keras Attention()举一个代码的例子

好的，下面是一个使用Keras的Attention层的示例代码：

from keras.layers import *
from keras.models import Model

 定义Attention层
class Attention(Layer):
    def __init__(self, step_dim, **kwargs):
        self.supports_masking = True
        self.step_dim = step_dim
        super(Attention, self).__init__(**kwargs)

    def build(self, input_shape):
        assert len(input_shape) == 3

        self.W = self.add_weight(shape=(input_shape[-1],), 
                                 initializer='normal',
                                 trainable=True)
        super(Attention, self).build(input_shape)

    def call(self, x, mask=None):
        eij = K.reshape(K.dot(K.reshape(x, (-1, self.step_dim)), 
                              K.reshape(self.W, (self.step_dim, 1))), 
                        (-1, input_shape[1]))

        ai = K.exp(eij)
        weights = ai / K.sum(ai, axis=1).dimshuffle(0, 'x')

        weighted_input = x * weights.dimshuffle(0, 1, 'x')
        return weighted_input.sum(axis=1)

    def compute_output_shape(self, input_shape):
        return input_shape[0], input_shape[-1]

# 定义模型
inputs = Input(shape=(sequence_length,), dtype='int32')
embedding = Embedding(input_dim=vocab_size, output_dim=embedding_size, 
                      input_length=sequence_length)(inputs)
lstm = LSTM(units=hidden_size, return_sequences=True)(embedding)
attention = Attention(step_dim=sequence_length)(lstm)
output = Dense(units=num_classes, activation='softmax')(attention)
model = Model(inputs=inputs, outputs=output)

# 编译模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

这是一个基于Keras的Attention层实现的文本分类模型。在这个模型中，我们首先定义了一个Attention层，用于学习输入序列中每个时间步的权重，然后将这些权重应用于输入序列的LSTM层的输出，得到最终的注意力向量，最后将其输入到全连接层进行分类。