Keras深度学习使用Xception预训练神经网络实现猫狗分类，测试集准确率高达0.99
Keras深度学习使用Xception预训练神经网络实现猫狗分类，测试集准确率高达0.99
前面一篇文章呢，我使用了VGG16预训练神经网络实现了一下猫狗分类的案例，即Keras深度学习使用VGG16预训练神经网络实现猫狗分类，当时的训练集准确率为0.90，而测试集的准确率为0.89。
这篇文章来使用Xception预训练神经网络来实现一下猫狗分类的案例，其结果会比VGG16更好一些。
Xception（可分离卷积）
在ImageNet上预训练的Xception V1模型，在ImageNet上，该模型取得了验证集top1 0.790和top5 0.945的准去率（第一个命中结果和前五个包含结果）。
注意该模型只支持channels_last的维度顺序（高度, 宽度, 通道）。模型默认输入尺寸是299✖️299
实现过程
本次使用Xception预训练神经网络实现猫狗图像分类的过程与VGG16实现的过程相类似，只是改变了预训练神经网络。
模型训练
首先我们需要导入所需要的包，本次训练模型呢，使用了Xception预训练网络模型，对于没有GPU加持的小伙伴众多的预训练网络模型也算是福音。即可加快模型训练速度，也可以使小批量的数据集的准确率提高很多。
import keras
import tensorflow as tf
from keras import layers
import numpy as np
import os
import shutil
import matplotlib.pyplot as plt
%matplotlib inline
from keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import Xception
从网上找到的猫狗数据集资源，加载猫狗数据集和划分猫狗训练（train）数据和测试（test）数据。猫狗原数据集我已上传到百度云盘，需要的文章末尾请自取。
# 创建划分好的训练测试目录
BASE_DIR = './cat_dog'
train_dir = os.path.join(BASE_DIR, 'train')
train_dir_dog = os.path.join(train_dir, 'dog')
train_dir_cat = os.path.join(train_dir, 'cat')
test_dir = os.path.join(BASE_DIR, 'test')
test_dir_dog = os.path.join(test_dir, 'dog')
test_dir_cat = os.path.join(test_dir, 'cat')
train_dir_dog, test_dir_cat
os.mkdir(BASE_DIR)
os.mkdir(train_dir)
os.mkdir(train_dir_dog)
os.mkdir(train_dir_cat)
os.mkdir(test_dir)
os.mkdir(test_dir_dog)
os.mkdir(test_dir_cat)
# 数据集拷贝
source_dir = './source_data/train'
# 拷贝1000张猫的训练集到新划分的目录
fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
s = os.path.join(source_dir, fname)
d = os.path.join(train_dir_cat, fname)
shutil.copyfile(s, d)
# 拷贝1000张狗的训练集到新划分的目录
fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
s = os.path.join(source_dir, fname)
d = os.path.join(train_dir_dog, fname)
shutil.copyfile(s, d)
# 拷贝猫和狗测试集图片各500张，共1000张
fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:
s = os.path.join(source_dir, fname)
d = os.path.join(test_dir_dog, fname)
shutil.copyfile(s, d)
fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:
s = os.path.join(source_dir, fname)
d = os.path.join(test_dir_cat, fname)
shutil.copyfile(s, d)
建立图像数据迭代器，并将原始图像进行归一化处理
train_datagen = ImageDataGenerator(rescale=1 / 255)
test_datagen = ImageDataGenerator(rescale=1 / 255)
# 训练集数据生成器，从数据目录生成，读取成200*200的统一图像resize，本质是一个二分类问题，model我们使用binary
train_generator = train_datagen.flow_from_directory(train_dir,
target_size=(200, 200), batch_size=20, class_mode='binary')
# 测试集数据
test_generator = test_datagen.flow_from_directory(test_dir,
target_size=(200, 200), batch_size=20, class_mode='binary')
使用Matplotlib，我们可以将图像进行输出；图像的数据呢，本质上就是三个通道的颜色数据值，即RGB值。
# [批次](批次数据集, 批次二分类结果)[批次数据集下标] --- 对应迭代器的数据格式
# 0 为猫；1 为狗 --- 二分类结果表示
plt.imshow(train_generator[0][0][0])
print(train_generator[0][1][0])
初始化Xception预训练神经网络；使用Xception网络，使用imageNet权重，include_top是否包含最后的全连接层和输出层，
covn_base = Xception(weights='imagenet', include_top=False,
input_shape=(200,200,3))
使用summary()可以查看神经网络的结构
covn_base.summary()
Xception模型的结构：
Model: "xception"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 200, 200, 3 0 []
)]
block1_conv1 (Conv2D) (None, 99, 99, 32) 864 ['input_1[0][0]']
block1_conv1_bn (BatchNormaliz (None, 99, 99, 32) 128 ['block1_conv1[0][0]']
ation)
block1_conv1_act (Activation) (None, 99, 99, 32) 0 ['block1_conv1_bn[0][0]']
block1_conv2 (Conv2D) (None, 97, 97, 64) 18432 ['block1_conv1_act[0][0]']
block1_conv2_bn (BatchNormaliz (None, 97, 97, 64) 256 ['block1_conv2[0][0]']
ation)
block1_conv2_act (Activation) (None, 97, 97, 64) 0 ['block1_conv2_bn[0][0]']
block2_sepconv1 (SeparableConv (None, 97, 97, 128) 8768 ['block1_conv2_act[0][0]']
2D)
block2_sepconv1_bn (BatchNorma (None, 97, 97, 128) 512 ['block2_sepconv1[0][0]']
lization)
block2_sepconv2_act (Activatio (None, 97, 97, 128) 0 ['block2_sepconv1_bn[0][0]']
n)
block2_sepconv2 (SeparableConv (None, 97, 97, 128) 17536 ['block2_sepconv2_act[0][0]']
2D)
block2_sepconv2_bn (BatchNorma (None, 97, 97, 128) 512 ['block2_sepconv2[0][0]']
lization)
conv2d (Conv2D) (None, 49, 49, 128) 8192 ['block1_conv2_act[0][0]']
block2_pool (MaxPooling2D) (None, 49, 49, 128) 0 ['block2_sepconv2_bn[0][0]']
batch_normalization (BatchNorm (None, 49, 49, 128) 512 ['conv2d[0][0]']
alization)
add (Add) (None, 49, 49, 128) 0 ['block2_pool[0][0]',
'batch_normalization[0][0]']
block3_sepconv1_act (Activatio (None, 49, 49, 128) 0 ['add[0][0]']
n)
block3_sepconv1 (SeparableConv (None, 49, 49, 256) 33920 ['block3_sepconv1_act[0][0]']
2D)
block3_sepconv1_bn (BatchNorma (None, 49, 49, 256) 1024 ['block3_sepconv1[0][0]']
lization)
block3_sepconv2_act (Activatio (None, 49, 49, 256) 0 ['block3_sepconv1_bn[0][0]']
n)
block3_sepconv2 (SeparableConv (None, 49, 49, 256) 67840 ['block3_sepconv2_act[0][0]']
2D)
block3_sepconv2_bn (BatchNorma (None, 49, 49, 256) 1024 ['block3_sepconv2[0][0]']
lization)
conv2d_1 (Conv2D) (None, 25, 25, 256) 32768 ['add[0][0]']
block3_pool (MaxPooling2D) (None, 25, 25, 256) 0 ['block3_sepconv2_bn[0][0]']
batch_normalization_1 (BatchNo (None, 25, 25, 256) 1024 ['conv2d_1[0][0]']
rmalization)
add_1 (Add) (None, 25, 25, 256) 0 ['block3_pool[0][0]',
'batch_normalization_1[0][0]']
block4_sepconv1_act (Activatio (None, 25, 25, 256) 0 ['add_1[0][0]']
n)
block4_sepconv1 (SeparableConv (None, 25, 25, 728) 188672 ['block4_sepconv1_act[0][0]']
2D)
block4_sepconv1_bn (BatchNorma (None, 25, 25, 728) 2912 ['block4_sepconv1[0][0]']
lization)
block4_sepconv2_act (Activatio (None, 25, 25, 728) 0 ['block4_sepconv1_bn[0][0]']
n)
block4_sepconv2 (SeparableConv (None, 25, 25, 728) 536536 ['block4_sepconv2_act[0][0]']
2D)
block4_sepconv2_bn (BatchNorma (None, 25, 25, 728) 2912 ['block4_sepconv2[0][0]']
lization)
conv2d_2 (Conv2D) (None, 13, 13, 728) 186368 ['add_1[0][0]']
block4_pool (MaxPooling2D) (None, 13, 13, 728) 0 ['block4_sepconv2_bn[0][0]']
batch_normalization_2 (BatchNo (None, 13, 13, 728) 2912 ['conv2d_2[0][0]']
rmalization)
add_2 (Add) (None, 13, 13, 728) 0 ['block4_pool[0][0]',
'batch_normalization_2[0][0]']
block5_sepconv1_act (Activatio (None, 13, 13, 728) 0 ['add_2[0][0]']
n)
block5_sepconv1 (SeparableConv (None, 13, 13, 728) 536536 ['block5_sepconv1_act[0][0]']
2D)
block5_sepconv1_bn (BatchNorma (None, 13, 13, 728) 2912 ['block5_sepconv1[0][0]']
lization)
block5_sepconv2_act (Activatio (None, 13, 13, 728) 0 ['block5_sepconv1_bn[0][0]']
n)
block5_sepconv2 (SeparableConv (None, 13, 13, 728) 536536 ['block5_sepconv2_act[0][0]']
2D)
block5_sepconv2_bn (BatchNorma (None, 13, 13, 728) 2912 ['block5_sepconv2[0][0]']
lization)
block5_sepconv3_act (Activatio (None, 13, 13, 728) 0 ['block5_sepconv2_bn[0][0]']
n)
block5_sepconv3 (SeparableConv (None, 13, 13, 728) 536536 ['block5_sepconv3_act[0][0]']
2D)
block5_sepconv3_bn (BatchNorma (None, 13, 13, 728) 2912 ['block5_sepconv3[0][0]']
lization)
add_3 (Add) (None, 13, 13, 728) 0 ['block5_sepconv3_bn[0][0]',
'add_2[0][0]']
block6_sepconv1_act (Activatio (None, 13, 13, 728) 0 ['add_3[0][0]']
n)
block6_sepconv1 (SeparableConv (None, 13, 13, 728) 536536 ['block6_sepconv1_act[0][0]']
2D)
block6_sepconv1_bn (BatchNorma (None, 13, 13, 728) 2912 ['block6_sepconv1[0][0]']
lization)
block6_sepconv2_act (Activatio (None, 13, 13, 728) 0 ['block6_sepconv1_bn[0][0]']
n)
block6_sepconv2 (SeparableConv (None, 13, 13, 728) 536536 ['block6_sepconv2_act[0][0]']
2D)
block6_sepconv2_bn (BatchNorma (None, 13, 13, 728) 2912 ['block6_sepconv2[0][0]']
lization)
block6_sepconv3_act (Activatio (None, 13, 13, 728) 0 ['block6_sepconv2_bn[0][0]']
n)
block6_sepconv3 (SeparableConv (None, 13, 13, 728) 536536 ['block6_sepconv3_act[0][0]']
2D)
block6_sepconv3_bn (BatchNorma (None, 13, 13, 728) 2912 ['block6_sepconv3[0][0]']
lization)
add_4 (Add) (None, 13, 13, 728) 0 ['block6_sepconv3_bn[0][0]',
'add_3[0][0]']
block7_sepconv1_act (Activatio (None, 13, 13, 728) 0 ['add_4[0][0]']
n)
block7_sepconv1 (SeparableConv (None, 13, 13, 728) 536536 ['block7_sepconv1_act[0][0]']
2D)
block7_sepconv1_bn (BatchNorma (None, 13, 13, 728) 2912 ['block7_sepconv1[0][0]']
lization)
block7_sepconv2_act (Activatio (None, 13, 13, 728) 0 ['block7_sepconv1_bn[0][0]']
n)
block7_sepconv2 (SeparableConv (None, 13, 13, 728) 536536 ['block7_sepconv2_act[0][0]']
2D)
block7_sepconv2_bn (BatchNorma (None, 13, 13, 728) 2912 ['block7_sepconv2[0][0]']
lization)
block7_sepconv3_act (Activatio (None, 13, 13, 728) 0 ['block7_sepconv2_bn[0][0]']
n)
block7_sepconv3 (SeparableConv (None, 13, 13, 728) 536536 ['block7_sepconv3_act[0][0]']
2D)
block7_sepconv3_bn (BatchNorma (None, 13, 13, 728) 2912 ['block7_sepconv3[0][0]']
lization)
add_5 (Add) (None, 13, 13, 728) 0 ['block7_sepconv3_bn[0][0]',
'add_4[0][0]']
block8_sepconv1_act (Activatio (None, 13, 13, 728) 0 ['add_5[0][0]']
n)
block8_sepconv1 (SeparableConv (None, 13, 13, 728) 536536 ['block8_sepconv1_act[0][0]']
2D)
block8_sepconv1_bn (BatchNorma (None, 13, 13, 728) 2912 ['block8_sepconv1[0][0]']
lization)
block8_sepconv2_act (Activatio (None, 13, 13, 728) 0 ['block8_sepconv1_bn[0][0]']
n)
block8_sepconv2 (SeparableConv (None, 13, 13, 728) 536536 ['block8_sepconv2_act[0][0]']
2D)
block8_sepconv2_bn (BatchNorma (None, 13, 13, 728) 2912 ['block8_sepconv2[0][0]']
lization)
block8_sepconv3_act (Activatio (None, 13, 13, 728) 0 ['block8_sepconv2_bn[0][0]']
n)
block8_sepconv3 (SeparableConv (None, 13, 13, 728) 536536 ['block8_sepconv3_act[0][0]']
2D)
block8_sepconv3_bn (BatchNorma (None, 13, 13, 728) 2912 ['block8_sepconv3[0][0]']
lization)
add_6 (Add) (None, 13, 13, 728) 0 ['block8_sepconv3_bn[0][0]',
'add_5[0][0]']
block9_sepconv1_act (Activatio (None, 13, 13, 728) 0 ['add_6[0][0]']
n)
block9_sepconv1 (SeparableConv (None, 13, 13, 728) 536536 ['block9_sepconv1_act[0][0]']
2D)
block9_sepconv1_bn (BatchNorma (None, 13, 13, 728) 2912 ['block9_sepconv1[0][0]']
lization)
block9_sepconv2_act (Activatio (None, 13, 13, 728) 0 ['block9_sepconv1_bn[0][0]']
n)
block9_sepconv2 (SeparableConv (None, 13, 13, 728) 536536 ['block9_sepconv2_act[0][0]']
2D)
block9_sepconv2_bn (BatchNorma (None, 13, 13, 728) 2912 ['block9_sepconv2[0][0]']
lization)
block9_sepconv3_act (Activatio (None, 13, 13, 728) 0 ['block9_sepconv2_bn[0][0]']
n)
block9_sepconv3 (SeparableConv (None, 13, 13, 728) 536536 ['block9_sepconv3_act[0][0]']
2D)
block9_sepconv3_bn (BatchNorma (None, 13, 13, 728) 2912 ['block9_sepconv3[0][0]']
lization)
add_7 (Add) (None, 13, 13, 728) 0 ['block9_sepconv3_bn[0][0]',
'add_6[0][0]']
block10_sepconv1_act (Activati (None, 13, 13, 728) 0 ['add_7[0][0]']
on)
block10_sepconv1 (SeparableCon (None, 13, 13, 728) 536536 ['block10_sepconv1_act[0][0]']
v2D)
block10_sepconv1_bn (BatchNorm (None, 13, 13, 728) 2912 ['block10_sepconv1[0][0]']
alization)
block10_sepconv2_act (Activati (None, 13, 13, 728) 0 ['block10_sepconv1_bn[0][0]']
on)
block10_sepconv2 (SeparableCon (None, 13, 13, 728) 536536 ['block10_sepconv2_act[0][0]']
v2D)
block10_sepconv2_bn (BatchNorm (None, 13, 13, 728) 2912 ['block10_sepconv2[0][0]']
alization)
block10_sepconv3_act (Activati (None, 13, 13, 728) 0 ['block10_sepconv2_bn[0][0]']
on)
block10_sepconv3 (SeparableCon (None, 13, 13, 728) 536536 ['block10_sepconv3_act[0][0]']
v2D)
block10_sepconv3_bn (BatchNorm (None, 13, 13, 728) 2912 ['block10_sepconv3[0][0]']
alization)
add_8 (Add) (None, 13, 13, 728) 0 ['block10_sepconv3_bn[0][0]',
'add_7[0][0]']
block11_sepconv1_act (Activati (None, 13, 13, 728) 0 ['add_8[0][0]']
on)
block11_sepconv1 (SeparableCon (None, 13, 13, 728) 536536 ['block11_sepconv1_act[0][0]']
v2D)
block11_sepconv1_bn (BatchNorm (None, 13, 13, 728) 2912 ['block11_sepconv1[0][0]']
alization)
block11_sepconv2_act (Activati (None, 13, 13, 728) 0 ['block11_sepconv1_bn[0][0]']
on)
block11_sepconv2 (SeparableCon (None, 13, 13, 728) 536536 ['block11_sepconv2_act[0][0]']
v2D)
block11_sepconv2_bn (BatchNorm (None, 13, 13, 728) 2912 ['block11_sepconv2[0][0]']
alization)
block11_sepconv3_act (Activati (None, 13, 13, 728) 0 ['block11_sepconv2_bn[0][0]']
on)
block11_sepconv3 (SeparableCon (None, 13, 13, 728) 536536 ['block11_sepconv3_act[0][0]']
v2D)
block11_sepconv3_bn (BatchNorm (None, 13, 13, 728) 2912 ['block11_sepconv3[0][0]']
alization)
add_9 (Add) (None, 13, 13, 728) 0 ['block11_sepconv3_bn[0][0]',
'add_8[0][0]']
block12_sepconv1_act (Activati (None, 13, 13, 728) 0 ['add_9[0][0]']
on)
block12_sepconv1 (SeparableCon (None, 13, 13, 728) 536536 ['block12_sepconv1_act[0][0]']
v2D)
block12_sepconv1_bn (BatchNorm (None, 13, 13, 728) 2912 ['block12_sepconv1[0][0]']
alization)
block12_sepconv2_act (Activati (None, 13, 13, 728) 0 ['block12_sepconv1_bn[0][0]']
on)
block12_sepconv2 (SeparableCon (None, 13, 13, 728) 536536 ['block12_sepconv2_act[0][0]']
v2D)
block12_sepconv2_bn (BatchNorm (None, 13, 13, 728) 2912 ['block12_sepconv2[0][0]']
alization)
block12_sepconv3_act (Activati (None, 13, 13, 728) 0 ['block12_sepconv2_bn[0][0]']
on)
block12_sepconv3 (SeparableCon (None, 13, 13, 728) 536536 ['block12_sepconv3_act[0][0]']
v2D)
block12_sepconv3_bn (BatchNorm (None, 13, 13, 728) 2912 ['block12_sepconv3[0][0]']
alization)
add_10 (Add) (None, 13, 13, 728) 0 ['block12_sepconv3_bn[0][0]',
'add_9[0][0]']
block13_sepconv1_act (Activati (None, 13, 13, 728) 0 ['add_10[0][0]']
on)
block13_sepconv1 (SeparableCon (None, 13, 13, 728) 536536 ['block13_sepconv1_act[0][0]']
v2D)
block13_sepconv1_bn (BatchNorm (None, 13, 13, 728) 2912 ['block13_sepconv1[0][0]']
alization)
block13_sepconv2_act (Activati (None, 13, 13, 728) 0 ['block13_sepconv1_bn[0][0]']
on)
block13_sepconv2 (SeparableCon (None, 13, 13, 1024 752024 ['block13_sepconv2_act[0][0]']
v2D) )
block13_sepconv2_bn (BatchNorm (None, 13, 13, 1024 4096 ['block13_sepconv2[0][0]']
alization) )
conv2d_3 (Conv2D) (None, 7, 7, 1024) 745472 ['add_10[0][0]']
block13_pool (MaxPooling2D) (None, 7, 7, 1024) 0 ['block13_sepconv2_bn[0][0]']
batch_normalization_3 (BatchNo (None, 7, 7, 1024) 4096 ['conv2d_3[0][0]']
rmalization)
add_11 (Add) (None, 7, 7, 1024) 0 ['block13_pool[0][0]',
'batch_normalization_3[0][0]']
block14_sepconv1 (SeparableCon (None, 7, 7, 1536) 1582080 ['add_11[0][0]']
v2D)
block14_sepconv1_bn (BatchNorm (None, 7, 7, 1536) 6144 ['block14_sepconv1[0][0]']
alization)
block14_sepconv1_act (Activati (None, 7, 7, 1536) 0 ['block14_sepconv1_bn[0][0]']
on)
block14_sepconv2 (SeparableCon (None, 7, 7, 2048) 3159552 ['block14_sepconv1_act[0][0]']
v2D)
block14_sepconv2_bn (BatchNorm (None, 7, 7, 2048) 8192 ['block14_sepconv2[0][0]']
alization)
block14_sepconv2_act (Activati (None, 7, 7, 2048) 0 ['block14_sepconv2_bn[0][0]']
on)
==================================================================================================
Total params: 20,861,480
Trainable params: 20,806,952
Non-trainable params: 54,528
__________________________________________________________________________________________________
使用Xception网络把图片的特征值提取出来，在放入线性网络中进行训练，以提高速度
batch_size = 20
def extract_features(data_generator, sample_count):
i = 0
features = np.zeros(shape=(sample_count, 7, 7, 2048))
labels = np.zeros(shape=(sample_count))
for inputs_batch, labels_batch in data_generator:
features_batch = covn_base.predict(inputs_batch)
features[i * batch_size : (i+1)*batch_size] = features_batch
labels[i*batch_size:(i+1)*batch_size] = labels_batch
i+=1
if i * batch_size >= sample_count:
break
return features, labels
train_featrues, train_labels = extract_features(train_generator, 2000)
test_featrues, test_labels = extract_features(test_generator, 1000)
搭建自己模型的全连接Dense层，对结果进行输出；使用GlobalAveragePooling2D对Xception处理的图像数据进行扁平化处理（即变成一维数据），最终归结为y=w1x1+w2x2…+b的问题，对结果进行输出；使用relu激活函数；使用Dropout抑制过拟合；最后输出结果，因为结果为二分类，即0为猫，1为狗。故输出结果只有一个，所以使用sigmoid函数进行二分类结果的输出。
model = keras.Sequential()
model.add(layers.GlobalAveragePooling2D(input_shape=(7, 7, 2048)))
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dropout(0.7))
model.add(layers.Dense(1, activation='sigmoid'))
编译模型；使用Adam激活函数，并调整优化速率；因为是二分类问题，所以这里损失函数使用binary_crossentropy
model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.0005/10), loss='binary_crossentropy', metrics=['acc'])
开始训练模型；在训练时对测试集进行测试，这里共训练50次
history = model.fit(train_featrues,train_labels, epochs=50, batch_size=50, validation_data=(test_featrues, test_labels))
以下为训练结果。其中loss为训练集损失值，acc为训练集准确率；val_loss为测试集损失值，val_acc为测试集准确率。可以看到结果还是比较理想的，其训练集和测试集的准确率均能达到99%左右，而且拟合的很好。
使用Matplotlib绘制以下训练集和测试集的准确率曲线，可以更清晰的看出，训练过程的变化。
plt.plot(range(50), history.history.get('val_acc'), c='r', label='val_acc')
plt.plot(range(50), history.history.get('acc'), c='b', label='acc')
plt.legend
将训练好的模型保存为本地的h5类型文件
model.save('cat_dog_model.h5')
以上训练过程完毕，接下来使用保存训练好的模型对真实数据进行测试
模型测试
在模型测试中，为了方便，我们借助OpenCV，来帮我们将网络上获取的图片进行resize处理和方便展示输出结果。
导入所需要的包。
import tensorflow as tf
import numpy as np
from keras.models import load_model
import cv2
定义OpenCV图像展示函数
def show(image):
cv2.namedWindow('test', 0)
cv2.imshow('test', image)
# 0任意键终止窗口
cv2.waitKey(0)
cv2.destroyAllWindows()
加载Xception的权重以及保存的训练模型
covn_base = tf.keras.applications.Xception(weights='imagenet', include_top=False, input_shape=(200, 200, 3))
cat_dog_model = load_model('./cat_dog_model.h5')
使用OpenCV读取图片，并将图片resize为200✖️200的大小，将图像数据扩展为Xception所需要的数据格式
image = cv2.imread('cat1.jpeg')
resize_image = cv2.resize(image, (200, 200), interpolation=cv2.INTER_AREA)
input_data = np.expand_dims(resize_image, axis=0)
分别使用Xception和自己训练好的模型对图像进行predict预测
result = int(cat_dog_model.predict(covn_base.predict(input_data))[0][0])
输出识别结果，并展示输入图像
if result == 1:
print("狗")
if result == 0:
print("猫")
show(resize_image)
可以看到以下为识别的结果，猫，准确无误
识别狗的图像，结果准确无误
文章到此结束，这个案例就算是出入Keras深度学习的小试牛刀，希望同样可以作为大家初入深度学习的小案例之一。
猫狗数据集百度网盘链接
链接: https://pan.baidu.com/s/16K4P5Nb1k5_sfFml-qEF2g 提取码: mchl