环境的搭建可以参考另一篇文章。

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:

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:

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/