NAME

mlpack_perceptron - perceptron

SYNOPSIS

 mlpack_perceptron [-m unknown] [-l string] [-n int] [-T string] [-t string] [-V bool] [-o string] [-M unknown] [-P string] [-h -v] 

DESCRIPTION

This program implements a perceptron, which is a single level neural network. The perceptron makes its predictions based on a linear predictor function combining a set of weights with the feature vector. The perceptron learning rule is able to converge, given enough iterations (specified using the ’--max_iterations (-n)' parameter), if the data supplied is linearly separable. The perceptron is parameterized by a matrix of weight vectors that denote the numerical weights of the neural network.

This program allows loading a perceptron from a model (via the ’--input_model_file (-m)' parameter) or training a perceptron given training data (via the '--training_file (-t)' parameter), or both those things at once. In addition, this program allows classification on a test dataset (via the ’--test_file (-T)' parameter) and the classification results on the test set may be saved with the '--predictions_file (-P)' output parameter. The perceptron model may be saved with the '--output_model_file (-M)' output parameter.

Note: the following parameter is deprecated and will be removed in mlpack 4.0.0: '--output_file (-o)'. Use '--predictions_file (-P)' instead of '--output_file (-o)'.

The training data given with the '--training_file (-t)' option may have class labels as its last dimension (so, if the training data is in CSV format, labels should be the last column). Alternately, the '--labels_file (-l)' parameter may be used to specify a separate matrix of labels.

All these options make it easy to train a perceptron, and then re-use that perceptron for later classification. The invocation below trains a perceptron on 'training_data.csv' with labels 'training_labels.csv', and saves the model to 'perceptron_model.bin'.

$ mlpack_perceptron --training_file training_data.csv --labels_file training_labels.csv --output_model_file perceptron_model.bin

Then, this model can be re-used for classification on the test data ’test_data.csv'. The example below does precisely that, saving the predicted classes to 'predictions.csv'.

$ mlpack_perceptron --input_model_file perceptron_model.bin --test_file test_data.csv --predictions_file predictions.csv

Note that all of the options may be specified at once: predictions may be calculated right after training a model, and model training can occur even if an existing perceptron model is passed with the '--input_model_file (-m)' parameter. However, note that the number of classes and the dimensionality of all data must match. So you cannot pass a perceptron model trained on 2 classes and then re-train with a 4-class dataset. Similarly, attempting classification on a 3-dimensional dataset with a perceptron that has been trained on 8 dimensions will cause an error.

OPTIONAL INPUT OPTIONS

--help (-h) [bool]

Default help info.

--info [string]

Print help on a specific option. Default value ''.

--input_model_file (-m) [unknown]

Input perceptron model.

--labels_file (-l) [string]

A matrix containing labels for the training set.

--max_iterations (-n) [int]

The maximum number of iterations the perceptron is to be run Default value 1000.

--test_file (-T) [string]

A matrix containing the test set.

--training_file (-t) [string]

A matrix containing the training set.

--verbose (-v) [bool]

Display informational messages and the full list of parameters and timers at the end of execution.

--version (-V) [bool]

Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

--output_file (-o) [string]

The matrix in which the predicted labels for the test set will be written.

--output_model_file (-M) [unknown]

Output for trained perceptron model.

--predictions_file (-P) [string]

The matrix in which the predicted labels for the test set will be written.

ADDITIONAL INFORMATION

For further information, including relevant papers, citations, and theory, consult the documentation found at http://www.mlpack.org or included with your distribution of mlpack.