1mlpack_perceptron(1) General Commands Manual mlpack_perceptron(1)
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6 mlpack_perceptron - perceptron
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9 mlpack_perceptron [-h] [-v]
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12 This program implements a perceptron, which is a single level neural
13 network. The perceptron makes its predictions based on a linear pre‐
14 dictor function combining a set of weights with the feature vector. The
15 perceptron learning rule is able to converge, given enough iterations
16 using the --max_iterations (-n) parameter, if the data supplied is lin‐
17 early separable. The perceptron is parameterized by a matrix of weight
18 vectors that denote the numerical weights of the neural network.
19 This program allows loading a perceptron from a model (-m) or training
21 a perceptron given training data (-t), or both those things at once. In
22 addition, this program allows classification on a test dataset (-T) and
23 will save the classification results to the given output file (-o). The
24 perceptron model itself may be saved with a file specified using the -M
25 option.
26 The training data given with the -t option should have class labels as
28 its last dimension (so, if the training data is in CSV format, labels
29 should be the last column). Alternately, the -l (--labels_file) option
30 may be used to specify a separate file of labels.
31 All these options make it easy to train a perceptron, and then re-use
33 that perceptron for later classification. The invocation below trains a
34 perceptron on 'training_data.csv' (and 'training_labels.csv)' and saves
35 the model to ’perceptron.xml'.
36 $ perceptron -t training_data.csv -l training_labels.csv -M percep‐
38 tron.xml
39 Then, this model can be re-used for classification on 'test_data.csv'.
41 The example below does precisely that, saving the predicted classes to
42 ’predictions.csv'.
43 $ perceptron -m perceptron.xml -T test_data.csv -o predictions.csv
45 Note that all of the options may be specified at once: predictions may
47 be calculated right after training a model, and model training can
48 occur even if an existing perceptron model is passed with -m
49 (--input_model_file). However, note that the number of classes and the
50 dimensionality of all data must match. So you cannot pass a perceptron
51 model trained on 2 classes and then re-train with a 4-class dataset.
52 Similarly, attempting classification on a 3-dimensional dataset with a
53 perceptron that has been trained on 8 dimensions will cause an error.
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56 --help (-h)
57 Default help info.
58 --info [string]
60 Get help on a specific module or option. Default value ''.
61 --input_model_file (-m) [string] File containing input percep‐
62 tron model. Default value ''.
63 --labels_file (-l) [string]
65 A file containing labels for the training set. Default value
66 ''.
67 --max_iterations (-n) [int]
69 The maximum number of iterations the perceptron is to be run
70 Default value 1000.
71 --test_file (-T) [string]
73 A file containing the test set. Default value ’'. --train‐
74 ing_file (-t) [string] A file containing the training set.
75 Default value ''.
76 --verbose (-v)
78 Display informational messages and the full list of parameters
79 and timers at the end of execution.
80 --version (-V)
82 Display the version of mlpack.
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85 --output_file (-o) [string]
86 The file in which the predicted labels for the test set will be
87 written. Default value ''. --output_model_file (-M) [string]
88 File to save trained perceptron model to. Default value ''.
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92 For further information, including relevant papers, citations, and the‐
93 ory, For further information, including relevant papers, citations, and
94 theory, consult the documentation found at http://www.mlpack.org or
95 included with your consult the documentation found at
96 http://www.mlpack.org or included with your DISTRIBUTION OF MLPACK.
97 DISTRIBUTION OF MLPACK.
98 mlpack_perceptron(1)