LSTM-CNN文本分类算法代码

以下是基于LSTM-CNN的文本分类算法的Python代码：

import numpy as np
import tensorflow as tf

class LSTM_CNN_Model:
    def __init__(self, sequence_length, num_classes, vocab_size, embedding_size, filter_sizes, num_filters, hidden_size, dropout_rate):
        self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name='input_x')
        self.input_y = tf.placeholder(tf.float32, [None, num_classes], name='input_y')
        self.dropout_rate = tf.placeholder(tf.float32, name='dropout_rate')
        self.global_step = tf.Variable(0, trainable=False, name='global_step')
        
        with tf.name_scope('embedding'):
            self.W = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), name='W')
            self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)
            self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)
        
        pooled_outputs = []
        for i, filter_size in enumerate(filter_sizes):
            with tf.name_scope('conv-maxpool-%s' % filter_size):
                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(self.embedded_chars_expanded, W, strides=[1, 1, 1, 1], padding='VALID', name='conv')
                h = tf.nn.relu(tf.nn.bias_add(conv, b), name='relu')
                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)
        
        num_filters_total = num_filters * len(filter_sizes)
        self.h_pool = tf.concat(pooled_outputs, 3)
        self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])
        
        with tf.name_scope('lstm'):
            lstm_cell = tf.contrib.rnn.BasicLSTMCell(hidden_size)
            lstm_cell = tf.contrib.rnn.DropoutWrapper(lstm_cell, output_keep_prob=1.0 - self.dropout_rate)
            outputs, _ = tf.nn.dynamic_rnn(lstm_cell, self.embedded_chars, dtype=tf.float32)
            lstm_out = tf.reduce_mean(outputs, axis=1)
        
        with tf.name_scope('output'):
            W = tf.Variable(tf.truncated_normal([num_filters_total + hidden_size, num_classes], stddev=0.1), name='W')
            b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name='b')
            self.scores = tf.nn.xw_plus_b(tf.concat([self.h_pool_flat, lstm_out], axis=1), W, b, name='scores')
            self.predictions = tf.argmax(self.scores, 1, name='predictions')
        
        with tf.name_scope('loss'):
            losses = tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.scores, labels=self.input_y)
            self.loss = tf.reduce_mean(losses)
        
        with tf.name_scope('accuracy'):
            correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
            self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, 'float'), name='accuracy')
        
        self.optimizer = tf.train.AdamOptimizer(learning_rate=1e-3).minimize(self.loss, global_step=self.global_step)

这段代码实现了一个LSTM和CNN结合的文本分类模型，使用了dropout和Adam优化器。其中，`sequence_length`表示输入序列的长度，`num_classes`表示分类数，`vocab_size`表示词汇表的大小，`embedding_size`表示词向量的维度，`filter_sizes`表示卷积核的大小，`num_filters`表示卷积核的数量，`hidden_size`表示LSTM隐藏层的大小，`dropout_rate`表示dropout的概率。在模型的构建中，首先将输入的词序列转换为词向量，然后通过卷积核进行卷积和池化操作，得到一个固定长度的向量表示。接着，将词向量输入到LSTM中，得到一个平均池化的向量表示。最后将卷积和LSTM的输出拼接在一起，通过一个全连接层进行分类。