我不想下载‘reuters.csv’，我想用“from nltk.corpus import reuters”

好的，我们可以使用nltk中自带的reuters数据集。以下是基于reuters数据集的RNN模型构建和实践代码：

import numpy as np
from sklearn.preprocessing import LabelEncoder
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense
from nltk.corpus import reuters

# 读取数据
documents = reuters.fileids()
train_docs_id = list(filter(lambda doc: doc.startswith("train"), documents))
test_docs_id = list(filter(lambda doc: doc.startswith("test"), documents))
train_docs = [reuters.raw(doc_id) for doc_id in train_docs_id]
test_docs = [reuters.raw(doc_id) for doc_id in test_docs_id]
train_labels = [reuters.categories(doc_id) for doc_id in train_docs_id]
test_labels = [reuters.categories(doc_id) for doc_id in test_docs_id]

# 将标签转换为数字
encoder = LabelEncoder()
encoder.fit(train_labels)
y_train = encoder.transform(train_labels)
y_test = encoder.transform(test_labels)

# 将文本转换为数字序列
max_words = 10000
tokenizer = Tokenizer(num_words=max_words)
tokenizer.fit_on_texts(train_docs)
x_train = tokenizer.texts_to_sequences(train_docs)
x_test = tokenizer.texts_to_sequences(test_docs)

# 将序列填充到固定长度
maxlen = 100
x_train = pad_sequences(x_train, padding="post", maxlen=maxlen)
x_test = pad_sequences(x_test, padding="post", maxlen=maxlen)

# 创建一个Sequential模型
model = Sequential()

# 添加一个嵌入层
embedding_size = 100
model.add(Embedding(input_dim=max_words, output_dim=embedding_size, input_length=maxlen))

# 添加一个LSTM层
lstm_size = 128
model.add(LSTM(lstm_size))

# 添加一个全连接层
num_classes = len(np.unique(y_train))
model.add(Dense(num_classes, activation="softmax"))

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

# 训练模型
batch_size = 128
epochs = 10
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test))

# 在测试集上评估模型
score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])

这样，我们就完成了基于nltk.corpus.reuters的RNN模型构建和实践。