利用TransR方法实现对知识图谱的实体和关系嵌入成向量并输出结果python代码实现

好的，以下是利用TransR方法实现对知识图谱的实体和关系嵌入成向量并输出结果的Python代码实现：

import numpy as np
import tensorflow as tf

# 定义TransR模型
class TransR:
    def __init__(self, n_entity, n_relation, dim_e, dim_r, margin, learning_rate):
        self.n_entity = n_entity
        self.n_relation = n_relation
        self.dim_e = dim_e
        self.dim_r = dim_r
        self.margin = margin
        self.learning_rate = learning_rate
        
        # 定义实体向量和关系向量
        self.entity_embedding = tf.get_variable('entity_embedding', [n_entity, dim_e], initializer=tf.contrib.layers.xavier_initializer())
        self.relation_embedding = tf.get_variable('relation_embedding', [n_relation, dim_r], initializer=tf.contrib.layers.xavier_initializer())
        
        # 定义投影矩阵
        self.projection_matrix = tf.get_variable('projection_matrix', [dim_r, dim_e], initializer=tf.contrib.layers.xavier_initializer())
        
        # 定义三元组的头实体、关系和尾实体
        self.head = tf.placeholder(tf.int32, [None])
        self.relation = tf.placeholder(tf.int32, [None])
        self.tail = tf.placeholder(tf.int32, [None])
        
        # 定义训练过程
        self.pos_score = self._calc_score(self.head, self.relation, self.tail)
        self.neg_head = tf.placeholder(tf.int32, [None])
        self.neg_tail = tf.placeholder(tf.int32, [None])
        self.neg_head_score = self._calc_score(self.neg_head, self.relation, self.tail)
        self.neg_tail_score = self._calc_score(self.head, self.relation, self.neg_tail)
        self.loss = tf.reduce_sum(tf.maximum(0.0, self.pos_score - self.neg_head_score + self.margin) + tf.maximum(0.0, self.pos_score - self.neg_tail_score + self.margin))
        self.optimizer = tf.train.AdamOptimizer(self.learning_rate)
        self.train_op = self.optimizer.minimize(self.loss)
        
    # 定义分数计算函数
    def _calc_score(self, h, r, t):
        h_e = tf.nn.embedding_lookup(self.entity_embedding, h)
        r_e = tf.nn.embedding_lookup(self.relation_embedding, r)
        t_e = tf.nn.embedding_lookup(self.entity_embedding, t)
        proj = tf.matmul(r_e, self.projection_matrix)
        h_proj = tf.matmul(h_e, proj)
        t_proj = tf.matmul(t_e, proj)
        score = tf.reduce_sum(tf.square(h_proj + r_e - t_proj), axis=1)
        return score
    
    # 定义训练函数
    def train(self, sess, pos_triplets, neg_head_triplets, neg_tail_triplets):
        feed_dict = {
            self.head: pos_triplets[:, 0],
            self.relation: pos_triplets[:, 1],
            self.tail: pos_triplets[:, 2],
            self.neg_head: neg_head_triplets[:, 0],
            self.neg_tail: neg_tail_triplets[:, 2]
        }
        _, loss = sess.run([self.train_op, self.loss], feed_dict=feed_dict)
        return loss
    
    # 定义测试函数
    def test(self, sess, triplets):
        feed_dict = {
            self.head: triplets[:, 0],
            self.relation: triplets[:, 1],
            self.tail: triplets[:, 2]
        }
        score = sess.run(self.pos_score, feed_dict=feed_dict)
        return score
    
# 定义训练数据和测试数据
train_triplets = np.array([
    [0, 0, 1],
    [0, 1, 2],
    [1, 0, 2],
    [2, 1, 0],
    [1, 1, 1],
    [2, 0, 1],
    [2, 1, 2],
    [1, 2, 0]
], dtype=np.int32)

test_triplets = np.array([
    [0, 2, 1],
    [1, 1, 0],
    [2, 2, 0]
], dtype=np.int32)

# 定义TransR模型参数
n_entity = 3
n_relation = 3
dim_e = 3
dim_r = 3
margin = 1.0
learning_rate = 0.01

# 定义训练模型和测试模型
train_model = TransR(n_entity, n_relation, dim_e, dim_r, margin, learning_rate)
test_model = TransR(n_entity, n_relation, dim_e, dim_r, margin, learning_rate)

# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(1000):
        neg_head_triplets = np.array([(np.random.randint(0, n_entity), r, t) for h, r, t in train_triplets])
        neg_tail_triplets = np.array([(h, r, np.random.randint(0, n_entity)) for h, r, t in train_triplets])
        loss = train_model.train(sess, train_triplets, neg_head_triplets, neg_tail_triplets)
        if i % 100 == 0:
            print('epoch %d, loss %.2f' % (i, loss))
    
    # 测试模型
    pos_score = test_model.test(sess, test_triplets)
    print('test score:', pos_score)
    
    # 输出所有实体和关系的嵌入向量
    entity_embedding = sess.run(train_model.entity_embedding)
    relation_embedding = sess.run(train_model.relation_embedding)
    print('entity embedding:')
    print(entity_embedding)
    print('relation embedding:')
    print(relation_embedding)

以上代码实现了一个简单的TransR模型，并使用训练数据进行了模型的训练。在训练过程中，我们采用负采样的方式生成负样本并进行训练。训练完成后，我们使用测试数据进行模型的测试，并输出所有实体和关系的嵌入向量。