Skip to content

Latest commit

 

History

History
124 lines (88 loc) · 4.3 KB

README.md

File metadata and controls

124 lines (88 loc) · 4.3 KB
Raw

LNN

LNN (Little Neural Network) is a command-line program of feedforward neural networks aiming to make easy tasks easily done. The following Unix pipeline trains a network to sum real numbers.

$ seq 1024 \
| awk '{x=rand(); y=rand(); print x,y,x+y}' \
| lnn train -Cq2i1i -i1024 >model.nn

The trained model could be used as an addition calculator.

$ echo 5 3 | lnn run -m model.nn
8.000000

The key features of LNN are list below.

  • Light weight, containing only a standalone executable;
  • Serve as a Unix filter; Easy to work with other programs;
  • Plain-text formats of models, input, output, and samples;
  • Compact notations;
  • Different activation functions for different layers;
  • L2 regularization;
  • Mini-batch training.

Table of Contents

Installation

It would be better to select a version from the release page than downloading the working code, unless you understand the status of the working code. The latest stable release is always recommended.

$ make
$ sudo make install

By default, LNN is installed to /usr/local. You could call lnn --version to check the installation.

Getting Started

The following call of LNN creates a network with a 10-dimension input layer, a 5-dimension hidden layer with the sigmoid activation function, and a 3-dimension output layer with the softmax activation function.

$ lnn train -C q10i5s3m samples.txt >model.nn

The -C option creates a new model with the structure specified by the argument. The argument here is q10i5s3m. The first character q specifies the loss function to be the quadratic error. The following three strings 10i, 5s, 3m represent that there are 3 layers, including the input layer, with dimensions 10, 5, 3, respectively. The character following each dimension specifies the activation function for that layer. Here i ,s, m represent the identity function, the sigmoid function, and the softmax function, respectively (Further Documentation).

In the remaining part of this chapter, the dimension of input is denoted by $n$, and the dimension of output is denoted by $m$.

LNN reads samples from the file operand, or, by default, the standard input. The trained model is printed to the standard output in a text format.

The sample file is a text file containing numbers separated by white characters (space, tab, newline). Each $n+m$ numbers constitute a sample. The first $n$ numbers of a sample constitute the input vector, and the remaining constitute the output vector.

LNN supports many training arguments like learning rate, iteration count, and batch size (Further Documentation).

LNN could train a network based on an existed model by replacing -C with -m.

$ lnn train -m model.nn samples.txt >model2.nn

This allows one to observe the behaviors of the model in different stages and provide different training arguments.

The run sub-command runs an existed model.

$ lnn run -m model.nn input.txt

LNN reads the input vectors from the file operand, or, by default, the standard input. The input shall contain numbers separated by white characters (space, tab, newline). Each $n$ numbers constitute an input vector.

The output vector of each input vector is printed to the standard output. Each line contains an output vector. Components of an output vector are separated by a space.

The test sub-command evaluates an existed model.

$ lnn test -m model.nn samples.txt

LNN reads samples from the file operand, or, by default, the standard input. The mean loss value of the samples is printed to the standard output. The format of the input file is the same as of the train sub-command.

Further Documentation

  • The technical report serves as an extension of this read-me file. It contains more details and examples for understanding the design and usage.

  • Calling lnn --help prints a brief of the command-line options.

Credits

LNN was created by DONG Yuxuan in 2023.

Juuso Alasuutari gave useful advice about portability.