好好学习第5天：Xception动物识别

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一、Xception介绍

Xception作为Inception v3的改进，主要是在Inception v3的基础上引入了depthwise separable convolution，在基本不增加网络复杂度的前提下提高了模型的效果。

简化 的Inception模块

 简化 的Inception模块的严格重构

  Inception 模块的“极端”版本，1x1 卷积的每个输出通道都有一个空间卷积。

 

参考论文：Xception: Deep Learning with Depthwise Separable Convolutions

 将卷积神经网络中的 Inception 模块解释为介于常规卷积和深度可分离卷积操作（深度卷积后跟点卷积）之间的中间步骤。从这个角度来看，深度可分离卷积可以理解为具有最大数量的塔的 Inception 模块。这一观察使我们提出了一种受 Inception 启发的新型深度卷积神经网络架构，其中 Inception 模块已被深度可分离卷积取代。我们表明，这种被称为 Xception 的架构在 ImageNet 数据集（Inception V3 的设计目标）上略微优于 Inception V3，并且在包含 3.5 亿张图像和 17,000 个类别的更大图像分类数据集上显着优于 Inception V3。由于 Xception 架构与 Inception V3 具有相同数量的参数，因此性能提升不是由于容量增加，而是更有效地使用模型参数。

深度可分离卷积，由深度卷积组成，即在输入的每个通道上独立执行的空间卷积，然后是逐点卷积，即 1x1 卷积，将深度卷积输出的通道投影到新的通道空间上。不要将其与空间可分离卷积混淆，后者在图像处理社区中通常也称为“可分离卷积”。
 

Xception网络结构：

 Xception 架构有 36 个卷积层，构成网络的特征提取基础。在论文的实验评估中，专门研究图像分类，因此卷积基础之后将是逻辑回归层。可以选择在逻辑回归层之前插入全连接层。36 个卷积层被构造成 14 个模块，除了第一个和最后一个模块外，所有这些模块周围都有线性残差连接。

二、实验过程

1.导入库

# 查看当前kernel下已安装的包  list packages
!pip list --format=columns

pip install tensorflow==2.4.1

刚开始使用的tensorflow版本是1.4.0，后面会报错。

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

2.导入数据集

data_dir = "data-xception"
 
data_dir = pathlib.Path(data_dir)

image_count = len(list(data_dir.glob('*/*')))
 
print("图片总数为：",image_count)

 应该是4000，不知道为啥只有3998。

batch_size = 2
img_height = 299
img_width  = 299

就下面这一段代码需要tensorflow版本2.2.0以上

"""
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)

 

class_names = train_ds.class_names
print(class_names)

 

为什么会出现“.ipynb_checkpoints”？

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

 

AUTOTUNE = tf.data.AUTOTUNE
 
train_ds = (
    train_ds.cache()
    .shuffle(1000)
#     .map(train_preprocessing)    # 这里可以设置预处理函数
#     .batch(batch_size)           # 在image_dataset_from_directory处已经设置了batch_size
    .prefetch(buffer_size=AUTOTUNE)
)
 
val_ds = (
    val_ds.cache()
    .shuffle(1000)
#     .map(val_preprocessing)    # 这里可以设置预处理函数
#     .batch(batch_size)         # 在image_dataset_from_directory处已经设置了batch_size
    .prefetch(buffer_size=AUTOTUNE)
)

 3.网络结构

#====================================#
#     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()

4.训练过程

# 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)

 

model.compile(optimizer=optimizer,
              loss     ='sparse_categorical_crossentropy',
              metrics  =['accuracy'])

epochs = 3
 
history = model.fit(
    train_ds,
    validation_data=val_ds,
    epochs=epochs
)

 才训练两下就搞不动了，CPU不给力啊，我真的会谢！

5.评估

# 模型评估
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()

等我搞出来上一步！