- #WEKA JAR FILE HAS THE CORRECT PATH INSTALL#
- #WEKA JAR FILE HAS THE CORRECT PATH ZIP FILE#
- #WEKA JAR FILE HAS THE CORRECT PATH CODE#
Simply invoke the tool from the commandline: $ java -cp weka.Run. and WekaDeeplearning4j will check your GPU's availability. Once WekaDeeplearning4j is installed, you can find IsGPUAvailable in the Tools menu in the GUIChooser: If the tool returns false, your GPU is not available to WekaDeeplearning4j (e.g., caused by incorrect drivers) and will If the tool returns true, your GPU is setup correctly and ready to use! GPU is identified and available to WekaDeeplearning4j. install-cuda-libs.sh ~/Downloads/wekaDeeplearning4j-cuda-10.2-1.6.0-linux-x86_64.zipĮnsuring your GPU is setup correctly may be difficult so to help out we've provided IsGPUAvailable, a simple diagnostic tool to test whether your If you want to download the library zip yourself, choose the appropriate combination of your platform and CUDA version from the latest release and point the installation script to the file, e.g.
#WEKA JAR FILE HAS THE CORRECT PATH INSTALL#
The install script automatically downloads the libraries and copies them into your wekaDeeplearning4j package installation. Make sure CUDA is installed on your system as explained here. To add GPU support, download and run the latest install-cuda-libs.sh for Linux/Macosx or install-cuda-libs.ps1 for Windows. Which results in Installed Repository Loaded Packageġ.5.6 - Yes : Weka wrappers for Deeplearning4j You can check whether the installation was successful with $ java -cp \ Where must be replaced by the path pointing to the Weka jar file, and is the wekaDeeplearning4j package zip file. Weka packages can be easily installed either via the user interface as described here, or simply via the commandline: $ java -cp \
![weka jar file has the correct path weka jar file has the correct path](https://i.stack.imgur.com/wDEfV.png)
Nvidia provides some good installation instructions for all platforms: The GPU additions needs the CUDA Toolkit 10.0, 10.1, or 10.2 backend with the appropriate cuDNN library to be installed on your system. CPUįor the package no further requisites are necessary.
#WEKA JAR FILE HAS THE CORRECT PATH ZIP FILE#
You need to unzip the Weka zip file to a directory of your choice. WekaDeeplearning4j package latest version ( here).
![weka jar file has the correct path weka jar file has the correct path](https://static.wixstatic.com/media/2d4c97_996154e6c0094050aaf37b6d3aa4b38b~mv2.png)
The following call java MessageClassifier -m email1023.txt -t messageclassifier. ClassifyingĬlassifying an unseen message is quite straight-forward, one just omits the class option (" -c"). Repeat this for all the messages you want to have classified. Here's an example, that labels the message email1.txt as miss: java MessageClassifier -m email1.txt -c miss -t messageclassifier.model Since the data and the model are kept for future use, one has to specify a filename, where the MessageClassifier is serialized to (" -t"). If you run the MessageClassifier for the first time, you need to provide labeled examples to build a classifier from, i.e., messages (" -m") and the corresponding classes (" -c"). Note: The classpath handling is omitted from here on. Javac -classpath /path/to/weka.jar MessageClassifier.java
#WEKA JAR FILE HAS THE CORRECT PATH CODE#
![weka jar file has the correct path weka jar file has the correct path](https://deeplearning.cms.waikato.ac.nz/img/gui/GUIChooserToolsMenu.png)
MessageClassifier ( book, stable-3.6, developer) Source codeĭepending on the version of the book, download the corresponding version (this article is based on the 2nd edition): In the following you'll find some information about the MessageClassifier from the 2nd edition of the Data Mining book by Witten and Frank.