卷积神经网络（Xception）动物识别

朴灿烈づ我的快乐病毒、 2024-05-07 17:08 72阅读 0赞

> 活动地址：[CSDN21天学习挑战赛][CSDN21]

> ## 什么是Xception模型？ ##
> 
> Xception是谷歌公司继Inception后，提出的InceptionV3的一种改进模型，其改进的主要内容为采用depthwise separable convolution来替换原来Inception v3中的多尺寸卷积核特征响应操作。在讲Xception模型之前，首先要讲一下什么是depthwise separable convolution。

###    对于一个卷积点而言： ###

>   
> 假设有一个3×3大小的卷积层，其输入通道为16、输出通道为32。具体为，32个3×3大小的卷积核会遍历16个通道中的每个数据，最后可得到所需的32个输出通道，所需参数为16×32×3×3=4608个。  
> 应用深度可分离卷积，用16个3×3大小的卷积核分别遍历16通道的数据，得到了16个特征图谱。在融合操作之前，接着用32个1×1大小的卷积核遍历这16个特征图谱，所需参数为16×3×3+16×32×1×1=656个。可以看出来depthwise separable convolution可以减少模型的参数。

> 通俗地理解就是卷积核厚度只有一层，然后在输入张量上一层一层地滑动，所以一个卷积核就对应了一个输出通道，当卷积完成后，在利用1x1的卷积调整厚度。
> 
> 不过Xception模型中的depthwise separable convolution和普通的不太一样，普通的depthwise separable convolution是先进行3x3操作再进行1x1操作（就好像mobileNet中的一样），而Xception模型中则是先进行1x1操作然后再进行3x3操作，中间为了保证数据不被破坏，没有添加relu层，而mobileNet添加了relu层。在建立模型的时候，可以使用Keras中的SeparableConv2D层建立相应的功能。

![4f8c3ba2910944fca9078be6a72f0a52.png][]

--------------------

## 一、前期工作 ##

### 1.设置GPU ###

建议大家还是使用GPU八，不然训练模型太慢了。

import tensorflow as tf
    
    gpus = tf.config.list_physical_devices("GPU")
    
    if gpus:
        tf.config.experimental.set_memory_growth(gpus[0], True)  #设置GPU显存用量按需使用
        tf.config.set_visible_devices([gpus[0]],"GPU")
    
    # 打印显卡信息，确认GPU可用
    print(gpus)

### 2.导入数据 ###

import matplotlib.pyplot as plt
    # 支持中文
    plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
    plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
    
    import os,PIL
    
    # 设置随机种子尽可能使结果可以重现
    import numpy as np
    np.random.seed(1)
    
    # 设置随机种子尽可能使结果可以重现
    import tensorflow as tf
    tf.random.set_seed(1)
    
    import pathlib
    data_dir = "data"
    
    data_dir = pathlib.Path(data_dir)

### 3.查看数据 ###

image_count = len(list(data_dir.glob('*/*')))
    
    print("图片总数为：",image_count)

> 图片总数为： 4000

## 二、数据预处理 ##

### 1.加载数据 ###

batch_size = 2
    img_height = 299
    img_width  = 299

"""
    关于image_dataset_from_directory()的详细介绍可以参考文章：https://mtyjkh.blog.csdn.net/article/details/117018789
    """
    train_ds = tf.keras.preprocessing.image_dataset_from_directory(
        data_dir,
        validation_split=0.2,
        subset="training",
        seed=12,
        image_size=(img_height, img_width),
        batch_size=batch_size)

> Found 4000 files belonging to 4 classes.Using 3200 files for training.

"""
    关于image_dataset_from_directory()的详细介绍可以参考文章：https://mtyjkh.blog.csdn.net/article/details/117018789
    """
    val_ds = tf.keras.preprocessing.image_dataset_from_directory(
        data_dir,
        validation_split=0.2,
        subset="validation",
        seed=12,
        image_size=(img_height, img_width),
        batch_size=batch_size)

> Found 4000 files belonging to 4 classes.Using 800 files for validation.

查看标签

class_names = train_ds.class_names
    print(class_names)

> \['cat', 'chook', 'dog', 'horse'\]

### 2.再次检查数据 ###

查看维度

for image_batch, labels_batch in train_ds:
        print(image_batch.shape)
        print(labels_batch.shape)
        break

> (2, 299, 299, 3)  
> (2,)

###  ###

### 3.配置数据集 ###

AUTOTUNE = tf.data.AUTOTUNE
    
    train_ds = (
        train_ds.cache()
        .shuffle(1000)
    #     .map(train_preprocessing)    # 这里可以设置预处理函数
    #     .batch(batch_size)           # 在image_dataset_from_directory处已经设置了batch_size
        .prefetch(buffer_size=AUTOTUNE)
    )
    
    val_ds = (
        val_ds.cache()
        .shuffle(1000)
    #     .map(val_preprocessing)    # 这里可以设置预处理函数
    #     .batch(batch_size)         # 在image_dataset_from_directory处已经设置了batch_size
        .prefetch(buffer_size=AUTOTUNE)
    )

## 三、构建模型 ##

### 1.构建Xception模型 ###

#====================================#
    #     Xception的网络部分
    #====================================#
    from tensorflow.keras.preprocessing import image
    
    from tensorflow.keras.models import Model
    from tensorflow.keras import layers
    from tensorflow.keras.layers import Dense,Input,BatchNormalization,Activation,Conv2D,SeparableConv2D,MaxPooling2D
    from tensorflow.keras.layers import GlobalAveragePooling2D,GlobalMaxPooling2D
    from tensorflow.keras import backend as K
    from tensorflow.keras.applications.imagenet_utils import decode_predictions
    
    
    def Xception(input_shape = [299,299,3],classes=1000):
    
        img_input = Input(shape=input_shape)
        
        #=================#
        #   Entry flow
        #=================#
        #  block1
        # 299,299,3 -> 149,149,64
        x = Conv2D(32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')(img_input)
        x = BatchNormalization(name='block1_conv1_bn')(x)
        x = Activation('relu', name='block1_conv1_act')(x)
        x = Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x)
        x = BatchNormalization(name='block1_conv2_bn')(x)
        x = Activation('relu', name='block1_conv2_act')(x)
    
    
        # block2
        # 149,149,64 -> 75,75,128
        residual = Conv2D(128, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
        residual = BatchNormalization()(residual)
    
        x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')(x)
        x = BatchNormalization(name='block2_sepconv1_bn')(x)
        x = Activation('relu', name='block2_sepconv2_act')(x)
        x = SeparableConv2D(128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')(x)
        x = BatchNormalization(name='block2_sepconv2_bn')(x)
    
        x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block2_pool')(x)
        x = layers.add([x, residual])
    
        # block3
        # 75,75,128 -> 38,38,256
        residual = Conv2D(256, (1, 1), strides=(2, 2),padding='same', use_bias=False)(x)
        residual = BatchNormalization()(residual)
    
        x = Activation('relu', name='block3_sepconv1_act')(x)
        x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')(x)
        x = BatchNormalization(name='block3_sepconv1_bn')(x)
        x = Activation('relu', name='block3_sepconv2_act')(x)
        x = SeparableConv2D(256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')(x)
        x = BatchNormalization(name='block3_sepconv2_bn')(x)
    
        x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block3_pool')(x)
        x = layers.add([x, residual])
    
        # block4
        # 38,38,256 -> 19,19,728
        residual = Conv2D(728, (1, 1), strides=(2, 2),padding='same', use_bias=False)(x)
        residual = BatchNormalization()(residual)
    
        x = Activation('relu', name='block4_sepconv1_act')(x)
        x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')(x)
        x = BatchNormalization(name='block4_sepconv1_bn')(x)
        x = Activation('relu', name='block4_sepconv2_act')(x)
        x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')(x)
        x = BatchNormalization(name='block4_sepconv2_bn')(x)
    
        x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block4_pool')(x)
        x = layers.add([x, residual])
    
        #=================#
        # Middle flow
        #=================#
        # block5--block12
        # 19,19,728 -> 19,19,728
        for i in range(8):
            residual = x
            prefix = 'block' + str(i + 5)
    
            x = Activation('relu', name=prefix + '_sepconv1_act')(x)
            x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv1')(x)
            x = BatchNormalization(name=prefix + '_sepconv1_bn')(x)
            x = Activation('relu', name=prefix + '_sepconv2_act')(x)
            x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv2')(x)
            x = BatchNormalization(name=prefix + '_sepconv2_bn')(x)
            x = Activation('relu', name=prefix + '_sepconv3_act')(x)
            x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv3')(x)
            x = BatchNormalization(name=prefix + '_sepconv3_bn')(x)
    
            x = layers.add([x, residual])
    
        #=================#
        #    Exit flow
        #=================#
        # block13
        # 19,19,728 -> 10,10,1024
        residual = Conv2D(1024, (1, 1), strides=(2, 2),
                          padding='same', use_bias=False)(x)
        residual = BatchNormalization()(residual)
    
        x = Activation('relu', name='block13_sepconv1_act')(x)
        x = SeparableConv2D(728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')(x)
        x = BatchNormalization(name='block13_sepconv1_bn')(x)
        x = Activation('relu', name='block13_sepconv2_act')(x)
        x = SeparableConv2D(1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')(x)
        x = BatchNormalization(name='block13_sepconv2_bn')(x)
    
        x = MaxPooling2D((3, 3), strides=(2, 2), padding='same', name='block13_pool')(x)
        x = layers.add([x, residual])
    
        # block14
        # 10,10,1024 -> 10,10,2048
        x = SeparableConv2D(1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')(x)
        x = BatchNormalization(name='block14_sepconv1_bn')(x)
        x = Activation('relu', name='block14_sepconv1_act')(x)
    
        x = SeparableConv2D(2048, (3, 3), padding='same', use_bias=False, name='block14_sepconv2')(x)
        x = BatchNormalization(name='block14_sepconv2_bn')(x)
        x = Activation('relu', name='block14_sepconv2_act')(x)
    
        x = GlobalAveragePooling2D(name='avg_pool')(x)
        x = Dense(classes, activation='softmax', name='predictions')(x)
    
        inputs = img_input
    
        model = Model(inputs, x, name='xception')
    
        return model

将模型打印出来：

model = Xception()
    # 打印模型信息
    model.summary()

Model: "xception"
    __________________________________________________________________________________________________
    Layer (type)                    Output Shape         Param #     Connected to                     
    ==================================================================================================
    input_1 (InputLayer)            [(None, 299, 299, 3) 0                                            
    __________________________________________________________________________________________________
    block1_conv1 (Conv2D)           (None, 149, 149, 32) 864         input_1[0][0]                    
    __________________________________________________________________________________________________
    block1_conv1_bn (BatchNormaliza (None, 149, 149, 32) 128         block1_conv1[0][0]               
    __________________________________________________________________________________________________
    block1_conv1_act (Activation)   (None, 149, 149, 32) 0           block1_conv1_bn[0][0]            
    __________________________________________________________________________________________________
    block1_conv2 (Conv2D)           (None, 147, 147, 64) 18432       block1_conv1_act[0][0]           
    __________________________________________________________________________________________________
    block1_conv2_bn (BatchNormaliza (None, 147, 147, 64) 256         block1_conv2[0][0]               
    __________________________________________________________________________________________________
    block1_conv2_act (Activation)   (None, 147, 147, 64) 0           block1_conv2_bn[0][0]            
    __________________________________________________________________________________________________
    block2_sepconv1 (SeparableConv2 (None, 147, 147, 128 8768        block1_conv2_act[0][0]           
    __________________________________________________________________________________________________
    block2_sepconv1_bn (BatchNormal (None, 147, 147, 128 512         block2_sepconv1[0][0]            
    __________________________________________________________________________________________________
    block2_sepconv2_act (Activation (None, 147, 147, 128 0           block2_sepconv1_bn[0][0]         
    __________________________________________________________________________________________________
    block2_sepconv2 (SeparableConv2 (None, 147, 147, 128 17536       block2_sepconv2_act[0][0]        
    __________________________________________________________________________________________________
    block2_sepconv2_bn (BatchNormal (None, 147, 147, 128 512         block2_sepconv2[0][0]            
    __________________________________________________________________________________________________
    conv2d (Conv2D)                 (None, 74, 74, 128)  8192        block1_conv2_act[0][0]           
    __________________________________________________________________________________________________
    block2_pool (MaxPooling2D)      (None, 74, 74, 128)  0           block2_sepconv2_bn[0][0]         
    __________________________________________________________________________________________________
    batch_normalization (BatchNorma (None, 74, 74, 128)  512         conv2d[0][0]                     
    __________________________________________________________________________________________________
    add (Add)                       (None, 74, 74, 128)  0           block2_pool[0][0]                
                                                                     batch_normalization[0][0]        
    __________________________________________________________________________________________________
    block3_sepconv1_act (Activation (None, 74, 74, 128)  0           add[0][0]                        
    __________________________________________________________________________________________________
    block3_sepconv1 (SeparableConv2 (None, 74, 74, 256)  33920       block3_sepconv1_act[0][0]        
    __________________________________________________________________________________________________
    block3_sepconv1_bn (BatchNormal (None, 74, 74, 256)  1024        block3_sepconv1[0][0]            
    __________________________________________________________________________________________________
    block3_sepconv2_act (Activation (None, 74, 74, 256)  0           block3_sepconv1_bn[0][0]         
    __________________________________________________________________________________________________
    block3_sepconv2 (SeparableConv2 (None, 74, 74, 256)  67840       block3_sepconv2_act[0][0]        
    __________________________________________________________________________________________________
    block3_sepconv2_bn (BatchNormal (None, 74, 74, 256)  1024        block3_sepconv2[0][0]            
    __________________________________________________________________________________________________
    conv2d_1 (Conv2D)               (None, 37, 37, 256)  32768       add[0][0]                        
    __________________________________________________________________________________________________
    block3_pool (MaxPooling2D)      (None, 37, 37, 256)  0           block3_sepconv2_bn[0][0]         
    __________________________________________________________________________________________________
    batch_normalization_1 (BatchNor (None, 37, 37, 256)  1024        conv2d_1[0][0]                   
    __________________________________________________________________________________________________
    add_1 (Add)                     (None, 37, 37, 256)  0           block3_pool[0][0]                
                                                                     batch_normalization_1[0][0]      
    __________________________________________________________________________________________________
    block4_sepconv1_act (Activation (None, 37, 37, 256)  0           add_1[0][0]                      
    __________________________________________________________________________________________________
    block4_sepconv1 (SeparableConv2 (None, 37, 37, 728)  188672      block4_sepconv1_act[0][0]        
    __________________________________________________________________________________________________
    block4_sepconv1_bn (BatchNormal (None, 37, 37, 728)  2912        block4_sepconv1[0][0]            
    __________________________________________________________________________________________________
    block4_sepconv2_act (Activation (None, 37, 37, 728)  0           block4_sepconv1_bn[0][0]         
    __________________________________________________________________________________________________
    block4_sepconv2 (SeparableConv2 (None, 37, 37, 728)  536536      block4_sepconv2_act[0][0]        
    __________________________________________________________________________________________________
    block4_sepconv2_bn (BatchNormal (None, 37, 37, 728)  2912        block4_sepconv2[0][0]            
    __________________________________________________________________________________________________
    conv2d_2 (Conv2D)               (None, 19, 19, 728)  186368      add_1[0][0]                      
    __________________________________________________________________________________________________
    block4_pool (MaxPooling2D)      (None, 19, 19, 728)  0           block4_sepconv2_bn[0][0]         
    __________________________________________________________________________________________________
    batch_normalization_2 (BatchNor (None, 19, 19, 728)  2912        conv2d_2[0][0]                   
    __________________________________________________________________________________________________
    add_2 (Add)                     (None, 19, 19, 728)  0           block4_pool[0][0]                
                                                                     batch_normalization_2[0][0]      
    __________________________________________________________________________________________________
    block5_sepconv1_act (Activation (None, 19, 19, 728)  0           add_2[0][0]                      
    __________________________________________________________________________________________________
    block5_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block5_sepconv1_act[0][0]        
    __________________________________________________________________________________________________
    block5_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block5_sepconv1[0][0]            
    __________________________________________________________________________________________________
    block5_sepconv2_act (Activation (None, 19, 19, 728)  0           block5_sepconv1_bn[0][0]         
    __________________________________________________________________________________________________
    block5_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block5_sepconv2_act[0][0]        
    __________________________________________________________________________________________________
    block5_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block5_sepconv2[0][0]            
    __________________________________________________________________________________________________
    block5_sepconv3_act (Activation (None, 19, 19, 728)  0           block5_sepconv2_bn[0][0]         
    __________________________________________________________________________________________________
    block5_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block5_sepconv3_act[0][0]        
    __________________________________________________________________________________________________
    block5_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block5_sepconv3[0][0]            
    __________________________________________________________________________________________________
    add_3 (Add)                     (None, 19, 19, 728)  0           block5_sepconv3_bn[0][0]         
                                                                     add_2[0][0]                      
    __________________________________________________________________________________________________
    block6_sepconv1_act (Activation (None, 19, 19, 728)  0           add_3[0][0]                      
    __________________________________________________________________________________________________
    block6_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block6_sepconv1_act[0][0]        
    __________________________________________________________________________________________________
    block6_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block6_sepconv1[0][0]            
    __________________________________________________________________________________________________
    block6_sepconv2_act (Activation (None, 19, 19, 728)  0           block6_sepconv1_bn[0][0]         
    __________________________________________________________________________________________________
    block6_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block6_sepconv2_act[0][0]        
    __________________________________________________________________________________________________
    block6_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block6_sepconv2[0][0]            
    __________________________________________________________________________________________________
    block6_sepconv3_act (Activation (None, 19, 19, 728)  0           block6_sepconv2_bn[0][0]         
    __________________________________________________________________________________________________
    block6_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block6_sepconv3_act[0][0]        
    __________________________________________________________________________________________________
    block6_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block6_sepconv3[0][0]            
    __________________________________________________________________________________________________
    add_4 (Add)                     (None, 19, 19, 728)  0           block6_sepconv3_bn[0][0]         
                                                                     add_3[0][0]                      
    __________________________________________________________________________________________________
    block7_sepconv1_act (Activation (None, 19, 19, 728)  0           add_4[0][0]                      
    __________________________________________________________________________________________________
    block7_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block7_sepconv1_act[0][0]        
    __________________________________________________________________________________________________
    block7_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block7_sepconv1[0][0]            
    __________________________________________________________________________________________________
    block7_sepconv2_act (Activation (None, 19, 19, 728)  0           block7_sepconv1_bn[0][0]         
    __________________________________________________________________________________________________
    block7_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block7_sepconv2_act[0][0]        
    __________________________________________________________________________________________________
    block7_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block7_sepconv2[0][0]            
    __________________________________________________________________________________________________
    block7_sepconv3_act (Activation (None, 19, 19, 728)  0           block7_sepconv2_bn[0][0]         
    __________________________________________________________________________________________________
    block7_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block7_sepconv3_act[0][0]        
    __________________________________________________________________________________________________
    block7_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block7_sepconv3[0][0]            
    __________________________________________________________________________________________________
    add_5 (Add)                     (None, 19, 19, 728)  0           block7_sepconv3_bn[0][0]         
                                                                     add_4[0][0]                      
    __________________________________________________________________________________________________
    block8_sepconv1_act (Activation (None, 19, 19, 728)  0           add_5[0][0]                      
    __________________________________________________________________________________________________
    block8_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block8_sepconv1_act[0][0]        
    __________________________________________________________________________________________________
    block8_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block8_sepconv1[0][0]            
    __________________________________________________________________________________________________
    block8_sepconv2_act (Activation (None, 19, 19, 728)  0           block8_sepconv1_bn[0][0]         
    __________________________________________________________________________________________________
    block8_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block8_sepconv2_act[0][0]        
    __________________________________________________________________________________________________
    block8_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block8_sepconv2[0][0]            
    __________________________________________________________________________________________________
    block8_sepconv3_act (Activation (None, 19, 19, 728)  0           block8_sepconv2_bn[0][0]         
    __________________________________________________________________________________________________
    block8_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block8_sepconv3_act[0][0]        
    __________________________________________________________________________________________________
    block8_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block8_sepconv3[0][0]            
    __________________________________________________________________________________________________
    add_6 (Add)                     (None, 19, 19, 728)  0           block8_sepconv3_bn[0][0]         
                                                                     add_5[0][0]                      
    __________________________________________________________________________________________________
    block9_sepconv1_act (Activation (None, 19, 19, 728)  0           add_6[0][0]                      
    __________________________________________________________________________________________________
    block9_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block9_sepconv1_act[0][0]        
    __________________________________________________________________________________________________
    block9_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block9_sepconv1[0][0]            
    __________________________________________________________________________________________________
    block9_sepconv2_act (Activation (None, 19, 19, 728)  0           block9_sepconv1_bn[0][0]         
    __________________________________________________________________________________________________
    block9_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block9_sepconv2_act[0][0]        
    __________________________________________________________________________________________________
    block9_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block9_sepconv2[0][0]            
    __________________________________________________________________________________________________
    block9_sepconv3_act (Activation (None, 19, 19, 728)  0           block9_sepconv2_bn[0][0]         
    __________________________________________________________________________________________________
    block9_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block9_sepconv3_act[0][0]        
    __________________________________________________________________________________________________
    block9_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block9_sepconv3[0][0]            
    __________________________________________________________________________________________________
    add_7 (Add)                     (None, 19, 19, 728)  0           block9_sepconv3_bn[0][0]         
                                                                     add_6[0][0]                      
    __________________________________________________________________________________________________
    block10_sepconv1_act (Activatio (None, 19, 19, 728)  0           add_7[0][0]                      
    __________________________________________________________________________________________________
    block10_sepconv1 (SeparableConv (None, 19, 19, 728)  536536      block10_sepconv1_act[0][0]       
    __________________________________________________________________________________________________
    block10_sepconv1_bn (BatchNorma (None, 19, 19, 728)  2912        block10_sepconv1[0][0]           
    __________________________________________________________________________________________________
    block10_sepconv2_act (Activatio (None, 19, 19, 728)  0           block10_sepconv1_bn[0][0]        
    __________________________________________________________________________________________________
    block10_sepconv2 (SeparableConv (None, 19, 19, 728)  536536      block10_sepconv2_act[0][0]       
    __________________________________________________________________________________________________
    block10_sepconv2_bn (BatchNorma (None, 19, 19, 728)  2912        block10_sepconv2[0][0]           
    __________________________________________________________________________________________________
    block10_sepconv3_act (Activatio (None, 19, 19, 728)  0           block10_sepconv2_bn[0][0]        
    __________________________________________________________________________________________________
    block10_sepconv3 (SeparableConv (None, 19, 19, 728)  536536      block10_sepconv3_act[0][0]       
    __________________________________________________________________________________________________
    block10_sepconv3_bn (BatchNorma (None, 19, 19, 728)  2912        block10_sepconv3[0][0]           
    __________________________________________________________________________________________________
    add_8 (Add)                     (None, 19, 19, 728)  0           block10_sepconv3_bn[0][0]        
                                                                     add_7[0][0]                      
    __________________________________________________________________________________________________
    block11_sepconv1_act (Activatio (None, 19, 19, 728)  0           add_8[0][0]                      
    __________________________________________________________________________________________________
    block11_sepconv1 (SeparableConv (None, 19, 19, 728)  536536      block11_sepconv1_act[0][0]       
    __________________________________________________________________________________________________
    block11_sepconv1_bn (BatchNorma (None, 19, 19, 728)  2912        block11_sepconv1[0][0]           
    __________________________________________________________________________________________________
    block11_sepconv2_act (Activatio (None, 19, 19, 728)  0           block11_sepconv1_bn[0][0]        
    __________________________________________________________________________________________________
    block11_sepconv2 (SeparableConv (None, 19, 19, 728)  536536      block11_sepconv2_act[0][0]       
    __________________________________________________________________________________________________
    block11_sepconv2_bn (BatchNorma (None, 19, 19, 728)  2912        block11_sepconv2[0][0]           
    __________________________________________________________________________________________________
    block11_sepconv3_act (Activatio (None, 19, 19, 728)  0           block11_sepconv2_bn[0][0]        
    __________________________________________________________________________________________________
    block11_sepconv3 (SeparableConv (None, 19, 19, 728)  536536      block11_sepconv3_act[0][0]       
    __________________________________________________________________________________________________
    block11_sepconv3_bn (BatchNorma (None, 19, 19, 728)  2912        block11_sepconv3[0][0]           
    __________________________________________________________________________________________________
    add_9 (Add)                     (None, 19, 19, 728)  0           block11_sepconv3_bn[0][0]        
                                                                     add_8[0][0]                      
    __________________________________________________________________________________________________
    block12_sepconv1_act (Activatio (None, 19, 19, 728)  0           add_9[0][0]                      
    __________________________________________________________________________________________________
    block12_sepconv1 (SeparableConv (None, 19, 19, 728)  536536      block12_sepconv1_act[0][0]       
    __________________________________________________________________________________________________
    block12_sepconv1_bn (BatchNorma (None, 19, 19, 728)  2912        block12_sepconv1[0][0]           
    __________________________________________________________________________________________________
    block12_sepconv2_act (Activatio (None, 19, 19, 728)  0           block12_sepconv1_bn[0][0]        
    __________________________________________________________________________________________________
    block12_sepconv2 (SeparableConv (None, 19, 19, 728)  536536      block12_sepconv2_act[0][0]       
    __________________________________________________________________________________________________
    block12_sepconv2_bn (BatchNorma (None, 19, 19, 728)  2912        block12_sepconv2[0][0]           
    __________________________________________________________________________________________________
    block12_sepconv3_act (Activatio (None, 19, 19, 728)  0           block12_sepconv2_bn[0][0]        
    __________________________________________________________________________________________________
    block12_sepconv3 (SeparableConv (None, 19, 19, 728)  536536      block12_sepconv3_act[0][0]       
    __________________________________________________________________________________________________
    block12_sepconv3_bn (BatchNorma (None, 19, 19, 728)  2912        block12_sepconv3[0][0]           
    __________________________________________________________________________________________________
    add_10 (Add)                    (None, 19, 19, 728)  0           block12_sepconv3_bn[0][0]        
                                                                     add_9[0][0]                      
    __________________________________________________________________________________________________
    block13_sepconv1_act (Activatio (None, 19, 19, 728)  0           add_10[0][0]                     
    __________________________________________________________________________________________________
    block13_sepconv1 (SeparableConv (None, 19, 19, 728)  536536      block13_sepconv1_act[0][0]       
    __________________________________________________________________________________________________
    block13_sepconv1_bn (BatchNorma (None, 19, 19, 728)  2912        block13_sepconv1[0][0]           
    __________________________________________________________________________________________________
    block13_sepconv2_act (Activatio (None, 19, 19, 728)  0           block13_sepconv1_bn[0][0]        
    __________________________________________________________________________________________________
    block13_sepconv2 (SeparableConv (None, 19, 19, 1024) 752024      block13_sepconv2_act[0][0]       
    __________________________________________________________________________________________________
    block13_sepconv2_bn (BatchNorma (None, 19, 19, 1024) 4096        block13_sepconv2[0][0]           
    __________________________________________________________________________________________________
    conv2d_3 (Conv2D)               (None, 10, 10, 1024) 745472      add_10[0][0]                     
    __________________________________________________________________________________________________
    block13_pool (MaxPooling2D)     (None, 10, 10, 1024) 0           block13_sepconv2_bn[0][0]        
    __________________________________________________________________________________________________
    batch_normalization_3 (BatchNor (None, 10, 10, 1024) 4096        conv2d_3[0][0]                   
    __________________________________________________________________________________________________
    add_11 (Add)                    (None, 10, 10, 1024) 0           block13_pool[0][0]               
                                                                     batch_normalization_3[0][0]      
    __________________________________________________________________________________________________
    block14_sepconv1 (SeparableConv (None, 10, 10, 1536) 1582080     add_11[0][0]                     
    __________________________________________________________________________________________________
    block14_sepconv1_bn (BatchNorma (None, 10, 10, 1536) 6144        block14_sepconv1[0][0]           
    __________________________________________________________________________________________________
    block14_sepconv1_act (Activatio (None, 10, 10, 1536) 0           block14_sepconv1_bn[0][0]        
    __________________________________________________________________________________________________
    block14_sepconv2 (SeparableConv (None, 10, 10, 2048) 3159552     block14_sepconv1_act[0][0]       
    __________________________________________________________________________________________________
    block14_sepconv2_bn (BatchNorma (None, 10, 10, 2048) 8192        block14_sepconv2[0][0]           
    __________________________________________________________________________________________________
    block14_sepconv2_act (Activatio (None, 10, 10, 2048) 0           block14_sepconv2_bn[0][0]        
    __________________________________________________________________________________________________
    avg_pool (GlobalAveragePooling2 (None, 2048)         0           block14_sepconv2_act[0][0]       
    __________________________________________________________________________________________________
    predictions (Dense)             (None, 1000)         2049000     avg_pool[0][0]                   
    ==================================================================================================
    Total params: 22,910,480
    Trainable params: 22,855,952
    Non-trainable params: 54,528
    __________________________________________________________________________________________________

## 四、设置动态学习率 ##

# 设置初始学习率
    initial_learning_rate = 1e-4
    
    lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
            initial_learning_rate, 
            decay_steps=300,      # 敲黑板！！！这里是指 steps，不是指epochs
            decay_rate=0.96,     # lr经过一次衰减就会变成 decay_rate*lr
            staircase=True)
    
    # 将指数衰减学习率送入优化器
    optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)

## 五、编译 ##

model.compile(optimizer=optimizer,
                  loss     ='sparse_categorical_crossentropy',
                  metrics  =['accuracy'])

## 六、训练模型 ##

epochs = 15
    
    history = model.fit(
        train_ds,
        validation_data=val_ds,
        epochs=epochs
    )

## 七、模型评估 ##

### 1.Accuracy 和 Loss图 ###

acc = history.history['accuracy']
    val_acc = history.history['val_accuracy']
    
    loss = history.history['loss']
    val_loss = history.history['val_loss']
    
    epochs_range = range(epochs)
    
    plt.figure(figsize=(12, 4))
    plt.subplot(1, 2, 1)
    
    plt.plot(epochs_range, acc, label='Training Accuracy')
    plt.plot(epochs_range, val_acc, label='Validation Accuracy')
    plt.legend(loc='lower right')
    plt.title('Training and Validation Accuracy')
    
    plt.subplot(1, 2, 2)
    plt.plot(epochs_range, loss, label='Training Loss')
    plt.plot(epochs_range, val_loss, label='Validation Loss')
    plt.legend(loc='upper right')
    plt.title('Training and Validation Loss')
    plt.show()

### 2.混淆矩阵 ###

from sklearn.metrics import confusion_matrix
    import seaborn as sns
    import pandas as pd
    
    # 定义一个绘制混淆矩阵图的函数
    def plot_cm(labels, predictions):
        
        # 生成混淆矩阵
        conf_numpy = confusion_matrix(labels, predictions)
        # 将矩阵转化为 DataFrame
        conf_df = pd.DataFrame(conf_numpy, index=class_names ,columns=class_names)  
        
        plt.figure(figsize=(8,7))
        
        sns.heatmap(conf_df, annot=True, fmt="d", cmap="BuPu")
        
        plt.title('混淆矩阵',fontsize=15)
        plt.ylabel('真实值',fontsize=14)
        plt.xlabel('预测值',fontsize=14)
    val_pre   = []
    val_label = []
    
    for images, labels in val_ds:#这里可以取部分验证数据（.take(1)）生成混淆矩阵
        for image, label in zip(images, labels):
            # 需要给图片增加一个维度
            img_array = tf.expand_dims(image, 0) 
            # 使用模型预测图片中的人物
            prediction = model.predict(img_array)
    
            val_pre.append(class_names[np.argmax(prediction)])
            val_label.append(class_names[label])
    plot_cm(val_label, val_pre)

## 八、保存和加载模型 ##

# 保存模型
    model.save('model/24_model.h5')
    # 加载模型
    new_model = tf.keras.models.load_model('model/24_model.h5')

参考：

> **本文为[?365天深度学习训练营][365] 中的学习记录博客**
> 
>  **参考文章地址： [?深度学习100例 | 第24天-卷积神经网络（Xception）：动物识别][100_ _ _24_-_Xception]**

[CSDN21]: https://marketing.csdn.net/p/bdabfb52c5d56532133df2adc1a728fd
[4f8c3ba2910944fca9078be6a72f0a52.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/05/07/b479ba68ef0c46ae89b51e6f4c2df0cf.png
[365]: https://mp.weixin.qq.com/s/k-vYaC8l7uxX51WoypLkTw
[100_ _ _24_-_Xception]: https://mtyjkh.blog.csdn.net/article/details/120073717