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- Description:
Nen
Neural Network Implementation in Java
3-layer neural network for regression with sigmoid activation function and command line interface inspired by LibSVM.
Quick Start: "java -jar nen.jar"
Data Format:
Nen reads and writes LibSVM datasets.
Command-Line-Examples:
java -jar nen.jar scale abalone abalone_scaled java -jar nen.jar split abalone_scaled abalone_train abalone_test java -jar nen.jar train abalone_train abalone_model java -jar nen.jar predict abalone_test abalone_model abalone_predictions
Command-Line-Parameters:
Usage for Training: java -jar nen.jar train [options] train_file model_file Options: -t model_type : set type of model (default 1) 1 -- Regression 2 -- Classification (not yet implemented) -h hidden : set number of hidden units (default 4) -n steps : set number of iterations (default 10000) -lrstart l : set start-value of learnrate (default 0.005) -lrend l : set end-value of learnrate (default 0.0000001) Usage for Prediction: java -jar nen.jar predict test_file model_file output_file Usage for Scaling: java -jar nen.jar scale [options] input_file output_file Options: -t type : set scaling-type (default 1) 1 -- Scale both X and Y (for Regression problems) 2 -- Scale only X (for Classification problems) Usage for Split: java -jar nen.jar split [options] input_file train_outputfile test_outputfile Options: -t data_type : set type of data (default 1) 1 -- Regression 2 -- Classification (not yet implemented) -r seed : set seed for split (default 0) -p train_percentage : set trainsize in % (default 80)
Java-Example:
// Create data (1000 data points, 8 attributes) float[][] x=new float[1000][8]; float[][] y=new float[1000][1]; // Fill your data: .............. .............. // Create an 8x16x1 regression network: Nen nen=new Nen(Type.Regression,8,16,1); // Train network 10000 steps, learnrate 0.005..0.0000001, not quiet: nen.train(x,y,10000,0.005f,0.0000001f,false); // Get Predictions for training set (true for threadsafe): float[][] p=nen.get(x,true); // Get Training Error (Mean-Squared) float e=nen.getError(p,y);
- Changes to previous version:
Initial Announcement on mloss.org.
- BibTeX Entry: Download
- Supported Operating Systems: Platform Independent
- Data Formats: Libsvm
- Tags: Neural Networks
- Archive: download here
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