基于tensorflow的DCNN代码

以下是一个基于TensorFlow的DCNN代码示例：

import tensorflow as tf

# 定义DCNN模型
class DCNN(tf.keras.Model):
    def __init__(self, num_filters, kernel_size, vocab_size, embedding_dim, maxlen, num_classes, drop_rate=0.5):
        super(DCNN, self).__init__()

        self.embedding = tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=maxlen)
        self.conv1 = tf.keras.layers.Conv1D(filters=num_filters, kernel_size=kernel_size, activation='relu')
        self.pool1 = tf.keras.layers.GlobalMaxPooling1D()
        self.conv2 = tf.keras.layers.Conv1D(filters=num_filters, kernel_size=kernel_size, activation='relu')
        self.pool2 = tf.keras.layers.GlobalMaxPooling1D()
        self.drop = tf.keras.layers.Dropout(rate=drop_rate)
        self.dense = tf.keras.layers.Dense(units=num_classes, activation='softmax')

    def call(self, inputs):
        x = self.embedding(inputs)
        x = self.conv1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.pool2(x)
        x = self.drop(x)
        x = self.dense(x)
        return x

# 加载数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.imdb.load_data(num_words=10000)

# 数据预处理
maxlen = 500
x_train = tf.keras.preprocessing.sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = tf.keras.preprocessing.sequence.pad_sequences(x_test, maxlen=maxlen)

# 定义超参数
vocab_size = 10000
embedding_dim = 50
num_filters = 64
kernel_size = 5
num_classes = 2
drop_rate = 0.5
batch_size = 32
epochs = 5

# 创建模型
model = DCNN(num_filters, kernel_size, vocab_size, embedding_dim, maxlen, num_classes, drop_rate)

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

# 训练模型
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test))

# 评估模型
test_loss, test_acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print('Test Loss:', test_loss)
print('Test Accuracy:', test_acc)

此代码实现了一个简单的DCNN模型，用于情感分析任务。它包括一个卷积层和一个全局最大池化层，以及一个dropout层和一个全连接层。在训练过程中，使用了IMDB数据集进行训练和评估。