java手写图像识别_Java分布式神经网络库Deeplearning4j之上手实践手写数字图像识别与模型训练...

不念不忘少年蓝@ 2022-11-09 12:42 213阅读 0赞

环境的搭建可以参考另一篇文章。

![0818b9ca8b590ca3270a3433284dd417.png][]

第一步运行MnistImagePipelineExampleSave代码下载数据集，并进行训练和保存

需要下载一个文件(windows默认保存在C:\\Users\\Administrator\\AppData\\Local\\Temp\\dl4j\_Mnist)。文件存在git。如果网络不好。建议手动下载并解压。然后注释掉代码中的下载方法即可。如图所示:

![0818b9ca8b590ca3270a3433284dd417.png][]

训练需要一段时间等待即可。时间长短取决于自己电脑配置。

第二步运行MnistImagePipelineLoadChooser代码。并选中一个手写数字图像。进行识别测试

package org.deeplearning4j.examples.dataexamples;

import org.datavec.image.loader.NativeImageLoader;

import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;

import org.deeplearning4j.util.ModelSerializer;

import org.nd4j.linalg.api.ndarray.INDArray;

import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;

import org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler;

import org.slf4j.Logger;

import org.slf4j.LoggerFactory;

import javax.swing.\*;

import java.io.File;

import java.util.Arrays;

import java.util.List;

/\*\*

\* 给定用户一个文件选择框来选中要测试的手写数字图像

\* 0-9数字 白色或者黑色背景进行识别

\*/

public class MnistImagePipelineLoadChooser \{

private static Logger log = LoggerFactory.getLogger(MnistImagePipelineLoadChooser.class);

/\*

Create a popup window to allow you to chose an image file to test against the

trained Neural Network

Chosen images will be automatically

scaled to 28\*28 grayscale

\*/

public static String fileChose()\{

JFileChooser fc = new JFileChooser();

int ret = fc.showOpenDialog(null);

if (ret == JFileChooser.APPROVE\_OPTION)

File file = fc.getSelectedFile();

String filename = file.getAbsolutePath();

return filename;

else \{

return null;

public static void main(String\[\] args) throws Exception\{

int height = 28;

int width = 28;

int channels = 1;

List labelList = Arrays.asList(0,1,2,3,4,5,6,7,8,9);

// pop up file chooser

String filechose = fileChose().toString();

//LOAD NEURAL NETWORK

// MnistImagePipelineExampleSave训练并保存模型

File locationToSave = new File("trained\_mnist\_model.zip");

// 检查保存的模型是否存在

if(locationToSave.exists())\{

System.out.println("\\n\#\#\#\#\#\#存在保存的训练模型\#\#\#\#\#\#\\n");

\}else\{

System.out.println("\\n\\n\#\#\#\#\#\#\#File not found!\#\#\#\#\#\#\#");

System.out.println("This example depends on running ");

System.out.println("MnistImagePipelineExampleSave");

System.out.println("Run that Example First");

System.out.println("\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\#\\n\\n");

System.exit(0);

MultiLayerNetwork model = ModelSerializer.restoreMultiLayerNetwork(locationToSave);

log.info("\*\*\*\*\*\*\*\*\*TEST YOUR IMAGE AGAINST SAVED NETWORK\*\*\*\*\*\*\*\*");

// 选择一个文件

File file = new File(filechose);

// 使用NativeImageLoader转换为数值矩阵

NativeImageLoader loader = new NativeImageLoader(height, width, channels);

// 得到图像并赋值INDArray

INDArray image = loader.asMatrix(file);

// 0-255

// 0-1

DataNormalization scaler = new ImagePreProcessingScaler(0,1);

scaler.transform(image);

// 传递到神经网络 并得到概率值

INDArray output = model.output(image);

log.info("\#\# The FILE CHOSEN WAS " + filechose);

log.info("\#\# The Neural Nets Pediction \#\#");

log.info("\#\# list of probabilities per label \#\#");

//log.info("\#\# List of Labels in Order\#\# ");

//有序状态

log.info(output.toString());

log.info(labelList.toString());

选择图片运行后的结果

\#\#\#\#\#\#Saved Model Found\#\#\#\#\#\#

o.n.l.f.Nd4jBackend - Loaded \[CpuBackend\] backend

o.n.n.NativeOpsHolder - Number of threads used for NativeOps: 2

o.n.n.Nd4jBlas - Number of threads used for BLAS: 2

o.n.l.a.o.e.DefaultOpExecutioner - Backend used: \[CPU\]; OS: \[Windows 7\]

o.n.l.a.o.e.DefaultOpExecutioner - Cores: \[4\]; Memory: \[1.8GB\];

o.n.l.a.o.e.DefaultOpExecutioner - Blas vendor: \[OPENBLAS\]

o.d.n.m.MultiLayerNetwork - Starting MultiLayerNetwork with WorkspaceModes set to \[training: NONE; inference: SEPARATE\]

o.d.e.d.MnistImagePipelineLoadChooser - \*\*\*\*\*\*\*\*\*TEST YOUR IMAGE AGAINST SAVED NETWORK\*\*\*\*\*\*\*\*

o.d.e.d.MnistImagePipelineLoadChooser - \#\# The FILE CHOSEN WAS C:\\Users\\Administrator\\Desktop\\93.png

o.d.e.d.MnistImagePipelineLoadChooser - \#\# The Neural Nets Pediction \#\#

o.d.e.d.MnistImagePipelineLoadChooser - \#\# list of probabilities per label \#\#

o.d.e.d.MnistImagePipelineLoadChooser - \[0.00, 0.00, 0.00, 1.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00\]

o.d.e.d.MnistImagePipelineLoadChooser - \[0, 1, 2, 3, 4, 5, 6, 7, 8, 9\]

图中的数字为: 3

数字的置信度为:100.0%

Process finished with exit code 0

选择的图片为:

![0818b9ca8b590ca3270a3433284dd417.png][]

可见模型对黑白的手写数字识别度还算是可以的。

相关资料。建议还是去官网查阅。本博客只是进行上手实践

https://deeplearning4j.org/cn/

[0818b9ca8b590ca3270a3433284dd417.png]: /images/20221023/5669a93946d9405baa8f0b9f90d433b9.png

java手写图像识别_Java分布式神经网络库Deeplearning4j之上手实践手写数字图像识别与模型训练...

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