self-attention代码keras

以下是一个简单的self-attention代码示例，使用Keras实现：

from keras.layers import Input, Dense, Dropout, Lambda, Layer
from keras.models import Model
import keras.backend as K

class SelfAttention(Layer):
    def __init__(self, units, **kwargs):
        self.units = units
        super(SelfAttention, self).__init__(**kwargs)

    def build(self, input_shape):
        self.W = self.add_weight(name='W', shape=(input_shape[-1], self.units), initializer='glorot_uniform', trainable=True)
        self.b = self.add_weight(name='b', shape=(self.units,), initializer='zeros', trainable=True)
        self.u = self.add_weight(name='u', shape=(self.units,), initializer='glorot_uniform', trainable=True)
        super(SelfAttention, self).build(input_shape)

    def call(self, x):
        u_it = K.tanh(K.dot(x, self.W) + self.b)
        a_it = K.dot(u_it, self.u)
        a_it = K.softmax(a_it)
        weighted_input = x * a_it[..., None]
        output = K.sum(weighted_input, axis=1)
        return output

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

# 定义模型
inputs = Input(shape=(100,))
x = Dense(64, activation='relu')(inputs)
x = Dropout(.5)(x)
x = Dense(32, activation='relu')(x)
x = Dropout(.5)(x)
outputs = SelfAttention(units=16)(x)
model = Model(inputs=inputs, outputs=outputs)

# 编译模型
model.compile(optimizer='adam', loss='mse')

# 训练模型
model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_test, y_test))

这个模型包含一个自注意力层，它将输入张量的每个时间步的特征向量转换为一个加权平均值，其中权重由该时间步的特征向量和其他时间步的特征向量之间的相似度计算得出。该模型使用MSE损失函数和Adam优化器进行训练。