帮我做一个完整的LSTM中文文本分类的示例，基于tensorflow2.0

当然，我很乐意帮你完成这个任务。以下是一个基于 TensorFlow 2.0 的 LSTM 中文文本分类示例：

首先，我们需要导入必要的库：

import tensorflow as tf
import numpy as np
import pandas as pd
import jieba
from sklearn.model_selection import train_test_split

接下来，我们需要加载数据集。这里我们使用 THUCNews 数据集，它包含了 14 个不同类别的新闻文章。你可以在这里下载数据集：[THUCNews](http://thuctc.thunlp.org/message)

# 加载数据集
def load_data():
    # 读取数据集
    df = pd.read_csv('thucnews.csv')
    # 分词
    df['content'] = df['content'].apply(lambda x: ' '.join(jieba.cut(x)))
    # 将标签转换为数字
    label2id = {label: idx for idx, label in enumerate(set(df['label']))}
    df['label'] = df['label'].apply(lambda x: label2id[x])
    # 划分训练集和测试集
    X_train, X_test, y_train, y_test = train_test_split(df['content'], df['label'], test_size=0.2, random_state=42)
    return X_train, X_test, y_train, y_test

接下来，我们需要对文本进行预处理。这里我们使用 TensorFlow 的 TextVectorization 层来将文本转换为数字序列。

# 对文本进行预处理
def preprocess(X_train, X_test):
    # 定义 TextVectorization 层
    vectorizer = tf.keras.layers.experimental.preprocessing.TextVectorization(max_tokens=5000, output_sequence_length=500)
    # 适配训练集
    vectorizer.adapt(X_train.to_numpy())
    # 转换训练集和测试集
    X_train = vectorizer(X_train.to_numpy())
    X_test = vectorizer(X_test.to_numpy())
    return X_train, X_test

接下来，我们可以定义 LSTM 模型。这里我们使用一个简单的 LSTM 模型，包含一个嵌入层、一个 LSTM 层和一个全连接层。

# 定义 LSTM 模型
def build_model():
    model = tf.keras.Sequential([
        tf.keras.layers.Embedding(input_dim=5000, output_dim=64),
        tf.keras.layers.LSTM(64),
        tf.keras.layers.Dense(14, activation='softmax')
    ])
    model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

最后，我们可以训练模型并评估其性能。

# 训练模型
def train_model(X_train, y_train):
    model = build_model()
    model.fit(X_train, y_train, epochs=10, batch_size=64)
    return model

# 评估模型
def evaluate_model(model, X_test, y_test):
    loss, accuracy = model.evaluate(X_test, y_test)
    print('Test accuracy:', accuracy)

完整代码如下：

import tensorflow as tf
import numpy as np
import pandas as pd
import jieba
from sklearn.model_selection import train_test_split

# 加载数据集
def load_data():
    # 读取数据集
    df = pd.read_csv('thucnews.csv')
    # 分词
    df['content'] = df['content'].apply(lambda x: ' '.join(jieba.cut(x)))
    # 将标签转换为数字
    label2id = {label: idx for idx, label in enumerate(set(df['label']))}
    df['label'] = df['label'].apply(lambda x: label2id[x])
    # 划分训练集和测试集
    X_train, X_test, y_train, y_test = train_test_split(df['content'], df['label'], test_size=0.2, random_state=42)
    return X_train, X_test, y_train, y_test

# 对文本进行预处理
def preprocess(X_train, X_test):
    # 定义 TextVectorization 层
    vectorizer = tf.keras.layers.experimental.preprocessing.TextVectorization(max_tokens=5000, output_sequence_length=500)
    # 适配训练集
    vectorizer.adapt(X_train.to_numpy())
    # 转换训练集和测试集
    X_train = vectorizer(X_train.to_numpy())
    X_test = vectorizer(X_test.to_numpy())
    return X_train, X_test

# 定义 LSTM 模型
def build_model():
    model = tf.keras.Sequential([
        tf.keras.layers.Embedding(input_dim=5000, output_dim=64),
        tf.keras.layers.LSTM(64),
        tf.keras.layers.Dense(14, activation='softmax')
    ])
    model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

# 训练模型
def train_model(X_train, y_train):
    model = build_model()
    model.fit(X_train, y_train, epochs=10, batch_size=64)
    return model

# 评估模型
def evaluate_model(model, X_test, y_test):
    loss, accuracy = model.evaluate(X_test, y_test)
    print('Test accuracy:', accuracy)

# 加载数据集
X_train, X_test, y_train, y_test = load_data()

# 对文本进行预处理
X_train, X_test = preprocess(X_train, X_test)

# 训练模型
model = train_model(X_train, y_train)

# 评估模型
evaluate_model(model, X_test, y_test)

希望这个示例对你有所帮助！