在resnet50中引入通道注意力机制（SE）代码

在ResNet50中引入通道注意力机制（SE）的代码如下所示：

import tensorflow as tf
from tensorflow.keras import layers

def se_block(input_tensor, reduction_ratio=16):
    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1
    channel = input_tensor.shape[channel_axis]

    se = layers.GlobalAveragePooling2D()(input_tensor)
    se = layers.Reshape((1, 1, channel))(se)
    se = layers.Dense(channel // reduction_ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
    se = layers.Dense(channel, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)

    if tf.keras.backend.image_data_format() == 'channels_first':
        se = layers.Permute((3, 1, 2))(se)

    x = layers.multiply([input_tensor, se])
    return x

def resnet_block(input_tensor, filters, kernel_size, strides, reduction_ratio=16):
    x = layers.Conv2D(filters, kernel_size=kernel_size, strides=strides, padding='same')(input_tensor)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)

    x = layers.Conv2D(filters, kernel_size=kernel_size, padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = se_block(x, reduction_ratio)  # 添加SE模块
    x = layers.add([x, input_tensor])
    x = layers.Activation('relu')(x)
    return x

def build_resnet50(input_shape=(224, 224, 3), num_classes=1000):
    input_tensor = layers.Input(shape=input_shape)

    x = layers.Conv2D(64, kernel_size=7, strides=2, padding='same')(input_tensor)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    x = layers.MaxPooling2D(pool_size=3, strides=2, padding='same')(x)

    x = resnet_block(x, filters=64, kernel_size=3, strides=1)
    x = resnet_block(x, filters=64, kernel_size=3, strides=1)
    x = resnet_block(x, filters=64, kernel_size=3, strides=1)

    x = resnet_block(x, filters=128, kernel_size=3, strides=2)
    x = resnet_block(x, filters=128, kernel_size=3, strides=1)
    x = resnet_block(x, filters=128, kernel_size=3, strides=1)
    x = resnet_block(x, filters=128, kernel_size=3, strides=1)

    x = resnet_block(x, filters=256, kernel_size=3, strides=2)
    for _ in range(5):
        x = resnet_block(x, filters=256, kernel_size=3, strides=1)

    x = resnet_block(x, filters=512, kernel_size=3, strides=2)
    for _ in range(2):
        x = resnet_block(x, filters=512, kernel_size=3, strides=1)

    x = layers.GlobalAveragePooling2D()(x)
    x = layers.Dense(num_classes, activation='softmax')(x)

    model = tf.keras.Model(inputs=input_tensor, outputs=x)
    return model

这段代码定义了一个ResNet50模型，并在每个残差块中引入了通道注意力机制（SE）。通道注意力机制通过全局平均池化、全连接层和Sigmoid激活函数来计算通道权重，然后将通道权重与输入特征图相乘，实现通道的自适应加权。这样可以增强模型对重要通道的关注，提升模型性能。