在tensorflow2.0中用LSTM训练路透社数据集进行文本分量，给出python代码

好的，以下是用LSTM训练路透社数据集进行文本分类的Python代码：

import numpy as np
from tensorflow.keras.datasets import reuters
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM, Embedding
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import to_categorical

# Load Reuters dataset
(x_train, y_train), (x_test, y_test) = reuters.load_data(num_words=10000)

# Get word index dictionary
word_index = reuters.get_word_index()

# Reverse word index to get words from indices
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])

# Decode input sequences
decoded_newswire = ' '.join([reverse_word_index.get(i - 3, '?') for i in x_train[0]])

# Pad input sequences to have same length
maxlen = 500
x_train = pad_sequences(x_train, maxlen=maxlen)
x_test = pad_sequences(x_test, maxlen=maxlen)

# One-hot encode labels
num_classes = np.max(y_train) + 1
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)

# Build LSTM model
model = Sequential()
model.add(Embedding(10000, 32))
model.add(LSTM(32))
model.add(Dense(num_classes, activation='softmax'))
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

# Train LSTM model
history = model.fit(x_train, y_train, epochs=10, batch_size=128, validation_split=0.2)

# Evaluate LSTM model on test data
test_loss, test_acc = model.evaluate(x_test, y_test)
print('Test accuracy:', test_acc)

这段代码中，我们首先加载路透社数据集，并将其分为训练集和测试集。然后，我们获取数据集的词汇表，并使用其构建一个反向词汇表，以便我们可以从整数序列中获取单词。接着，我们将输入序列填充到相同的长度，并对标签进行one-hot编码。然后，我们使用Embedding层和LSTM层构建一个LSTM模型，并使用rmsprop优化器和分类交叉熵损失函数进行编译。最后，我们在训练集的20%上进行验证，并在测试集上评估模型的性能。