写一个训练神经网络分词模型程序
时间: 2023-12-01 11:02:15 浏览: 35
好的,以下是一个简单的训练神经网络分词模型的程序:
```
import numpy as np
import tensorflow as tf
# 加载数据
def load_data(file_path):
with open(file_path, 'r', encoding='utf-8') as f:
data = f.read()
data = data.split('\n')
return data
# 构建词表
def build_vocab(data):
vocab = set()
for sentence in data:
words = sentence.split(' ')
for word in words:
vocab.add(word)
word2id = {w: i+2 for i, w in enumerate(vocab)}
word2id['<PAD>'] = 0
word2id['<UNK>'] = 1
return word2id
# 把句子转成 id 序列
def sentence2id(sentence, word2id, max_length):
words = sentence.split(' ')
ids = [word2id.get(word, 1) for word in words]
if len(ids) < max_length:
ids += [0] * (max_length - len(ids))
else:
ids = ids[:max_length]
return ids
# 加载和处理数据
train_data = load_data('train.txt')
test_data = load_data('test.txt')
word2id = build_vocab(train_data + test_data)
max_length = 20
train_x = [sentence2id(s, word2id, max_length) for s in train_data]
train_y = [s.split(' ') for s in train_data]
test_x = [sentence2id(s, word2id, max_length) for s in test_data]
test_y = [s.split(' ') for s in test_data]
# 构建模型
vocab_size = len(word2id)
embedding_size = 128
hidden_size = 256
num_classes = 2
batch_size = 32
num_epochs = 10
learning_rate = 0.001
inputs = tf.keras.layers.Input(shape=(max_length,))
embedding = tf.keras.layers.Embedding(vocab_size, embedding_size)(inputs)
lstm = tf.keras.layers.LSTM(hidden_size)(embedding)
outputs = tf.keras.layers.Dense(num_classes, activation='softmax')(lstm)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# 训练模型
model.fit(np.array(train_x), np.array(train_y),
batch_size=batch_size,
epochs=num_epochs,
validation_data=(np.array(test_x), np.array(test_y)))
# 保存模型
model.save('segmentation_model.h5')
```
这个程序基于 TensorFlow 构建了一个简单的 LSTM 模型,用来进行中文分词。首先,我们加载数据并构建词表。然后,我们把句子转成 id 序列,并把标签转成单词列表。接下来,我们构建模型,包括一个嵌入层、一个 LSTM 层和一个全连接层。最后,我们编译模型并训练它。训练完成后,我们可以保存模型,以备后续使用。