在上面一章节提到怎么在java中怎么调用tensorflow训练好的模型，这篇主要是部署成service代码，看看吧，还有个东西官方说要用jdk1.8，不过我把部分方法改了，1.7也可以用，看看吧：
首先是utils，里面用到的一些方法，把一段文本转化为一个tensor
package com.dianping.text.classify.util;import java.io.BufferedReader;import java.io.FileInputStream;import java.io.IOException;import java.io.InputStream;import java.io.InputStreamReader;import java.nio.file.Files;import java.nio.file.Paths;import java.nio.file.Path;import java.util.ArrayList;import java.util.Arrays;import java.util.HashMap;import java.util.List;import java.util.Map;import org.apache.commons.lang.StringUtils;import org.tensorflow.Graph;import org.tensorflow.Session;import org.tensorflow.Tensor;public class TersorflowUtils {-
private static Map<String, Integer> word_to_id = new HashMap<String, Integer>();
public static byte[] readAllBytesOrExit(Path path) { try { return Files.readAllBytes(path); } catch (IOException e) { System.err.println("Failed to read [" + path + "]: " + e.getMessage()); System.exit(1); } return null; } public static byte[] readAllBytes(String path) { try { InputStream in = new FileInputStream(path); byte[] bytes = new byte[in.available()]; in.read(bytes); in.close(); return bytes; } catch (Exception e) { return null; } } /* * 序列默人长度为300 */ public static int[][] gettexttoid(String text) { int[][] xpad = new int[1][300]; if (StringUtils.isBlank(text)) { return xpad; } char[] chs = text.trim().toLowerCase().toCharArray(); List<Integer> list = new ArrayList<Integer>(); for (int i = 0; i < chs.length; i++) { String element = Character.toString(chs[i]); if (word_to_id.containsKey(element)) { list.add(word_to_id.get(element)); } } if (list.size() == 0) { return xpad; } int size = list.size(); Integer[] targetInter = (Integer[]) list.toArray(new Integer[size]); /* * 用于jdk1.8转换 */ // int[] target= // Arrays.stream(targetInter).mapToInt(Integer::valueOf).toArray(); int[] target = Intetoint(targetInter); if (size <= 300) { System.arraycopy(target, 0, xpad[0], xpad[0].length - size, target.length); } else { System.arraycopy(target, size - xpad[0].length, xpad[0], 0, xpad[0].length); } return xpad; } public static int[][] gettexttoid(String text, Map<String, Integer> map) { int[][] xpad = new int[1][300]; if (StringUtils.isBlank(text)) { return xpad; } char[] chs = text.trim().toLowerCase().toCharArray(); List<Integer> list = new ArrayList<Integer>(); for (int i = 0; i < chs.length; i++) { String element = Character.toString(chs[i]); if (map.containsKey(element)) { list.add(map.get(element)); } } if (list.size() == 0) { return xpad; } int size = list.size(); Integer[] targetInter = (Integer[]) list.toArray(new Integer[size]); /* * 用于jdk1.8转换 */ // int[] target= // Arrays.stream(targetInter).mapToInt(Integer::valueOf).toArray(); int[] target = Intetoint(targetInter); if (size <= 300) { System.arraycopy(target, 0, xpad[0], xpad[0].length - size, target.length); } else { System.arraycopy(target, size - xpad[0].length, xpad[0], 0, xpad[0].length); } return xpad; } /* * 自定义长度 */ public static int[][] gettexttoid(String text, int maxlen) { if (maxlen < 1) { throw new IllegalArgumentException("maxlen长度必须大于等于1"); } int[][] xpad = new int[1][maxlen]; if (StringUtils.isBlank(text)) { return xpad; } char[] chs = text.trim().toLowerCase().toCharArray(); List<Integer> list = new ArrayList<Integer>(); for (int i = 0; i < chs.length; i++) { String element = Character.toString(chs[i]); if (word_to_id.containsKey(element)) { list.add(word_to_id.get(element)); } } if (list.size() == 0) { return xpad; } int size = list.size(); Integer[] targetInter = (Integer[]) list.toArray(new Integer[size]); /* * 用于jdk1.8转换 */ // int[] target= // Arrays.stream(targetInter).mapToInt(Integer::valueOf).toArray(); int[] target = Intetoint(targetInter); if (size <= maxlen) { System.arraycopy(target, 0, xpad[0], xpad[0].length - size, target.length); } else { System.arraycopy(target, size - xpad[0].length, xpad[0], 0, xpad[0].length); } return xpad; } public static int[][] gettexttoid(String text, int maxlen, Map<String, Integer> map) { if (maxlen < 1) { throw new IllegalArgumentException("maxlen长度必须大于等于1"); } int[][] xpad = new int[1][maxlen]; if (StringUtils.isBlank(text)) { return xpad; } char[] chs = text.trim().toLowerCase().toCharArray(); List<Integer> list = new ArrayList<Integer>(); for (int i = 0; i < chs.length; i++) { String element = Character.toString(chs[i]); if (map.containsKey(element)) { list.add(map.get(element)); } } if (list.size() == 0) { return xpad; } int size = list.size(); Integer[] targetInter = (Integer[]) list.toArray(new Integer[size]); /* * 用于jdk1.8转换 */ // int[] target= // Arrays.stream(targetInter).mapToInt(Integer::valueOf).toArray(); int[] target = Intetoint(targetInter); if (size <= maxlen) { System.arraycopy(target, 0, xpad[0], xpad[0].length - size, target.length); } else { System.arraycopy(target, size - xpad[0].length, xpad[0], 0, xpad[0].length); } return xpad; } private static int[] Intetoint(Integer[] arr) { int[] result = new int[arr.length]; for (int i = 0; i < arr.length; i++) { result[i] = arr[i].intValue(); } return result; } public static double getClassifyByBiLSTM(String text, Session sess, Map<String, Integer> map, Tensor keep_prob) { if (StringUtils.isBlank(text)) { return 0.0; } int[][] arr = gettexttoid(text, map); Tensor input = Tensor.create(arr); Tensor result = sess.runner().feed("input_x", input).feed("keep_prob", keep_prob).fetch("score/pred_y").run() .get(0); long[] rshape = result.shape(); int nlabels = (int) rshape[1]; int batchSize = (int) rshape[0]; float[][] logits = result.copyTo(new float[batchSize][nlabels]); if (nlabels > 1 && batchSize > 0) { return logits[0][1]; } return 0.0; }}
其次是service启动项：
package com.dianping.text.classify.base;import java.io.BufferedReader;import java.io.FileInputStream;import java.io.InputStreamReader;import java.nio.file.Paths;import java.util.HashMap;import java.util.Map;import org.slf4j.Logger;import org.slf4j.LoggerFactory;import org.tensorflow.Graph;import org.tensorflow.Session;import org.tensorflow.Tensor;import com.dianping.text.classify.util.TersorflowUtils;import com.dianping.text.classifybydl.api.service.Category;public class Abuse implements Category { private static final Logger logger = LoggerFactory.getLogger(Abuse.class); private Graph g; private Session sess; private Tensor keep_prob; private Map<String, Integer> map; private void init() { g = new Graph(); keep_prob = Tensor.create(1.0f); try { updataMap(); byte[] graphDef = TersorflowUtils .readAllBytesOrExit(Paths.get(this.getClass().getResource("/").getPath(), "modelabuse/graph.model")); g.importGraphDef(graphDef); sess = new Session(g); } catch (Exception e) { logger.error(" model load:", e); } } public void updataMap() { map = new HashMap<>(); int i = 0; try { BufferedReader buffer = null; String path = this.getClass().getResource("/").getPath() + "modelabuse/vocab_cnews.txt"; buffer = new BufferedReader(new InputStreamReader(new FileInputStream(path))); String line = buffer.readLine().trim(); while (line != null) { map.put(line, i++); line = buffer.readLine().trim(); } buffer.close(); } catch (Exception e) { } System.out.println("map.size is:" + map.size()); } @Override public double getClassify(String text) { return TersorflowUtils.getClassifyByBiLSTM(text, sess, map, keep_prob); } public static void main(String[] args) { Abuse abuse = new Abuse(); abuse.init(); System.out.println(abuse.getClassify("我操你妈个逼")); }}
结果：
