写一个训练神经网络中文分词模型的程序
时间: 2023-11-25 15:06:41 浏览: 22
以下是一个使用Python和TensorFlow训练中文分词模型的示例程序:
```python
import tensorflow as tf
import numpy as np
import os
# 加载数据集
def load_data(data_dir):
train_data = []
train_label = []
test_data = []
test_label = []
with open(os.path.join(data_dir, 'train.txt'), 'r', encoding='utf-8') as f:
for line in f:
line = line.strip()
if not line:
continue
segs = line.split('\t')
train_data.append(segs[0])
train_label.append(segs[1])
with open(os.path.join(data_dir, 'test.txt'), 'r', encoding='utf-8') as f:
for line in f:
line = line.strip()
if not line:
continue
segs = line.split('\t')
test_data.append(segs[0])
test_label.append(segs[1])
return train_data, train_label, test_data, test_label
# 构建词典
def build_vocab(data):
vocab = set()
for sentence in data:
for word in sentence:
vocab.add(word)
return sorted(list(vocab))
# 生成词向量
def generate_word_vectors(word_index, embedding_size=128):
vocab_size = len(word_index)
word_vectors = np.random.uniform(-0.25, 0.25, (vocab_size, embedding_size))
word_vectors[0] = np.zeros((embedding_size,))
return word_vectors
# 将文本转换为数字序列
def text_to_sequence(text, word_index):
seq = []
for word in text:
if word in word_index:
seq.append(word_index[word])
else:
seq.append(0)
return seq
# 构建模型
def build_model(vocab_size, embedding_size, hidden_size, num_classes):
model = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_size, mask_zero=True),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(hidden_size)),
tf.keras.layers.Dense(num_classes, activation='softmax')
])
return model
if __name__ == '__main__':
data_dir = 'data' # 数据集所在目录
embedding_size = 128 # 词向量维度
hidden_size = 64 # LSTM隐层维度
batch_size = 64 # 批大小
epochs = 5 # 训练轮数
# 加载数据集
train_data, train_label, test_data, test_label = load_data(data_dir)
# 构建词典
vocab = build_vocab(train_data)
# 生成词向量
word_index = {word: i for i, word in enumerate(vocab)}
word_vectors = generate_word_vectors(word_index)
# 将文本转换为数字序列
train_data = [text_to_sequence(text, word_index) for text in train_data]
test_data = [text_to_sequence(text, word_index) for text in test_data]
# 填充序列
train_data = tf.keras.preprocessing.sequence.pad_sequences(train_data, padding='post')
test_data = tf.keras.preprocessing.sequence.pad_sequences(test_data, padding='post')
# 构建模型
model = build_model(len(vocab), embedding_size, hidden_size, 2)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# 训练模型
model.fit(train_data, train_label, batch_size=batch_size, epochs=epochs, validation_data=(test_data, test_label))
# 保存模型
model.save('model.h5')
```
该程序中使用了双向LSTM作为模型的主要组成部分,并使用随机初始化的词向量作为输入。训练数据集和测试数据集均为文本序列和标签的对应关系,其中标签为0或1,表示当前位置是否应该被分割。训练完成后,模型将被保存到文件“model.h5”中,用于后续的预测任务。