Xception实现动物识别(TensorFlow)

目录

1.项目数据及源码

可在github下载:

https://github.com/chenshunpeng/Animal-recognition-based-on-xception

2.任务介绍

数据结构为:

data
├── cat(文件夹含1000张图像)
│
├── chook(文件夹含1000张图像)
│
├── dog(文件夹含1000张图像)
│
└── horse(文件夹含1000张图像)

需要把数据分成训练集train和验证集val,对train数据集进行训练,达到给定val数据集中的一张猫 / 狗的图片,识别其是猫还是狗的目的

3.数据处理

3.1.数据预处理

设置GPU环境进行训练:

import tensorflow as tf

gpus = tf.config.list_physical_devices("GPU")

if gpus:
    tf.config.experimental.set_memory_growth(gpus[0], True)
    tf.config.set_visible_devices([gpus[0]],"GPU")

print(gpus)

输出:

[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

导入图片数据:

import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

import os,PIL

import numpy as np
np.random.seed(1)

import tensorflow as tf
tf.random.set_seed(1)

import pathlib

data_dir = "./data"

data_dir = pathlib.Path(data_dir)

image_count = len(list(data_dir.glob('*/*')))

print("图片总数为:",image_count)

输出:

 图片总数为: 4000

之后初始化参数,并使用 image_dataset_from_directory方法将磁盘中的数据加载到 tf.data.Dataset

函数原型:

tf.keras.preprocessing.image_dataset_from_directory(
    directory,
    labels="inferred",
    label_mode="int",
    class_names=None,
    color_mode="rgb",
    batch_size=32,
    image_size=(256, 256),
    shuffle=True,
    seed=None,
    validation_split=None,
    subset=None,
    interpolation="bilinear",
    follow_links=False,
)

官网介绍:tf.keras.utils.image_dataset_from_directory

代码:

batch_size = 4
img_height = 299
img_width  = 299

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)

输出:

Found 4000 files belonging to 4 classes.

Using 3200 files for training.

同理配置验证集:

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)

输出:

Found 4000 files belonging to 4 classes.

Using 800 files for validation.

我们可以通过 class_names输出数据集的标签,标签将按字母顺序对应于目录名称

class_names = train_ds.class_names
print(class_names)

输出:

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

查看batch的数据类型:

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

输出:

(4, 299, 299, 3)
(4,)

3.2.可视化数据

plt.figure(figsize=(10, 5))
plt.suptitle("数据展示")

num = -1
for images, labels in train_ds.take(2):
    for i in range(4):
        num = num + 1
        ax = plt.subplot(2, 4, num + 1)
        plt.imshow(images[i].numpy().astype("uint8"))
        plt.title(class_names[labels[i]])
        plt.savefig('pic1.jpg', dpi=600)
        plt.axis("off")

输出:

Xception实现动物识别(TensorFlow)

3.3.配置数据集

shuffle() : 打乱数据,详细可参考:数据集shuffle方法中buffer_size的理解

prefetch() :预取数据,加速运行,详细可参考:Better performance with the tf.data API

cache() :将数据集缓存到内存当中,加速运行

AUTOTUNE = tf.data.AUTOTUNE

train_ds = (
    train_ds.cache()
    .shuffle(1000)

    .prefetch(buffer_size=AUTOTUNE)
)

val_ds = (
    val_ds.cache()
    .shuffle(1000)

    .prefetch(buffer_size=AUTOTUNE)
)

4.网络设计

4.1.Xception简单介绍

详细可看:知乎

论文地址:Xception: Deep Learning with Depthwise Separable Convolutions

工程代码:https://github.com/keras-team/keras-applications/blob/master/keras_applications/xception.py

Xception是Google2016年10月提出的,时间在Google家的MobileNet v1之后,MobileNet v2之前。其吸纳了ResNet、Inception、MobileNet v1的设计思想,直接以Inception v3为模子,将里面的基本Inception module的卷积替换为使用 Depthwise Separable Convolution,又外加了残差连接

Xception 的结构基于ResNet,整个网络被分为了三个部分: EntryMiddleExit

  • Entry 部分主要是用来不断下采样,减小空间维度
  • Middle 部分则是不断学习关联关系,优化特征,其有8个部分;所有的普通卷积和可分离卷积后面都接了BN,不过图中没有给出
  • 最终 Exit部分则是汇总、整理特征,最后交由FC来进行表达

网络的整个流程如下图,Xception架构有36个卷积层作为网络特征提取的基础,这36个卷积层被分为14个模块,除了第一个和最后一个,其他每一个模块都使用了残差连接

Xception实现动物识别(TensorFlow)

简而言之,Xception架构是一个深度可分离卷积层的线性叠加,这个架构易于修改,仅使用30-40行代码就可以完成

; 4.2.设计网络模型


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)

    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)

    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])

    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])

    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])

    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])

    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])

    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

打印模型信息:

model = Xception()

model.summary()

输出:

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 (BatchNormaliz  (None, 149, 149, 32  128        ['block1_conv1[0][0]']
 ation)                         )

 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 (BatchNormaliz  (None, 147, 147, 64  256        ['block1_conv2[0][0]']
 ation)                         )

 block1_conv2_act (Activation)  (None, 147, 147, 64  0           ['block1_conv2_bn[0][0]']
                                )

 block2_sepconv1 (SeparableConv  (None, 147, 147, 12  8768       ['block1_conv2_act[0][0]']
 2D)                            8)

 block2_sepconv1_bn (BatchNorma  (None, 147, 147, 12  512        ['block2_sepconv1[0][0]']
 lization)                      8)

 block2_sepconv2_act (Activatio  (None, 147, 147, 12  0          ['block2_sepconv1_bn[0][0]']
 n)                             8)

 block2_sepconv2 (SeparableConv  (None, 147, 147, 12  17536      ['block2_sepconv2_act[0][0]']
 2D)                            8)

 block2_sepconv2_bn (BatchNorma  (None, 147, 147, 12  512        ['block2_sepconv2[0][0]']
 lization)                      8)

 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 (BatchNorm  (None, 74, 74, 128)  512        ['conv2d[0][0]']
 alization)

 add (Add)                      (None, 74, 74, 128)  0           ['block2_pool[0][0]',
                                                                  'batch_normalization[0][0]']

 block3_sepconv1_act (Activatio  (None, 74, 74, 128)  0          ['add[0][0]']
 n)

 block3_sepconv1 (SeparableConv  (None, 74, 74, 256)  33920      ['block3_sepconv1_act[0][0]']
 2D)

 block3_sepconv1_bn (BatchNorma  (None, 74, 74, 256)  1024       ['block3_sepconv1[0][0]']
 lization)

 block3_sepconv2_act (Activatio  (None, 74, 74, 256)  0          ['block3_sepconv1_bn[0][0]']
 n)

 block3_sepconv2 (SeparableConv  (None, 74, 74, 256)  67840      ['block3_sepconv2_act[0][0]']
 2D)

 block3_sepconv2_bn (BatchNorma  (None, 74, 74, 256)  1024       ['block3_sepconv2[0][0]']
 lization)

 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 (BatchNo  (None, 37, 37, 256)  1024       ['conv2d_1[0][0]']
 rmalization)

 add_1 (Add)                    (None, 37, 37, 256)  0           ['block3_pool[0][0]',
                                                                  'batch_normalization_1[0][0]']

 block4_sepconv1_act (Activatio  (None, 37, 37, 256)  0          ['add_1[0][0]']
 n)

 block4_sepconv1 (SeparableConv  (None, 37, 37, 728)  188672     ['block4_sepconv1_act[0][0]']
 2D)

 block4_sepconv1_bn (BatchNorma  (None, 37, 37, 728)  2912       ['block4_sepconv1[0][0]']
 lization)

 block4_sepconv2_act (Activatio  (None, 37, 37, 728)  0          ['block4_sepconv1_bn[0][0]']
 n)

 block4_sepconv2 (SeparableConv  (None, 37, 37, 728)  536536     ['block4_sepconv2_act[0][0]']
 2D)

 block4_sepconv2_bn (BatchNorma  (None, 37, 37, 728)  2912       ['block4_sepconv2[0][0]']
 lization)

 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 (BatchNo  (None, 19, 19, 728)  2912       ['conv2d_2[0][0]']
 rmalization)

 add_2 (Add)                    (None, 19, 19, 728)  0           ['block4_pool[0][0]',
                                                                  'batch_normalization_2[0][0]']

 block5_sepconv1_act (Activatio  (None, 19, 19, 728)  0          ['add_2[0][0]']
 n)

 block5_sepconv1 (SeparableConv  (None, 19, 19, 728)  536536     ['block5_sepconv1_act[0][0]']
 2D)

 block5_sepconv1_bn (BatchNorma  (None, 19, 19, 728)  2912       ['block5_sepconv1[0][0]']
 lization)

 block5_sepconv2_act (Activatio  (None, 19, 19, 728)  0          ['block5_sepconv1_bn[0][0]']
 n)

 block5_sepconv2 (SeparableConv  (None, 19, 19, 728)  536536     ['block5_sepconv2_act[0][0]']
 2D)

 block5_sepconv2_bn (BatchNorma  (None, 19, 19, 728)  2912       ['block5_sepconv2[0][0]']
 lization)

 block5_sepconv3_act (Activatio  (None, 19, 19, 728)  0          ['block5_sepconv2_bn[0][0]']
 n)

 block5_sepconv3 (SeparableConv  (None, 19, 19, 728)  536536     ['block5_sepconv3_act[0][0]']
 2D)

 block5_sepconv3_bn (BatchNorma  (None, 19, 19, 728)  2912       ['block5_sepconv3[0][0]']
 lization)

 add_3 (Add)                    (None, 19, 19, 728)  0           ['block5_sepconv3_bn[0][0]',
                                                                  'add_2[0][0]']

 block6_sepconv1_act (Activatio  (None, 19, 19, 728)  0          ['add_3[0][0]']
 n)

 block6_sepconv1 (SeparableConv  (None, 19, 19, 728)  536536     ['block6_sepconv1_act[0][0]']
 2D)

 block6_sepconv1_bn (BatchNorma  (None, 19, 19, 728)  2912       ['block6_sepconv1[0][0]']
 lization)

 block6_sepconv2_act (Activatio  (None, 19, 19, 728)  0          ['block6_sepconv1_bn[0][0]']
 n)

 block6_sepconv2 (SeparableConv  (None, 19, 19, 728)  536536     ['block6_sepconv2_act[0][0]']
 2D)

 block6_sepconv2_bn (BatchNorma  (None, 19, 19, 728)  2912       ['block6_sepconv2[0][0]']
 lization)

 block6_sepconv3_act (Activatio  (None, 19, 19, 728)  0          ['block6_sepconv2_bn[0][0]']
 n)

 block6_sepconv3 (SeparableConv  (None, 19, 19, 728)  536536     ['block6_sepconv3_act[0][0]']
 2D)

 block6_sepconv3_bn (BatchNorma  (None, 19, 19, 728)  2912       ['block6_sepconv3[0][0]']
 lization)

 add_4 (Add)                    (None, 19, 19, 728)  0           ['block6_sepconv3_bn[0][0]',
                                                                  'add_3[0][0]']

 block7_sepconv1_act (Activatio  (None, 19, 19, 728)  0          ['add_4[0][0]']
 n)

 block7_sepconv1 (SeparableConv  (None, 19, 19, 728)  536536     ['block7_sepconv1_act[0][0]']
 2D)

 block7_sepconv1_bn (BatchNorma  (None, 19, 19, 728)  2912       ['block7_sepconv1[0][0]']
 lization)

 block7_sepconv2_act (Activatio  (None, 19, 19, 728)  0          ['block7_sepconv1_bn[0][0]']
 n)

 block7_sepconv2 (SeparableConv  (None, 19, 19, 728)  536536     ['block7_sepconv2_act[0][0]']
 2D)

 block7_sepconv2_bn (BatchNorma  (None, 19, 19, 728)  2912       ['block7_sepconv2[0][0]']
 lization)

 block7_sepconv3_act (Activatio  (None, 19, 19, 728)  0          ['block7_sepconv2_bn[0][0]']
 n)

 block7_sepconv3 (SeparableConv  (None, 19, 19, 728)  536536     ['block7_sepconv3_act[0][0]']
 2D)

 block7_sepconv3_bn (BatchNorma  (None, 19, 19, 728)  2912       ['block7_sepconv3[0][0]']
 lization)

 add_5 (Add)                    (None, 19, 19, 728)  0           ['block7_sepconv3_bn[0][0]',
                                                                  'add_4[0][0]']

 block8_sepconv1_act (Activatio  (None, 19, 19, 728)  0          ['add_5[0][0]']
 n)

 block8_sepconv1 (SeparableConv  (None, 19, 19, 728)  536536     ['block8_sepconv1_act[0][0]']
 2D)

 block8_sepconv1_bn (BatchNorma  (None, 19, 19, 728)  2912       ['block8_sepconv1[0][0]']
 lization)

 block8_sepconv2_act (Activatio  (None, 19, 19, 728)  0          ['block8_sepconv1_bn[0][0]']
 n)

 block8_sepconv2 (SeparableConv  (None, 19, 19, 728)  536536     ['block8_sepconv2_act[0][0]']
 2D)

 block8_sepconv2_bn (BatchNorma  (None, 19, 19, 728)  2912       ['block8_sepconv2[0][0]']
 lization)

 block8_sepconv3_act (Activatio  (None, 19, 19, 728)  0          ['block8_sepconv2_bn[0][0]']
 n)

 block8_sepconv3 (SeparableConv  (None, 19, 19, 728)  536536     ['block8_sepconv3_act[0][0]']
 2D)

 block8_sepconv3_bn (BatchNorma  (None, 19, 19, 728)  2912       ['block8_sepconv3[0][0]']
 lization)

 add_6 (Add)                    (None, 19, 19, 728)  0           ['block8_sepconv3_bn[0][0]',
                                                                  'add_5[0][0]']

 block9_sepconv1_act (Activatio  (None, 19, 19, 728)  0          ['add_6[0][0]']
 n)

 block9_sepconv1 (SeparableConv  (None, 19, 19, 728)  536536     ['block9_sepconv1_act[0][0]']
 2D)

 block9_sepconv1_bn (BatchNorma  (None, 19, 19, 728)  2912       ['block9_sepconv1[0][0]']
 lization)

 block9_sepconv2_act (Activatio  (None, 19, 19, 728)  0          ['block9_sepconv1_bn[0][0]']
 n)

 block9_sepconv2 (SeparableConv  (None, 19, 19, 728)  536536     ['block9_sepconv2_act[0][0]']
 2D)

 block9_sepconv2_bn (BatchNorma  (None, 19, 19, 728)  2912       ['block9_sepconv2[0][0]']
 lization)

 block9_sepconv3_act (Activatio  (None, 19, 19, 728)  0          ['block9_sepconv2_bn[0][0]']
 n)

 block9_sepconv3 (SeparableConv  (None, 19, 19, 728)  536536     ['block9_sepconv3_act[0][0]']
 2D)

 block9_sepconv3_bn (BatchNorma  (None, 19, 19, 728)  2912       ['block9_sepconv3[0][0]']
 lization)

 add_7 (Add)                    (None, 19, 19, 728)  0           ['block9_sepconv3_bn[0][0]',
                                                                  'add_6[0][0]']

 block10_sepconv1_act (Activati  (None, 19, 19, 728)  0          ['add_7[0][0]']
 on)

 block10_sepconv1 (SeparableCon  (None, 19, 19, 728)  536536     ['block10_sepconv1_act[0][0]']
 v2D)

 block10_sepconv1_bn (BatchNorm  (None, 19, 19, 728)  2912       ['block10_sepconv1[0][0]']
 alization)

 block10_sepconv2_act (Activati  (None, 19, 19, 728)  0          ['block10_sepconv1_bn[0][0]']
 on)

 block10_sepconv2 (SeparableCon  (None, 19, 19, 728)  536536     ['block10_sepconv2_act[0][0]']
 v2D)

 block10_sepconv2_bn (BatchNorm  (None, 19, 19, 728)  2912       ['block10_sepconv2[0][0]']
 alization)

 block10_sepconv3_act (Activati  (None, 19, 19, 728)  0          ['block10_sepconv2_bn[0][0]']
 on)

 block10_sepconv3 (SeparableCon  (None, 19, 19, 728)  536536     ['block10_sepconv3_act[0][0]']
 v2D)

 block10_sepconv3_bn (BatchNorm  (None, 19, 19, 728)  2912       ['block10_sepconv3[0][0]']
 alization)

 add_8 (Add)                    (None, 19, 19, 728)  0           ['block10_sepconv3_bn[0][0]',
                                                                  'add_7[0][0]']

 block11_sepconv1_act (Activati  (None, 19, 19, 728)  0          ['add_8[0][0]']
 on)

 block11_sepconv1 (SeparableCon  (None, 19, 19, 728)  536536     ['block11_sepconv1_act[0][0]']
 v2D)

 block11_sepconv1_bn (BatchNorm  (None, 19, 19, 728)  2912       ['block11_sepconv1[0][0]']
 alization)

 block11_sepconv2_act (Activati  (None, 19, 19, 728)  0          ['block11_sepconv1_bn[0][0]']
 on)

 block11_sepconv2 (SeparableCon  (None, 19, 19, 728)  536536     ['block11_sepconv2_act[0][0]']
 v2D)

 block11_sepconv2_bn (BatchNorm  (None, 19, 19, 728)  2912       ['block11_sepconv2[0][0]']
 alization)

 block11_sepconv3_act (Activati  (None, 19, 19, 728)  0          ['block11_sepconv2_bn[0][0]']
 on)

 block11_sepconv3 (SeparableCon  (None, 19, 19, 728)  536536     ['block11_sepconv3_act[0][0]']
 v2D)

 block11_sepconv3_bn (BatchNorm  (None, 19, 19, 728)  2912       ['block11_sepconv3[0][0]']
 alization)

 add_9 (Add)                    (None, 19, 19, 728)  0           ['block11_sepconv3_bn[0][0]',
                                                                  'add_8[0][0]']

 block12_sepconv1_act (Activati  (None, 19, 19, 728)  0          ['add_9[0][0]']
 on)

 block12_sepconv1 (SeparableCon  (None, 19, 19, 728)  536536     ['block12_sepconv1_act[0][0]']
 v2D)

 block12_sepconv1_bn (BatchNorm  (None, 19, 19, 728)  2912       ['block12_sepconv1[0][0]']
 alization)

 block12_sepconv2_act (Activati  (None, 19, 19, 728)  0          ['block12_sepconv1_bn[0][0]']
 on)

 block12_sepconv2 (SeparableCon  (None, 19, 19, 728)  536536     ['block12_sepconv2_act[0][0]']
 v2D)

 block12_sepconv2_bn (BatchNorm  (None, 19, 19, 728)  2912       ['block12_sepconv2[0][0]']
 alization)

 block12_sepconv3_act (Activati  (None, 19, 19, 728)  0          ['block12_sepconv2_bn[0][0]']
 on)

 block12_sepconv3 (SeparableCon  (None, 19, 19, 728)  536536     ['block12_sepconv3_act[0][0]']
 v2D)

 block12_sepconv3_bn (BatchNorm  (None, 19, 19, 728)  2912       ['block12_sepconv3[0][0]']
 alization)

 add_10 (Add)                   (None, 19, 19, 728)  0           ['block12_sepconv3_bn[0][0]',
                                                                  'add_9[0][0]']

 block13_sepconv1_act (Activati  (None, 19, 19, 728)  0          ['add_10[0][0]']
 on)

 block13_sepconv1 (SeparableCon  (None, 19, 19, 728)  536536     ['block13_sepconv1_act[0][0]']
 v2D)

 block13_sepconv1_bn (BatchNorm  (None, 19, 19, 728)  2912       ['block13_sepconv1[0][0]']
 alization)

 block13_sepconv2_act (Activati  (None, 19, 19, 728)  0          ['block13_sepconv1_bn[0][0]']
 on)

 block13_sepconv2 (SeparableCon  (None, 19, 19, 1024  752024     ['block13_sepconv2_act[0][0]']
 v2D)                           )

 block13_sepconv2_bn (BatchNorm  (None, 19, 19, 1024  4096       ['block13_sepconv2[0][0]']
 alization)                     )

 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 (BatchNo  (None, 10, 10, 1024  4096       ['conv2d_3[0][0]']
 rmalization)                   )

 add_11 (Add)                   (None, 10, 10, 1024  0           ['block13_pool[0][0]',
                                )                                 'batch_normalization_3[0][0]']

 block14_sepconv1 (SeparableCon  (None, 10, 10, 1536  1582080    ['add_11[0][0]']
 v2D)                           )

 block14_sepconv1_bn (BatchNorm  (None, 10, 10, 1536  6144       ['block14_sepconv1[0][0]']
 alization)                     )

 block14_sepconv1_act (Activati  (None, 10, 10, 1536  0          ['block14_sepconv1_bn[0][0]']
 on)                            )

 block14_sepconv2 (SeparableCon  (None, 10, 10, 2048  3159552    ['block14_sepconv1_act[0][0]']
 v2D)                           )

 block14_sepconv2_bn (BatchNorm  (None, 10, 10, 2048  8192       ['block14_sepconv2[0][0]']
 alization)                     )

 block14_sepconv2_act (Activati  (None, 10, 10, 2048  0          ['block14_sepconv2_bn[0][0]']
 on)                            )

 avg_pool (GlobalAveragePooling  (None, 2048)        0           ['block14_sepconv2_act[0][0]']
 2D)

 predictions (Dense)            (None, 1000)         2049000     ['avg_pool[0][0]']

==================================================================================================
Total params: 22,910,480
Trainable params: 22,855,952
Non-trainable params: 54,528
__________________________________________________________________________________________________

设置动态学习率


initial_learning_rate = 1e-4

lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
        initial_learning_rate,
        decay_steps=300,
        decay_rate=0.96,
        staircase=True)

optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)

模型的编译

  • 损失函数(loss):用于衡量模型在训练期间的准确率,这里用 sparse_categorical_crossentropy,原理与 categorical_crossentropy(多类交叉熵损失 )一样,不过真实值采用的整数编码(例如第0个类用数字0表示,第3个类用数字3表示,官方可看:tf.keras.losses.SparseCategoricalCrossentropy
  • 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,这里是 Adam(官方可看:tf.keras.optimizers.Adam
  • 评价函数(metrics):用于监控训练和测试步骤,本次使用 accuracy,即被正确分类的图像的比率(官方可看:tf.keras.metrics.Accuracy
model.compile(optimizer=optimizer,
              loss     ='sparse_categorical_crossentropy',
              metrics  =['accuracy'])

训练模型

epochs = 20

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

训练结果:

Epoch 1/20
800/800 [==============================] - 464s 564ms/step - loss: 1.4314 - accuracy: 0.4584 - val_loss: 1.0577 - val_accuracy: 0.5475
Epoch 2/20
800/800 [==============================] - 447s 559ms/step - loss: 0.9087 - accuracy: 0.6228 - val_loss: 0.8191 - val_accuracy: 0.6612
Epoch 3/20
800/800 [==============================] - 446s 558ms/step - loss: 0.6728 - accuracy: 0.7403 - val_loss: 0.8190 - val_accuracy: 0.6687
Epoch 4/20
800/800 [==============================] - 447s 559ms/step - loss: 0.3362 - accuracy: 0.8841 - val_loss: 0.8249 - val_accuracy: 0.6913
Epoch 5/20
800/800 [==============================] - 447s 559ms/step - loss: 0.1415 - accuracy: 0.9566 - val_loss: 0.9374 - val_accuracy: 0.6975
Epoch 6/20
800/800 [==============================] - 446s 558ms/step - loss: 0.0840 - accuracy: 0.9809 - val_loss: 1.2619 - val_accuracy: 0.6737
Epoch 7/20
800/800 [==============================] - 447s 558ms/step - loss: 0.0574 - accuracy: 0.9862 - val_loss: 0.7897 - val_accuracy: 0.7738
Epoch 8/20
800/800 [==============================] - 446s 558ms/step - loss: 0.0369 - accuracy: 0.9912 - val_loss: 0.8976 - val_accuracy: 0.7350
Epoch 9/20
800/800 [==============================] - 446s 557ms/step - loss: 0.0276 - accuracy: 0.9966 - val_loss: 0.7896 - val_accuracy: 0.7725
Epoch 10/20
800/800 [==============================] - 446s 558ms/step - loss: 0.0223 - accuracy: 0.9969 - val_loss: 0.7084 - val_accuracy: 0.7812
Epoch 11/20
800/800 [==============================] - 446s 558ms/step - loss: 0.0108 - accuracy: 0.9978 - val_loss: 0.8445 - val_accuracy: 0.7588
Epoch 12/20
800/800 [==============================] - 446s 557ms/step - loss: 0.0102 - accuracy: 0.9975 - val_loss: 0.7577 - val_accuracy: 0.7850
Epoch 13/20
800/800 [==============================] - 446s 558ms/step - loss: 0.0062 - accuracy: 0.9991 - val_loss: 0.7447 - val_accuracy: 0.7837
Epoch 14/20
800/800 [==============================] - 445s 557ms/step - loss: 0.0034 - accuracy: 0.9987 - val_loss: 1.0870 - val_accuracy: 0.7063
Epoch 15/20
800/800 [==============================] - 445s 557ms/step - loss: 0.0100 - accuracy: 0.9978 - val_loss: 0.8212 - val_accuracy: 0.7725
Epoch 16/20
800/800 [==============================] - 446s 557ms/step - loss: 0.0089 - accuracy: 0.9981 - val_loss: 0.8604 - val_accuracy: 0.7688
Epoch 17/20
800/800 [==============================] - 446s 557ms/step - loss: 0.0068 - accuracy: 0.9984 - val_loss: 0.7941 - val_accuracy: 0.7887
Epoch 18/20
800/800 [==============================] - 446s 557ms/step - loss: 0.0037 - accuracy: 0.9994 - val_loss: 0.9039 - val_accuracy: 0.7650
Epoch 19/20
800/800 [==============================] - 446s 557ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.8278 - val_accuracy: 0.7812
Epoch 20/20
800/800 [==============================] - 446s 557ms/step - loss: 6.7889e-04 - accuracy: 1.0000 - val_loss: 0.8216 - val_accuracy: 0.7812

5.模型评估

5.1.准确率评估

Accuracy与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()

Xception实现动物识别(TensorFlow)

5.2.绘制混淆矩阵

confusion_matrix()介绍可看:sklearn.metrics.confusion_matrix

Seaborn:基于 Matplotlib 核心库进行了更高阶的 API 封装,其优势在配色更加舒服、以及图形元素的样式更加细腻

定义一个绘制混淆矩阵图的函数 plot_cm

from sklearn.metrics import confusion_matrix
import seaborn as sns
import pandas as pd

def plot_cm(labels, predictions):

    conf_numpy = confusion_matrix(labels, predictions)

    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)
    plt.savefig('pic3.jpg', dpi=600)

输出:

Xception实现动物识别(TensorFlow)

保存模型:


model.save('model/model.h5')

new_model = tf.keras.models.load_model('model/model.h5')

5.3.进行预测

plt.figure(figsize=(15, 7))
plt.suptitle("预测结果展示")

num = -1
for images, labels in val_ds.take(2):
    for i in range(4):
        num = num + 1
        plt.subplots_adjust(left=None, bottom=None, right=None, top=None , wspace=0.2, hspace=0.2)
        if num >= 8:
            break
        ax = plt.subplot(2, 4, num + 1)

        plt.imshow(images[i].numpy().astype("uint8"))

        img_array = tf.expand_dims(images[i], 0)

        predictions = model.predict(img_array)
        plt.title("True value: {}\npredictive value: {}".format(class_names[labels[i]],class_names[np.argmax(predictions)]))
        plt.savefig('pic4.jpg', dpi=400)
        plt.axis("off")

结果:

Xception实现动物识别(TensorFlow)

Original: https://blog.csdn.net/qq_45550375/article/details/126455124
Author: csp_
Title: Xception实现动物识别(TensorFlow)

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