深度学习实战07-卷积神经网络（Xception）实现动物识别
深度学习实战07-卷积神经网络（Xception）实现动物识别
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- 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
参考文章：
https://blog.csdn.net/qq_38389717/article/details/108904221
一、前期准备
1. 设置GPU
如果使用的是cpu，可以去掉这部分代码。
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 = "./datasets/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)
二、数据预处理
1. 加载数据
使用image_dataset_from_directory方法将磁盘中的数据加载到tf.data.Dataset中。
batch_size = 10
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)
"""
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)
我们可以通过class_names输出数据集的标签。将标签按字母顺序对应目录的名称。
class_names = train_ds.class_names
print(class_names)
补充：去掉.ipynb_checkpoints类的方法：
# %autosave 0
# os.removedirs("./datasets/data/" + "/.ipynb_checkpoints")
3. 再次检查数据
for image_batch, labels_batch in train_ds:
print(image_batch.shape)
print(labels_batch.shape)
break
- Image_batch是形状的张量（2,299,299,3）。这是一批形状2402403的8张图片（最后一维指的是彩色通道RGB）。
- Label_batch是形状（8，）的张量，这些标签对应8张图片。
4. 配置数据集
- shuffle()：打乱数据，关于此函数的详细介绍可以参考：
https://zhuanlan.zhihu.com/p/42417456 - prefetch()：预取数据，加速运行。
-cache()：将数据集缓存到内存当中，加速运行。
# AUTOTUNE = tf.data.AUTOTUNE
# 报错：AttributeError: module 'tensorflow._api.v2.data' has no attribute 'AUTOTUNE'
# 解决方法：
AUTOTUNE = tf.data.experimental.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
Xception是谷歌公司继Inception后，提出的InceptionV3的一种改进模型，其中Inception模块已被深度可分离卷积（depthwise separable convolution）替换。它与Inception-v1（23M）的参数数量大致相同。
1. 深度可分离卷积
深度可分离卷积其实是一种可分解卷积操作（factorized convolutions）。其中可以分解为两个更小的操作：depthwise convolution和pointwise convolution。
（1）标准卷积
下面先学习标准卷积的操作：
输入一个12 12 3的一个输入特征图，经过5 5 3的卷积核得到一个881的输出特征图。如果我们此时有256个卷积核，我们将会得到一个8 8 256的输出特征图。
以上就是标准卷积做的活，那么深度卷积和逐点卷积呢？
（2）深度卷积
与标准卷积不一样的是，这里会将卷积核拆分成单通道形式，在不改变输入特征图像的深度的情况下，对每一通道进行卷积操作，这样就得到了和输入特征图通道数一致的输出特征图。如上图，输入12 * 12 * 3的特征图，经过5 * 5 * 1 * 5的深度卷积后，得到了8 * 8 * 3的输出特征图。输入和输出的维度是不变的3，这样就会有一个问题，通道数太少，特征图的维度太少，能获得足够的有效信息吗？
（3）逐点卷积
逐点卷积就是1 * 1卷积，主要作用就是对特征图进行升维和降维，如下图：
在深度卷积的过程中，我们得到了8 * 8 * 3的输出特征图，我们用256个1 * 1 * 3的卷积核对输入特征图进行卷积操作，输出的特征图和标准卷积操作一样都是8 * 8 * 256了。
标准卷积和深度可分离卷积的过程对比如下：
（4）为什么要用深度可分离卷积？
深度可分离卷积可以实现更少的参数，更少的运算量。
2. 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
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
先罗列以下学习率大与小的优缺点：
- 学习率大
- 优点
- 加快学习速率。
- 有助于跳出局部最优值。
- 缺点
- 导致模型训练不收敛。
- 单单使用大学习率容易导致模型不精确。
- 优点
- 学习率小
- 优点
- 有助于模型收敛、模型细化。
- 提高模型精度。
- 缺点
- 很难跳出局部最优值。
- 收敛缓慢。
- 优点
注意：这里设置的动态学习率为：指数衰减（ExponentialDecay）。在每一个epoch开始前，学习率（learning_rate）都将会重置为初始学习率（initial_learning_rate），然后再重新开始衰减。计算公式如下：
== learning_rate = initial_learning_rate * decay_rate^(step / decay_steps) ==
# 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
在准备对模型进行训练之前，还需要再对其进行一些设置。以下内容是在模型的编译步骤中添加的：
- 优化器（optimizer）：决定模型如何根据其看到的数据和自身的损失函数进行更新。
- 损失函数（loss）：用于估量预测值与真实值的不一致程度。
- 评价函数（metrics）：用于监控训练步骤和测试步骤。以下示例使用了准确率，即被正确分类的图像的比例。
model.compile(optimizer=optimizer,
loss ='sparse_categorical_crossentropy',
metrics =['accuracy'])
六、训练模型
epochs = 5
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
Seaborn是一个画图库，它基于Matplotlib核心库进行了更高阶的API封装，可以让你轻松画出更漂亮的图形。Seaborn的漂亮主要体现在配色更加舒服，以及图形元素的样式更加细腻。
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')
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