写一个基于tensorflow实现在多变量时序预测任务中，利用多头注意力机制求得各变量之间的相关系的代码

以下是一个基于tensorflow的多变量时序预测任务的代码，其中包含了多头注意力机制来求解各变量之间的相关性：

import tensorflow as tf
import numpy as np

# 定义输入变量的维度
num_inputs = 3
num_outputs = 1

# 定义神经网络的超参数
num_epochs = 100
batch_size = 16
learning_rate = 0.001
num_heads = 4

# 定义输入数据和输出数据
x = np.random.randn(100, num_inputs)
y = np.random.randn(100, num_outputs)

# 定义输入占位符
inputs = tf.placeholder(tf.float32, shape=[None, num_inputs])
targets = tf.placeholder(tf.float32, shape=[None, num_outputs])

# 定义多头注意力机制的函数
def multi_head_attention(inputs, num_heads):
    # 获取输入数据的维度
    input_dim = inputs.get_shape().as_list()[-1]

    # 计算每个头的维度
    head_dim = input_dim // num_heads

    # 分割输入数据，每个头的维度为head_dim
    inputs_split = tf.split(inputs, num_heads, axis=-1)

    # 定义查询向量、键向量和值向量的权重矩阵
    wq = tf.Variable(tf.random_normal([input_dim, head_dim]))
    wk = tf.Variable(tf.random_normal([input_dim, head_dim]))
    wv = tf.Variable(tf.random_normal([input_dim, head_dim]))

    # 计算查询向量、键向量和值向量
    q = tf.matmul(inputs_split, wq)
    k = tf.matmul(inputs_split, wk)
    v = tf.matmul(inputs_split, wv)

    # 计算注意力分数
    attention_scores = tf.matmul(q, tf.transpose(k, [0, 2, 1]))

    # 对注意力分数进行归一化
    attention_scores_normalized = tf.nn.softmax(attention_scores, axis=-1)

    # 计算注意力加权后的值向量
    attention_output = tf.matmul(attention_scores_normalized, v)

    # 将多头注意力加权后的值向量拼接起来
    attention_output_concat = tf.concat(attention_output, axis=-1)

    # 定义多头注意力机制的输出矩阵
    wo = tf.Variable(tf.random_normal([input_dim, input_dim]))

    # 对多头注意力机制的输出进行线性变换
    output = tf.matmul(attention_output_concat, wo)

    return output

# 定义神经网络的结构
def neural_network(inputs):
    # 定义多头注意力机制的输出
    multi_head_attention_output = multi_head_attention(inputs, num_heads)

    # 对多头注意力机制的输出进行全连接层的处理
    fc1 = tf.layers.dense(multi_head_attention_output, 128, activation=tf.nn.relu)
    fc2 = tf.layers.dense(fc1, 64, activation=tf.nn.relu)
    fc3 = tf.layers.dense(fc2, 32, activation=tf.nn.relu)

    # 对全连接层的输出进行线性变换
    output = tf.layers.dense(fc3, num_outputs)

    return output

# 定义损失函数和优化器
predictions = neural_network(inputs)
loss = tf.reduce_mean(tf.square(predictions - targets))
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)

# 训练神经网络
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    for epoch in range(num_epochs):
        num_batches = int(len(x) / batch_size)

        for i in range(num_batches):
            # 获取当前批次的数据
            x_batch = x[i * batch_size:(i + 1) * batch_size]
            y_batch = y[i * batch_size:(i + 1) * batch_size]

            # 运行优化器
            _, loss_val = sess.run([optimizer, loss], feed_dict={inputs: x_batch, targets: y_batch})

        # 打印当前迭代的损失值
        print('Epoch:', epoch + 1, 'Loss:', loss_val)

    # 使用训练好的模型进行预测
    predictions_val = sess.run(predictions, feed_dict={inputs: x})