知识要点

• AlexNet 是2012年ISLVRC 2012竞赛的冠军网络。

• VGG 在2014年由牛津大学著名研究组 VGG 提出。

• 10 monkey数据集是10个种类的猴子分类.

一 AlexNet详解

1.1 Alexnet简介

AlexNet 是2012年ISLVRC 2012（ImageNet Large Scale Visual Recognition  Challenge）竞赛的冠军网络，分类准确率由传统的 70%+提升到 80%+。 它是由Hinton和他的学生Alex Krizhevsky设计的。也是在那年之后，深度学习开始迅速发展。

• ISLVRC 2012竞赛

  • 训练集：1,281,167张已标注图片

  • 验证集：50,000张已标注图片

  • 测试集：100,000张未标注图片

该网络的亮点在于：

1. 首次利用 GPU 进行网络加速训练。
2. 使用了 ReLU 激活函数，而不是传统的 Sigmoid 激活函数以及 Tanh 激活函数。
3. 使用了 LRN 局部响应归一化。
4. 在全连接层的前两层中使用了 Dropout 随机失活神经元操作，以减少过拟合。

过拟合：根本原因是特征维度过多，模型假设过于复杂，参数 过多，训练数据过少，噪声过多，导致拟合的函数完美的预测 训练集，但对新数据的测试集预测结果差。 过度的拟合了训练 数据，而没有考虑到泛化能力。

使用 Dropout 的方式在网络正向传播过程中随机失活一部分神经元。

经卷积后的矩阵尺寸大小计算公式为：N = (W − F + 2P ) / S + 1

1. 输入图片大小 W×W

2. Filter大小 F×F

3. 步长 S

4. padding的像素数 P

1.2 模型网络内部

1.2.1 conv1层

Conv1:  kernels:48*2=96  kernel_size:11  padding:[1, 2]  stride:4

• input_size:  [224, 224, 3]

• output_size: [55, 55, 96]

N = (W − F + 2P ) / S + 1   = [224-11+(1+2)]/4+1 = 55

1.2.2 Maxpool1层

Conv1:  kernels:48*2=96  kernel_size:11  padding: [1, 2]  stride:4  output_size:  [55, 55, 96]

Maxpool1:  kernel_size:3  pading: 0  stride:2

• input_size:  [55, 55, 96]

• output_size: [27, 27, 96]

• N = (W − F + 2P ) / S + 1    =(55-3)/2+1 = 27

1.2.3 Conv2层

Conv1:  kernels:48*2=96  kernel_size:11  padding: [1, 2]  stride:4  output_size:  [55, 55, 96]

Maxpool1:  kernel_size:3  pading: 0  stride:2    output_size:  [27, 27, 96]

Conv2:  kernels:128*2=256  kernel_size:5  padding: [2, 2]  stride:1

• input_size:  [27, 27, 96]

• output_size: [27, 27, 256]

N = (W − F + 2P ) / S + 1  =(27-5+4)/1+1 = 27

1.2.4 Maxpool2层

Conv2: kernels:128*2=256  kernel_size:5  padding: [2, 2]  stride:1  output_size: [27, 27, 256]

Maxpool2:  kernel_size:3  pading: 0  stride:2

• input_size:  [27, 27, 256]

• output_size: [13, 13, 256]

N = (W − F + 2P ) / S + 1 = (27-3)/2+1 = 13

1.2.5 Conv3层

Maxpool2:  kernel_size:3  pading: 0  stride:2    output_size: [13, 13, 256]

Conv3:  kernels:192*2=384  kernel_size:3  padding: [1, 1]  stride:1

• input_size:  [13, 13, 256]

• output_size: [13, 13, 384]

N = (W − F + 2P ) / S + 1 =(13-3+2)/1+1  = 13

1.2.6 Conv4层

Conv3:  kernels:192*2=384  kernel_size:3  padding: [1, 1]  stride:1  output_size:  [13, 13, 384]

Conv4:  kernels:192*2=384  kernel_size:3  padding: [1, 1]  stride:1

• input_size:   [13, 13, 384]

• output_size: [13, 13, 384]

N = (W − F + 2P ) / S + 1 = (13-3+2)/1+1

1.2.7 Conv5层

Conv4:  kernels:192*2=384  kernel_size:3  padding: [1, 1]  stride:1  output_size:  [13, 13, 256]

Conv5:  kernels:128*2=256  kernel_size:3  padding: [1, 1]  stride:1

• input_size:   [13, 13, 384]

• output_size: [13, 13, 256]

N = (W − F + 2P ) / S + 1 = (13-3+2)/1+1

1.2.8 Maxpool3层

Conv5:  kernels:128*2=256  kernel_size:3  padding: [1, 1]  stride:1   output_size:  [13, 13, 256]

Maxpool3:   kernel_size:3  padding:0  stride:2

• input_size:   [13, 13, 256]

• output_size: [6, 6, 256]

N = (W − F + 2P ) / S + 1 = (13-3)/2+1 = 6

1.3 图像内部尺寸变换

1.4 代码实现

1.4.1 导包

from tensorflow import keras
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as pltcpu=tf.config.list_physical_devices("CPU")
tf.config.set_visible_devices(cpu)
print(tf.config.list_logical_devices())

1.4.2 函数式建模

# 函数式写法
def AlexNet(im_height=224, im_width=224, num_classes=1000):# 输入层input_image = keras.layers.Input(shape =(im_height, im_width, 3), dtype = tf.float32)# 手动实现padding, 周边补零填充x = keras.layers.ZeroPadding2D(((1, 2), (1, 2)))(input_image)# 卷积x = keras.layers.Conv2D(48, kernel_size = 11, strides = 4, activation = 'relu')(x)# 池化x = keras.layers.MaxPool2D(pool_size = 3, strides = 2)(x)# 第二层卷积x = keras.layers.Conv2D(128, kernel_size = 5, padding = 'same', activation = 'relu')(x)# 池化x = keras.layers.MaxPool2D(pool_size = 3, strides = 2)(x)# 卷积x = keras.layers.Conv2D(192, kernel_size = 3, padding = 'same', activation = 'relu')(x)x = keras.layers.Conv2D(192, kernel_size = 3, padding = 'same', activation = 'relu')(x)x = keras.layers.Conv2D(128, kernel_size = 3, padding = 'same', activation = 'relu')(x)# 池化 pool_sizex = keras.layers.MaxPool2D(pool_size = 3, strides = 2)(x)# 传链接x = keras.layers.Flatten()(x)# 加dropoutx = keras.layers.Dropout(0.2)(x)x = keras.layers.Dense(2048, activation = 'relu')(x)x = keras.layers.Dropout(0.2)(x)x = keras.layers.Dense(2048, activation = 'relu')(x)# 输出层x = keras.layers.Dense(num_classes)(x)# 预测predict = keras.layers.Softmax()(x)model = keras.models.Model(inputs = input_image, outputs = predict)return modelmodel = AlexNet(im_height= 224, im_width= 224, num_classes= 10)
model.summary()

1.4.3 数据处理

# 用10mokeys 进行使用举例
train_dir = './training/training/'
valid_dir = './validation/validation/'
# 数据整理  # 图片数据生成器
train_datagen = keras.preprocessing.image.ImageDataGenerator(rescale = 1.0/ 255,rotation_range= 40, width_shift_range= 0.2,height_shift_range= 0.2,shear_range = 0.2, zoom_range = 0.2,horizontal_flip = True,vertical_flip= True,fill_mode= 'nearest')height = 224
width = 224
channels = 3
batch_size = 32
num_classes = 10
train_generator = train_datagen.flow_from_directory(train_dir, target_size= (height, width),batch_size = batch_size,shuffle= True,seed = 7,class_mode = 'categorical')valid_dategen = keras.preprocessing.image.ImageDataGenerator(rescale = 1. / 255)
valid_generator = valid_dategen.flow_from_directory(valid_dir, target_size= (height, width),batch_size = batch_size,shuffle= True,seed = 7,class_mode = 'categorical')print(train_generator.samples)   # 1098
print(valid_generator.samples)   # 272

1.4.4 模型训练

model.compile(optimizer = 'adam',   # optimizer 优化器, 防止过拟合 loss = 'categorical_crossentropy',metrics = ['accuracy'])histroy = model.fit(train_generator,steps_per_epoch= train_generator.samples // batch_size,epochs = 10,validation_data= valid_generator,validation_steps= valid_generator.samples // batch_size)

二 VGG详解

2.1 简介

VGG在2014年由牛津大学著名研究组VGG (Visual Geometry  Group) 提出，斩获该年ImageNet竞  中 Localization Task (定位 任务) 第一名 和 Classification Task (分类任务) 第二名。

网络中的亮点：通过堆叠多个 3x3的卷积核 来替代大尺度卷积核（减少所需参数）

论文中提到，可以通过堆叠两个3x3的卷 积核替代5x5的卷积核，堆叠三个3x3的卷积核替代7x7的卷积核。

2.2 基本概念拓展—CNN感受野

在卷积神经网络中，决定某一层输出结果中一个元素所对应的输入层的区域大小，被称作感受野(receptive field)。通俗的解释是，输出feature map上的一个单元对应输入层上的区域大小。

论文中提到，可以通过堆叠两个3x3的卷积核替代5x5的卷积核，

堆叠三个3x3的卷积核替代7x7的卷积核。

使用7x7卷积核所需参数，与堆叠三个3x3卷积核所需参数(假设输入输出channel为C)

• conv的stride为1，padding为1
• maxpool的size为2，stride为2

2.3 代码实现

2.3.1 导包

from tensorflow import keras
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as pltcpu=tf.config.list_physical_devices("CPU")
tf.config.set_visible_devices(cpu)
print(tf.config.list_logical_devices())

2.3.2 创建模型

# 函数式写法
cfgs = {'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M']}  # M:池化

def make_feature(cfg):feature_layers = []for v in cfg:if v == 'M':feature_layers.append(keras.layers.MaxPool2D(pool_size = 2, strides = 2))else:feature_layers.append(keras.layers.Conv2D(v, kernel_size = 3,padding = 'SAME',activation = 'relu'))return keras.Sequential(feature_layers, name = 'feature')  # 整体当做一层

# 定义网络结构
def VGG(feature, im_height = 224, im_width = 224, num_classes = 1000):input_image = keras.layers.Input(shape = (im_height, im_width, 3), dtype = 'float32')x = feature(input_image)x = keras.layers.Flatten()(x)  # 将flatten当做一个函数# dropout, 防止过拟合, 每次放弃部分参数x = keras.layers.Dropout(rate = 0.5)(x)# 原论文为4096x = keras.layers.Dense(512, activation = 'relu')(x)x = keras.layers.Dropout(rate = 0.5)(x)x = keras.layers.Dense(512, activation = 'relu')(x)x = keras.layers.Dense(num_classes)(x)output = keras.layers.Softmax()(x)model = keras.models.Model(inputs = input_image, outputs = output)return model

# 定义网络模型
def vgg(model_name = 'vgg16', im_height = 224, im_width = 224, num_classes = 1000):cfg = cfgs[model_name]model = VGG(make_feature(cfg), im_height = im_height, im_width= im_width, num_classes= num_classes)return model
vgg16 = vgg(num_classes = 10)

2.3.3 数据导入

# 用10mokeys 进行使用举例
train_dir = './training/training/'
valid_dir = './validation/validation/'# 数据整理
# 图片数据生成器
train_datagen = keras.preprocessing.image.ImageDataGenerator(rescale = 1.0/ 255,rotation_range= 40, width_shift_range= 0.2,height_shift_range= 0.2,shear_range = 0.2, zoom_range = 0.2,horizontal_flip = True,vertical_flip= True,fill_mode= 'nearest')height = 224
width = 224
channels = 3
batch_size = 32
num_classes = 10
train_generator = train_datagen.flow_from_directory(train_dir, target_size= (height, width),batch_size = batch_size,shuffle= True,seed = 7,class_mode = 'categorical')valid_dategen = keras.preprocessing.image.ImageDataGenerator(rescale = 1. / 255)
valid_generator = valid_dategen.flow_from_directory(valid_dir, target_size= (height, width),batch_size = batch_size,shuffle= True,seed = 7,class_mode = 'categorical')print(train_generator.samples)
print(valid_generator.samples)

2.3.4 模型训练

vgg16.compile(optimizer = 'adam',   # optimizer 优化器, 防止过拟合 loss = 'categorical_crossentropy',metrics = ['accuracy'])histroy = vgg16.fit(train_generator,steps_per_epoch= train_generator.samples // batch_size,epochs = 10,validation_data= valid_generator,validation_steps= valid_generator.samples // batch_size)