import jieba import pynlpir import numpy as np import tensorflow as tf from sklearn.model_selection import train_test_split # 读取文本文件 with open('1.txt', 'r', encoding='utf-8') as f: text = f.read() # 对文本进行分词 word_list = list(jieba.cut(text, cut_all=False)) # 打开pynlpir分词器 pynlpir.open() # 对分词后的词语进行词性标注 pos_list = pynlpir.segment(text, pos_tagging=True) # 将词汇表映射成整数编号 vocab = set(word_list) vocab_size = len(vocab) word_to_int = {word: i for i, word in enumerate(vocab)} int_to_word = {i: word for i, word in enumerate(vocab)} # 将词语和词性标记映射成整数编号 pos_tags = set(pos for word, pos in pos_list) num_tags = len(pos_tags) tag_to_int = {tag: i for i, tag in enumerate(pos_tags)} int_to_tag = {i: tag for i, tag in enumerate(pos_tags)} # 将文本和标签转换成整数序列 X = np.array([word_to_int[word] for word in word_list]) y = np.array([tag_to_int[pos] for word, pos in pos_list]) # 将数据划分成训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # 定义模型参数 embedding_size = 128 rnn_size = 256 batch_size = 128 epochs = 10 # 定义RNN模型 model = tf.keras.Sequential([ tf.keras.layers.Embedding(vocab_size, embedding_size), tf.keras.layers.SimpleRNN(rnn_size), tf.keras.layers.Dense(num_tags, activation='softmax') ]) # 编译模型 model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # 训练模型 model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_test, y_test)) # 对测试集进行预测 y_pred = model.predict(X_test) y_pred = np.argmax(y_pred, axis=1) # 计算模型准确率 accuracy = np.mean(y_pred == y_test) print('Accuracy: {:.2f}%'.format(accuracy * 100)) # 将模型保存到文件中 model.save('model.h5')出现下述问题：ValueError: Found input variables with inconsistent numbers of samples:

根据您提供的代码和错误信息，问题出在将分词后的文本和标签转换成整数序列时，训练集和测试集的样本数量不一致。这是由于您在分割数据集时，只对特征数据进行了分割，而标签数据没有进行相应的分割，导致训练集和测试集的标签数量不一致。

为了解决这个问题，您可以将训练集和测试集的特征数据和标签数据一起进行分割，代码如下：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

其中，X是特征矩阵，y是目标量，test_size参数指定分割的测试集占总数据集的比例，random_state参数用于确定数据集分割的随机种子，以确保每次分割的结果相同。

修改后的完整代码如下：

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

# 读取文本文件
with open('1.txt', 'r', encoding='utf-8') as f:
    text = f.read()

# 对文本进行分词
word_list = list(jieba.cut(text, cut_all=False))

# 打开pynlpir分词器
pynlpir.open()

# 对分词后的词语进行词性标注
pos_list = pynlpir.segment(text, pos_tagging=True)

# 将词汇表映射成整数编号
vocab = set(word_list)
vocab_size = len(vocab)
word_to_int = {word: i for i, word in enumerate(vocab)}
int_to_word = {i: word for i, word in enumerate(vocab)}

# 将词语和词性标记映射成整数编号
pos_tags = set(pos for word, pos in pos_list)
num_tags = len(pos_tags)
tag_to_int = {tag: i for i, tag in enumerate(pos_tags)}
int_to_tag = {i: tag for i, tag in enumerate(pos_tags)}

# 将文本和标签转换成整数序列
X = np.array([word_to_int[word] for word in word_list])
y = np.array([tag_to_int[pos] for word, pos in pos_list])

# 将数据划分成训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义模型参数
embedding_size = 128
rnn_size = 256
batch_size = 128
epochs = 10

# 定义RNN模型
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, embedding_size),
    tf.keras.layers.SimpleRNN(rnn_size),
    tf.keras.layers.Dense(num_tags, activation='softmax')
])

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

# 训练模型
model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_test, y_test))

# 对测试集进行预测
y_pred = model.predict(X_test)
y_pred = np.argmax(y_pred, axis=1)

# 计算模型准确率
accuracy = np.mean(y_pred == y_test)
print('Accuracy: {:.2f}%'.format(accuracy * 100))

# 将模型保存到文件中
model.save('model.h5')

希望这可以帮助您解决问题。