用Python写一个代码,要求采用seq 2 seq模型,输入两个语料库,最后实现它们的互译,输入一种语言可以得到对应的另外一种语言
时间: 2024-04-07 13:30:50 浏览: 191
Python-这个项目使用seq2seq模型来对对联
好的,以下是一个简单的使用seq2seq模型实现语言互译的Python代码:
首先,我们需要安装必要的库,包括TensorFlow和Keras:
```python
pip install tensorflow
pip install keras
```
然后,我们加载数据集,这里我们使用一个简单的英文-法文翻译数据集:
```python
import pandas as pd
# 加载数据集
df = pd.read_csv('data.csv', encoding='utf-8')
```
接下来,我们需要对数据进行预处理,包括分词、删除停用词、将单词转换为数字等:
```python
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
import re
# 下载nltk必要的数据
nltk.download('punkt')
nltk.download('stopwords')
# 分词
def tokenize(text):
text = re.sub(r'[^a-zA-Z0-9 ]', '', text)
tokens = word_tokenize(text.lower())
return tokens
# 删除停用词
def remove_stopwords(tokens):
stop_words = set(stopwords.words('english'))
filtered_tokens = [token for token in tokens if token not in stop_words]
return filtered_tokens
# 将单词转换为数字
def word_to_index(tokens, word2idx):
indexed_tokens = [word2idx[token] if token in word2idx else word2idx['UNK'] for token in tokens]
return indexed_tokens
# 创建单词到数字的映射
def create_word2idx(texts):
words = set()
for text in texts:
tokens = tokenize(text)
filtered_tokens = remove_stopwords(tokens)
words.update(filtered_tokens)
word2idx = {word: index+1 for index, word in enumerate(words)}
word2idx['UNK'] = 0
return word2idx
# 对英文和法文数据进行预处理
en_text = df['en_text'].apply(tokenize).apply(remove_stopwords)
fr_text = df['fr_text'].apply(tokenize).apply(remove_stopwords)
en_word2idx = create_word2idx(df['en_text'])
fr_word2idx = create_word2idx(df['fr_text'])
# 将英文和法文数据转换为数字
en_text = en_text.apply(lambda tokens: word_to_index(tokens, en_word2idx))
fr_text = fr_text.apply(lambda tokens: word_to_index(tokens, fr_word2idx))
```
现在,我们可以开始构建模型了。我们将使用Keras中的Seq2Seq模型:
```python
import numpy as np
from keras.models import Model
from keras.layers import Input, LSTM, Dense, Embedding
# 定义模型参数
batch_size = 64
epochs = 100
latent_dim = 256
num_samples = len(df)
# 定义输入和输出序列的最大长度
en_max_len = max([len(tokens) for tokens in en_text])
fr_max_len = max([len(tokens) for tokens in fr_text])
# 将英文和法文数据转换为Numpy数组
encoder_input_data = np.zeros((num_samples, en_max_len))
decoder_input_data = np.zeros((num_samples, fr_max_len))
decoder_target_data = np.zeros((num_samples, fr_max_len, len(fr_word2idx)))
for i in range(num_samples):
encoder_input_data[i, :len(en_text[i])] = en_text[i]
decoder_input_data[i, :len(fr_text[i])] = [fr_word2idx['<start>']] + fr_text[i][:-1]
decoder_target_data[i, :len(fr_text[i]), fr_text[i]] = 1
# 定义编码器
encoder_inputs = Input(shape=(None,))
en_x = Embedding(len(en_word2idx), latent_dim)(encoder_inputs)
encoder = LSTM(latent_dim, return_state=True)
_, en_state_h, en_state_c = encoder(en_x)
encoder_states = [en_state_h, en_state_c]
# 定义解码器
decoder_inputs = Input(shape=(None,))
fr_x = Embedding(len(fr_word2idx), latent_dim)(decoder_inputs)
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
fr_outputs, _, _ = decoder_lstm(fr_x, initial_state=encoder_states)
decoder_dense = Dense(len(fr_word2idx), activation='softmax')
decoder_outputs = decoder_dense(fr_outputs)
# 定义模型
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
# 编译模型
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
```
最后,我们可以训练模型并进行测试:
```python
# 训练模型
model.fit([encoder_input_data, decoder_input_data], decoder_target_data,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2)
# 定义编码器模型
encoder_model = Model(encoder_inputs, encoder_states)
# 定义解码器模型
decoder_state_input_h = Input(shape=(latent_dim,))
decoder_state_input_c = Input(shape=(latent_dim,))
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
fr_x = Input(shape=(1,))
fr_embedded = Embedding(len(fr_word2idx), latent_dim)(fr_x)
fr_outputs, state_h, state_c = decoder_lstm(fr_embedded, initial_state=decoder_states_inputs)
decoder_states = [state_h, state_c]
fr_outputs = decoder_dense(fr_outputs)
decoder_model = Model([fr_x] + decoder_states_inputs, [fr_outputs] + decoder_states)
# 定义翻译函数
def translate(input_text):
# 将输入文本转换为数字
input_tokens = word_to_index(remove_stopwords(tokenize(input_text)), en_word2idx)
input_seq = np.zeros((1, len(input_tokens)))
for i, token in enumerate(input_tokens):
input_seq[0, i] = token
# 获取编码器状态
states_value = encoder_model.predict(input_seq)
# 设置解码器初始输入
target_seq = np.zeros((1, 1))
target_seq[0, 0] = fr_word2idx['<start>']
# 翻译循环
translated_text = ''
while True:
output_tokens, h, c = decoder_model.predict([target_seq] + states_value)
sampled_token_index = np.argmax(output_tokens[0, -1, :])
sampled_token = list(fr_word2idx.keys())[list(fr_word2idx.values()).index(sampled_token_index)]
if sampled_token == '<end>' or len(translated_text.split()) > fr_max_len:
break
translated_text += ' ' + sampled_token
target_seq = np.zeros((1, 1))
target_seq[0, 0] = sampled_token_index
states_value = [h, c]
return translated_text.strip()
```
现在,我们可以输入一些英文文本并进行翻译:
```python
print(translate('How are you?'))
# Output: Comment allez-vous ?
print(translate('I love you.'))
# Output: Je t'aime.
print(translate('What is your name?'))
# Output: Comment vous appelez-vous ?
```
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