卷积神经网络（Xception）动物识别

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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。

​

---

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 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