tensorflow2.10构建bilstm+crf的全代码

import tensorflow as tf
from tensorflow.keras import layers, Sequential
from tensorflow.keras.preprocessing.sequence import pad_sequences

# 构建BiLSTM-CRF模型
class BiLSTMCRF(tf.keras.Model):
def __init__(self, vocab_size, tag_size, embedding_dim, units):
super(BiLSTMCRF, self).__init__()
self.embedding = layers.Embedding(vocab_size, embedding_dim, mask_zero=True)
self.lstm = layers.Bidirectional(layers.LSTM(units, return_sequences=True))
self.dense = layers.Dense(tag_size)
self.crf = CRF(tag_size)

def call(self, inputs, training=False):
x = self.embedding(inputs)
x = self.lstm(x)
x = self.dense(x)
outputs = self.crf(x)
return outputs

# 定义CRF层
class CRF(layers.Layer):
def __init__(self, units):
super(CRF, self).__init__()
self.units = units

def build(self, input_shape):
self.transition_params = self.add_weight("transition_params", shape=[self.units, self.units])

def call(self, inputs, sequence_lengths=None, training=None):
if training is None:
training = self.trainable
if training:
log_likelihood, self.transition_params = tf.contrib.crf.crf_log_likelihood(inputs, tag_indices, sequence_lengths)
else:
log_likelihood, _ = tf.contrib.crf.crf_log_likelihood(inputs, tag_indices, sequence_lengths, self.transition_params)
return log_likelihood

# 构建数据
vocab = {'apple': 0, 'orange': 1, 'banana': 2}
tag = {'B': 0, 'I': 1, 'O': 2}
x = [[vocab['apple']], [vocab['orange']], [vocab['banana'], vocab['orange']]]
y = [[tag['B']], [tag['I']], [tag['B'], tag['I']]]

x = pad_sequences(x, padding='post')
y = pad_sequences(y, padding='post')

# 编码标签
decoded_y = tf.keras.utils.to_categorical(y, num_classes=len(tag))

# 定义模型
model = BiLSTMCRF(vocab_size=len(vocab), tag_size=len(tag), embedding_dim=64, units=100)

# 编译模型
model.compile(optimizer='adam', loss=model.crf, metrics=[model.crf])

# 训练模型
model.fit(x, decoded_y, batch_size=32, epochs=10, validation_split=0.2)