博主用PyTorch训练神经网络替代评分卡，测试效果良好准备上生产环境。因生产环境主要是Java技术栈，需徒手撸简单模型运行框架。介绍了实现思路，包括了解神经网络正向传播、编写多线程矩阵相乘Java代码等，还给出Python和Java端代码，最终Java输出与Python一致，并附上JSON结构。
背景
最近用PyTorch训练了一个神经网络,用来替代原来数据团队建立的评分卡;
作为一个Java架构,既要会分布式,又要学点数据分析,这样才有核心竞争力;
从测试集的效果来看,比评分卡的效果好不少,准备上生产环境做引流陪跑.
生产环境目前最主要是Java技术栈,陪跑最简单的是徒手撸一个简单的模型运行框架
(Java直接运行原生的PyTorch模型依赖诸多,下载一堆dll文件,还有版本限制)
因为不涉及反向传播的代码,其实最主要的是文件格式设计和解析,以及兼顾部分性能;
思路
1. 对于不了解神经网络的同学,先 了解神经网络是怎么正向传播和向量化的;
2. 多线程矩阵相乘的Java代码;
3. 将PyTorch的模型导出成多层的List 格式的Json;
4. 目前先实现 nn.Linear,nn.Dropout,nn.ReLU(),nn.Sigmoid()等解析,预留扩展能力 ;
Python端代码
import torch
import torch.nn as nn
import json
in_features=10
## 定义模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.net=nn.Sequential(
nn.Linear(in_features=in_features,out_features=in_features*2),nn.ReLU(),
nn.Linear(in_features*2,in_features*4),nn.ReLU(),
nn.Linear(in_features*4,in_features*4),nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(in_features*4,in_features*2),nn.ReLU(),
nn.Linear(in_features*2,1),nn.Sigmoid(),
)
def forward(self, input:torch.FloatTensor):
return self.net(input)
## 创建模型实例
net = Net()
### ... 略训练和测试代码
## 模型dump 成JSON
def dump_w_b(model,file):
model.eval()
res=[]
for layer in model.net:
json_obj={'name':layer.__class__.__name__}
if json_obj['name']=="Linear" :
json_obj['w']=layer.weight.detach().numpy().T.tolist()
json_obj['b']=layer.bias.detach().numpy().tolist()
res.append(json_obj)
with open(file, "w") as file:
file.write(json.dumps(res))
### dump到对应目录
dump_w_b(net,"test.json")
result=torch.from_numpy(np.array([[ 0, 0.0093474167290689, 17, 0.016589247191179823, 2, 18, 0.029496849269328967, 0, 1, 0]
])).float()
###测试结果 tensor([[0.4746]], grad_fn=<SigmoidBackward0>)
print(net(result))
Java端代码
package hex.wang.kdb.ai.run;
import com.fasterxml.jackson.databind.ObjectMapper;
import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.concurrent.CountDownLatch;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.function.BiConsumer;
import java.util.function.Function;
public class ModelRuns {
private final static ExecutorService executor = Executors.newFixedThreadPool(Runtime.getRuntime().availableProcessors() * 2);
/**
* 计算一层网络
* 最主要的知识点: 矩阵相乘,使用多线程等提升性能
*
* @param matrix 输入
* @param w 参数: 设置w为list是因为Jackson解析的时候是List
* @param bias 偏置 设置bias为list是因为Jackson解析的时候是List
* @param g 激活函数: 一个Liner后面跟多个时 g为依次执行
* @return
*/
public static double[][] cal(double[][] matrix, List<List<Double>> w, List<Double> bias, Function<Double, Double> g) {
int rowsA = matrix.length;
int colsA = matrix[0].length;
int colsB = w.get(0).size();
double[][] resultMatrix = new double[rowsA][colsB];
//使用ContDownLatch 等待线程执行结果
CountDownLatch latch = new CountDownLatch(rowsA);
for (int i = 0; i < rowsA; i++) {
final int indexI = i;
executor.execute(() -> {
try {
for (int j = 0; j < colsB; j++) {
final int indexJ = j;
for (int k = 0; k < colsA; k++) {
resultMatrix[indexI][indexJ] += matrix[indexI][k] * w.get(k).get(indexJ);
}
}
if (bias != null) {
for (int j = 0; j < colsB; j++) {
resultMatrix[indexI][j] += bias.get(j);
if (g != null) {
resultMatrix[indexI][j] = g.apply(resultMatrix[indexI][j]);
}
}
}
//防止报错 线程池挂起
} finally {
latch.countDown();
}
});
}
try {
latch.await();
} catch (InterruptedException e) {
return null;
}
return resultMatrix;
}
List<Function<double[][], double[][]>> strategy = new ArrayList<>();
//对应函数
final static Map<String, Function<Double, Double>> FUNCTIONS = new HashMap<>();
static {
FUNCTIONS.put("ReLU", (d) -> Math.max(0, d));
FUNCTIONS.put("Sigmoid", (d) -> 1 / (1 + Math.exp(-d)));
//Dropout在评估模式下直接忽略
FUNCTIONS.put("Dropout", (d) -> d);
}
/**
* 从Json文件中加载成 List<Function<double[][], double[][]>>每一层list对应一次计算
* 这样一次加载就可以多次运行
*
* @param inputStream 输入流
*/
public void load(InputStream inputStream) {
try (BufferedReader br = new BufferedReader(new InputStreamReader(inputStream));) {
String line = null;
StringBuilder sb = new StringBuilder();
while ((line = br.readLine()) != null) {
sb.append(line);
}
ObjectMapper om = new ObjectMapper();
List<Map<String, Object>> list = om.readValue(sb.toString(), List.class);
BiConsumer<Map<String, Object>, List<Function<Double, Double>>> add = (l, fs) -> {
if (l != null) {
Function<double[][], double[][]> function = (matrix) ->
cal(matrix, (List<List<Double>>) l.get("w"), (List<Double>) l.get("b"), (d) -> {
double res = d;
for (Function<Double, Double> f : fs) {
res = f.apply(res);
}
return res;
});
strategy.add(function);
}
};
Map<String, Object> preLiner = null;
List<Function<Double, Double>> fun = new ArrayList<>();
for (int i = 0; i < list.size(); i = i + 1) {
Map<String, Object> map = list.get(i);
String name = map.get("name").toString();
if ("Linear".equals(name)) {
if (preLiner != null) {
add.accept(preLiner, fun);
}
preLiner = map;
fun = new ArrayList<>();
} else {
fun.add(FUNCTIONS.get(name));
}
}
add.accept(preLiner, fun);
} catch (Exception e) {
e.printStackTrace();
}
}
/**
* 模型计算
*
* @param input 输入
* @return
*/
public double[][] run(double[][] input) {
double[][] res = input;
for (Function<double[][], double[][]> function : strategy) {
res = function.apply(res);
}
return res;
}
public static void main(String[] args) throws Exception {
ModelRuns modelRun = new ModelRuns();
modelRun.load(new FileInputStream("src/main/java/hex/wang/kdb/ai/run/test.json"));
double[][] run = modelRun.run(new double[][]{{
0, 0.0093474167290689, 17, 0.016589247191179823, 2, 18, 0.029496849269328967, 0, 1, 0,
}});
//0.47463949292259966 与Python tensor([[0.4746]] 一致
System.out.println(run[0][0]);
}
}
结果
Java 输出的0.47463949292259966 与Python tensor([[0.4746]] 一致
附JSON结构
[{
"name": "Linear",
"w": [
[0.049014121294021606, 0.2260240614414215, -0.07569430768489838, 0.098835289478302, 0.011676609516143799, -0.05673441290855408, -0.12183792889118195, -0.1594104766845703, -0.1001812219619751, -0.07556363940238953, 0.15874972939491272, 0.1815515160560608, 0.03197541832923889, -0.164956733584404, 0.174170583486557, 0.1978684961795807, 0.2124955952167511, 0.02494204044342041, 0.07737991213798523, 0.223506897687912],
[0.12570449709892273, -0.05074012279510498, -0.11532723903656006, -0.03410300612449646, 0.2813394367694855, 0.2930663526058197, 0.3154285252094269, -0.1515667736530304, -0.12630736827850342, 0.09046667814254761, -0.227073073387146, -0.2909892499446869, 0.05439966917037964, -0.2955554127693176, -0.24468174576759338, -0.19554121792316437, -0.15940998494625092, -0.05569705367088318, 0.00027039647102355957, -0.2155320942401886],
[-0.26284104585647583, -0.16819967329502106, 0.011911123991012573, -0.24299904704093933, -0.095997154712677, -0.2535367012023926, 0.08970555663108826, 0.20097044110298157, 0.09061428904533386, -0.22959811985492706, 0.28205230832099915, -0.29438868165016174, 0.04723566770553589, -0.04720774292945862, 0.1277734935283661, -0.1816496104001999, 0.23394206166267395, 0.2871434986591339, 0.006114661693572998, -0.2690645456314087],
[-0.09173348546028137, 0.13575807213783264, 0.033074796199798584, -0.21160010993480682, -0.21243597567081451, -0.10736063122749329, 0.20223191380500793, 0.058478862047195435, 0.21479639410972595, -0.05793687701225281, -0.24128517508506775, -0.019178837537765503, -0.31491270661354065, 0.13123178482055664, -0.07667243480682373, 0.2122458517551422, -0.12257552146911621, -0.3118617534637451, -0.28478720784187317, -0.3143037259578705],
[-0.06639860570430756, -0.28937414288520813, -0.1739385575056076, 0.05930858850479126, -0.254612535238266, 0.26860710978507996, -0.18121059238910675, 0.2772187292575836, 0.2881365120410919, 0.18908390402793884, -0.2356683611869812, -0.31167733669281006, 0.0186120867729187, -0.3137657940387726, 0.30255261063575745, -0.2437284141778946, 0.11783036589622498, -0.018902599811553955, -0.24420180916786194, 0.12368589639663696],
[-0.29502832889556885, -0.20623940229415894, 0.04622131586074829, 0.25302961468696594, 0.29329541325569153, 0.08533713221549988, -0.012216299772262573, -0.15748517215251923, -0.04242083430290222, -0.11765123903751373, 0.19834378361701965, 0.14504572749137878, -0.21566975116729736, -0.17975513637065887, 0.31473585963249207, -0.06384009122848511, 0.038017839193344116, -0.01097080111503601, 0.10975286364555359, -0.07214650511741638],
[0.02500438690185547, 0.14519497752189636, 0.06151047348976135, -0.2938093841075897, 0.11705482006072998, -0.21894656121730804, -0.2777327001094818, 0.08499741554260254, -0.0693095475435257, 0.07211506366729736, -0.09737703204154968, 0.13412350416183472, 0.2603215277194977, 0.21599414944648743, -0.06963473558425903, 0.05242541432380676, 0.08793532848358154, -0.18096353113651276, 0.07554766535758972, 0.25916674733161926],
[-0.01806628704071045, -0.3128723204135895, -0.15232957899570465, -0.138676255941391, 0.12906679511070251, -0.07649528980255127, -0.11042334139347076, 0.06186613440513611, -0.07655452191829681, -0.11588402092456818, -0.2687896490097046, 0.05440652370452881, 0.0017803609371185303, 0.2700250446796417, -0.14676597714424133, 0.030959486961364746, 0.10642212629318237, -0.26338377594947815, -0.12073969841003418, -0.22476939857006073],
[0.10149875283241272, 0.03437066078186035, 0.03299066424369812, 0.25902315974235535, -0.002107083797454834, -0.010185003280639648, 0.08062568306922913, -0.15752992033958435, 0.09863567352294922, -0.24676844477653503, -0.3031960725784302, -0.27956250309944153, 0.29883500933647156, -0.275065153837204, -0.14314286410808563, -0.23479683697223663, 0.131430983543396, 0.13150906562805176, -0.08927637338638306, -0.23571382462978363],
[-0.046410977840423584, 0.3143695294857025, 0.027437061071395874, 0.017752259969711304, -0.25573018193244934, -0.0971163958311081, -0.18945296108722687, -0.14200326800346375, 0.22860798239707947, -0.28282952308654785, -0.015413850545883179, 0.06427007913589478, 0.04342859983444214, 0.03424161672592163, -0.07318872213363647, -0.022868573665618896, -0.0752001702785492, 0.3017915189266205, -0.1901322305202484, 0.2968579828739166]
],
"b": [-0.13250604271888733, -0.11769258975982666, -0.1811736524105072, 0.22289356589317322, -0.08990490436553955, 0.17470836639404297, 0.07157939672470093, -0.24236173927783966, -0.08122795820236206, -0.29916709661483765, -0.1942247450351715, -0.09729926288127899, -0.27487605810165405, 0.19307461380958557, -0.1549651026725769, 0.10388568043708801, 0.3092552125453949, -0.023724794387817383, -0.2036224603652954, 0.05324620008468628]
}, {
"name": "ReLU"
}, {
"name": "Linear",
"w": [
[0.1922588348388672, 0.0136394202709198, 0.08746951818466187, 0.2179190218448639, -0.017637580633163452, -0.03615671396255493, 0.10848012566566467, 0.21108904480934143, 0.033155202865600586, 0.010118648409843445, 0.14674389362335205, 0.10173043608665466, -0.0554722398519516, 0.13705900311470032, 0.08886870741844177, 0.07400226593017578, -0.20269621908664703, 0.19583174586296082, 0.07895553112030029, -0.0015076100826263428, 0.014361143112182617, 0.04023033380508423, 0.21307826042175293, 0.14562004804611206, 0.02970433235168457, -0.13546988368034363, 0.1775062084197998, -0.16776126623153687, -0.14171022176742554, -0.11940547823905945, 0.0030667930841445923, 0.049405425786972046, 0.21791428327560425, 0.14226192235946655, 0.14752787351608276, -0.12400484085083008, -0.13669149577617645, 0.05897292494773865, -0.059449851512908936, 0.05242118239402771],
[-0.22351296246051788, -0.1193380355834961, -0.06748509407043457, 0.16007000207901, 0.0941629707813263, -0.1178944781422615, 0.1718026101589203, 0.2122698426246643, -0.09016260504722595, -0.08463726937770844, 0.09498369693756104, 0.2190772294998169, -0.11027245968580246, 0.08658209443092346, 0.1803257167339325, 0.11671000719070435, 0.09169134497642517, -0.18820494413375854, -0.05112457275390625, 0.0858578085899353, -0.013051420450210571, 0.20337018370628357, 0.1053956151008606, 0.18569058179855347, 0.06581428647041321, -0.00011286139488220215, 0.1826930046081543, 0.15422332286834717, -0.18590250611305237, -0.12348419427871704, 0.0607684850692749, -0.22095155715942383, -0.16659224033355713, -0.16052591800689697, 0.15464138984680176, 0.16252866387367249, -0.13462640345096588, 0.1175067126750946, 0.218968003988266, 0.0652596652507782],
[0.09606620669364929, -0.12617993354797363, 0.11081892251968384, 0.22160306572914124, 0.10488474369049072, 0.218475341796875, 0.21625542640686035, -0.03234720230102539, 0.10515350103378296, -0.13301467895507812, 0.12133929133415222, -0.06942488253116608, 0.05325409770011902, -0.05524377524852753, 0.160069078207016, -0.1944463551044464, 0.020570367574691772, -0.07300648093223572, -0.1253926008939743, -0.14663410186767578, 0.046961843967437744, 0.1037103533744812, 0.2117903232574463, 0.08869683742523193, -0.06209324300289154, -0.12749817967414856, -0.11325114220380783, -0.0014680474996566772, 0.19310206174850464, 0.21917927265167236, 0.13614308834075928, 0.22262758016586304, -0.19062909483909607, 0.06130579113960266, 0.025560051202774048, 0.133678138256073, 0.08372607827186584, 0.11900779604911804, 0.06332391500473022, -0.0009699910879135132],
[-0.17619559168815613, 0.15528380870819092, 0.1371418535709381, 0.13537591695785522, 0.1730223000049591, 0.18615520000457764, -0.06463044881820679, -0.14263948798179626, 0.07556876540184021, 0.06907919049263, -0.12464959174394608, 0.09518611431121826, -0.20022884011268616, 0.14389464259147644, 0.19469141960144043, 0.02674943208694458, -0.20712736248970032, 0.1232571005821228, -0.08194366097450256, 0.024827778339385986, 0.1444879174232483, 0.0593796968460083, -0.03827512264251709, -0.21100495755672455, -0.01871877908706665, 0.19265753030776978, 0.07645559310913086, -0.015240833163261414, -0.07626664638519287, 0.02431541681289673, 0.058705657720565796, -0.14781716465950012, 0.09367415308952332, -0.176688089966774, 0.07896983623504639, -0.03725510835647583, -0.058721959590911865, -0.031992003321647644, -0.04971158504486084, -0.06632593274116516],
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}, {
"name": "ReLU"
}, {
"name": "Linear",
"w": [
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}, {
"name": "Sigmoid"
}]
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