Keras实现CA注意力机制

CA（Channel Attention）注意力机制是一种用于图像分类和目标检测的注意力机制。Keras实现CA注意力机制的步骤如下：

1. 定义输入

inputs = Input(shape=(height, width, channels))

2. 定义卷积层

x = Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding='same')(inputs)

3. 定义全局平均池化层

pooling = GlobalAveragePooling2D()(x)

4. 定义全连接层

fc = Dense(units=units)(pooling)

5. 定义激活函数

activation = Activation('relu')(fc)

6. 定义softmax层

softmax = Dense(units=filters, activation='softmax')(activation)

7. 定义乘法层

multiply = Multiply()([x, softmax])

8. 定义输出

outputs = multiply

完整代码如下：

from keras.layers import Input, Conv2D, GlobalAveragePooling2D, Dense, Activation, Multiply
from keras.models import Model

def ca_block(inputs, filters, kernel_size, strides):
    x = Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding='same')(inputs)
    pooling = GlobalAveragePooling2D()(x)
    fc = Dense(units=filters)(pooling)
    activation = Activation('relu')(fc)
    softmax = Dense(units=filters, activation='softmax')(activation)
    multiply = Multiply()([x, softmax])
    outputs = multiply
    return outputs

inputs = Input(shape=(height, width, channels))
ca = ca_block(inputs, filters=64, kernel_size=3, strides=1)
outputs = ca_block(ca, filters=64, kernel_size=3, strides=1)

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