs);
y.addi(yIncs).subiRowVector(y.mean(0));
INDArray tiled = Nd4j.tile(y.mean(0), new int[] {y.rows(), y.columns()});
y.subi(tiled);
}
@Override
public void computeGradientAndScore() {
if (input == null || labels == null) return;
INDArray output = output(input);
Pair<Gradient, INDArray> pair = getGradientsAndDelta(output);
this.gradient = pair.getFirst();
setScoreWithZ(output);
}
/**
* Convert data to probability co-occurrences (aka calculating the kernel)
*
* @param d the data to convert
* @param u the perplexity of the model
* @return the probabilities of co-occurrence
*/
public INDArray computeGaussianPerplexity(final INDArray d, double u) {
int n = d.rows();
final INDArray p = zeros(n, n);
final INDArray beta = ones(n, 1);
final double logU = Math.log(u);
log.info("Calculating probabilities of data similarities..");
for (int i = 0; i < n; i++) {
if (i % 500 == 0 && i > 0) log.info("Handled " + i + " records");
double betaMin = Double.NEGATIVE_INFINITY;
double betaMax = Double.POSITIVE_INFINITY;
int[] vals = Ints.concat(ArrayUtil.range(0, i), ArrayUtil.range(i + 1, d.columns()));
INDArrayIndex[] range = new INDArrayIndex[] {new NDArrayIndex(vals)};
INDArray row = d.slice(i).get(range);
Pair<INDArray, INDArray> pair = hBeta(row, beta.getDouble(i));
INDArray hDiff = pair.getFirst().sub(logU);
int tries = 0;
// while hdiff > tolerance
while (BooleanIndexing.and(abs(hDiff), Conditions.greaterThan(tolerance)) && tries < 50) {
// if hdiff > 0
if (BooleanIndexing.and(hDiff, Conditions.greaterThan(0))) {
if (Double.isInfinite(betaMax)) beta.putScalar(i, beta.getDouble(i) * 2.0);
else beta.putScalar(i, (beta.getDouble(i) + betaMax) / 2.0);
betaMin = beta.getDouble(i);
} else {
if (Double.isInfinite(betaMin)) beta.putScalar(i, beta.getDouble(i) / 2.0);
else beta.putScalar(i, (beta.getDouble(i) + betaMin) / 2.0);
betaMax = beta.getDouble(i);
}
pair = hBeta(row, beta.getDouble(i));
hDiff = pair.getFirst().subi(logU);
tries++;
}
p.slice(i).put(range, pair.getSecond());
}
// dont need data in memory after
log.info("Mean value of sigma " + sqrt(beta.rdiv(1)).mean(Integer.MAX_VALUE));
BooleanIndexing.applyWhere(p, Conditions.isNan(), new Value(realMin));
// set 0 along the diagonal
INDArray permute = p.transpose();
INDArray pOut = p.add(permute);
pOut.divi(pOut.sum(Integer.MAX_VALUE));
BooleanIndexing.applyWhere(
pOut, Conditions.lessThan(Nd4j.EPS_THRESHOLD), new Value(Nd4j.EPS_THRESHOLD));
// ensure no nans
return pOut;
}
@Test
public void testFeedForwardActivationsAndDerivatives() {
MultiLayerNetwork network = new MultiLayerNetwork(getConf());
network.init();
DataSet data = new IrisDataSetIterator(1, 150).next();
network.fit(data);
Pair result = network.feedForwardActivationsAndDerivatives();
List<INDArray> first = (List) result.getFirst();
List<INDArray> second = (List) result.getSecond();
assertEquals(first.size(), second.size());
}
@Override
public Pair<Gradient, INDArray> backpropGradient(
INDArray epsilon, Gradient nextGradient, Layer layer) {
Pair<Gradient, INDArray> pair =
getGradientsAndDelta(
output(input)); // Returns Gradient and delta^(this), not Gradient and epsilon^(this-1)
INDArray delta = pair.getSecond();
INDArray epsilonNext =
params.get(DefaultParamInitializer.WEIGHT_KEY).mmul(delta.transpose()).transpose();
return new Pair<>(pair.getFirst(), epsilonNext);
}
public static void main(String[] args) throws Exception {
// STEP 1: Initialization
int iterations = 100;
// create an n-dimensional array of doubles
DataTypeUtil.setDTypeForContext(DataBuffer.Type.DOUBLE);
List<String> cacheList =
new ArrayList<>(); // cacheList is a dynamic array of strings used to hold all words
// STEP 2: Turn text input into a list of words
log.info("Load & Vectorize data....");
File wordFile = new ClassPathResource("words.txt").getFile(); // Open the file
// Get the data of all unique word vectors
Pair<InMemoryLookupTable, VocabCache> vectors = WordVectorSerializer.loadTxt(wordFile);
VocabCache cache = vectors.getSecond();
INDArray weights =
vectors.getFirst().getSyn0(); // seperate weights of unique words into their own list
for (int i = 0; i < cache.numWords(); i++) // seperate strings of words into their own list
cacheList.add(cache.wordAtIndex(i));
// STEP 3: build a dual-tree tsne to use later
log.info("Build model....");
BarnesHutTsne tsne =
new BarnesHutTsne.Builder()
.setMaxIter(iterations)
.theta(0.5)
.normalize(false)
.learningRate(500)
.useAdaGrad(false)
// .usePca(false)
.build();
// STEP 4: establish the tsne values and save them to a file
log.info("Store TSNE Coordinates for Plotting....");
String outputFile = "target/archive-tmp/tsne-standard-coords.csv";
(new File(outputFile)).getParentFile().mkdirs();
tsne.plot(weights, 2, cacheList, outputFile);
// This tsne will use the weights of the vectors as its matrix, have two dimensions, use the
// words strings as
// labels, and be written to the outputFile created on the previous line
// !!! Possible error: plot was recently deprecated. Might need to re-do the last line
}
// note precision is off on this test but the numbers are close
// investigation in a future release should determine how to resolve
@Test
public void testBackpropResultsContained() {
Layer layer = getContainedConfig();
INDArray input = getContainedData();
INDArray col = getContainedCol();
INDArray epsilon = Nd4j.ones(1, 2, 4, 4);
INDArray expectedBiasGradient =
Nd4j.create(new double[] {0.16608272, 0.16608272}, new int[] {1, 2});
INDArray expectedWeightGradient =
Nd4j.create(
new double[] {
0.17238397,
0.17238397,
0.33846668,
0.33846668,
0.17238397,
0.17238397,
0.33846668,
0.33846668
},
new int[] {2, 1, 2, 2});
INDArray expectedEpsilon =
Nd4j.create(
new double[] {
0.00039383, 0.00039383, 0.00039383, 0.00039383, 0.00039383,
0.00039383, 0., 0., 0.00039383, 0.00039383,
0.00039383, 0.00039383, 0.00039383, 0.00039383, 0.,
0., 0.02036651, 0.02036651, 0.02036651, 0.02036651,
0.02036651, 0.02036651, 0., 0., 0.02036651,
0.02036651, 0.02036651, 0.02036651, 0.02036651, 0.02036651,
0., 0., 0.00039383, 0.00039383, 0.00039383,
0.00039383, 0.00039383, 0.00039383, 0., 0.,
0.00039383, 0.00039383, 0.00039383, 0.00039383, 0.00039383,
0.00039383, 0., 0., 0., 0.,
0., 0., 0., 0., 0.,
0., 0., 0., 0., 0.,
0., 0., 0., 0.
},
new int[] {1, 1, 8, 8});
layer.setInput(input);
org.deeplearning4j.nn.layers.convolution.ConvolutionLayer layer2 =
(org.deeplearning4j.nn.layers.convolution.ConvolutionLayer) layer;
layer2.setCol(col);
Pair<Gradient, INDArray> pair = layer2.backpropGradient(epsilon);
assertArrayEquals(expectedEpsilon.shape(), pair.getSecond().shape());
assertArrayEquals(expectedWeightGradient.shape(), pair.getFirst().getGradientFor("W").shape());
assertArrayEquals(expectedBiasGradient.shape(), pair.getFirst().getGradientFor("b").shape());
assertEquals(expectedEpsilon, pair.getSecond());
assertEquals(expectedWeightGradient, pair.getFirst().getGradientFor("W"));
assertEquals(expectedBiasGradient, pair.getFirst().getGradientFor("b"));
}
@Test
public void testBackpropResults() {
Layer layer = getContainedConfig();
INDArray col = getContainedCol();
INDArray expectedWeightGradient =
Nd4j.create(
new double[] {-1440., -1440., -1984., -1984., -1440., -1440., -1984., -1984.},
new int[] {2, 1, 2, 2});
INDArray expectedBiasGradient =
Nd4j.create(
new double[] {-544., -544.},
new int[] {
2,
});
INDArray expectedEpsilon =
Nd4j.create(
new double[] {
-12., -12., -12., -12., -12., -12., -12., -12., -12., -12., -12.,
-12., -12., -12., -12., -12., -56., -56., -56., -56., -56., -56.,
-56., -56., -56., -56., -56., -56., -56., -56., -56., -56., -12.,
-12., -12., -12., -12., -12., -12., -12., -12., -12., -12., -12.,
-12., -12., -12., -12., -56., -56., -56., -56., -56., -56., -56.,
-56., -56., -56., -56., -56., -56., -56., -56., -56.
},
new int[] {1, 1, 8, 8});
org.deeplearning4j.nn.layers.convolution.ConvolutionLayer layer2 =
(org.deeplearning4j.nn.layers.convolution.ConvolutionLayer) layer;
layer2.setCol(col);
Pair<Gradient, INDArray> pair = layer2.backpropGradient(epsilon);
assertEquals(expectedEpsilon.shape(), pair.getSecond().shape());
assertEquals(expectedWeightGradient.shape(), pair.getFirst().getGradientFor("W").shape());
assertEquals(expectedBiasGradient.shape(), pair.getFirst().getGradientFor("b").shape());
assertEquals(expectedEpsilon, pair.getSecond());
assertEquals(expectedWeightGradient, pair.getFirst().getGradientFor("W"));
assertEquals(expectedBiasGradient, pair.getFirst().getGradientFor("b"));
}
public FloatDataSet(Pair<FloatMatrix, FloatMatrix> pair) {
this(pair.getFirst(), pair.getSecond());
}
/**
* Train on the corpus
*
* @param rdd the rdd to train
* @return the vocab and weights
*/
public Pair<VocabCache, GloveWeightLookupTable> train(JavaRDD<String> rdd) {
TextPipeline pipeline = new TextPipeline(rdd);
final Pair<VocabCache, Long> vocabAndNumWords = pipeline.process();
SparkConf conf = rdd.context().getConf();
JavaSparkContext sc = new JavaSparkContext(rdd.context());
vocabCacheBroadcast = sc.broadcast(vocabAndNumWords.getFirst());
final GloveWeightLookupTable gloveWeightLookupTable =
new GloveWeightLookupTable.Builder()
.cache(vocabAndNumWords.getFirst())
.lr(conf.getDouble(GlovePerformer.ALPHA, 0.025))
.maxCount(conf.getDouble(GlovePerformer.MAX_COUNT, 100))
.vectorLength(conf.getInt(GlovePerformer.VECTOR_LENGTH, 300))
.xMax(conf.getDouble(GlovePerformer.X_MAX, 0.75))
.build();
gloveWeightLookupTable.resetWeights();
gloveWeightLookupTable.getBiasAdaGrad().historicalGradient =
Nd4j.zeros(gloveWeightLookupTable.getSyn0().rows());
gloveWeightLookupTable.getWeightAdaGrad().historicalGradient =
Nd4j.create(gloveWeightLookupTable.getSyn0().shape());
log.info(
"Created lookup table of size "
+ Arrays.toString(gloveWeightLookupTable.getSyn0().shape()));
CounterMap<String, String> coOccurrenceCounts =
rdd.map(new TokenizerFunction(tokenizerFactoryClazz))
.map(new CoOccurrenceCalculator(symmetric, vocabCacheBroadcast, windowSize))
.fold(new CounterMap<String, String>(), new CoOccurrenceCounts());
List<Triple<String, String, Double>> counts = new ArrayList<>();
Iterator<Pair<String, String>> pairIter = coOccurrenceCounts.getPairIterator();
while (pairIter.hasNext()) {
Pair<String, String> pair = pairIter.next();
counts.add(
new Triple<>(
pair.getFirst(),
pair.getSecond(),
coOccurrenceCounts.getCount(pair.getFirst(), pair.getSecond())));
}
log.info("Calculated co occurrences");
JavaRDD<Triple<String, String, Double>> parallel = sc.parallelize(counts);
JavaPairRDD<String, Tuple2<String, Double>> pairs =
parallel.mapToPair(
new PairFunction<Triple<String, String, Double>, String, Tuple2<String, Double>>() {
@Override
public Tuple2<String, Tuple2<String, Double>> call(
Triple<String, String, Double> stringStringDoubleTriple) throws Exception {
return new Tuple2<>(
stringStringDoubleTriple.getFirst(),
new Tuple2<>(
stringStringDoubleTriple.getFirst(), stringStringDoubleTriple.getThird()));
}
});
JavaPairRDD<VocabWord, Tuple2<VocabWord, Double>> pairsVocab =
pairs.mapToPair(
new PairFunction<
Tuple2<String, Tuple2<String, Double>>, VocabWord, Tuple2<VocabWord, Double>>() {
@Override
public Tuple2<VocabWord, Tuple2<VocabWord, Double>> call(
Tuple2<String, Tuple2<String, Double>> stringTuple2Tuple2) throws Exception {
return new Tuple2<>(
vocabCacheBroadcast.getValue().wordFor(stringTuple2Tuple2._1()),
new Tuple2<>(
vocabCacheBroadcast.getValue().wordFor(stringTuple2Tuple2._2()._1()),
stringTuple2Tuple2._2()._2()));
}
});
for (int i = 0; i < iterations; i++) {
JavaRDD<GloveChange> change =
pairsVocab.map(
new Function<Tuple2<VocabWord, Tuple2<VocabWord, Double>>, GloveChange>() {
@Override
public GloveChange call(
Tuple2<VocabWord, Tuple2<VocabWord, Double>> vocabWordTuple2Tuple2)
throws Exception {
VocabWord w1 = vocabWordTuple2Tuple2._1();
VocabWord w2 = vocabWordTuple2Tuple2._2()._1();
INDArray w1Vector = gloveWeightLookupTable.getSyn0().slice(w1.getIndex());
INDArray w2Vector = gloveWeightLookupTable.getSyn0().slice(w2.getIndex());
INDArray bias = gloveWeightLookupTable.getBias();
double score = vocabWordTuple2Tuple2._2()._2();
double xMax = gloveWeightLookupTable.getxMax();
double maxCount = gloveWeightLookupTable.getMaxCount();
// w1 * w2 + bias
double prediction = Nd4j.getBlasWrapper().dot(w1Vector, w2Vector);
prediction += bias.getDouble(w1.getIndex()) + bias.getDouble(w2.getIndex());
double weight = Math.pow(Math.min(1.0, (score / maxCount)), xMax);
double fDiff =
score > xMax ? prediction : weight * (prediction - Math.log(score));
if (Double.isNaN(fDiff)) fDiff = Nd4j.EPS_THRESHOLD;
// amount of change
double gradient = fDiff;
// update(w1,w1Vector,w2Vector,gradient);
// update(w2,w2Vector,w1Vector,gradient);
Pair<INDArray, Double> w1Update =
update(
gloveWeightLookupTable.getWeightAdaGrad(),
gloveWeightLookupTable.getBiasAdaGrad(),
gloveWeightLookupTable.getSyn0(),
gloveWeightLookupTable.getBias(),
w1,
w1Vector,
w2Vector,
gradient);
Pair<INDArray, Double> w2Update =
update(
gloveWeightLookupTable.getWeightAdaGrad(),
gloveWeightLookupTable.getBiasAdaGrad(),
gloveWeightLookupTable.getSyn0(),
gloveWeightLookupTable.getBias(),
w2,
w2Vector,
w1Vector,
gradient);
return new GloveChange(
w1,
w2,
w1Update.getFirst(),
w2Update.getFirst(),
w1Update.getSecond(),
w2Update.getSecond(),
fDiff);
}
});
JavaRDD<Double> error =
change.map(
new Function<GloveChange, Double>() {
@Override
public Double call(GloveChange gloveChange) throws Exception {
gloveChange.apply(gloveWeightLookupTable);
return gloveChange.getError();
}
});
final Accumulator<Double> d = sc.accumulator(0.0);
error.foreach(
new VoidFunction<Double>() {
@Override
public void call(Double aDouble) throws Exception {
d.$plus$eq(aDouble);
}
});
log.info("Error at iteration " + i + " was " + d.value());
}
return new Pair<>(vocabAndNumWords.getFirst(), gloveWeightLookupTable);
}
/**
* Check backprop gradients for a MultiLayerNetwork.
*
* @param mln MultiLayerNetwork to test. This must be initialized.
* @param epsilon Usually on the order/ of 1e-4 or so.
* @param maxRelError Maximum relative error. Usually < 1e-5 or so, though maybe more for deep
* networks or those with nonlinear activation
* @param minAbsoluteError Minimum absolute error to cause a failure. Numerical gradients can be
* non-zero due to precision issues. For example, 0.0 vs. 1e-18: relative error is 1.0, but
* not really a failure
* @param print Whether to print full pass/failure details for each parameter gradient
* @param exitOnFirstError If true: return upon first failure. If false: continue checking even if
* one parameter gradient has failed. Typically use false for debugging, true for unit tests.
* @param input Input array to use for forward pass. May be mini-batch data.
* @param labels Labels/targets to use to calculate backprop gradient. May be mini-batch data.
* @return true if gradients are passed, false otherwise.
*/
public static boolean checkGradients(
MultiLayerNetwork mln,
double epsilon,
double maxRelError,
double minAbsoluteError,
boolean print,
boolean exitOnFirstError,
INDArray input,
INDArray labels) {
// Basic sanity checks on input:
if (epsilon <= 0.0 || epsilon > 0.1)
throw new IllegalArgumentException(
"Invalid epsilon: expect epsilon in range (0,0.1], usually 1e-4 or so");
if (maxRelError <= 0.0 || maxRelError > 0.25)
throw new IllegalArgumentException("Invalid maxRelativeError: " + maxRelError);
if (!(mln.getOutputLayer() instanceof BaseOutputLayer))
throw new IllegalArgumentException("Cannot check backprop gradients without OutputLayer");
// Check network configuration:
int layerCount = 0;
for (NeuralNetConfiguration n : mln.getLayerWiseConfigurations().getConfs()) {
org.deeplearning4j.nn.conf.Updater u = n.getLayer().getUpdater();
if (u == org.deeplearning4j.nn.conf.Updater.SGD) {
// Must have LR of 1.0
double lr = n.getLayer().getLearningRate();
if (lr != 1.0) {
throw new IllegalStateException(
"When using SGD updater, must also use lr=1.0 for layer "
+ layerCount
+ "; got "
+ u
+ " with lr="
+ lr);
}
} else if (u != org.deeplearning4j.nn.conf.Updater.NONE) {
throw new IllegalStateException(
"Must have Updater.NONE (or SGD + lr=1.0) for layer " + layerCount + "; got " + u);
}
}
mln.setInput(input);
mln.setLabels(labels);
mln.computeGradientAndScore();
Pair<Gradient, Double> gradAndScore = mln.gradientAndScore();
Updater updater = UpdaterCreator.getUpdater(mln);
updater.update(mln, gradAndScore.getFirst(), 0, mln.batchSize());
INDArray gradientToCheck =
gradAndScore
.getFirst()
.gradient()
.dup(); // need dup: gradients are a *view* of the full gradient array (which will
// change every time backprop is done)
INDArray originalParams =
mln.params().dup(); // need dup: params are a *view* of full parameters
int nParams = originalParams.length();
int totalNFailures = 0;
double maxError = 0.0;
for (int i = 0; i < nParams; i++) {
// (w+epsilon): Do forward pass and score
INDArray params = originalParams.dup();
params.putScalar(i, params.getDouble(i) + epsilon);
mln.setParameters(params);
mln.computeGradientAndScore();
double scorePlus = mln.score();
// (w-epsilon): Do forward pass and score
params.putScalar(i, params.getDouble(i) - 2 * epsilon); // +eps - 2*eps = -eps
mln.setParameters(params);
mln.computeGradientAndScore();
double scoreMinus = mln.score();
// Calculate numerical parameter gradient:
double scoreDelta = scorePlus - scoreMinus;
double numericalGradient = scoreDelta / (2 * epsilon);
if (Double.isNaN(numericalGradient))
throw new IllegalStateException(
"Numerical gradient was NaN for parameter " + i + " of " + nParams);
double backpropGradient = gradientToCheck.getDouble(i);
// http://cs231n.github.io/neural-networks-3/#gradcheck
// use mean centered
double relError =
Math.abs(backpropGradient - numericalGradient)
/ (Math.abs(numericalGradient) + Math.abs(backpropGradient));
if (backpropGradient == 0.0 && numericalGradient == 0.0)
relError = 0.0; // Edge case: i.e., RNNs with time series length of 1.0
if (relError > maxError) maxError = relError;
if (relError > maxRelError || Double.isNaN(relError)) {
double absError = Math.abs(backpropGradient - numericalGradient);
if (absError < minAbsoluteError) {
log.info(
"Param "
+ i
+ " passed: grad= "
+ backpropGradient
+ ", numericalGrad= "
+ numericalGradient
+ ", relError= "
+ relError
+ "; absolute error = "
+ absError
+ " < minAbsoluteError = "
+ minAbsoluteError);
} else {
if (print)
log.info(
"Param "
+ i
+ " FAILED: grad= "
+ backpropGradient
+ ", numericalGrad= "
+ numericalGradient
+ ", relError= "
+ relError
+ ", scorePlus="
+ scorePlus
+ ", scoreMinus= "
+ scoreMinus);
if (exitOnFirstError) return false;
totalNFailures++;
}
} else if (print) {
log.info(
"Param "
+ i
+ " passed: grad= "
+ backpropGradient
+ ", numericalGrad= "
+ numericalGradient
+ ", relError= "
+ relError);
}
}
if (print) {
int nPass = nParams - totalNFailures;
log.info(
"GradientCheckUtil.checkGradients(): "
+ nParams
+ " params checked, "
+ nPass
+ " passed, "
+ totalNFailures
+ " failed. Largest relative error = "
+ maxError);
}
return totalNFailures == 0;
}
/**
* Check backprop gradients for a ComputationGraph
*
* @param graph ComputationGraph to test. This must be initialized.
* @param epsilon Usually on the order of 1e-4 or so.
* @param maxRelError Maximum relative error. Usually < 0.01, though maybe more for deep networks
* @param minAbsoluteError Minimum absolute error to cause a failure. Numerical gradients can be
* non-zero due to precision issues. For example, 0.0 vs. 1e-18: relative error is 1.0, but
* not really a failure
* @param print Whether to print full pass/failure details for each parameter gradient
* @param exitOnFirstError If true: return upon first failure. If false: continue checking even if
* one parameter gradient has failed. Typically use false for debugging, true for unit tests.
* @param inputs Input arrays to use for forward pass. May be mini-batch data.
* @param labels Labels/targets (output) arrays to use to calculate backprop gradient. May be
* mini-batch data.
* @return true if gradients are passed, false otherwise.
*/
public static boolean checkGradients(
ComputationGraph graph,
double epsilon,
double maxRelError,
double minAbsoluteError,
boolean print,
boolean exitOnFirstError,
INDArray[] inputs,
INDArray[] labels) {
// Basic sanity checks on input:
if (epsilon <= 0.0 || epsilon > 0.1)
throw new IllegalArgumentException(
"Invalid epsilon: expect epsilon in range (0,0.1], usually 1e-4 or so");
if (maxRelError <= 0.0 || maxRelError > 0.25)
throw new IllegalArgumentException("Invalid maxRelativeError: " + maxRelError);
if (graph.getNumInputArrays() != inputs.length)
throw new IllegalArgumentException(
"Invalid input arrays: expect " + graph.getNumInputArrays() + " inputs");
if (graph.getNumOutputArrays() != labels.length)
throw new IllegalArgumentException(
"Invalid labels arrays: expect " + graph.getNumOutputArrays() + " outputs");
// Check configuration
int layerCount = 0;
for (String vertexName : graph.getConfiguration().getVertices().keySet()) {
GraphVertex gv = graph.getConfiguration().getVertices().get(vertexName);
if (!(gv instanceof LayerVertex)) continue;
LayerVertex lv = (LayerVertex) gv;
org.deeplearning4j.nn.conf.Updater u = lv.getLayerConf().getLayer().getUpdater();
if (u == org.deeplearning4j.nn.conf.Updater.SGD) {
// Must have LR of 1.0
double lr = lv.getLayerConf().getLayer().getLearningRate();
if (lr != 1.0) {
throw new IllegalStateException(
"When using SGD updater, must also use lr=1.0 for layer \""
+ vertexName
+ "\"; got "
+ u);
}
} else if (u != org.deeplearning4j.nn.conf.Updater.NONE) {
throw new IllegalStateException(
"Must have Updater.NONE (or SGD + lr=1.0) for layer \"" + vertexName + "\"; got " + u);
}
}
for (int i = 0; i < inputs.length; i++) graph.setInput(i, inputs[i]);
for (int i = 0; i < labels.length; i++) graph.setLabel(i, labels[i]);
graph.computeGradientAndScore();
Pair<Gradient, Double> gradAndScore = graph.gradientAndScore();
ComputationGraphUpdater updater = new ComputationGraphUpdater(graph);
updater.update(graph, gradAndScore.getFirst(), 0, graph.batchSize());
INDArray gradientToCheck =
gradAndScore
.getFirst()
.gradient()
.dup(); // need dup: gradients are a *view* of the full gradient array (which will
// change every time backprop is done)
INDArray originalParams =
graph.params().dup(); // need dup: params are a *view* of full parameters
int nParams = originalParams.length();
int totalNFailures = 0;
double maxError = 0.0;
for (int i = 0; i < nParams; i++) {
// (w+epsilon): Do forward pass and score
INDArray params = originalParams.dup();
params.putScalar(i, params.getDouble(i) + epsilon);
graph.setParams(params);
graph.computeGradientAndScore();
double scorePlus = graph.score();
// (w-epsilon): Do forward pass and score
params.putScalar(i, params.getDouble(i) - 2 * epsilon); // +eps - 2*eps = -eps
graph.setParams(params);
graph.computeGradientAndScore();
double scoreMinus = graph.score();
// Calculate numerical parameter gradient:
double scoreDelta = scorePlus - scoreMinus;
double numericalGradient = scoreDelta / (2 * epsilon);
if (Double.isNaN(numericalGradient))
throw new IllegalStateException(
"Numerical gradient was NaN for parameter " + i + " of " + nParams);
double backpropGradient = gradientToCheck.getDouble(i);
// http://cs231n.github.io/neural-networks-3/#gradcheck
// use mean centered
double relError =
Math.abs(backpropGradient - numericalGradient)
/ (Math.abs(numericalGradient) + Math.abs(backpropGradient));
if (backpropGradient == 0.0 && numericalGradient == 0.0)
relError = 0.0; // Edge case: i.e., RNNs with time series length of 1.0
if (relError > maxError) maxError = relError;
if (relError > maxRelError || Double.isNaN(relError)) {
double absError = Math.abs(backpropGradient - numericalGradient);
if (absError < minAbsoluteError) {
log.info(
"Param "
+ i
+ " passed: grad= "
+ backpropGradient
+ ", numericalGrad= "
+ numericalGradient
+ ", relError= "
+ relError
+ "; absolute error = "
+ absError
+ " < minAbsoluteError = "
+ minAbsoluteError);
} else {
if (print)
log.info(
"Param "
+ i
+ " FAILED: grad= "
+ backpropGradient
+ ", numericalGrad= "
+ numericalGradient
+ ", relError= "
+ relError
+ ", scorePlus="
+ scorePlus
+ ", scoreMinus= "
+ scoreMinus);
if (exitOnFirstError) return false;
totalNFailures++;
}
} else if (print) {
log.info(
"Param "
+ i
+ " passed: grad= "
+ backpropGradient
+ ", numericalGrad= "
+ numericalGradient
+ ", relError= "
+ relError);
}
}
if (print) {
int nPass = nParams - totalNFailures;
log.info(
"GradientCheckUtil.checkGradients(): "
+ nParams
+ " params checked, "
+ nPass
+ " passed, "
+ totalNFailures
+ " failed. Largest relative error = "
+ maxError);
}
return totalNFailures == 0;
}
@Override
public void computeGradientAndScore() {
int k = layerConf().getK();
// POSITIVE PHASE
Pair<INDArray, INDArray> probHidden = sampleHiddenGivenVisible(input());
/*
* Start the gibbs sampling.
*/
INDArray chainStart = probHidden.getSecond();
/*
* Note that at a later date, we can explore alternative methods of
* storing the chain transitions for different kinds of sampling
* and exploring the search space.
*/
Pair<Pair<INDArray, INDArray>, Pair<INDArray, INDArray>> matrices;
// negative visible means or expected values
INDArray nvMeans = null;
// negative value samples
INDArray nvSamples = null;
// negative hidden means or expected values
INDArray nhMeans = null;
// negative hidden samples
INDArray nhSamples = null;
/*
* K steps of gibbs sampling. This is the positive phase of contrastive divergence.
*
* There are 4 matrices being computed for each gibbs sampling.
* The samples from both the positive and negative phases and their expected values
* or averages.
*
*/
for (int i = 0; i < k; i++) {
// NEGATIVE PHASE
if (i == 0) matrices = gibbhVh(chainStart);
else matrices = gibbhVh(nhSamples);
// get the cost updates for sampling in the chain after k iterations
nvMeans = matrices.getFirst().getFirst();
nvSamples = matrices.getFirst().getSecond();
nhMeans = matrices.getSecond().getFirst();
nhSamples = matrices.getSecond().getSecond();
}
/*
* Update gradient parameters
*/
INDArray wGradient =
input().transposei().mmul(probHidden.getSecond()).subi(nvSamples.transpose().mmul(nhMeans));
INDArray hBiasGradient;
if (layerConf().getSparsity() != 0)
// all hidden units must stay around this number
hBiasGradient = probHidden.getSecond().rsub(layerConf().getSparsity()).sum(0);
else
// update rule: the expected values of the hidden input - the negative hidden means adjusted
// by the learning rate
hBiasGradient = probHidden.getSecond().sub(nhMeans).sum(0);
// update rule: the expected values of the input - the negative samples adjusted by the learning
// rate
INDArray delta = input.sub(nvSamples);
INDArray vBiasGradient = delta.sum(0);
Gradient ret = new DefaultGradient();
ret.gradientForVariable().put(PretrainParamInitializer.VISIBLE_BIAS_KEY, vBiasGradient);
ret.gradientForVariable().put(PretrainParamInitializer.BIAS_KEY, hBiasGradient);
ret.gradientForVariable().put(PretrainParamInitializer.WEIGHT_KEY, wGradient);
gradient = ret;
setScoreWithZ(delta);
}
private void init() {
if (rng == null) rng = new MersenneTwister(123);
MultiDimensionalSet<String, String> binaryProductions = MultiDimensionalSet.hashSet();
if (simplifiedModel) {
binaryProductions.add("", "");
} else {
// TODO
// figure out what binary productions we have in these trees
// Note: the current sentiment training data does not actually
// have any constituent labels
throw new UnsupportedOperationException("Not yet implemented");
}
Set<String> unaryProductions = new HashSet<>();
if (simplifiedModel) {
unaryProductions.add("");
} else {
// TODO
// figure out what unary productions we have in these trees (preterminals only, after the
// collapsing)
throw new UnsupportedOperationException("Not yet implemented");
}
identity = FloatMatrix.eye(numHidden);
binaryTransform = MultiDimensionalMap.newTreeBackedMap();
binaryFloatTensors = MultiDimensionalMap.newTreeBackedMap();
binaryClassification = MultiDimensionalMap.newTreeBackedMap();
// When making a flat model (no semantic untying) the
// basicCategory function will return the same basic category for
// all labels, so all entries will map to the same matrix
for (Pair<String, String> binary : binaryProductions) {
String left = basicCategory(binary.getFirst());
String right = basicCategory(binary.getSecond());
if (binaryTransform.contains(left, right)) {
continue;
}
binaryTransform.put(left, right, randomTransformMatrix());
if (useFloatTensors) {
binaryFloatTensors.put(left, right, randomBinaryFloatTensor());
}
if (!combineClassification) {
binaryClassification.put(left, right, randomClassificationMatrix());
}
}
numBinaryMatrices = binaryTransform.size();
binaryTransformSize = numHidden * (2 * numHidden + 1);
if (useFloatTensors) {
binaryFloatTensorSize = numHidden * numHidden * numHidden * 4;
} else {
binaryFloatTensorSize = 0;
}
binaryClassificationSize = (combineClassification) ? 0 : numOuts * (numHidden + 1);
unaryClassification = new TreeMap<>();
// When making a flat model (no semantic untying) the
// basicCategory function will return the same basic category for
// all labels, so all entries will map to the same matrix
for (String unary : unaryProductions) {
unary = basicCategory(unary);
if (unaryClassification.containsKey(unary)) {
continue;
}
unaryClassification.put(unary, randomClassificationMatrix());
}
binaryClassificationSize = (combineClassification) ? 0 : numOuts * (numHidden + 1);
numUnaryMatrices = unaryClassification.size();
unaryClassificationSize = numOuts * (numHidden + 1);
featureVectors.put(UNKNOWN_FEATURE, randomWordVector());
numUnaryMatrices = unaryClassification.size();
unaryClassificationSize = numOuts * (numHidden + 1);
classWeights = new HashMap<>();
}
/**
* Load word vectors from the given pair
*
* @param pair the given pair
* @return a read only word vectors impl based on the given lookup table and vocab
*/
public static WordVectors fromPair(Pair<InMemoryLookupTable, VocabCache> pair) {
WordVectorsImpl vectors = new WordVectorsImpl();
vectors.setLookupTable(pair.getFirst());
vectors.setVocab(pair.getSecond());
return vectors;
}
