xenial (1) mlpack_perceptron.1.gz

Provided by: mlpack-bin_2.0.1-1_amd64 bug

NAME

       mlpack_perceptron - perceptron

SYNOPSIS

        mlpack_perceptron [-h] [-v] [-m string] [-l string] [-n int] [-o string] [-M string] [-T string] [-t string] -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 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 (-m) or training a perceptron given training data
       (-t), or both those things at once. In addition, this program allows classification  on  a  test  dataset
       (-T)  and will save the classification results to the given output file (-o). The perceptron model itself
       may be saved with a file specified using the -M option.

       The training data given with the -t option should have class labels as its last  dimension  (so,  if  the
       training  data  is  in CSV format, labels should be the last column). Alternately, the -l (--labels_file)
       option may be used to specify a separate file 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'   (and
       'training_labels.csv)' and saves the model to ’perceptron.xml'.

       $ perceptron -t training_data.csv -l training_labels.csv -m perceptron.csv

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

       $ perceptron -i perceptron.xml -T test_data.csv -o 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  -m
       (--input_model_file).  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.

OPTIONS

       --help (-h)
              Default help info.

       --info [string]
              Get help on a specific module or option.  Default value ''.  --input_model_file (-m) [string] File
              containing input perceptron model. Default value ''.

       --labels_file (-l) [string]
              A file containing labels for the training set.  Default value ''.

       --max_iterations (-n) [int]
              The maximum number of iterations the perceptron is to be run Default value 1000.

       --output_file (-o) [string]
              The  file  in  which  the  predicted  labels  for  the  test  set  will  be written. Default value
              ’output.csv'.  --output_model_file (-M)  [string]  File  to  save  trained  perceptron  model  to.
              Default value ''.

       --test_file (-T) [string]
              A file containing the test set. Default value ’'.  --training_file (-t) [string] A file containing
              the training set. Default value ''.

       --verbose (-v)
              Display informational messages and the full list of parameters and timers at the end of execution.

       --version (-V)
              Display the version of mlpack.

ADDITIONAL INFORMATION

ADDITIONAL INFORMATION

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

                                                                                            mlpack_perceptron(1)