CNN-LSTM-BiGRU分类模型实现Python代码

CNN-LSTM-BiGRU分类模型是一种常用于文本分类任务的深度学习模型，它结合了卷积神经网络（CNN）、长短期记忆网络（LSTM）和双向门控循环单元（BiGRU）的特性。下面是一个简单的Python代码示例，用于实现CNN-LSTM-BiGRU分类模型：

import tensorflow as tf
from tensorflow.keras.layers import Embedding, Conv1D, LSTM, Bidirectional, Dense

# 定义模型
class CNN_LSTM_BiGRU_Model(tf.keras.Model):
    def __init__(self, vocab_size, embedding_dim, num_filters, kernel_size, lstm_units, num_classes):
        super(CNN_LSTM_BiGRU_Model, self).__init__()
        self.embedding = Embedding(vocab_size, embedding_dim)
        self.conv1d = Conv1D(num_filters, kernel_size, activation='relu')
        self.lstm = LSTM(lstm_units, return_sequences=True)
        self.bigru = Bidirectional(tf.keras.layers.GRU(lstm_units))
        self.dense = Dense(num_classes, activation='softmax')

    def call(self, inputs):
        x = self.embedding(inputs)
        x = self.conv1d(x)
        x = self.lstm(x)
        x = self.bigru(x)
        x = self.dense(x)
        return x

# 设置超参数
vocab_size = 10000  # 词汇表大小
embedding_dim = 100  # 词嵌入维度
num_filters = 128  # 卷积核数量
kernel_size = 5  # 卷积核大小
lstm_units = 64  # LSTM隐藏单元数量
num_classes = 10  # 分类类别数量

# 创建模型实例
model = CNN_LSTM_BiGRU_Model(vocab_size, embedding_dim, num_filters, kernel_size, lstm_units, num_classes)

# 编译模型
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(train_data, train_labels, epochs=10, batch_size=32)

# 使用模型进行预测
predictions = model.predict(test_data)

请注意，上述代码仅为示例，实际应用中可能需要根据具体任务进行调整和优化。另外，代码中的`train_data`、`train_labels`、`test_data`等变量需要根据实际情况进行替换。