Я пытаюсь настроить проект maven в IntelliJ, и мне нужны полные современные инструкции о том, как выполнить настройку для deeplearning4j, потому что я продолжаю сталкиваться с ошибками, такими как:
java .lang.NoClassDefFoundError: Не удалось инициализировать класс org.nd4j.linalg.factory.Nd4j и java .lang.ExceptionInInitializerError (Это когда я использую Kotlin REPL). Я также получаю эти предупреждения, когда я запускаю программу в обычном режиме: log4j: ПРЕДУПРЕЖДЕНИЕ. Для регистратора не найдено ни одного наставника (org.nd4j.linalg.factory.Nd4jBackend).
Это мой pom. xml file:
<?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>
Это моя программа (взято из примеров nd4j):
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)
}