将自注意力嵌入到二维卷积神经网络中的keras代码

下面是将自注意力层嵌入到二维卷积神经网络中的Keras代码：

from keras.layers import Input, Conv2D, Activation, Add, Multiply, Lambda
from keras.layers import GlobalAveragePooling2D, Dense, Reshape
from keras.initializers import he_normal
from keras.models import Model
import keras.backend as K

def conv2d_bn(x, filters, kernel_size, strides=1, padding='same'):
    x = Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding=padding)(x)
    x = Activation('relu')(x)
    return x

def self_attention(x, channels):
    f = conv2d_bn(x, channels // 8, 1)
    g = conv2d_bn(x, channels // 8, 1)
    h = conv2d_bn(x, channels, 1)
    shape_f = K.shape(f)
    shape_g = K.shape(g)
    shape_h = K.shape(h)
    flatten_f = Reshape((-1, shape_f[-1]))(f)
    flatten_g = Reshape((-1, shape_g[-1]))(g)
    flatten_h = Reshape((-1, shape_h[-1]))(h)
    attention = Lambda(lambda x: K.batch_dot(x[0], x[1], axes=[2, 2]) / K.sqrt(K.cast(shape_f[-1], dtype='float32'))) \
        ([flatten_g, flatten_f])
    attention = Activation('softmax')(attention)
    multiply = Lambda(lambda x: K.batch_dot(x[0], x[1]))([attention, flatten_h])
    multiply = Reshape(shape_h[1:])(multiply)
    return multiply

def residual_block(x, channels):
    shortcut = x
    x = conv2d_bn(x, channels, 3)
    x = conv2d_bn(x, channels, 3)
    x = self_attention(x, channels)
    x = Add()([x, shortcut])
    return x

def build_model(input_shape, num_classes):
    inputs = Input(shape=input_shape)
    x = conv2d_bn(inputs, 64, 7, strides=2)
    x = residual_block(x, 64)
    x = residual_block(x, 64)
    x = residual_block(x, 64)
    x = conv2d_bn(x, 128, 3, strides=2)
    x = residual_block(x, 128)
    x = residual_block(x, 128)
    x = residual_block(x, 128)
    x = conv2d_bn(x, 256, 3, strides=2)
    x = residual_block(x, 256)
    x = residual_block(x, 256)
    x = residual_block(x, 256)
    x = GlobalAveragePooling2D()(x)
    x = Dense(num_classes, kernel_initializer=he_normal())(x)
    outputs = Activation('softmax')(x)
    model = Model(inputs=inputs, outputs=outputs)
    return model

这个代码定义了一个包含自注意力层的残差网络，可以用于图像分类任务。其中`conv2d_bn`函数定义了一个包含卷积和ReLU激活的层，`self_attention`函数定义了自注意力层，`residual_block`函数定义了一个包含注意力层的残差块，`build_model`函数定义了整个模型。在`build_model`函数中，我们首先进行了一个`7x7`的卷积操作，然后进行了三个残差块，每个残差块包含了三个卷积层和一个自注意力层，最后通过全局平均池化和一个全连接层得到最终的分类结果。