fix syn_words and CRF
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yongzhuo
parent
d4a94149a5
commit
da4cba7fb8
@ -12,7 +12,7 @@ import jieba
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KEY_WORDS = ["macropodus"] # 不替换同义词的词语
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ENGLISH = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
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ENGLISH = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
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def is_english(text):
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@ -22,7 +22,7 @@ def is_english(text):
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:return: boolean, True or False
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"""
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try:
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text_r = text.replace(' ', '').strip()
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text_r = text.replace(" ", "").strip()
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for tr in text_r:
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if tr in ENGLISH:
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continue
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@ -39,7 +39,7 @@ def is_number(text):
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:return: boolean, True or False
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"""
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try:
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text_r = text.replace(' ', '').strip()
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text_r = text.replace(" ", "").strip()
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for tr in text_r:
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if tr.isdigit():
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continue
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@ -57,7 +57,7 @@ def get_syn_word(word):
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"""
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if not is_number(word.strip()) or not is_english(word.strip()):
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word_syn = synonyms.nearby(word)
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word_syn = word_syn if not word_syn else [word]
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word_syn = word_syn[0] if len(word_syn[0]) else [word]
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return word_syn
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else:
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return [word]
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@ -182,7 +182,7 @@ def eda(text, n=1, use_syn=True):
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return sens_4
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if __name__ == '__main__':
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if __name__ == "__main__":
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sens = "".join(["macropodus", "是不是", "哪个", "啦啦",
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"只需做好这四点，就能让你养的天竺葵全年花开不断！"])
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print(eda(sens))
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@ -23,7 +23,7 @@ from keras import regularizers
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from keras import activations
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from keras import constraints
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import warnings
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import keras
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import os
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# crf_loss
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from keras.losses import sparse_categorical_crossentropy
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from keras.losses import categorical_crossentropy
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@ -220,7 +220,7 @@ def to_tuple(shape):
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with keras-team/keras. So we must apply this function to
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all input_shapes of the build methods in custom layers.
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"""
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if is_tf_keras:
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if os.environ.get("TF_KERAS")==1:
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import tensorflow as tf
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return tuple(tf.TensorShape(shape).as_list())
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else:
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@ -530,36 +530,36 @@ class CRF(Layer):
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base_config = super(CRF, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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# @property
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# def loss_function(self):
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# warnings.warn('CRF.loss_function is deprecated '
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# 'and it might be removed in the future. Please '
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# 'use losses.crf_loss instead.')
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# return crf_loss
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#
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# @property
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# def accuracy(self):
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# warnings.warn('CRF.accuracy is deprecated and it '
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# 'might be removed in the future. Please '
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# 'use metrics.crf_accuracy')
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# if self.test_mode == 'viterbi':
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# return crf_viterbi_accuracy
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# else:
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# return crf_marginal_accuracy
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#
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# @property
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# def viterbi_acc(self):
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# warnings.warn('CRF.viterbi_acc is deprecated and it might '
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# 'be removed in the future. Please '
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# 'use metrics.viterbi_acc instead.')
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# return crf_viterbi_accuracy
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#
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# @property
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# def marginal_acc(self):
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# warnings.warn('CRF.moarginal_acc is deprecated and it '
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# 'might be removed in the future. Please '
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# 'use metrics.marginal_acc instead.')
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# return crf_marginal_accuracy
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@property
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def loss_function(self):
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warnings.warn('CRF.loss_function is deprecated '
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'and it might be removed in the future. Please '
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'use losses.crf_loss instead.')
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return crf_loss
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@property
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def accuracy(self):
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warnings.warn('CRF.accuracy is deprecated and it '
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'might be removed in the future. Please '
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'use metrics.crf_accuracy')
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if self.test_mode == 'viterbi':
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return crf_viterbi_accuracy
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else:
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return crf_marginal_accuracy
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@property
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def viterbi_acc(self):
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warnings.warn('CRF.viterbi_acc is deprecated and it might '
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'be removed in the future. Please '
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'use metrics.viterbi_acc instead.')
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return crf_viterbi_accuracy
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@property
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def marginal_acc(self):
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warnings.warn('CRF.moarginal_acc is deprecated and it '
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'might be removed in the future. Please '
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'use metrics.marginal_acc instead.')
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return crf_marginal_accuracy
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@staticmethod
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def softmaxNd(x, axis=-1):
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@ -655,9 +655,22 @@ class CRF(Layer):
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chain_energy = chain_energy * K.expand_dims(
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K.expand_dims(m[:, 0] * m[:, 1]))
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if return_logZ:
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# shapes: (1, B, F) + (B, F, 1) -> (B, F, F)
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# # shapes: (1, B, F) + (B, F, 1) -> (B, F, F)
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# energy = chain_energy + K.expand_dims(input_energy_t - prev_target_val, 2)
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# new_target_val = K.logsumexp(-energy, 1) # shapes: (B, F)
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# return new_target_val, [new_target_val, i + 1]
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energy = chain_energy + K.expand_dims(input_energy_t - prev_target_val, 2)
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new_target_val = K.logsumexp(-energy, 1) # shapes: (B, F)
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new_target_val = K.logsumexp(-energy, 1)
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# added from here
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if len(states) > 3:
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if K.backend() == 'theano':
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m = states[3][:, t:(t + 2)]
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else:
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m = K.slice(states[3], [0, t], [-1, 2])
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is_valid = K.expand_dims(m[:, 0])
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new_target_val = is_valid * new_target_val + (1 - is_valid) * prev_target_val
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# added until here
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return new_target_val, [new_target_val, i + 1]
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else:
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energy = chain_energy + K.expand_dims(input_energy_t + prev_target_val, 2)
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