用python生成一段代码，实现注意力机制对一段话进行分类，并标记出话中的关键词

好的，以下是一个简单的示例代码，它使用注意力机制对输入的文本进行分类，并标记出文本中的关键词。

import numpy as np
import tensorflow as tf

# 定义注意力机制函数
def attention(inputs, attention_size):
    hidden_size = inputs.shape[2].value  # 获取输入的向量维度
    W_omega = tf.Variable(tf.random_normal([hidden_size, attention_size], stddev=0.1))  # 定义权重矩阵W
    b_omega = tf.Variable(tf.random_normal([attention_size], stddev=0.1))  # 定义偏置向量b
    u_omega = tf.Variable(tf.random_normal([attention_size], stddev=0.1))  # 定义参数u

    with tf.name_scope('v'):
        v = tf.tanh(tf.tensordot(inputs, W_omega, axes=1) + b_omega)  # 计算v

    # 计算attention权重
    vu = tf.tensordot(v, u_omega, axes=1, name='vu')
    alphas = tf.nn.softmax(vu, name='alphas')

    # 计算加权平均值
    output = tf.reduce_sum(inputs * tf.expand_dims(alphas, -1), 1)

    return output


# 定义输入数据
input_text = "This is a sample sentence for testing attention mechanism in natural language processing."
input_text = input_text.lower().split()

# 定义词汇表和词向量
vocab = set(input_text)
vocab_size = len(vocab)
word2idx = {w: i for i, w in enumerate(vocab)}
idx2word = {i: w for i, w in enumerate(vocab)}
embedding_dim = 50
embeddings = np.random.randn(vocab_size, embedding_dim)

# 将输入数据转化为词向量
input_indices = [word2idx[w] for w in input_text]
input_vecs = np.array([embeddings[i] for i in input_indices])

# 定义标签和标签向量
labels = ['positive', 'negative']
label2idx = {l: i for i, l in enumerate(labels)}
idx2label = {i: l for i, l in enumerate(labels)}
label_vecs = np.eye(len(labels))

# 定义模型参数
attention_size = 50
hidden_size = 100
output_size = len(labels)

# 定义占位符
inputs = tf.placeholder(tf.float32, [None, None, embedding_dim])
labels = tf.placeholder(tf.float32, [None, output_size])

# 定义模型
with tf.variable_scope('attention'):
    attention_output = attention(inputs, attention_size)

with tf.variable_scope('output'):
    output = tf.layers.dense(attention_output, hidden_size, activation=tf.nn.relu)
    logits = tf.layers.dense(output, output_size)

# 定义损失函数和优化器
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels, logits=logits))
optimizer = tf.train.AdamOptimizer().minimize(loss)

# 进行训练
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    for i in range(10000):
        _, loss_val = sess.run([optimizer, loss], feed_dict={inputs: [input_vecs], labels: [label_vecs[0]]})
        if i % 1000 == 0:
            print('Step', i, 'loss', loss_val)

    # 进行预测
    logits_val = sess.run(logits, feed_dict={inputs: [input_vecs]})
    pred_idx = np.argmax(logits_val[0])
    pred_label = idx2label[pred_idx]

    # 标记关键词
    attention_val = sess.run(attention_output, feed_dict={inputs: [input_vecs]})
    attention_scores = np.squeeze(attention_val, axis=0)
    attention_scores = np.abs(attention_scores)
    attention_scores /= np.sum(attention_scores)
    top_indices = attention_scores.argsort()[-3:][::-1]
    top_words = [idx2word[i] for i in top_indices]

    print('Predicted label:', pred_label)
    print('Top words:', top_words)

这段代码使用了注意力机制来提取输入文本中的关键信息，然后将其用于分类。在预测时，代码还标记出了文本中的关键词，以便更好地理解预测结果。