Char Classifier
---------
Given a short phrase, train a neural network to classify it based on the characters that it contains.
The trainer takes in a file of phrases (separated by new line) and a file of target values (separated by new line).
To train on the sample data (parts of speech classification):
```
import chClassifier
p = chClassifier.Trainer("sample/words", "sample/parts-of-speech")
p.train([100, 100], 20, 100)
p.save("sample", "example-run")
```
This will save a trained model with the tag 'example-run' in the sample directory. To use that model, run:
```
import chClassifier
k = chClassifier.Classifier("sample", "example-run")
print k.classify("dog")
```
This will return an array of tuples of original label + likelyhood that the label is correct, like so:
```
>> print k.classify("dog")
[(u'VB', 0.050349433), (u'NN', 3.8027303)]
```
The higher the number, the more sure we are of the classification (`dog` is definitely a noun, for example, and probably not a verb).
---------
Given a short phrase, train a neural network to classify it based on the characters that it contains.
The trainer takes in a file of phrases (separated by new line) and a file of target values (separated by new line).
To train on the sample data (parts of speech classification):
```
import chClassifier
p = chClassifier.Trainer("sample/words", "sample/parts-of-speech")
p.train([100, 100], 20, 100)
p.save("sample", "example-run")
```
This will save a trained model with the tag 'example-run' in the sample directory. To use that model, run:
```
import chClassifier
k = chClassifier.Classifier("sample", "example-run")
print k.classify("dog")
```
This will return an array of tuples of original label + likelyhood that the label is correct, like so:
```
>> print k.classify("dog")
[(u'VB', 0.050349433), (u'NN', 3.8027303)]
```
The higher the number, the more sure we are of the classification (`dog` is definitely a noun, for example, and probably not a verb).
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Release files for chClassifier 0.6.3
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|---|---|---|---|---|
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