Neural network implementation in octave
copy config.sample.csv to config.csv and change the options. The configuration options are loaded in a struct with:
config = config();
After that, options are available with
config.layers
config.input_path
...
New variables will be added automatically when added to the config file
The value of this field can be both relative and absolute. Use ./ for input files in the same folder as main.m.
the layers option is the number of layers in the neural network. Please enter a value of 2 or higher! A network with 1 layer can only learn linear divisable models, which can be calculated with a single comparison.
The number of neurons in each internal layer.
When layers is equal to 2, the only internal layer has the layer_size neurons. The matrix of weights between the input layer and the internal layer has layer_size * input_size values, with input_size the number of columns in the input csv file.
Also when layers is equal to 2, the weight matrix between the only internal layer and the output layer has layer_size * output_size values, with output_size the number of columns in the output csv file.
The main.m script requires one command line argument, which is the name of the input file (without .csv). The input file is retrieved from the folder in the input_path variable. The csv with the output variables should have the same name, but should end with -out.csv. Both the input and the output file should have integers, separated with comma's (,). The output file only has 0 and 1 values. Do not confuse the output file with the saved weights file.
To try this neural network please start with input_path, ./data/ and run
$ octave main.m example
With only 50 iterations an training accuracy of 99% can be reached.
- dividing training set to do training and cross checking
- selection of
lambdaby checking precision & recall on cross checking set - Principle Component Analysis
- export weights to csv
- New repository; a generic recommender system.
MIT