活动地址：CSDN21天学习挑战赛

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 tfgpus = 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.AUTOTUNEtrain_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 imagefrom 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_predictionsdef Xception(input_shape = [299,299,3],classes=1000):img_input = Input(shape=input_shape)#=================##   Entry flow#=================##  block1# 299,299,3 -> 149,149,64x = 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 -> 75,75,128residual = 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 -> 38,38,256residual = 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 -> 19,19,728residual = 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 -> 19,19,728for i in range(8):residual = xprefix = '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 -> 10,10,1024residual = 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 -> 10,10,2048x = 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_inputmodel = 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-4lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(initial_learning_rate, decay_steps=300,      # 敲黑板！！！这里是指 steps，不是指epochsdecay_rate=0.96,     # lr经过一次衰减就会变成 decay_rate*lrstaircase=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 = 15history = 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 DataFrameconf_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

卷积神经网络（Xception）动物识别相关推荐

卷积神经网络和图像分类识别
Andrew Kirillov 著 Conmajia 译 2019 年 1 月 15 日原文发表于 CodeProject(2018 年 10 月 28 日). 中文版有小幅修改,已获作者本人授权. ...

卷积神经网络-猫狗识别（附源码）
一,项目描述该项目将使用卷积神经网络算法,识别图片中的动物是猫还是狗数据集地址:https://momodel.cn/explore/5efc77dbc018c95e69fb2a81?type=d ...

基于深度卷积神经网络进行人脸识别的原理是什么？
原文:https://www.zhihu.com/question/60759296 基于深度卷积神经网络进行人脸识别的原理是什么? 这里的人脸识别包括但不限于:人脸检测,人脸对齐,身份验证识别,和表 ...

【深度学习】Tensorflow搭建卷积神经网络实现情绪识别
[深度学习]Tensorflow搭建卷积神经网络实现情绪识别文章目录 1 Tensorflow的基本使用方法1.1 计算图1.2 Feed1.3 Fetch1.4 其他解释 2 训练一个Tensor ...

cnn图像二分类 python_人工智能Keras图像分类器（CNN卷积神经网络的图片识别篇）...
上期文章我们分享了人工智能Keras图像分类器(CNN卷积神经网络的图片识别的训练模型),本期我们使用预训练模型对图片进行识别:Keras CNN卷积神经网络模型训练导入第三方库 from kera ...

利用卷积神经网络实现人脸识别（python+TensorFlow）
利用CNN卷积神经网络实现人脸识别(python+TensorFlow) 使用的人脸数据是耶鲁大学的一个人脸数据集Yale_64x64.mat,数据集已经上传Yale 64x64.mat 程序: '' ...

PyTorch实现基于卷积神经网络的面部表情识别
基于卷积神经网络的面部表情识别(Pytorch实现)----台大李宏毅机器学习作业3(HW3) 一.项目说明给定数据集train.csv,要求使用卷积神经网络CNN,根据每个样本的面部图片判断出其表 ...

【图像识别】基于卷积神经网络实现手写汉字识别附matlab代码
1 内容介绍 1.1. 数据集的获取数据集的获取来自模式识别国家重点实验室共享,这个不解释直接上网址http://www.nlpr.ia.ac.cn/databases/download/featu ...

【转】干货｜孙启超：卷积神经网络在人脸识别技术中的应用
2018-08-27 21:45:01 随着 iPhone X 的发布,Face ID 人脸识别技术开始进入人们的日常生活中,当我们拿起手机并看着它的时候就可以实现手机解锁的功能.而人脸识别中的关键技 ...

毕设深度学习卷积神经网络的花卉识别
文章目录 0 前言 1 项目背景 2 花卉识别的基本原理 3 算法实现 3.1 预处理 3.2 特征提取和选择 3.3 分类器设计和决策 3.4 卷积神经网络基本原理 4 算法实现 4.1 花卉图像数 ...

最新文章

开源！《AI 算法工程师手册》中文教程正式发布！

用C++ 和OpenCV 实现视频目标检测（YOLOv4模型）

2019-4-25 html学习笔记

Angry IP Scanner 获取设备的IP

详述JavaScript数组

程序、进程以及线程的爱恨情仇

Java黑皮书课后题第9章：*9.4（使用Random类）编写一个程序，创建一个种子为1000的Random对象，然后使用nextInt(100)方法显示0到100之间的前50个随机整数

leetcode1414. 和为 K 的最少斐波那契数字数目（贪心算法）

php mysql cpu100_解决mysql占用cpu资源100%的问题_MySQL

Java高并发编程详解系列-Balking设计模式

java kafka client_Kafka Client API 基本使用

C++中#和##的特殊使用

python list倒序_python数据分析(1):内建序列函数

UML教程2：用例图

HTML5 实现拼图游戏

JUnit with Ant

403forbidden

Redis连接报错【redis.clients.jedis.exceptions.JedisDataException: ERR Client sent AUTH, but no password 】

市面上比较好用的钣金展开软件都有哪些？

倾斜摄影三维建模软件photoscan教程 [转]

热门文章

linux挂载nfs文件失败,挂载nfs文件系统时错误

TISS 携手知名公链发起“公链成长计划”，共同推动公链应用落地 | TokenInsight...

期末作业代码网页设计代码——优分期大学生购物商城(7页) 大学生大学生购物商城作业模板下载静态HTML购物商城网页制作下载_网页设计代码

薪酬管理系统的作用是什么？

攻克了Orvibo厂商MixPad S的调试密码

android自定义计步器形状,Android自定义view-QQ计步器效果

Nmon的安装及使用

glusterfs分布式存储架构集群【分布式卷、条带卷、复制卷、分布式条带卷、分布式复制卷】创建

计算机主板的选购技巧,如何看芯选板？各种主板选购技巧推荐

mysql查询两个字的人名_mysql 怎么通过一条sql查询出两个人员的名字