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144 lines
5.5 KiB
Python
144 lines
5.5 KiB
Python
import pickle
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import numpy as np
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import pandas as pd
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import platform
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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_contrib.layers import CRF
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"""
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# padding: pre(默认) 向前补充0 post 向后补充0
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# truncating: 文本超过 pad_num, pre(默认) 删除前面 post 删除后面
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# x_train = pad_sequences(x, maxlen=pad_num, value=0, padding='post', truncating="post")
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# print("--- ", x_train[0][:20])
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使用keras_bert、keras_contrib的crf时bug记录
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TypeError: Tensors in list passed to 'values' of 'ConcatV2' Op have types [bool, float32] that don't all match
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解决方案, 修改crf.py 516行：
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mask2 = K.cast(K.concatenate([mask, K.zeros_like(mask[:, :1])], axis=1),
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为:
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mask2 = K.cast(K.concatenate([mask, K.cast(K.zeros_like(mask[:, :1]), mask.dtype)], axis=1),
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"""
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from keras.preprocessing.sequence import pad_sequences
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from config.setting import Config
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def load_data():
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train = _parse_data(Config.nlp_ner.path_train)
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test = _parse_data(Config.nlp_ner.path_test)
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print("--- init 数据加载解析完成 ---")
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# Counter({'的': 8, '中': 7, '致': 7, '党': 7})
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word_counts = Counter(row[0].lower() for sample in train for row in sample)
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vocab = [w for w, f in iter(word_counts.items()) if f >= 2]
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chunk_tags = ['O', 'B-PER', 'I-PER', 'B-LOC', 'I-LOC', "B-ORG", "I-ORG"]
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# 存储保留的有效个数的 vovab 和 对应 chunk_tags
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with open(Config.nlp_ner.path_config, 'wb') as outp:
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pickle.dump((vocab, chunk_tags), outp)
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print("--- init 配置文件保存成功 ---")
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train = _process_data(train, vocab, chunk_tags)
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test = _process_data(test , vocab, chunk_tags)
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print("--- init 对数据进行编码，生成训练需要的数据格式 ---")
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return train, test, (vocab, chunk_tags)
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def _parse_data(filename):
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"""
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以单下划线开头（_foo）的代表不能直接访问的类属性
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用于解析数据，用于模型训练
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:param filename: 文件地址
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:return: data: 解析数据后的结果
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[[['中', 'B-ORG'], ['共', 'I-ORG']], [['中', 'B-ORG'], ['国', 'I-ORG']]]
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"""
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with open(filename, 'rb') as fn:
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split_text = '\n'
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# 主要是分句: split_text 默认每个句子都是一行，所以原来换行就需要 两个split_text
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texts = fn.read().decode('utf-8').strip().split(split_text + split_text)
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# 对于每个字需要 split_text, 而字的内部需要用空格分隔
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data = [[row.split() for row in text.split(split_text)] for text in texts]
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return data
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def _process_data(data, vocab, chunk_tags, maxlen=None, onehot=False):
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if maxlen is None:
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maxlen = max(len(s) for s in data)
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# 对每个字进行编码
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word2idx = dict((w, i) for i, w in enumerate(vocab))
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# 如果不在 vocab里面，就给 unk 值为 1
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x = [[word2idx.get(w[0].lower(), 1) for w in s] for s in data]
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y_chunk = [[chunk_tags.index(w[1]) for w in s] for s in data]
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x = pad_sequences(x, maxlen) # left padding
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y_chunk = pad_sequences(y_chunk, maxlen, value=-1)
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if onehot:
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# 返回一个onehot 编码的多维数组
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y_chunk = np.eye(len(chunk_tags), dtype='float32')[y_chunk]
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else:
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# np.expand_dims:用于扩展数组的形状
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# https://blog.csdn.net/hong615771420/article/details/83448878
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y_chunk = np.expand_dims(y_chunk, 2)
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return x, y_chunk
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def process_data(data, vocab, maxlen=100):
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word2idx = dict((w, i) for i, w in enumerate(vocab))
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x = [word2idx.get(w[0].lower(), 1) for w in data]
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length = len(x)
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x = pad_sequences([x], maxlen) # left padding
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return x, length
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def create_model(len_vocab, len_chunk_tags):
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model = Sequential()
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model.add(Embedding(len_vocab, Config.nlp_ner.EMBED_DIM, mask_zero=True)) # Random embedding
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model.add(Bidirectional(LSTM(Config.nlp_ner.BiLSTM_UNITS // 2, return_sequences=True)))
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crf = CRF(len_chunk_tags, 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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return model
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def train():
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(train_x, train_y), (test_x, test_y), (vocab, chunk_tags) = load_data()
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model = create_model(len(vocab), len(chunk_tags))
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# train model
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model.fit(train_x, train_y, batch_size=16, epochs=Config.nlp_ner.EPOCHS, validation_data=[test_x, test_y])
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model.save(Config.nlp_ner.path_model)
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def test():
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with open(Config.nlp_ner.path_config, 'rb') as inp:
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(vocab, chunk_tags) = pickle.load(inp)
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model = create_model(len(vocab), len(chunk_tags))
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predict_text = '中华人民共和国国务院总理周恩来在外交部长陈毅的陪同下，连续访问了埃塞俄比亚等非洲10国以及阿尔巴尼亚'
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text_EMBED, length = process_data(predict_text, vocab)
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model.load_weights(Config.nlp_ner.path_model)
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raw = model.predict(text_EMBED)[0][-length:]
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result = [np.argmax(row) for row in raw]
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result_tags = [chunk_tags[i] for i in result]
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per, loc, org = '', '', ''
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for s, t in zip(predict_text, result_tags):
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if t in ('B-PER', 'I-PER'):
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per += ' ' + s if (t == 'B-PER') else s
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if t in ('B-ORG', 'I-ORG'):
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org += ' ' + s if (t == 'B-ORG') else s
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if t in ('B-LOC', 'I-LOC'):
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loc += ' ' + s if (t == 'B-LOC') else s
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print(['person:' + per, 'location:' + loc, 'organzation:' + org])
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def main():
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# print("--")
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train()
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# if __name__ == "__main__":
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# train()
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