使用卷积神经网络在R语言下构建文本分类模型的代码该怎么写？

以下是使用卷积神经网络在R语言下构建文本分类模型的代码示例：

library(keras)

max_words <- 10000
maxlen <- 100

# 加载数据集
imdb <- dataset_imdb(num_words = max_words)
x_train <- pad_sequences(imdb$train$x, maxlen = maxlen)
x_test <- pad_sequences(imdb$test$x, maxlen = maxlen)
y_train <- imdb$train$y
y_test <- imdb$test$y

# 定义模型
model <- keras_model_sequential() %>%
  layer_embedding(input_dim = max_words, output_dim = 128, input_length = maxlen) %>%
  layer_dropout(0.5) %>%
  layer_conv_1d(filters = 64, kernel_size = 3, padding = 'valid', activation = 'relu') %>%
  layer_global_max_pooling_1d() %>%
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(0.5) %>%
  layer_dense(units = 1, activation = 'sigmoid')

# 编译模型
model %>% compile(
  optimizer = 'adam',
  loss = 'binary_crossentropy',
  metrics = c('accuracy')
)

# 训练模型
model %>% fit(
  x_train, y_train,
  epochs = 5,
  batch_size = 64,
  validation_split = 0.2
)

# 测试模型
model %>% evaluate(x_test, y_test)

希望对您有所帮助。