Quickstart for Java using Maven
This is everything you need to run DL4J examples and begin your own projects.
We recommend that you join our community forum. There you can request help and give feedback, but please do use this guide before asking questions we've answered below. If you are new to deep learning, we've included a road map for beginners with links to courses, readings and other resources.
We are currently reworking the Getting Started Guide.
If you find that you have trouble following along here, take a look at the Konduit blog, as it features some getting started guides from the community.
Deeplearning4j is a domain-specific language to configure deep neural networks, which are made of multiple layers. Everything starts with a MultiLayerConfiguration, which organizes those layers and their hyperparameters.
Hyperparameters are variables that determine how a neural network learns. They include how many times to update the weights of the model, how to initialize those weights, which activation function to attach to the nodes, which optimization algorithm to use, and how fast the model should learn. This is what one configuration would look like:
With Deeplearning4j, you add a layer by calling layer on the NeuralNetConfiguration.Builder(), specifying its place in the order of layers (the zero-indexed layer below is the input layer), the number of input and output nodes, nIn and nOut, as well as the type: DenseLayer.
Once you've configured your net, you train the model with model.fit.
Java (developer version) 1.7 or later (Only 64-Bit versions supported)
Apache Maven (automated build and dependency manager)
IntelliJ IDEA or Eclipse
You should have these installed to use this QuickStart guide. DL4J targets professional Java developers who are familiar with production deployments, IDEs and automated build tools. Working with DL4J will be easiest if you already have experience with these.
If you are new to Java or unfamiliar with these tools, read the details below for help with installation and setup. Otherwise, skip to DL4J Examples.
If you don't have Java 1.7 or later, download the current Java Development Kit (JDK) here. To check if you have a compatible version of Java installed, use the following command:
Please make sure you have a 64-Bit version of java installed, as you will see an error telling you no jnind4j in java.library.path if you decide to try to use a 32-Bit version instead. Make sure the JAVA_HOME environment variable is set.
Maven is a dependency management and automated build tool for Java projects. It works well with IDEs such as IntelliJ and lets you install DL4J project libraries easily. Install or update Maven to the latest release following their instructions for your system. To check if you have the most recent version of Maven installed, enter the following:
If you are working on a Mac, you can simply enter the following into the command line:
Maven is widely used among Java developers and it's pretty much mandatory for working with DL4J. If you come from a different background, and Maven is new to you, check out Apache's Maven overview and our introduction to Maven for non-Java programmers, which includes some additional troubleshooting tips. Other build tools such as Ivy and Gradle can also work, but we support Maven best.
An Integrated Development Environment (IDE) allows you to work with our API and configure neural networks in a few steps. We strongly recommend using IntelliJ, which communicates with Maven to handle dependencies. The community edition of IntelliJ is free.
There are other popular IDEs such as Eclipse and Netbeans. However, IntelliJ is preferred, and using it will make finding help on the community forums easier if you need it.
Install the latest version of Git. If you already have Git, you can update to the latest version using Git itself:
The latest version of Mac's Mojave OS breaks git, producing the following error message:
xcrun: error: invalid active developer path (/Library/Developer/CommandLineTools), missing xcrun at: /Library/Developer/CommandLineTools/usr/bin/xcrun
This can be fixed by running:
Use the command line to enter the following:
Open IntelliJ and choose Import Project. Then select the main 'dl4j-examples' directory. (Note: the example in the illustration below refers to an outdated repository named dl4j-0.4-examples. However, the repository that you will download and install will be called dl4j-examples)..png)
Choose 'Import project from external model' and ensure that Maven is selected.
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Continue through the wizard's options. Select the SDK that begins with jdk. (You may need to click on a plus sign to see your options...) Then click Finish. Wait a moment for IntelliJ to download all the dependencies. You'll see the horizontal bar working on the lower right.
Pick an example from the file tree on the left. Right-click the file to run.
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To run DL4J in your own projects, we highly recommend using Maven for Java users, or a tool such as SBT for Scala. The basic set of dependencies and their versions are shown below. This includes:
deeplearning4j-core, which contains the neural network implementations
nd4j-native-platform, the CPU version of the ND4J library that powers DL4J
datavec-api - Datavec is our library vectorizing and loading data
Every Maven project has a POM file. Here is how the POM file should appear when you run your examples.
Within IntelliJ, you will need to choose the first Deeplearning4j example you're going to run. We suggest MLPClassifierLinear, as you will almost immediately see the network classify two groups of data in our UI. The file on Github can be found here.
To run the example, right click on it and select the green button in the drop-down menu. You will see, in IntelliJ's bottom window, a series of scores. The rightmost number is the error score for the network's classifications. If your network is learning, then that number will decrease over time with each batch it processes. At the end, this window will tell you how accurate your neural-network model has become:
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In another window, a graph will appear, showing you how the multilayer perceptron (MLP) has classified the data in the example. It will look like this:
Congratulations! You just trained your first neural network with Deeplearning4j.
Join our community forums on community.konduit.ai.
Read the introduction to deep neural networks.
Check out the more detailed Comprehensive Setup Guide.
Python folks: If you plan to run benchmarks on Deeplearning4j comparing it to well-known Python framework [x], please read these instructions on how to optimize heap space, garbage collection and ETL on the JVM. By following them, you will see at least a 10x speedup in training time.
Q: I'm using a 64-Bit Java on Windows and still get the no jnind4j in java.library.path error
A: You may have incompatible DLLs on your PATH. To tell DL4J to ignore those, you have to add the following as a VM parameter (Run -> Edit Configurations -> VM Options in IntelliJ):
Q: SPARK ISSUES I am running the examples and having issues with the Spark based examples such as distributed training or datavec transform options.
A: You may be missing some dependencies that Spark requires. See this Stack Overflow discussion for a discussion of potential dependency issues. Windows users may need the winutils.exe from Hadoop.
Download winutils.exe from https://github.com/steveloughran/winutils and put it into the null/bin/winutils.exe (or create a hadoop folder and add that to HADOOP_HOME)
Windows users might be seeing something like:
If that is the issue, see this page. In this case replace with "Nd4jCpu".
Now that you've learned how to run the different examples, we've made a template available for you that has a basic MNIST trainer with simple evaluation code.
The Quickstart template is available at https://github.com/eclipse/deeplearning4j-examples/tree/master/mvn-project-template.
To use the template:
Copy the standalone-sample-project from the examples and give it the name of your project.
Import the folder into IntelliJ.
Start coding!