关于TensorFlow实现CRF的方法我在网上找了很久也没有找到很合适的，目前最多关注的是自己写出来的CRF，比较复杂。在翻阅TensorFlow文档的时候偶然间发现TensorFlow1.4.0版本已经实现了CRF，并找到了官方例程，实现简单，在这里跟大家分享一下
import numpy as np
import tensorflow as tf# 参数设置
num_examples = 10
num_words = 20
num_features = 100
num_tags = 5# 构建随机特征
x = np.random.rand(num_examples, num_words, num_features).astype(np.float32)# 构建随机tag
y = np.random.randint(num_tags, size=[num_examples, num_words]).astype(np.int32)# 获取样本句长向量（因为每一个样本可能包含不一样多的词），在这里统一设为 num_words - 1，真实情况下根据需要设置
sequence_lengths = np.full(num_examples, num_words - 1, dtype=np.int32)# 训练，评估模型
with tf.Graph().as_default():with tf.Session() as session:x_t = tf.constant(x)y_t = tf.constant(y)sequence_lengths_t = tf.constant(sequence_lengths)# 在这里设置一个无偏置的线性层weights = tf.get_variable("weights", [num_features, num_tags])matricized_x_t = tf.reshape(x_t, [-1, num_features])matricized_unary_scores = tf.matmul(matricized_x_t, weights)unary_scores = tf.reshape(matricized_unary_scores,[num_examples, num_words, num_tags])# 计算log-likelihood并获得transition_paramslog_likelihood, transition_params = tf.contrib.crf.crf_log_likelihood(unary_scores, y_t, sequence_lengths_t)# 进行解码（维特比算法），获得解码之后的序列viterbi_sequence和分数viterbi_scoreviterbi_sequence, viterbi_score = tf.contrib.crf.crf_decode(unary_scores, transition_params, sequence_lengths_t)loss = tf.reduce_mean(-log_likelihood)train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)session.run(tf.global_variables_initializer())mask = (np.expand_dims(np.arange(num_words), axis=0) < # np.arange()创建等差数组np.expand_dims(sequence_lengths, axis=1)) # np.expand_dims()扩张维度# 得到一个num_examples*num_words的二维数组，数据类型为布尔型，目的是对句长进行截断# 将每个样本的sequence_lengths加起来，得到标签的总数total_labels = np.sum(sequence_lengths)# 进行训练for i in range(1000):tf_viterbi_sequence, _ = session.run([viterbi_sequence, train_op])if i % 100 == 0:correct_labels = np.sum((y == tf_viterbi_sequence) * mask)accuracy = 100.0 * correct_labels / float(total_labels)print("Accuracy: %.2f%%" % accuracy)