I am trying to set up a maven project in IntelliJ and I need complete up to date instructions on how to do the set up for deeplearning4j because I keep running into errors such as:
java.lang.NoClassDefFoundError: Could not initialize class org.nd4j.linalg.factory.Nd4j and java.lang.ExceptionInInitializerError (These are when I use Kotlin REPL). I also get these warnings when I run the program normally: log4j:WARN No appenders could be found for logger (org.nd4j.linalg.factory.Nd4jBackend).
This is my 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>
This is my program (taken from nd4j examples):
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)
}
