mirror of
https://github.com/wassname/keras-contrib.git
synced 2026-06-27 16:10:11 +08:00
Eric Xihui Lin
d1f4b6ba75
* implemented CRF * added test for CRF * added a chunking example for CRF * changed to use up2date tensorflow * added conll2000 data * minimize package dependency * using logsumexp in keras instead
95 lines
3.1 KiB
Python
95 lines
3.1 KiB
Python
"""Train CRF and BiLSTM-CRF on CONLL2000 chunking data, similar to https://arxiv.org/pdf/1508.01991v1.pdf.
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"""
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from __future__ import absolute_import
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from __future__ import print_function
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from __future__ import division
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import numpy
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from collections import Counter
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from keras.models import Sequential
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from keras.layers import Embedding, Bidirectional, LSTM
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from keras.preprocessing.sequence import pad_sequences
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from keras_contrib.layers import CRF
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from keras_contrib.datasets import conll2000
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EPOCHS = 10
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EMBED_DIM = 200
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BiRNN_UNITS = 200
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def classification_report(y_true, y_pred, labels):
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'''Similar to the one in sklearn.metrics, reports per classs recall, precision and F1 score'''
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y_true = numpy.asarray(y_true).ravel()
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y_pred = numpy.asarray(y_pred).ravel()
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corrects = Counter(yt for yt, yp in zip(y_true, y_pred) if yt == yp)
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y_true_counts = Counter(y_true)
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y_pred_counts = Counter(y_pred)
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report = ((lab, # label
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corrects[i] / max(1, y_true_counts[i]), # recall
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corrects[i] / max(1, y_pred_counts[i]), # precision
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y_true_counts[i] # support
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) for i, lab in enumerate(labels))
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report = [(l, r, p, 2 * r * p / max(1e-9, r + p), s) for l, r, p, s in report]
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print('{:<15}{:>10}{:>10}{:>10}{:>10}\n'.format('', 'recall', 'precision', 'f1-score', 'support'))
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formatter = '{:<15}{:>10.2f}{:>10.2f}{:>10.2f}{:>10d}'.format
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for r in report:
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print(formatter(*r))
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print('')
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report2 = zip(*[(r * s, p * s, f1 * s) for l, r, p, f1, s in report])
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N = len(y_true)
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print(formatter('avg / total', sum(report2[0]) / N, sum(report2[1]) / N, sum(report2[2]) / N, N) + '\n')
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# ------
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# Data
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# -----
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# conll200 has two different targets, here will only use IBO like chunking as an example
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(train_x, _, train_y), (test_x, _, test_y), (vocab, _, class_labels) = conll2000.load_data()
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# --------------
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# 1. Regular CRF
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# --------------
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print('==== training CRF ====')
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model = Sequential()
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model.add(Embedding(len(vocab), EMBED_DIM, mask_zero=True)) # Random embedding
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crf = CRF(len(class_labels), sparse_target=True)
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model.add(crf)
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model.summary()
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model.compile('adam', loss=crf.loss_function, metrics=[crf.accuracy])
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model.fit(train_x, train_y, epochs=EPOCHS, validation_data=[test_x, test_y])
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test_y_pred = model.predict(test_x).argmax(-1)[test_x > 0]
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test_y_true = test_y[test_x > 0]
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print('\n---- Result of CRF ----\n')
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classification_report(test_y_true, test_y_pred, class_labels)
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# -------------
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# 2. BiLSTM-CRF
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# -------------
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print('==== training BiLSTM-CRF ====')
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model = Sequential()
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model.add(Embedding(len(vocab), EMBED_DIM, mask_zero=True)) # Random embedding
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model.add(Bidirectional(LSTM(BiRNN_UNITS // 2, return_sequences=True)))
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crf = CRF(len(class_labels), sparse_target=True)
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model.add(crf)
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model.summary()
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model.compile('adam', loss=crf.loss_function, metrics=[crf.accuracy])
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model.fit(train_x, train_y, epochs=EPOCHS, validation_data=[test_x, test_y])
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test_y_pred = model.predict(test_x).argmax(-1)[test_x > 0]
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test_y_true = test_y[test_x > 0]
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print('\n---- Result of BiLSTM-CRF ----\n')
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classification_report(test_y_true, test_y_pred, class_labels)
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