我正在尝试在 IntelliJ 中设置一个 maven 项目，我需要有关如何设置 deeplearning4j 的完整最新说明，因为我一直遇到以下错误：
java.lang.NoClassDefFoundError: 无法初始化类 org.nd4j.linalg.factory.Nd4j 和 java.lang.ExceptionInInitializerError（这些是我使用 Kotlin REPL 时的情况）。当我正常运行程序时，我也会收到这些警告：log4j:WARN No appenders could be found for logger (org.nd4j.linalg.factory.Nd4jBackend)。
这是我的 pom.xml 文件：
<?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/maven-v4_0_0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>org.example</groupId>
<artifactId>Test</artifactId>
<version>1.0-SNAPSHOT</version>
<packaging>jar</packaging>
<name>org.example Test</name>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<kotlin.version>1.3.61</kotlin.version>
<kotlin.code.style>official</kotlin.code.style>
<junit.version>4.12</junit.version>
</properties>
<dependencies>
<dependency>
<groupId>org.jetbrains.kotlin</groupId>
<artifactId>kotlin-stdlib</artifactId>
<version>${kotlin.version}</version>
</dependency>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-common</artifactId>
<version>1.0.0-beta6</version>
</dependency>
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-core</artifactId>
<version>1.0.0-beta4</version>
</dependency>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-native-platform</artifactId>
<version>1.0.0-beta4</version>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-api</artifactId>
<version>1.7.5</version>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-api</artifactId>
<version>1.7.5</version>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-api</artifactId>
<version>1.7.5</version>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
<version>1.7.5</version>
</dependency>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-jblas</artifactId>
<version>0.0.3.5.5.4-SNAPSHOT</version>
</dependency>
</dependencies>
<build>
<sourceDirectory>src/main/kotlin</sourceDirectory>
<testSourceDirectory>src/test/kotlin</testSourceDirectory>
<plugins>
<plugin>
<groupId>org.jetbrains.kotlin</groupId>
<artifactId>kotlin-maven-plugin</artifactId>
<version>${kotlin.version}</version>
<executions>
<execution>
<id>compile</id>
<phase>compile</phase>
<goals>
<goal>compile</goal>
</goals>
</execution>
<execution>
<id>test-compile</id>
<phase>test-compile</phase>
<goals>
<goal>addSources</goal>
<goal>test-compile</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
这是我的程序（取自 nd4j 示例）：
import org.nd4j.linalg.api.ndarray.INDArray
import org.nd4j.linalg.factory.Nd4j
import java.util.*
class Test3
{
}
fun main() { /*
Before we begin, let's review what an INDArray is:
A INDArray is a multi-dimensional array of numbers: a vector, matrix, or tensor for example.
Internally, it may store single precision or double precision floating point values for each entry.
Here, we'll see how you can get some basic information about INDArrays. In later examples, we'll see
the different ways to create INDArrays, and more operations we can do on them.
*/
//Let's start by creating a basic 2d array: a matrix with 3 rows and 5 columns. All elements are 0.0
val nRows = 3
val nColumns = 5
val myArray: INDArray = Nd4j.zeros(nRows, nColumns)
//Next, print some basic information about the array:
System.out.println("Basic INDArray information:")
System.out.println("Num. Rows: " + myArray.rows())
System.out.println("Num. Columns: " + myArray.columns())
System.out.println("Num. Dimensions: " + myArray.rank()) //2 dimensions -> rank 2
System.out.println("Shape: " + Arrays.toString(myArray.shape())) //[3,5] -> 3 rows, 5 columns
System.out.println("Length: " + myArray.length()) // 3 rows * 5 columns = 15 total elements
//We can print the array itself using toString method:
System.out.println("\nArray Contents:\n$myArray")
//There are some other ways we can get the same or similar info
System.out.println()
System.out.println("size(0) == nRows: " + myArray.size(0)) //Also equivalent to: .shape()[0]
System.out.println("size(1) == nCols: " + myArray.size(1)) //Also equivalent to: .shape()[1]
System.out.println("Is a vector: " + myArray.isVector())
System.out.println("Is a scalar: " + myArray.isScalar())
System.out.println("Is a matrix: " + myArray.isMatrix())
System.out.println("Is a square matrix: " + myArray.isSquare())
//Let's make some modifications to our array...
// Note that indexing starts at 0. Thus 0..2 are valid indices for rows, and 0..4 are valid indices for columns here
myArray.putScalar(0, 1, 2.0) //Set value at row 0, column 1 to value 2.0
myArray.putScalar(2, 3, 5.0) //Set value at row 2, column 3 to value 5.0
System.out.println("\nArray after putScalar operations:")
System.out.println(myArray)
//We can also get individual values:
val val0: Double = myArray.getDouble(0, 1) //Get the value at row 0, column 1 - expect value 2.0 as we set this earlier
System.out.println("\nValue at (0,1): $val0")
//Finally, there are many things we can do to the array... for example adding scalars:
val myArray2: INDArray = myArray.add(1.0) //Add 1.0 to each entry
System.out.println("\nNew INDArray, after adding 1.0 to each entry:")
System.out.println(myArray2)
val myArray3: INDArray = myArray2.mul(2.0) //Multiply each entry by 2.0
System.out.println("\nNew INDArray, after multiplying each entry by 2.0:")
System.out.println(myArray3)
}