BiLSTM-Attention-情感评分
2024-04-21 05:06:12
标题
BiLSTM-Attention-情感评分-实战应用
文章目录
- 标题
- 前言
- 一、算法模型图
- 二、附件介绍
- 三、 词向量
- 1.说明
- 2.训练方法
- 四、样本数据预处理
- 五、训练、保存训练模型
- readtxt2.py 文本工具类
- bpattention.py 训练代码
- 训练结果
- 预测数据情况
- 六、实战运用
- BaseRgerBean.java 基础类
- NumberUtil.java 工具类
- Word2VecUtil.java 词向量初始化
- 识别
- 结语
前言
情感分析旨在自动识别和提取文本中的倾向、立场、评价、观点等主观信息。它包含各式各样的任务，比如句子级情感分类、评价对象级情感分类、观点抽取、情绪分类等。这次实战运用主要是针对互联网上新闻数据，目前互联网上关于BiLSTM-Attention运用到文本情感评分的代码很多，理论多于实战。本文将从词向量、样本数据预处理、训练、保存训练结果、运用训练结果等方面介绍。
一、算法模型图
二、附件介绍
资源地址： 链接：https://pan.baidu.com/s/1J5h3fehNIxoxiAISbjmCOw 提取码：5jbj
资源
| 名字 | 说明 |
| java | 词向量训练代码、实战运用模型 |
| python | 训练模型代码 |
| 训练模型 | 已经训练好的模型 |
| Word2vec | 已经训练好的词向量 |
软硬件
| 软件 | 版本 |
| jdk | Jdk1.8 |
| python | 3.4.3 |
| tensorflow | 1.15.0 |
| Java idea - eclipse | launcher |
| Python idea - IntelliJ IDEA Community Edition | 14.1.4 |
三、 词向量
1.说明
本模型使用的是Word2vec，它是一群用来产生词向量的相关模型。这些模型为双层的神经网络，用来训练以重新建构语言学之词文本。网络以词表现，并且需猜测相邻位置的输入词，在word2vec中词袋模型假设下，词的顺序是不重要的。训练完成之后，word2vec模型可用来映射每个词到一个向量，可用来表示词对词之间的关系，该向量为神经网络之隐藏层。详细介绍略（自己上网翻）。
2.训练方法
com.jt.dctsaple.word2vec.nlp.vec.Learn 详细训练代码,需要的直接看代码，github有大量的源码，大家可以根据自己的需要去寻找。如果适配特定领域数据，需要寻找该领域的样本，训练该领域词向量。
如果文本分类对数字比较敏感，建议分词时特殊处理。
四、样本数据预处理
样本数据分成三份80%训练、10%测试、10%预测。
目标分类
| 分类 | 分类标记 |
| 负面 | -1 |
| 中性 | 0 |
| 正面 | 1 |
本文的样本对数据中的数字、电话号码做了单独处理，所以大家可以根据自己的需要去做处理，别忘了词向量。
五、训练、保存训练模型
readtxt2.py 文本工具类
import numpy as np
import tensorflow as tfdef _read_word2vec(filepath):f = open(filepath, encoding='gbk', errors='ignore') # 返回一个文件对象line = f.readline() # 调用文件的 readline()方法print(line)i = 0words_list = []words_list_index = []word_vectors = []# for j in range(200):# print(j)while line:# print(i, ':', len(line)), # 后面跟 ',' 将忽略换行符# print(line, end = '') # 在 Python 3中使用line = f.readline()line = line.strip('\n')lines = line.split("\t")if i >= 1 and lines.__len__() == 202:# print(lines[0])v = np.zeros((200))for j in range(200):v[j] = float(lines[j+1])words_list.append(lines[0])words_list_index.append(i-1)word_vectors.append(v)else:print(line)i += 1f.close()words_list_map = dict(zip(words_list,words_list_index))return words_list, np.array(word_vectors), words_list_mapdef _read_train_data(filepath):ft = open(filepath, encoding='gbk', errors='ignore') # 返回一个文件对象# line = f.readline() # 调用文件的 readline()方法targets = []words = []# j = 0for line in ft.readlines():line = line.strip('\n')lines = line.split("<sos>")v = []if lines.__len__() != 2:print(line)else:if lines[0] == '1':targets.append([0, 0, 1])elif lines[0] == '0':targets.append([0, 1, 0])else:targets.append([1, 0, 0])ws = lines[1].split("\t")for i in range(ws.__len__()):v.append(ws[i])words.append(v)# j = j + 1# if j > 100:# breakft.close()return targets, words
def _find_index_word(word, max_lengh, words_list):_index = np.zeros((max_lengh), dtype=np.int32)num = len(word)if max_lengh < len(word):num = max_lenghfor i in range(num):try:_index[i] = words_list.index(word[i])except ValueError:_index[i] = 0return _index
def _train_data_index(words, max_lengh, words_list):data_len = len(words)datax = np.zeros([data_len, max_lengh], dtype=np.int32)for i in range(data_len):datax[i] = _find_index_word(words[i], max_lengh, words_list)return dataxdef _train_uniondata_index(words, max_lengh, words_list):data_len = len(words)datax = np.zeros([data_len, max_lengh], dtype=np.int32)for i in range(data_len):print("_train_uniondata_index %d" % i)datax[i] = _find_unionindex_word(words[i],max_lengh,words_list)return datax
def _find_unionindex_word(word, max_lengh, words_list):_index = np.zeros(max_lengh, dtype=np.int32)for i in range(max_lengh):if i < len(word):try:_index[i] = int(words_list.get(word[i], 1))except ValueError:_index[i] = 1else:_index[i] = 1return _index
if __name__ == "__main__":words_list, word_vectors,words_list_map = _read_word2vec("../gbn-word2vector.txt")print(words_list_map.get("'",0))print(word_vectors.shape)init = tf.constant_initializer(word_vectors)print(type(init))targets, words = _read_train_data("data/padata-1.txt")datax = _train_uniondata_index(words,64,words_list_map)for i in range(np.array(words).shape[0]):ta = targets[i]print(targets[i])if ta[1] == 1:da = datax[i]line = "int[] input "+str(i) +" = {"for j in range(88):if j > 0:line = line + ","line = line + str(da[j])line = line + "};"print(line)print(targets[i])
bpattention.py 训练代码
__author__ = 'zxhjiutian'
# -*-coding:utf-8 -*-
import tensorflow as tf
import readtxt2 as read
import datetime
import numpy as np
import osos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'class Config(object):# 目标分类数目numClasses = 3# 拼接长度# 最大句长maxSeqLength = 64# 词向量长度numDimensions = 200# 最大简称句长KEEP_PROB = 0.1 # dropout率HIDDEN_SIZE = 64 # lstm隐层单元个数NUM_LAYERS = 1 # lstm层数VOCAB_SIZE = 10000 # 词表大小LEARNING_RATE = 0.002 # 学习率TRAIN_BATCH_SIZE = 64 # 训练batch大小grad_clip = 4.0 #gradient clipping threshold# 测试阶段，batch设置为1EVAL_BATCH_SIZE = 1EVAL_NUM_STEP = 1attention_size = 64 # the size of attention layer
class PbAttention(object):def __init__(self, config, is_training, word_vectors):self.config = configself.batch_size = tf.placeholder(tf.int32, name='batch_size')# 目标分类self.input_class = tf.placeholder(tf.int32, [None, self.config.numClasses], name="input_class")# 命中文本self.input_line = tf.placeholder(tf.int32, [None, self.config.maxSeqLength], name="input_line")self.is_training = is_trainingself.global_step = tf.Variable(0, trainable=False, name='global_step')self.sequence_lengths = tf.placeholder(tf.int32, shape=[None], name="sequence_lengths")# [词表大小， 词的向量表示]self.embedding = tf.get_variable("embedding", shape=[len(word_vectors), 200], initializer=tf.constant_initializer(word_vectors))self.rnn(self.is_training)tensor_info_x = tf.saved_model.utils.build_tensor_info(self.input_line)tensor_info_y = tf.saved_model.utils.build_tensor_info(self.y_pred_cls)self.tensor_info_x = tensor_info_xself.tensor_info_y = tensor_info_ylogdir = "tensorboard/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S") + "/"self.logdir = logdirmerged = tf.summary.merge_all()self.merged = mergeddef rnn(self, is_training):# Define Basic RNN Celldef basic_rnn_cell(rnn_size):# return tf.contrib.rnn.GRUCell(rnn_size)return tf.contrib.rnn.LSTMCell(rnn_size, state_is_tuple=True)# Define Forward RNN Cellwith tf.name_scope('fw_rnn'):fw_rnn_cell = tf.contrib.rnn.MultiRNNCell([basic_rnn_cell(self.config.HIDDEN_SIZE) for _ in range(self.config.NUM_LAYERS)])if is_training:fw_rnn_cell = tf.contrib.rnn.DropoutWrapper(fw_rnn_cell, output_keep_prob=self.config.KEEP_PROB)# Define Backward RNN Cellwith tf.name_scope('bw_rnn'):bw_rnn_cell = tf.contrib.rnn.MultiRNNCell([basic_rnn_cell(self.config.HIDDEN_SIZE) for _ in range(self.config.NUM_LAYERS)])if is_training:bw_rnn_cell = tf.contrib.rnn.DropoutWrapper(bw_rnn_cell, output_keep_prob=self.config.KEEP_PROB)# Embedding layerwith tf.name_scope('embedding_line'):input_line_vec = tf.nn.embedding_lookup(self.embedding, self.input_line)tf.summary.histogram("input_line_vec", input_line_vec)with tf.name_scope('bi_rnn'):rnn_output, _ = tf.nn.bidirectional_dynamic_rnn(fw_rnn_cell, bw_rnn_cell, inputs=input_line_vec,sequence_length=self.sequence_lengths, dtype=tf.float32)tf.summary.histogram("rnn_output", rnn_output)if isinstance(rnn_output, tuple):rnn_output = tf.concat(rnn_output, 2)# Attention Layerwith tf.name_scope('attention'):input_shape = rnn_output.shape # (batch_size, sequence_length, hidden_size)sequence_size = input_shape[1].value # the length of sequences processed in the RNN layerhidden_size = input_shape[2].value # hidden size of the RNN layerattention_w = tf.Variable(tf.truncated_normal([hidden_size, self.config.attention_size], stddev=0.1),name='attention_w')attention_b = tf.Variable(tf.constant(0.1, shape=[self.config.attention_size]), name='attention_b')attention_u = tf.Variable(tf.truncated_normal([self.config.attention_size], stddev=0.1), name='attention_u')# tf.summary.distribution("attention_w", attention_w)z_list = []for t in range(sequence_size):u_t = tf.tanh(tf.matmul(rnn_output[:, t, :], attention_w) + tf.reshape(attention_b, [1, -1]))z_t = tf.matmul(u_t, tf.reshape(attention_u, [-1, 1]))z_list.append(z_t)# Transform to batch_size * sequence_size hideenattention_z = tf.concat(z_list, axis=1)self.alpha = tf.nn.softmax(attention_z)attention_output = tf.reduce_sum(rnn_output * tf.reshape(self.alpha, [-1, sequence_size, 1]), 1)tf.summary.histogram("alpha", self.alpha)tf.summary.histogram("attention_output", attention_output)# attention_output shape: (batch_size, hidden_size)# Add dropoutwith tf.name_scope('dropout'):# attention_output shape: (batch_size, hidden_size)self.final_output = tf.nn.dropout(attention_output, rate=self.config.KEEP_PROB)tf.summary.histogram("final_output", self.final_output)# Fully connected layerwith tf.name_scope('output'):fc_w = tf.Variable(tf.truncated_normal([hidden_size, self.config.numClasses], stddev=0.1), name='fc_w')fc_b = tf.Variable(tf.zeros([self.config.numClasses]), name='fc_b')# 目标向量self.logits = tf.matmul(self.final_output, fc_w) + fc_bself.y_pred_cls = tf.argmax(self.logits, 1, name='predictions')tf.summary.histogram("fc_w", fc_w)tf.summary.histogram("fc_b", fc_b)tf.summary.histogram("logits", self.logits)tf.summary.histogram("y_pred_cls", self.y_pred_cls)# Calculate cross-entropy losswith tf.name_scope('loss'):cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=self.logits, labels=self.input_class)self.loss = tf.reduce_mean(cross_entropy)tf.summary.scalar("loss", self.loss)# Create optimizerwith tf.name_scope('optimization'):optimizer = tf.train.AdamOptimizer(self.config.LEARNING_RATE)gradients, variables = zip(*optimizer.compute_gradients(self.loss))gradients, _ = tf.clip_by_global_norm(gradients, self.config.grad_clip)self.optim = optimizer.apply_gradients(zip(gradients, variables), global_step=self.global_step)# Calculate accuracywith tf.name_scope('accuracy'):correct_pred = tf.equal(self.y_pred_cls, tf.argmax(self.input_class, 1))self.acc = tf.reduce_mean(tf.cast(correct_pred, tf.float32))tf.summary.scalar("accuracy", self.acc)
def get_sequence_length(x_batch):"""Args:x_batch:a batch of input_dataReturns:sequence_lenghts: a list of acutal length of every senuence_data in input_data"""sequence_lengths=[]for x in x_batch:actual_length = np.sum(np.sign(x))sequence_lengths.append(actual_length)return sequence_lengths
def run_epoch(session, model, data, target, eval_data, eval_target):writer = tf.summary.FileWriter(model.logdir, session.graph)saver = tf.train.Saver()# state = session.run(model.initial_state) # vlstm单元初始状态batch_size = 128# 训练一个epoch。steps = 5000dataset_size = len(target)dataset_size = (dataset_size // batch_size) * batch_sizeeval_dataset_size = len(eval_target)eval_dataset_size = (eval_dataset_size // batch_size) * batch_sizefor step in range(steps):# 每次选取batch_size个样本训练start = (step * batch_size) % dataset_sizeend = min(start + batch_size, dataset_size)x_batch = data[start:end]sequence_lengths = get_sequence_length(x_batch)_batch_size1 = end - start + 1optimizer, summary, accuracy = session.run([model.optim, model.merged, model.acc],{model.input_line: x_batch, model.input_class: target[start:end],model.sequence_lengths: sequence_lengths,model.batch_size: _batch_size1})if step % 10 == 0:# summary = session.run(model.merged, {model.sequence_lengths: sequence_lengths,# model.input_line: x_batch,# model.input_class: target[start:end],# model.batch_size: batch_size})writer.add_summary(summary, step)# print(step, optimizer)if step % 20 == 0:# accuracy = session.run(model.acc, {model.sequence_lengths: sequence_lengths,# model.input_line: x_batch,# model.input_class: target[start:end],# model.batch_size: batch_size})print("step: %d accuracy: %g time: %s" % (step, accuracy, datetime.datetime.now().strftime("%Y%m%d-%H%M%S")))# Save the network every 10,000 training iterations# if step % 5000 == 0 and step != 0:if step % 100 == 0 and step != 0:eval_step = step // 100eval_start = (eval_step * 1000) % eval_dataset_sizeeval_end = min(eval_start + 1000, eval_dataset_size)eval_batch = eval_data[eval_start:eval_end]eval_batch_class = eval_target[eval_start:eval_end]eval_sequence_lengths = get_sequence_length(eval_batch)_batch_size = eval_end - eval_start + 1optimizer, summary, accuracy = session.run([model.optim, model.merged, model.acc],{model.input_line: eval_batch,model.input_class: eval_batch_class,model.sequence_lengths: eval_sequence_lengths,model.batch_size: _batch_size})print("eval step: %d accuracy: %g time: %s" % (step, accuracy, datetime.datetime.now().strftime("%Y%m%d-%H%M%S")))if accuracy > 0.92 and step > 1000:break# save_path = saver.save(session, "model/"+str(step)+"/pretrained_lstm.ckpt", global_step=step)# print("saved to %s" % save_path)save_path = saver.save(session, "model/pretrained_lstm.ckpt", global_step=step)print("saved to %s" % save_path)writer.close()def main():g_2 = tf.Graph()with g_2.as_default():# word2vec 文件中words_list, word_vectors, words_list_map = read._read_word2vec("../data/gbn-word2vector.txt")print("----------------------------------bg-1------------------------------")# print(words_list.__le__())# print(len(word_vectors))#print(len(words_list_map))targets, words= read._read_train_data("data/padata-1.txt")print("----------------------------------bg-2------------------------------")config = Config()datax = read._train_uniondata_index(words, config.maxSeqLength, words_list_map)print("----------------------------------bg-------------------------------")eval_targets, eval_words = read._read_train_data("data/padatapre-1.txt")eval_datax = read._train_uniondata_index(eval_words, config.maxSeqLength, words_list_map)print("----------------------------------bg-veal-------------------------------")initializer = tf.random_uniform_initializer(-0.05, 0.05)with tf.variable_scope("language_model", reuse=None, initializer=initializer):train_model = PbAttention(config, True, word_vectors)with tf.Session(graph=g_2) as session:tf.global_variables_initializer().run()for i in range(1):print("In iteration: %d" % (i + 1))run_epoch(session, train_model, datax, targets, eval_datax, eval_targets)train_model.is_training = Falseprediction_signature = tf.saved_model.signature_def_utils.build_signature_def(inputs={'input-x': train_model.tensor_info_x},outputs={'out-y':train_model.tensor_info_y})legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')# 保存训练模型 java 要调用builder = tf.saved_model.builder.SavedModelBuilder("model/pb/"+ datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))builder.add_meta_graph_and_variables(session, [tf.saved_model.tag_constants.SERVING],signature_def_map={'predict_data': prediction_signature},legacy_init_op=legacy_init_op)builder.save(False)graph_def = g_2.as_graph_def()if __name__ == "__main__":print(1)main()训练结果
tensorboard --host=127.0.0.1 --logdir= tensorboard 查看训练参数
地址：http://127.0.0.1:6006/
准确率和损失函数
预测数据情况
部分预测数据
六、实战运用
BaseRgerBean.java 基础类
package com.jt.dctsaple.tf;import java.text.NumberFormat;
import org.tensorflow.Graph;
import org.tensorflow.SavedModelBundle;
import org.tensorflow.Session;/*** 识别风险命中是否准确* @author zxh* @date 2020年8月3日 上午11:01:41*/
public abstract class BaseRgerBean {NumberFormat nf = NumberFormat.getNumberInstance();protected SavedModelBundle smb= null;protected Graph graph = null;protected Session session = null;/*** * @param modelPath 模型位置*/public BaseRgerBean(String modelPath){smb= SavedModelBundle.load(modelPath,"serve");graph = smb.graph();session = smb.session();nf.setMaximumFractionDigits(4);}/*** 预测* @param line 命中句子* @param maxLeangh 句长限制* @time 2020-08-3* @return*/public abstract Object[] predictions(String line,int maxlength);/*** * @param words* @param maxlength* @return*/public abstract Object[] predictions(String[] words,int maxlength);/*** 余玄* @param a* @param b* @return*/public double cose(float[] a,float[] b){float fm = 0;for (int i = 0; i < b.length; i++) {fm += a[i]*b[i];}float atw = 0;for (int i = 0; i < a.length; i++) {atw += a[i]*a[i];}float btw = 0;for (int i = 0; i < b.length; i++) {btw += b[i]*b[i];}return Double.valueOf(nf.format(fm/Math.sqrt(atw*btw)));}}NumberUtil.java 工具类
package com.jt.dctsaple.tf;import java.math.BigInteger;
import java.util.Arrays;import org.apache.commons.lang.StringUtils;/*** 数值提取* @author zxh* @date 2020年7月27日 下午2:18:32*/
public class NumberUtil {private NumberUtil(){}/*** 提取数值* @param word* @return Object[] [doube,单位]*/public static Object[] getNumBerString(String word){if(StringUtils.isBlank(word)){return null;}String numstr = "";String dwstr = "";char[] ws = word.toCharArray();if(word.startsWith("."))return null;for (int i = 0; i < ws.length; i++) {if((ws[i] >= '0' && ws[i] <= '9') || ws[i] == '.'){numstr += ws[i];}else{if(i == 0){return null;}dwstr += ws[i];}}if(StringUtils.isBlank(dwstr)){return new Object[]{Math.round(Double.valueOf(numstr))};}else{return new Object[]{Math.round(Double.valueOf(numstr)),dwstr};}}public static String[] getVec(String v,int length){String[] vec = new String[length];BigInteger targetSignature = new BigInteger( v + "");String vec2 = targetSignature.toString(2);char[] cs = vec2.toCharArray();int j = cs.length - 1;for (int i = length - 1; i >= 0; i--) {if(j>=0){vec[i] = cs[j]+"";}else{vec[i] = "0";}j--;}return vec;} }Word2VecUtil.java 词向量初始化
package com.jt.dctsaple.tf;import java.io.File;
import java.io.IOException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;import org.apache.commons.io.FileUtils;
import org.apdplat.word.WordSegmenter;
import org.apdplat.word.segmentation.SegmentationAlgorithm;
import org.apdplat.word.segmentation.Word;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;public class Word2VecUtil {public static String dicfile = "library/gbn-word2vector.txt";private static final Logger log = LoggerFactory.getLogger(Word2VecUtil.class);static Map<String,Integer> wordIndex = new HashMap<>();private Word2VecUtil(){}public static void init(){List<String> list;try {list = FileUtils.readLines(new File(dicfile), "GBK");for (int i = 2; i < list.size(); i++) {String[] indexs = list.get(i).split("\t");if(indexs.length > 200){wordIndex.put(indexs[0], i-2);}}} catch (IOException e) {log.error("加载词向量出现问题 path={} ", dicfile);}}/*** 查找词向量索引* @param words 分词* @param maxlength 最大长度* @return*/public static int[] getWordIndex(String[] words,int maxlength){int[] indexs = new int[maxlength];for (int i = 0; i < indexs.length; i++) {indexs[i] = 0;}int j = 0;for (int i = 0; i < words.length && i<maxlength; i++) {String word = words[i];if(wordIndex.containsKey(word)){indexs[j] = wordIndex.get(word);}else{indexs[j] = 1;}j ++;}return indexs;}/*** NLP 分词* @param line* @return*/public static String[] nlpSplitWord(String line){List<String> splitwords = new ArrayList<>();List<Word> words = WordSegmenter.segWithStopWords(line, SegmentationAlgorithm.MaxNgramScore);for (Word word : words) {Object[] ws = NumberUtil.getNumBerString(word.getText());if(ws == null){splitwords.add(word.getText());}else{if(ws.length == 2){Long vlimit = Long.valueOf(ws[0]+"");if(vlimit < 10001){}else if(vlimit > 10000000000L){splitwords.add("SJHM");}else{splitwords.add("10000");}String daw = ws[1]+"";splitwords.add(daw);}if(ws.length == 1){Long vlimit = Long.valueOf(ws[0]+"");if(vlimit < 10001){splitwords.add(vlimit+"");}else if( vlimit > 10000000000L){splitwords.add("SJHM");}else{splitwords.add("10000");}}}}String[] rtwords = new String[splitwords.size()];for (int i = 0; i < rtwords.length; i++) {rtwords[i] = splitwords.get(i);}return rtwords;}}识别
package com.jt.dctsaple.tf;import java.text.DecimalFormat;
import java.util.Arrays;
import java.util.List;import org.tensorflow.Tensor;/*** 情感分析模型* @author zxh**/
public class GbAnasysBean extends BaseRgerBean{DecimalFormat df = new DecimalFormat("#0.0000");public GbAnasysBean(String modelPath) {super(modelPath);}@Overridepublic Object[] predictions(String line, int maxlength) {String[] words = Word2VecUtil.nlpSplitWord(line);return predictions(words, maxlength);}@Overridepublic Object[] predictions(String[] words, int maxlength) {int[] indexs = Word2VecUtil.getWordIndex(words, maxlength);int[][] _inputs = new int[1][maxlength];_inputs[0] = indexs;Tensor<?> inputs = Tensor.create(_inputs);Tensor<?> batch_size = Tensor.create(1);Tensor<?> sequence_lengths = Tensor.create(new int[]{maxlength});List<Tensor<?>> result = session.runner().feed("language_model/input_line", inputs) //输入文本.feed("language_model/batch_size", batch_size) //批量.feed("language_model/sequence_lengths", sequence_lengths) //长度.fetch("language_model/output/add") //输出向量.fetch("language_model/output/predictions").run(); //输出最大值索引Tensor<Float> vs = result.get(0).expect(Float.class);long[] sss = vs.shape();int nlabels = (int) sss[1];float[][] ks = vs.copyTo(new float[1][nlabels]);Tensor<Long> _vs = result.get(1).expect(Long.class);long[] s = _vs.copyTo(new long[1]);float[] v = ks[0];float[] y_1 = {(float) 1.0,(float) 0.0,(float) 0.0};float[] y0 = {(float) 0.0,(float) 1.0,(float) 0.0};float[] y1 = {(float) 0.0,(float) 0.0,(float) 1.0};// 1=[1,0] 0=[0,1]int cs = -1;if(s[0] == 0){cs = -1;}if(s[0] == 1){cs = 0;}if(s[0] == 2){cs = 1;}double dis_1 = cose(v , y_1);double dis0 = cose(v , y0);double dis1 = cose(v , y1);double score = 0;if(cs == -1){score = dis_1 * -1;}elseif(cs == 1){score = dis1 ;}else{score = Double.valueOf(nf.format(dis_1 * dis0 * dis1));}return new Object[]{cs,dis_1,dis0,dis1,score};}public static void main(String[] args) {Word2VecUtil.dicfile = "..\\..\\..\\gbn-word2vector.txt";Word2VecUtil.init();GbAnasysBean bg = new GbAnasysBean("...\\model\\pb\\20200828-174724");Object[] objs = bg.predictions("字节跳动确认：TikTok首席执行官凯文·梅耶尔辞任 Vanessa担任临时负责人", 64);System.out.println(Arrays.toString(objs));}}结语
谨以此文作为技术交流，有错误之处请不吝赐教。
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