写一个输入（64，3，128，128）的带CBAM、BN层和dropout层ResNet五分类网络要求效率高性能好

以下是一个满足要求的示例代码：

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, Activation, Add, GlobalAveragePooling2D, Dense, Dropout
from tensorflow.keras.layers import Rescaling, Multiply, Lambda, Softmax

def cbam_block(inputs, ratio=8):
    # 计算通道注意力权重
    avg_pool = Lambda(lambda x: tf.reduce_mean(x, axis=[1,2], keepdims=True))(inputs)
    max_pool = Lambda(lambda x: tf.reduce_max(x, axis=[1,2], keepdims=True))(inputs)
    concat = tf.keras.layers.Concatenate(axis=3)([avg_pool, max_pool])
    conv = Conv2D(filters=inputs.shape[-1] // ratio, kernel_size=1, strides=1, padding='same', activation='relu')(concat)
    attention_map = Conv2D(filters=inputs.shape[-1], kernel_size=1, strides=1, padding='same', activation='sigmoid')(conv)
    # 应用通道注意力权重
    output = Multiply()([inputs, attention_map])
    # 计算空间注意力权重
    channel_avg_pool = Lambda(lambda x: tf.reduce_mean(x, axis=3, keepdims=True))(output)
    channel_max_pool = Lambda(lambda x: tf.reduce_max(x, axis=3, keepdims=True))(output)
    channel_concat = tf.keras.layers.Concatenate(axis=3)([channel_avg_pool, channel_max_pool])
    channel_conv = Conv2D(filters=1, kernel_size=7, strides=1, padding='same', activation='sigmoid')(channel_concat)
    # 应用空间注意力权重
    output = Multiply()([output, channel_conv])
    return output

def resnet_block(inputs, filters, kernel_size, strides, use_cbam=True, use_bn=True, use_dropout=True):
    x = inputs
    # 第一层卷积
    x = Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding='same')(x)
    if use_bn:
        x = BatchNormalization()(x)
    x = Activation('relu')(x)
    # 第二层卷积
    x = Conv2D(filters=filters, kernel_size=kernel_size, strides=1, padding='same')(x)
    if use_bn:
        x = BatchNormalization()(x)
    if use_cbam:
        x = cbam_block(x)
    x = Add()([x, inputs])
    x = Activation('relu')(x)
    if use_dropout:
        x = Dropout(0.5)(x)
    return x

def build_resnet(input_shape, num_classes):
    inputs = Input(shape=input_shape)
    # 预处理层
    x = Rescaling(scale=1./255)(inputs)
    # 第一层卷积
    x = Conv2D(filters=64, kernel_size=7, strides=2, padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    # 池化层
    x = tf.keras.layers.MaxPooling2D(pool_size=3, strides=2, padding='same')(x)
    # 4个残差块
    x = resnet_block(x, filters=64, kernel_size=3, strides=1, use_cbam=True, use_bn=True, use_dropout=True)
    x = resnet_block(x, filters=64, kernel_size=3, strides=1, use_cbam=True, use_bn=True, use_dropout=True)
    x = resnet_block(x, filters=64, kernel_size=3, strides=1, use_cbam=True, use_bn=True, use_dropout=True)
    x = resnet_block(x, filters=64, kernel_size=3, strides=1, use_cbam=True, use_bn=True, use_dropout=True)
    # 全局平均池化层
    x = GlobalAveragePooling2D()(x)
    # 全连接层
    x = Dense(units=num_classes)(x)
    # Softmax激活层
    outputs = Softmax()(x)
    # 创建模型
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    return model

这个模型使用了CBAM块、BN层和dropout层，并且在ResNet的基础上进行了一些修改以提高效率和性能。建议在GPU上进行训练。