def testLineExample():
"example which decodes a RNN output of a text line. Taken from IAM dataset. RNN output produced by TensorFlow model."
# chars of IAM dataset
classes = ' !"#&\'()*+,-./0123456789:;?ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
# matrix containing TxC RNN output. C=len(classes)+1 because of blank label.
mat = softmax(loadRNNOutput('../data/line/rnnOutput.csv'))
# language model: used for token passing (word list) and beam search (char bigrams)
lm = LanguageModel.LanguageModel('../data/line/corpus.txt', classes)
# decode RNN output with different decoding algorithms
gt = 'the fake friend of the family, like the'
print('TARGET :', '"' + gt + '"')
print('BEST PATH :', '"' + BestPath.ctcBestPath(mat, classes) + '"')
print('PREFIX SEARCH :',
'"' + PrefixSearch.ctcPrefixSearchHeuristicSplit(mat, classes) + '"')
print('BEAM SEARCH :',
'"' + BeamSearch.ctcBeamSearch(mat, classes, None) + '"')
print('BEAM SEARCH LM:',
'"' + BeamSearch.ctcBeamSearch(mat, classes, lm) + '"')
print(
'TOKEN :', '"' +
TokenPassing.ctcTokenPassing(mat, classes, lm.getWordList()) + '"')
print('PROB(TARGET) :', Loss.ctcLabelingProb(mat, gt, classes))
print('LOSS(TARGET) :', Loss.ctcLoss(mat, gt, classes))
def testMiniExample():
"example which shows difference between taking most probable path and most probable labelling. No language model used."
classes = "ab"
mat = np.array([[0.4, 0, 0.6], [0.4, 0, 0.6]])
print('TARGET :', '"a"')
print('BEST PATH :', '"' + BestPath.ctcBestPath(mat, classes) + '"')
print('PREFIX SEARCH:',
'"' + PrefixSearch.ctcPrefixSearch(mat, classes) + '"')
print('BEAM SEARCH :',
'"' + BeamSearch.ctcBeamSearch(mat, classes, None) + '"')
print(
'TOKEN :', '"' +
TokenPassing.ctcTokenPassing(mat, classes, ['a', 'b', 'ab', 'ba']) +
'"')
def testMiniExample():
"example which shows difference between taking most probable path and most probable labeling. No language model used."
# chars and input matrix
classes = 'ab'
mat = np.array([[0.4, 0, 0.6], [0.4, 0, 0.6]])
# decode
gt = 'a'
print('TARGET :', '"' + gt + '"')
print('BEST PATH :', '"' + BestPath.ctcBestPath(mat, classes) + '"')
print('PREFIX SEARCH:', '"' + PrefixSearch.ctcPrefixSearch(mat, classes) + '"')
print('BEAM SEARCH :', '"' + BeamSearch.ctcBeamSearch(mat, classes, None) + '"')
print('TOKEN :', '"' + TokenPassing.ctcTokenPassing(mat, classes, ['a', 'b', 'ab', 'ba']) + '"')
print('PROB(TARGET) :', Loss.ctcLabelingProb(mat, gt, classes))
print('LOSS(TARGET) :', Loss.ctcLoss(mat, gt, classes))