Examples with DM org.deeplearning4j.models.embeddings.learning.impl.sequence.DM used on opensource projects

Example 1 with DM

use of org.deeplearning4j.models.embeddings.learning.impl.sequence.DM in project deeplearning4j by deeplearning4j.

the class ParagraphVectorsTest method testParagraphVectorsDM.

@Test
public void testParagraphVectorsDM() throws Exception {
    ClassPathResource resource = new ClassPathResource("/big/raw_sentences.txt");
    File file = resource.getFile();
    SentenceIterator iter = new BasicLineIterator(file);
    AbstractCache<VocabWord> cache = new AbstractCache.Builder<VocabWord>().build();
    TokenizerFactory t = new DefaultTokenizerFactory();
    t.setTokenPreProcessor(new CommonPreprocessor());
    LabelsSource source = new LabelsSource("DOC_");
    ParagraphVectors vec = new ParagraphVectors.Builder().minWordFrequency(1).iterations(2).seed(119).epochs(3).layerSize(100).learningRate(0.025).labelsSource(source).windowSize(5).iterate(iter).trainWordVectors(true).vocabCache(cache).tokenizerFactory(t).negativeSample(0).useHierarchicSoftmax(true).sampling(0).workers(1).usePreciseWeightInit(true).sequenceLearningAlgorithm(new DM<VocabWord>()).build();
    vec.fit();
    int cnt1 = cache.wordFrequency("day");
    int cnt2 = cache.wordFrequency("me");
    assertNotEquals(1, cnt1);
    assertNotEquals(1, cnt2);
    assertNotEquals(cnt1, cnt2);
    double simDN = vec.similarity("day", "night");
    log.info("day/night similariry: {}", simDN);
    double similarity1 = vec.similarity("DOC_9835", "DOC_12492");
    log.info("9835/12492 similarity: " + similarity1);
    //        assertTrue(similarity1 > 0.2d);
    double similarity2 = vec.similarity("DOC_3720", "DOC_16392");
    log.info("3720/16392 similarity: " + similarity2);
    //      assertTrue(similarity2 > 0.2d);
    double similarity3 = vec.similarity("DOC_6347", "DOC_3720");
    log.info("6347/3720 similarity: " + similarity3);
    //        assertTrue(similarity3 > 0.6d);
    double similarityX = vec.similarity("DOC_3720", "DOC_9852");
    log.info("3720/9852 similarity: " + similarityX);
    assertTrue(similarityX < 0.5d);
    // testing DM inference now
    INDArray original = vec.getWordVectorMatrix("DOC_16392").dup();
    INDArray inferredA1 = vec.inferVector("This is my work");
    INDArray inferredB1 = vec.inferVector("This is my work .");
    double cosAO1 = Transforms.cosineSim(inferredA1.dup(), original.dup());
    double cosAB1 = Transforms.cosineSim(inferredA1.dup(), inferredB1.dup());
    log.info("Cos O/A: {}", cosAO1);
    log.info("Cos A/B: {}", cosAB1);
}

Example 2 with DM

use of org.deeplearning4j.models.embeddings.learning.impl.sequence.DM in project deeplearning4j by deeplearning4j.

the class ParagraphVectorsTest method testGoogleModelForInference.

@Ignore
@Test
public void testGoogleModelForInference() throws Exception {
    WordVectors googleVectors = WordVectorSerializer.loadGoogleModelNonNormalized(new File("/ext/GoogleNews-vectors-negative300.bin.gz"), true, false);
    TokenizerFactory t = new DefaultTokenizerFactory();
    t.setTokenPreProcessor(new CommonPreprocessor());
    ParagraphVectors pv = new ParagraphVectors.Builder().tokenizerFactory(t).iterations(10).useHierarchicSoftmax(false).trainWordVectors(false).iterations(10).useExistingWordVectors(googleVectors).negativeSample(10).sequenceLearningAlgorithm(new DM<VocabWord>()).build();
    INDArray vec1 = pv.inferVector("This text is pretty awesome");
    INDArray vec2 = pv.inferVector("Fantastic process of crazy things happening inside just for history purposes");
    log.info("vec1/vec2: {}", Transforms.cosineSim(vec1, vec2));
}

Example 3 with DM

use of org.deeplearning4j.models.embeddings.learning.impl.sequence.DM in project deeplearning4j by deeplearning4j.

the class ParagraphVectorsTest method testDirectInference.

@Test
public void testDirectInference() throws Exception {
    ClassPathResource resource_sentences = new ClassPathResource("/big/raw_sentences.txt");
    ClassPathResource resource_mixed = new ClassPathResource("/paravec");
    SentenceIterator iter = new AggregatingSentenceIterator.Builder().addSentenceIterator(new BasicLineIterator(resource_sentences.getFile())).addSentenceIterator(new FileSentenceIterator(resource_mixed.getFile())).build();
    TokenizerFactory t = new DefaultTokenizerFactory();
    t.setTokenPreProcessor(new CommonPreprocessor());
    Word2Vec wordVectors = new Word2Vec.Builder().minWordFrequency(1).batchSize(250).iterations(1).epochs(3).learningRate(0.025).layerSize(150).minLearningRate(0.001).elementsLearningAlgorithm(new SkipGram<VocabWord>()).useHierarchicSoftmax(true).windowSize(5).iterate(iter).tokenizerFactory(t).build();
    wordVectors.fit();
    ParagraphVectors pv = new ParagraphVectors.Builder().tokenizerFactory(t).iterations(10).useHierarchicSoftmax(true).trainWordVectors(true).useExistingWordVectors(wordVectors).negativeSample(0).sequenceLearningAlgorithm(new DM<VocabWord>()).build();
    INDArray vec1 = pv.inferVector("This text is pretty awesome");
    INDArray vec2 = pv.inferVector("Fantastic process of crazy things happening inside just for history purposes");
    log.info("vec1/vec2: {}", Transforms.cosineSim(vec1, vec2));
}

Example 4 with DM

use of org.deeplearning4j.models.embeddings.learning.impl.sequence.DM in project deeplearning4j by deeplearning4j.

the class ParagraphVectorsTest method testGensimEquality.

/**
     * Special test to check d2v inference against pre-trained gensim model and
     */
@Ignore
@Test
public void testGensimEquality() throws Exception {
    INDArray expA = Nd4j.create(new double[] { -0.02461922, -0.00801059, -0.01821643, 0.0167951, 0.02240154, -0.00414107, -0.0022868, 0.00278438, -0.00651088, -0.02066556, -0.01045411, -0.02853066, 0.00153375, 0.02707097, -0.00754221, -0.02795872, -0.00275301, -0.01455731, -0.00981289, 0.01557207, -0.005259, 0.00355505, 0.01503531, -0.02185878, 0.0339283, -0.05049067, 0.02849454, -0.01242505, 0.00438659, -0.03037345, 0.01866657, -0.00740161, -0.01850279, 0.00851284, -0.01774663, -0.01976997, -0.03317627, 0.00372983, 0.01313218, -0.00041131, 0.00089357, -0.0156924, 0.01278253, -0.01596088, -0.01415407, -0.01795845, 0.00558284, -0.00529536, -0.03508032, 0.00725479, -0.01910841, -0.0008098, 0.00614283, -0.00926585, 0.01761538, -0.00272953, -0.01483113, 0.02062481, -0.03134528, 0.03416841, -0.0156226, -0.01418961, -0.00817538, 0.01848741, 0.00444605, 0.01090323, 0.00746163, -0.02490317, 0.00835013, 0.01091823, -0.0177979, 0.0207753, -0.00854185, 0.04269911, 0.02786852, 0.00179449, 0.00303065, -0.00127148, -0.01589409, -0.01110292, 0.01736244, -0.01177608, 0.00110929, 0.01790557, -0.01800732, 0.00903072, 0.00210271, 0.0103053, -0.01508116, 0.00336775, 0.00319031, -0.00982859, 0.02409827, -0.0079536, 0.01347831, -0.02555985, 0.00282605, 0.00350526, -0.00471707, -0.00592073, -0.01009063, -0.02396305, 0.02643895, -0.05487461, -0.01710705, -0.0082839, 0.01322765, 0.00098093, 0.01707118, 0.00290805, 0.03256396, 0.00277155, 0.00350602, 0.0096487, -0.0062662, 0.0331796, -0.01758772, 0.0295204, 0.00295053, -0.00670782, 0.02172252, 0.00172433, 0.0122977, -0.02401575, 0.01179839, -0.01646545, -0.0242724, 0.01318037, -0.00745518, -0.00400624, -0.01735787, 0.01627645, 0.04445697, -0.0189355, 0.01315041, 0.0131585, 0.01770667, -0.00114554, 0.00581599, 0.00745188, -0.01318868, -0.00801476, -0.00884938, 0.00084786, 0.02578231, -0.01312729, -0.02047793, 0.00485749, -0.00342519, -0.00744475, 0.01180929, 0.02871456, 0.01483848, -0.00696516, 0.02003011, -0.01721076, -0.0124568, -0.0114492, -0.00970469, 0.01971609, 0.01599673, -0.01426137, 0.00808409, -0.01431519, 0.01187332, 0.00144421, -0.00459554, 0.00384032, 0.00866845, 0.00265177, -0.01003456, 0.0289338, 0.00353483, -0.01664903, -0.03050662, 0.01305057, -0.0084294, -0.01615093, -0.00897918, 0.00768479, 0.02155688, 0.01594496, 0.00034328, -0.00557031, -0.00256555, 0.03939554, 0.00274235, 0.001288, 0.02933025, 0.0070212, -0.00573742, 0.00883708, 0.00829396, -0.01100356, -0.02653269, -0.01023274, 0.03079773, -0.00765917, 0.00949703, 0.01212146, -0.01362515, -0.0076843, -0.00290596, -0.01707907, 0.02899382, -0.00089925, 0.01510732, 0.02378234, -0.00947305, 0.0010998, -0.00558241, 0.00057873, 0.01098226, -0.02019168, -0.013942, -0.01639287, -0.00675588, -0.00400709, -0.02914054, -0.00433462, 0.01551765, -0.03552055, 0.01681101, -0.00629782, -0.01698086, 0.01891401, 0.03597684, 0.00888052, -0.01587857, 0.00935822, 0.00931327, -0.0128156, 0.05170929, -0.01811879, 0.02096679, 0.00897546, 0.00132624, -0.01796336, 0.01888563, -0.01142226, -0.00805926, 0.00049782, -0.02151541, 0.00747257, 0.023373, -0.00198183, 0.02968843, 0.00443042, -0.00328569, -0.04200815, 0.01306543, -0.01608924, -0.01604842, 0.03137267, 0.0266054, 0.00172526, -0.01205696, 0.00047532, 0.00321026, 0.00671424, 0.01710422, -0.01129941, 0.00268044, -0.01065434, -0.01107133, 0.00036135, -0.02991677, 0.02351665, -0.00343891, -0.01736755, -0.00100577, -0.00312481, -0.01083809, 0.00387084, 0.01136449, 0.01675043, -0.01978249, -0.00765182, 0.02746241, -0.01082247, -0.01587164, 0.01104732, -0.00878782, -0.00497555, -0.00186257, -0.02281011, 0.00141792, 0.00432851, -0.01290263, -0.00387155, 0.00802639, -0.00761913, 0.01508144, 0.02226428, 0.0107248, 0.01003709, 0.01587571, 0.00083492, -0.01632052, -0.00435973 });
    INDArray expB = Nd4j.create(new double[] { -0.02465764, 0.00756337, -0.0268607, 0.01588023, 0.01580242, -0.00150542, 0.00116652, 0.0021577, -0.00754891, -0.02441176, -0.01271976, -0.02015191, 0.00220599, 0.03722657, -0.01629612, -0.02779619, -0.01157856, -0.01937938, -0.00744667, 0.01990043, -0.00505888, 0.00573646, 0.00385467, -0.0282531, 0.03484593, -0.05528606, 0.02428633, -0.01510474, 0.00153177, -0.03637344, 0.01747423, -0.00090738, -0.02199888, 0.01410434, -0.01710641, -0.01446697, -0.04225266, 0.00262217, 0.00871943, 0.00471594, 0.0101348, -0.01991908, 0.00874325, -0.00606416, -0.01035323, -0.01376545, 0.00451507, -0.01220307, -0.04361237, 0.00026028, -0.02401881, 0.00580314, 0.00238946, -0.01325974, 0.01879044, -0.00335623, -0.01631887, 0.02222102, -0.02998703, 0.03190075, -0.01675236, -0.01799807, -0.01314015, 0.01950069, 0.0011723, 0.01013178, 0.01093296, -0.034143, 0.00420227, 0.01449351, -0.00629987, 0.01652851, -0.01286825, 0.03314656, 0.03485073, 0.01120341, 0.01298241, 0.0019494, -0.02420256, -0.0063762, 0.01527091, -0.00732881, 0.0060427, 0.019327, -0.02068196, 0.00876712, 0.00292274, 0.01312969, -0.01529114, 0.0021757, -0.00565621, -0.01093122, 0.02758765, -0.01342688, 0.01606117, -0.02666447, 0.00541112, 0.00375426, -0.00761796, 0.00136015, -0.01169962, -0.03012749, 0.03012953, -0.05491332, -0.01137303, -0.01392103, 0.01370098, -0.00794501, 0.0248435, 0.00319645, 0.04261713, -0.00364211, 0.00780485, 0.01182583, -0.00647098, 0.03291231, -0.02515565, 0.03480943, 0.00119836, -0.00490694, 0.02615346, -0.00152456, 0.00196142, -0.02326461, 0.00603225, -0.02414703, -0.02540966, 0.0072112, -0.01090273, -0.00505061, -0.02196866, 0.00515245, 0.04981546, -0.02237269, -0.00189305, 0.0169786, 0.01782372, -0.00430022, 0.00551226, 0.00293861, -0.01337168, -0.00302476, -0.01869966, 0.00270757, 0.03199976, -0.01614617, -0.02716484, 0.01560035, -0.01312686, -0.01604082, 0.01347521, 0.03229654, 0.00707219, -0.00588392, 0.02444809, -0.01068742, -0.0190814, -0.00556385, -0.00462766, 0.01283929, 0.02001247, -0.00837629, -0.00041943, -0.02298774, 0.00874839, 0.00434907, -0.00963332, 0.00476905, 0.00793049, -0.00212557, -0.01839353, 0.03345517, 0.00838255, -0.0157447, -0.0376134, 0.01059611, -0.02323246, -0.01326356, -0.01116734, 0.00598869, 0.0211626, 0.01872963, -0.0038276, -0.01208279, -0.00989125, 0.04147648, 0.00181867, -0.00369355, 0.02312465, 0.0048396, 0.00564515, 0.01317832, -0.0057621, -0.01882041, -0.02869064, -0.00670661, 0.02585443, -0.01108428, 0.01411031, 0.01204507, -0.01244726, -0.00962342, -0.00205239, -0.01653971, 0.02871559, -0.00772978, 0.0214524, 0.02035478, -0.01324312, 0.00169302, -0.00064739, 0.00531795, 0.01059279, -0.02455794, -0.00002782, -0.0068906, -0.0160858, -0.0031842, -0.02295724, 0.01481094, 0.01769004, -0.02925742, 0.02050495, -0.00029003, -0.02815636, 0.02467367, 0.03419458, 0.00654938, -0.01847546, 0.00999932, 0.00059222, -0.01722176, 0.05172159, -0.01548486, 0.01746444, 0.007871, 0.0078471, -0.02414417, 0.01898077, -0.01470176, -0.00299465, 0.00368212, -0.02474656, 0.01317451, 0.03706085, -0.00032923, 0.02655881, 0.0013586, -0.0120303, -0.05030316, 0.0222294, -0.0070967, -0.02150935, 0.03254268, 0.01369857, 0.00246183, -0.02253576, -0.00551247, 0.00787363, 0.01215617, 0.02439827, -0.01104699, -0.00774596, -0.01898127, -0.01407653, 0.00195514, -0.03466602, 0.01560903, -0.01239944, -0.02474852, 0.00155114, 0.00089324, -0.01725949, -0.00011816, 0.00742845, 0.01247074, -0.02467943, -0.00679623, 0.01988366, -0.00626181, -0.02396477, 0.01052101, -0.01123178, -0.00386291, -0.00349261, -0.02714747, -0.00563315, 0.00228767, -0.01303677, -0.01971108, 0.00014759, -0.00346399, 0.02220698, 0.01979946, -0.00526076, 0.00647453, 0.01428513, 0.00223467, -0.01690172, -0.0081715 });
    VectorsConfiguration configuration = new VectorsConfiguration();
    configuration.setIterations(5);
    configuration.setLearningRate(0.01);
    configuration.setUseHierarchicSoftmax(true);
    configuration.setNegative(0);
    Word2Vec w2v = WordVectorSerializer.readWord2VecFromText(new File("/home/raver119/Downloads/gensim_models_for_dl4j/word"), new File("/home/raver119/Downloads/gensim_models_for_dl4j/hs"), new File("/home/raver119/Downloads/gensim_models_for_dl4j/hs_code"), new File("/home/raver119/Downloads/gensim_models_for_dl4j/hs_mapping"), configuration);
    TokenizerFactory tokenizerFactory = new DefaultTokenizerFactory();
    tokenizerFactory.setTokenPreProcessor(new CommonPreprocessor());
    assertNotEquals(null, w2v.getLookupTable());
    assertNotEquals(null, w2v.getVocab());
    ParagraphVectors d2v = new ParagraphVectors.Builder(configuration).useExistingWordVectors(w2v).sequenceLearningAlgorithm(new DM<VocabWord>()).tokenizerFactory(tokenizerFactory).resetModel(false).build();
    assertNotEquals(null, d2v.getLookupTable());
    assertNotEquals(null, d2v.getVocab());
    assertTrue(d2v.getVocab() == w2v.getVocab());
    assertTrue(d2v.getLookupTable() == w2v.getLookupTable());
    String textA = "Donald Trump referred to President Obama as “your president” during the first presidential debate on Monday, much to many people’s chagrin on social media. Trump, made the reference after saying that the greatest threat facing the world is nuclear weapons. He then turned to Hillary Clinton and said, “Not global warming like you think and your President thinks,” referring to Obama.";
    String textB = "The comment followed Trump doubling down on his false claims about the so-called birther conspiracy theory about Obama. People following the debate were immediately angered that Trump implied Obama is not his president.";
    String textC = "practice of trust owned Trump for example indeed and conspiracy between provoke";
    INDArray arrayA = d2v.inferVector(textA);
    INDArray arrayB = d2v.inferVector(textB);
    INDArray arrayC = d2v.inferVector(textC);
    assertNotEquals(null, arrayA);
    assertNotEquals(null, arrayB);
    Transforms.unitVec(arrayA);
    Transforms.unitVec(arrayB);
    Transforms.unitVec(expA);
    Transforms.unitVec(expB);
    double simX = Transforms.cosineSim(arrayA, arrayB);
    double simC = Transforms.cosineSim(arrayA, arrayC);
    double simB = Transforms.cosineSim(arrayB, expB);
    log.info("SimilarityX: {}", simX);
    log.info("SimilarityC: {}", simC);
    log.info("SimilarityB: {}", simB);
}