请给我一个Attention模型的代码
时间: 2024-03-11 17:43:50 浏览: 9
以下是一个基于Keras的Attention模型代码示例,用于将一个序列转换为另一个序列:
```python
from keras.models import Model
from keras.layers import Input, LSTM, Dense, Dot, Concatenate
import numpy as np
class AttentionModel:
def __init__(self, input_vocab_size, output_vocab_size, hidden_size):
# Encoder
encoder_input = Input(shape=(None, input_vocab_size))
encoder_lstm = LSTM(hidden_size, return_sequences=True, return_state=True)
encoder_output, encoder_h, encoder_c = encoder_lstm(encoder_input)
# Decoder
decoder_input = Input(shape=(None, output_vocab_size))
decoder_lstm = LSTM(hidden_size, return_sequences=True, return_state=True)
decoder_output, _, _ = decoder_lstm(decoder_input, initial_state=[encoder_h, encoder_c])
# Attention
attention_dot = Dot(axes=[2, 2])
attention_concat = Concatenate(axis=-1)
attention_dense = Dense(1, activation='tanh')
attention_softmax = Dense(1, activation='softmax')
attention_weights = attention_softmax(attention_dense(attention_concat([decoder_output, encoder_output])))
attention_context = attention_dot([attention_weights, encoder_output])
decoder_combined_context = Concatenate(axis=-1)([decoder_output, attention_context])
# Output
output_dense = Dense(output_vocab_size, activation='softmax')
output = output_dense(decoder_combined_context)
# Model
self.model = Model([encoder_input, decoder_input], output)
def train(self, input_sequences, output_sequences, batch_size, epochs):
target_sequences = np.zeros(output_sequences.shape)
target_sequences[:, :-1] = output_sequences[:, 1:]
target_sequences = np.reshape(target_sequences, (-1, target_sequences.shape[1], 1))
self.model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy')
self.model.fit([input_sequences, output_sequences], target_sequences, batch_size=batch_size, epochs=epochs, validation_split=0.2)
def predict(self, input_sequence):
output_sequence = np.zeros((1, 1, self.output_vocab_size))
output_sequence[0, 0, 0] = 1
while True:
output_probabilities = self.model.predict([input_sequence, output_sequence])
output_sequence = np.zeros((1, 1, self.output_vocab_size))
output_sequence[0, 0, np.argmax(output_probabilities)] = 1
if np.argmax(output_probabilities) == 0:
break
return output_sequence
```
该模型使用了LSTM作为编码器和解码器,使用Dot和Concatenate层来计算注意力权重,使用Dense层来预测输出序列。在训练过程中,输入和输出序列以及目标序列都被传递给模型。在预测过程中,模型接受一个输入序列,并通过反复预测下一个单词来生成输出序列。