深度文字神经网络相似度代码
时间: 2023-09-03 08:07:01 浏览: 52
深度文本神经网络可以用于计算文本之间的相似度,以下是一个使用Keras实现的代码示例:
```python
from keras.layers import Input, Embedding, Bidirectional, LSTM, Dense, Dropout
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
import numpy as np
# 定义模型
def build_model(embedding_matrix, max_length):
# 定义输入层
input_layer = Input(shape=(max_length,))
# 定义嵌入层
embedding_layer = Embedding(embedding_matrix.shape[0], embedding_matrix.shape[1], weights=[embedding_matrix], trainable=False)(input_layer)
# 定义双向LSTM层
lstm_layer = Bidirectional(LSTM(128, return_sequences=True))(embedding_layer)
# 定义Dropout层
dropout_layer = Dropout(0.2)(lstm_layer)
# 定义全连接层
dense_layer = Dense(64, activation="relu")(dropout_layer)
# 定义输出层
output_layer = Dense(1, activation="sigmoid")(dense_layer)
# 定义模型
model = Model(inputs=input_layer, outputs=output_layer)
# 编译模型
model.compile(optimizer=Adam(lr=1e-3), loss="binary_crossentropy", metrics=["accuracy"])
return model
# 训练模型
def train_model(model, X_train, y_train, X_val, y_val, epochs):
model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=epochs, batch_size=256)
# 预处理文本数据
def preprocess_texts(texts, max_length):
tokenizer = Tokenizer()
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
padded_sequences = pad_sequences(sequences, maxlen=max_length)
return tokenizer, padded_sequences
# 计算文本相似度
def compute_similarity(model, tokenizer, text1, text2, max_length):
text1_sequence = tokenizer.texts_to_sequences([text1])
text2_sequence = tokenizer.texts_to_sequences([text2])
text1_padded_sequence = pad_sequences(text1_sequence, maxlen=max_length)
text2_padded_sequence = pad_sequences(text2_sequence, maxlen=max_length)
similarity = model.predict([text1_padded_sequence, text2_padded_sequence])[0][0]
return similarity
# 加载预训练的词向量
embedding_matrix = np.load("embedding_matrix.npy")
# 定义最大文本长度
max_length = 100
# 定义训练集和验证集
X_train = np.load("X_train.npy")
y_train = np.load("y_train.npy")
X_val = np.load("X_val.npy")
y_val = np.load("y_val.npy")
# 训练模型
model = build_model(embedding_matrix, max_length)
train_model(model, X_train, y_train, X_val, y_val, epochs=10)
# 测试模型
tokenizer, _ = preprocess_texts(["test text"])
similarity = compute_similarity(model, tokenizer, "text 1", "text 2", max_length)
print(similarity)
```
在以上代码中,模型使用了一个双向LSTM层和一个全连接层,通过训练文本分类任务,计算文本之间的相似度。预处理数据使用了Keras提供的Tokenizer和pad_sequences函数。
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