cnn-lstm注意力机制代码

以下是CNN-LSTM注意力机制的Python代码示例：

import tensorflow as tf

# 定义CNN-LSTM注意力模型
def cnn_lstm_attention_model(embedding_matrix, sequence_length, num_classes, filter_sizes, num_filters, hidden_size):
    # 输入层
    input_x = tf.placeholder(tf.int32, [None, sequence_length], name="input_x")
    input_y = tf.placeholder(tf.float32, [None, num_classes], name="input_y")
    dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")

    # embedding层
    with tf.name_scope("embedding"):
        W = tf.Variable(embedding_matrix, name="W")
        embedded_chars = tf.nn.embedding_lookup(W, input_x)
        embedded_chars_expanded = tf.expand_dims(embedded_chars, -1)

    # CNN层
    pooled_outputs = []
    for i, filter_size in enumerate(filter_sizes):
        with tf.name_scope("conv-maxpool-%s" % filter_size):
            # 卷积层
            filter_shape = [filter_size, embedding_size, 1, num_filters]
            W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
            b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")
            conv = tf.nn.conv2d(
                embedded_chars_expanded,
                W,
                strides=[1, 1, 1, 1],
                padding="VALID",
                name="conv")
            # 激活函数
            h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
            # 池化层
            pooled = tf.nn.max_pool(
                h,
                ksize=[1, sequence_length - filter_size + 1, 1, 1],
                strides=[1, 1, 1, 1],
                padding='VALID',
                name="pool")
            pooled_outputs.append(pooled)

    # 合并所有池化层的输出
    num_filters_total = num_filters * len(filter_sizes)
    h_pool = tf.concat(pooled_outputs, 3)
    h_pool_flat = tf.reshape(h_pool, [-1, num_filters_total])

    # LSTM层
    with tf.name_scope("lstm"):
        lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(hidden_size)
        lstm_cell = tf.nn.rnn_cell.DropoutWrapper(lstm_cell, output_keep_prob=dropout_keep_prob)
        _, final_state = tf.nn.dynamic_rnn(lstm_cell, h_pool_flat, dtype=tf.float32)

    # 注意力机制
    with tf.name_scope("attention"):
        attention_w = tf.Variable(tf.truncated_normal([hidden_size, 1], stddev=0.1))
        attention_b = tf.Variable(tf.constant(0.1, shape=[1]))
        u = tf.tanh(tf.matmul(h_pool_flat, attention_w) + attention_b)
        alpha = tf.nn.softmax(tf.matmul(u, tf.ones([num_filters_total, 1])))

    # 输出层
    with tf.name_scope("output"):
        output_w = tf.Variable(tf.truncated_normal([hidden_size, num_classes], stddev=0.1))
        output_b = tf.Variable(tf.constant(0.1, shape=[num_classes]))
        output = tf.matmul(final_state[1], output_w) + output_b
        output = tf.nn.dropout(output, dropout_keep_prob)

    # 损失函数和优化器
    with tf.name_scope("loss"):
        loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=input_y))
        optimizer = tf.train.AdamOptimizer(learning_rate=1e-3).minimize(loss)

    # 预测结果和准确率
    with tf.name_scope("accuracy"):
        predictions = tf.cast(tf.greater_equal(output, 0.5), tf.float32)
        correct_predictions = tf.equal(predictions, input_y)
        accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))

    return input_x, input_y, dropout_keep_prob, optimizer, loss, accuracy