在Practice_10_cifar100代码中添加SE注意力模块

好的，首先需要在代码中引入SE注意力模块所需的库和函数：

import tensorflow as tf

def se_block(input_feature, ratio=8):
    """Squeeze-and-excitation block"""
    channel_axis = 1 if tf.keras.backend.image_data_format() == "channels_first" else -1
    channel = input_feature.shape[channel_axis]
    se_feature = tf.keras.layers.GlobalAveragePooling2D()(input_feature)
    se_feature = tf.keras.layers.Reshape((1, 1, channel))(se_feature)
    se_feature = tf.keras.layers.Dense(channel // ratio, activation='relu', kernel_initializer='he_normal', use_bias=True)(se_feature)
    se_feature = tf.keras.layers.Dense(channel, activation='sigmoid', kernel_initializer='he_normal', use_bias=True)(se_feature)
    if tf.keras.backend.image_data_format() == 'channels_first':
        se_feature = tf.keras.layers.Permute((3, 1, 2))(se_feature)
    se_tensor = tf.keras.layers.multiply([input_feature, se_feature])
    return se_tensor

然后在代码中的残差块中添加SE注意力模块：

def residual_block(inputs, filters, strides=(1, 1), use_se=True):
    shortcut = inputs
    # first block
    x = tf.keras.layers.Conv2D(filters=filters, kernel_size=(3, 3), strides=strides, padding='same',
                               kernel_initializer='he_normal')(inputs)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)
    # second block
    x = tf.keras.layers.Conv2D(filters=filters, kernel_size=(3, 3), strides=(1, 1), padding='same',
                               kernel_initializer='he_normal')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    # SE block
    if use_se:
        x = se_block(x)
    # shortcut connection
    if strides != (1, 1) or inputs.shape[-1] != filters:
        shortcut = tf.keras.layers.Conv2D(filters=filters, kernel_size=(1, 1), strides=strides, padding='same',
                                          kernel_initializer='he_normal')(inputs)
        shortcut = tf.keras.layers.BatchNormalization()(shortcut)
    x = tf.keras.layers.Add()([x, shortcut])
    x = tf.keras.layers.Activation('relu')(x)
    return x

这样就在代码中添加了SE注意力模块。注意，这里的实现方式是在残差块中嵌入SE注意力模块，而不是在整个模型中添加。