将自注意力嵌入到卷积神经网络中的代码keras

以下是将自注意力嵌入到卷积神经网络中的Keras代码示例：

from keras.layers import Conv1D, MaxPooling1D, Dense, Flatten, Input, Dropout, Layer
from keras.models import Model
import keras.backend as K

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

    def build(self, input_shape):
        self.W = self.add_weight(name="att_weight", shape=(input_shape[-1], self.output_dim), initializer="uniform", trainable=True)
        self.b = self.add_weight(name="att_bias", shape=(self.output_dim,), initializer="uniform", trainable=True)
        self.u = self.add_weight(name="att_context", shape=(self.output_dim, 1), initializer="uniform", trainable=True)
        super(SelfAttention, self).build(input_shape)

    def call(self, x):
        e = K.tanh(K.dot(x, self.W) + self.b)
        a = K.softmax(K.dot(e, self.u), axis=1)
        output = x * a
        return K.sum(output, axis=1)

    def compute_output_shape(self, input_shape):
        return (input_shape[0], self.output_dim)

inputs = Input(shape=(input_shape,))
conv1 = Conv1D(filters=64, kernel_size=3, activation='relu')(inputs)
pool1 = MaxPooling1D(pool_size=2)(conv1)
drop1 = Dropout(rate=0.2)(pool1)
conv2 = Conv1D(filters=32, kernel_size=3, activation='relu')(drop1)
pool2 = MaxPooling1D(pool_size=2)(conv2)
drop2 = Dropout(rate=0.2)(pool2)
conv3 = Conv1D(filters=16, kernel_size=3, activation='relu')(drop2)
attention = SelfAttention(output_dim=16)(conv3)
flat = Flatten()(attention)
dense1 = Dense(units=64, activation='relu')(flat)
drop3 = Dropout(rate=0.2)(dense1)
outputs = Dense(units=output_shape, activation='softmax')(drop3)

model = Model(inputs=inputs, outputs=outputs)
model.summary()

上面的代码中添加了一个 `SelfAttention` 类，用于嵌入自注意力机制。在 `call` 方法中，首先通过全连接层将输入张量 `x` 转换为 `e`，再通过 softmax 函数计算注意力权重 `a`，最后将注意力权重与输入张量相乘并进行求和得到输出张量 `output`。在 `build` 方法中，定义了三个可训练的参数 `W`、`b` 和 `u`。`compute_output_shape` 方法返回输出张量的形状。

在模型的前向传播过程中，将卷积层的输出传递给 `SelfAttention` 层进行自注意力操作。最后通过全连接层和 softmax 函数得到最终输出。