使用DL4j加载keras模型时出错:“;nn.modelimport.keras.Hdf5Archive:对象条目内/之间输入的意外结束;
我使用的是经过keras和tensor flow训练的DL模型(保存到h5文件)。在spring boot中,微服务也已停靠 当我尝试使用此spring boot服务运行docker映像时,我需要帮助来解决此错误,该服务使用dl4j库调用keras模型: pom.xml如下所示:使用DL4j加载keras模型时出错:“;nn.modelimport.keras.Hdf5Archive:对象条目内/之间输入的意外结束;,keras,deep-learning,microservices,pom.xml,deeplearning4j,Keras,Deep Learning,Microservices,Pom.xml,Deeplearning4j,我使用的是经过keras和tensor flow训练的DL模型(保存到h5文件)。在spring boot中,微服务也已停靠 当我尝试使用此spring boot服务运行docker映像时,我需要帮助来解决此错误,该服务使用dl4j库调用keras模型: pom.xml如下所示: <?xml version="1.0" encoding="UTF-8"?> <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema- instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>com.autentia</groupId>
<artifactId>micro-service-spring-boot</artifactId>
<version>0.0.1-SNAPSHOT</version>
<packaging>jar</packaging>
<name>micro-service-spring-boot</name>
<description>Demo project microservice with Spring Boot and Docker</description>
<parent>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-parent</artifactId>
<version>1.4.0.M3</version>
<relativePath/> <!-- lookup parent from repository -->
</parent>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<java.version>1.8</java.version>
<docker.image.prefix>curso</docker.image.prefix>
<nd4j.version>1.0.0-beta2</nd4j.version>
<dl4j.version>1.0.0-beta2</dl4j.version>
</properties>
<dependencies>
<dependency>
<groupId>javax.xml.bind</groupId>
<artifactId>jaxb-api</artifactId>
<version>2.3.0</version>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-core</artifactId>
<version>1.0.0-beta2</version>
</dependency>
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-modelimport</artifactId>
<version>1.0.0-beta2</version>
</dependency>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-native-platform</artifactId>
<version>1.0.0-beta2</version>
</dependency>
<dependency>
<groupId>org.eclipse.jetty</groupId>
<artifactId>jetty-server</artifactId>
<version>9.4.9.v20180320</version>
</dependency>
<dependency>
<groupId>com.google.cloud.dataflow</groupId>
<artifactId>google-cloud-dataflow-java-sdk-all</artifactId>
<version>2.2.0</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-maven-plugin</artifactId>
</plugin>
<plugin>
<groupId>com.spotify</groupId>
<artifactId>docker-maven-plugin</artifactId>
<version>0.4.10</version>
<configuration>
<imageName>${docker.image.prefix}/${project.artifactId}</imageName>
<dockerDirectory>src/main/docker</dockerDirectory>
<serverId>docker-hub</serverId>
<registryUrl>https://index.docker.io/v1/</registryUrl>
<forceTags>true</forceTags>
<imageTags>
<imageTag>${project.version}</imageTag>
</imageTags>
<resources>
<resource>
<targetPath>/</targetPath>
<directory>${project.build.directory}</directory>
<include>${project.build.finalName}.jar</include>
</resource>
</resources>
</configuration>
<executions>
<execution>
<id>build-image</id>
<phase>package</phase>
<goals>
<goal>build</goal>
</goals>
</execution>
<execution>
<id>push-image</id>
<phase>install</phase>
<goals>
<goal>push</goal>
</goals>
<configuration>
<imageName>${docker.image.prefix}/${project.artifactId}:${project.version}</imageName>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
<repositories>
<repository>
<id>spring-snapshots</id>
<name>Spring Snapshots</name>
<url>https://repo.spring.io/snapshot</url>
<snapshots>
<enabled>true</enabled>
</snapshots>
</repository>
<repository>
<id>spring-milestones</id>
<name>Spring Milestones</name>
<url>https://repo.spring.io/milestone</url>
<snapshots>
<enabled>false</enabled>
</snapshots>
</repository>
</repositories>
<pluginRepositories>
<pluginRepository>
<id>spring-snapshots</id>
<name>Spring Snapshots</name>
<url>https://repo.spring.io/snapshot</url>
<snapshots>
<enabled>true</enabled>
</snapshots>
</pluginRepository>
<pluginRepository>
<id>spring-milestones</id>
<name>Spring Milestones</name>
<url>https://repo.spring.io/milestone</url>
<snapshots>
<enabled>false</enabled>
</snapshots>
</pluginRepository>
</pluginRepositories>
}
我已经用几个dl4j版本进行了检查,但问题仍然存在
有人能帮我吗
谢谢 您的屏幕截图中有两个问题:
tf.keras
,而不是独立的keras版本,对吗
不幸的是,从1.0.0-beta6版开始,deeplearning4j不支持导入使用
tf.keras
创建的模型 您的屏幕截图中有两个问题:
tf.keras
,而不是独立的keras版本,对吗
不幸的是,从1.0.0-beta6版开始,deeplearning4j不支持导入使用
tf.keras
创建的模型 是的,我正在使用tf.keras。我也尝试过不同的dl4j版本,但也有问题。我可以使用flask毫无问题地读取此h5文件。不幸的是,我的最后一句话确实适用于您的情况:目前不支持tf.keras导入。最后,我使用java 11和beta6 dl4j解决了它。谢谢是的,我正在使用tf.keras。我也尝试过不同的dl4j版本,但也有问题。我可以使用flask毫无问题地读取此h5文件。不幸的是,我的最后一句话确实适用于您的情况:目前不支持tf.keras导入。最后,我使用java 11和beta6 dl4j解决了它。谢谢
package com.EjemplosEva;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.http.HttpStatus;
import javax.servlet.http.HttpServletRequest;
import org.springframework.web.bind.annotation.ResponseBody;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;
import java.net.InetAddress;
import org.deeplearning4j.nn.modelimport.keras.KerasModelImport;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.io.ClassPathResource;
import org.deeplearning4j.nn.modelimport.keras.Hdf5Archive;
import org.deeplearning4j.nn.modelimport.keras.*;
@RestController
public class GuestbookController {
@RequestMapping(value = "/micro-service")
public String hello() throws Exception {
// load the model
final MultiLayerNetwork model;
try {
String simpleMlp = new ClassPathResource("modelLstm.h5").getFile().getPath();
System.out.println("File opened\n");
model = KerasModelImport.importKerasSequentialModelAndWeights(simpleMlp);
System.out.println("Fichero imported\n");
}
catch (Exception e) {
throw new RuntimeException(e);
}
return ("Hello world first step finished");
}