主流卷积神经网络结构探索和分析（三）

Tabitha ·

更新时间:2024-11-13

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主流卷积神经网络结构探索和分析（三）3 VGG16，VGG16_bigConv， GoogLeNet_V3及ResNet18网络结构详解及Keras实现方法3.1 VGG163.2 VGG16_bigConv3.3 GoogLeNet_V33.4 ResNet183.5 模型对比3.5.1 模型训练过程中的收敛情况对比附录A. keras踩坑记录A.1. 函数类A.1.1 categorical_crossentropy 和 sparse_categorical_crossentropy 的区别在哪？A.1.2 keras fit、fit_generator和train_on_batch的区分A.2. BUG类

接上一片的内容，本篇博客主要讨论（一）中分析的网络具体结构及keras实现方法

3 VGG16，VGG16_bigConv， GoogLeNet_V3及ResNet18网络结构详解及Keras实现方法 3.1 VGG16

前面的博客已经对VGG16的思路进行了阐述，这里直接给出VGG16的网络结构图。
VGG16详细网络结构图

keras代码如下：


def build_vgg16_model(weight_decay, num_classes, x_shape,learning_rate, lr_decay):
    # Build the network of vgg for 10 classes with massive dropout and weight decay as described in the paper.
    print('start model')
    model = Sequential()
    # weight_decay = self.weight_decay
    print('start 1st Conv2D')
    #model.add(Conv2D(64, (3, 3), padding='same', input_shape=x_shape, kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(Conv2D(64, (3, 3), padding='same', input_shape=x_shape, kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(0.3))
    print('start 2st Conv2D')
    model.add(Conv2D(64, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    print('start 3st Conv2D')
    model.add(Conv2D(128, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(0.4))
    print('start 4st Conv2D')
    model.add(Conv2D(128, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    print('start 5st Conv2D')
    model.add(Conv2D(256, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(0.4))
    print('start 6st Conv2D')
    model.add(Conv2D(256, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(0.4))
    print('start 7st Conv2D')
    model.add(Conv2D(256, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    print('start 8st Conv2D')
    model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(0.4))
    print('start 9st Conv2D')
    model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(0.4))
    print('start 10st Conv2D')
    model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    print('start 11st Conv2D')
    model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(0.4))
    print('start 12st Conv2D')
    model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(0.4))
    print('start 13st Conv2D')
    model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.5))
    print('start 14st Conv2D')
    model.add(Flatten())
    model.add(Dense(512, kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    print('start 15st Conv2D')
    model.add(Dropout(0.5))
    model.add(Dense(num_classes))
    model.add(Activation('softmax'))
    sgd = optimizers.SGD(lr=learning_rate, decay=lr_decay, momentum=0.9, nesterov=True)
    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
    return model

3.2 VGG16_bigConv

VGG16的基本思路是用若干个3x3的卷积层串联来模拟5x5或者7x7的感受野，同时降低网络的参数量。为了对比多个3x3卷积层串联与单个5x5,7x7的卷积层的效果。作者特意将VGG16中的3x3卷积核串联的层替换成了5x5,7x7的卷积层，以CIFAR10数据集对比识别的效果。

大感受野的VGG16网络结构图

keras代码如下：

def build_vgg16_big_model(weight_decay, num_classes, x_shape,learning_rate, lr_decay):
    # Build the network of vgg for 10 classes with massive dropout and weight decay as described in the paper.
    model = Sequential()
    model.add(Conv2D(64, (5, 5), padding='same',
                     input_shape=x_shape, kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(0.3))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(128, (5, 5), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(0.4))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(256, (7, 7), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(0.4))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(512, (7, 7), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(0.4))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(512, (7, 7), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(0.4))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.5))
    model.add(Flatten())
    model.add(Dense(512, kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes))
    model.add(Activation('softmax'))
    sgd = optimizers.SGD(lr=learning_rate, decay=lr_decay, momentum=0.9, nesterov=True)
    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
    return model

3.3 GoogLeNet_V3

GoogLeNet网络结构图

keras代码如下：

def GoogLeNet_submod(model,Co_array,weight_decay):
    # Co_arry [0] conv1x1 [1] conv3x3reduce [2] conv3x3 [3] conv5x5reduce [4] conv5x5 maxpooling [6] conv1x1
    x=model.output
    x1=Conv2D(Co_array[0], (1, 1), padding='same', kernel_regularizer=regularizers.l2(weight_decay))(x)
    x1=BatchNormalization()(x1)
    x1=Activation('relu')(x1)
    x2=Conv2D(Co_array[1], (1, 1), padding='same', kernel_regularizer=regularizers.l2(weight_decay))(x)
    x2=BatchNormalization()(x2)
    x2=Activation('relu')(x2)
    x2=Conv2D(Co_array[2], (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))(x2)
    x2=BatchNormalization()(x2)
    x2=Activation('relu')(x2)
    x3 = Conv2D(Co_array[3], (1, 1), padding='same', kernel_regularizer=regularizers.l2(weight_decay))(x)
    x3=BatchNormalization()(x3)
    x3=Activation('relu')(x3)
    x3 = Conv2D(Co_array[4], (5, 5), padding='same', kernel_regularizer=regularizers.l2(weight_decay))(x3)
    x3=BatchNormalization()(x3)
    x3=Activation('relu')(x3)
    x4= MaxPooling2D(pool_size=(2, 2), strides=1,padding='same')(x)
    x4=Conv2D(Co_array[5], (1, 1), padding='same', kernel_regularizer=regularizers.l2(weight_decay))(x)
    x4 = BatchNormalization()(x4)
    x4 = Activation('relu')(x4)
    xout=Concatenate(axis=-1)([x1,x2,x3,x4])
    modelout = Model(inputs=model.input, outputs=xout)
    return modelout
def build_inceptionV3_model(weight_decay, num_classes, x_shape,learning_rate, lr_decay):
    input1=Input(x_shape)
    x=Conv2D(64, (3, 3), padding='same',
                     input_shape=x_shape, kernel_regularizer=regularizers.l2(weight_decay))(input1)
    x=BatchNormalization()(x)
    x=Activation('relu')(x)
    x=MaxPooling2D(pool_size=(3, 3), strides=2,padding='same')(x)
    x=Conv2D(96, (5, 5), padding='same', kernel_regularizer=regularizers.l2(weight_decay))(x)
    x=BatchNormalization()(x)
    x=Activation('relu')(x)
    #model.add(Dropout(0.4))
    x=MaxPooling2D(pool_size=(3, 3), strides=2, padding='same')(x)
    model = Model(inputs=input1, outputs=x)
    sgd = optimizers.SGD(lr=learning_rate, decay=lr_decay, momentum=0.9, nesterov=True)
    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
    Co_array=np.array([64,96,128,16,32,32],dtype=int)
    Co_array=Co_array/2
    Co_array=Co_array.astype(np.int32)
    print(Co_array)
    model = GoogLeNet_submod(model, Co_array, weight_decay) #intercept 3a
    Co_array = np.array([128,128,192,32,96,64])
    Co_array=Co_array/2
    Co_array = Co_array.astype(np.int32)
    print(Co_array)
    model = GoogLeNet_submod(model, Co_array, weight_decay)  # intercept 3b
    #model.add(MaxPooling2D(pool_size=(3, 3), strides=2, padding='same'))
    x=MaxPooling2D(pool_size=(3, 3), strides=2, padding='same')(model.output)
    model = Model(inputs=input1, outputs=x)
    Co_array = np.array([192, 96, 208, 16, 48, 64])
    Co_array=Co_array/2
    Co_array = Co_array.astype(np.int32)
    print(Co_array)
    model = GoogLeNet_submod(model, Co_array, weight_decay)  # intercept 4a
    Co_array = np.array([160, 112, 224, 24, 64, 64])
    Co_array=Co_array/2
    Co_array = Co_array.astype(np.int32)
    print(Co_array)
    model = GoogLeNet_submod(model, Co_array, weight_decay)  # intercept 4b
    Co_array = np.array([128, 128, 256, 24, 64, 64])
    Co_array=Co_array/2
    Co_array = Co_array.astype(np.int32)
    print(Co_array)
    model = GoogLeNet_submod(model, Co_array, weight_decay)  # intercept 4c
    Co_array = np.array([112, 144, 288, 32, 64, 64])
    Co_array=Co_array/2
    Co_array = Co_array.astype(np.int32)
    print(Co_array)
    model = GoogLeNet_submod(model, Co_array, weight_decay)  # intercept 4d
    Co_array = np.array([256, 160, 320, 32, 128, 128])
    Co_array=Co_array/2
    Co_array = Co_array.astype(np.int32)
    print(Co_array)
    model = GoogLeNet_submod(model, Co_array, weight_decay)  # intercept 4e
    #model.add(MaxPooling2D(pool_size=(3, 3), strides=2, padding='same'))
    x = MaxPooling2D(pool_size=(3, 3), strides=2, padding='same')(model.output)
    model = Model(inputs=input1, outputs=x)
    Co_array = np.array([256, 160, 320, 32, 128, 128])
    Co_array=Co_array/2
    Co_array = Co_array.astype(np.int32)
    print(Co_array)
    model = GoogLeNet_submod(model, Co_array, weight_decay)  # intercept 5a
    Co_array = np.array([384, 192, 384, 48, 128, 128])
    Co_array=Co_array/2
    Co_array = Co_array.astype(np.int32)
    print(Co_array)
    model = GoogLeNet_submod(model, Co_array, weight_decay)  # intercept 5b
    x=GlobalMaxPooling2D()(model.output)
    #x=Flatten()(x)
    x=Dense(512, kernel_regularizer=regularizers.l2(weight_decay))(x)
    x=BatchNormalization()(x)
    x=Activation('relu')(x)
    x=Dropout(0.5)(x)
    x=Dense(num_classes)(x)
    x=Activation('softmax')(x)
    model = Model(inputs=input1, outputs=x)
    return model

3.4 ResNet18

ResNet18网络结构图

keras代码如下：

def build_resnet_block(model, Cout, Stride, weight_decay):
    x=model.output
    x1=Conv2D(Cout, (3, 3), padding='same', strides=Stride, kernel_regularizer=regularizers.l2(weight_decay))(x)
    x1=BatchNormalization()(x1)
    x1=Activation('relu')(x1)
    x1=Conv2D(Cout, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))(x1)
    x1=BatchNormalization()(x1)
    if Stride:
       x2=Conv2D(Cout, (1, 1), padding='same', strides=Stride, kernel_regularizer=regularizers.l2(weight_decay))(x)
       x2=BatchNormalization()(x2)
    else:
       x2=x
    x1=Add()([x1,x2])
    x=Activation('relu')(x1)
    x1=Conv2D(Cout, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))(x)
    x1=BatchNormalization()(x1)
    x1=Activation('relu')(x1)
    x1=Conv2D(Cout, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay))(x1)
    x1=BatchNormalization()(x1)
    x1=Add()([x1,x])
    x1=Activation('relu')(x1)
    modelout = Model(inputs=model.input, outputs=x1)
    return modelout
def build_ResNet18_model(weight_decay, num_classes, x_shape,learning_rate, lr_decay):
  input1=Input(x_shape)
  x=Conv2D(64, (3, 3), padding='same',
                     input_shape=x_shape, kernel_regularizer=regularizers.l2(weight_decay))(input1)
  x=BatchNormalization()(x)
  x=Activation('relu')(x)
  x=MaxPooling2D(pool_size=(3, 3), strides=2,padding='same')(x)
  model = Model(inputs=input1, outputs=x)
  sgd = optimizers.SGD(lr=learning_rate, decay=lr_decay, momentum=0.9, nesterov=True)
  model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
  model=build_resnet_block(model, 64, 1,weight_decay)
  model=build_resnet_block(model, 128, 2,weight_decay)
  model=build_resnet_block(model, 256, 2,weight_decay)
  model=build_resnet_block(model, 512, 2,weight_decay)
  x=model.output
  x=GlobalMaxPooling2D()(x)
  x=Dense(512, kernel_regularizer=regularizers.l2(weight_decay))(x)
  x=BatchNormalization()(x)
  x=Activation('relu')(x)
  x=Dropout(0.5)(x)
  x=Dense(num_classes)(x)
  x=Activation('softmax')(x)
  model = Model(inputs=input1, outputs=x)
  sgd = optimizers.SGD(lr=learning_rate, decay=lr_decay, momentum=0.9, nesterov=True)
  model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
  return model

3.5 模型对比 3.5.1 模型训练过程中的收敛情况对比

下图展示了训练过程中Loss函数值的变化情况。
四种网络的Loss变化曲线
下图展示了训练过程中分类精度的变化情况：
四种网络的分类精度变化曲线
下表展现了4种模型的完成训练后的分类精度比较：

model	accuracy (train) (%)	accuracy (valation) (%)	epoch
VGG16	97.93	93.04	100左右
VGG16-big	98.84	85.91	100左右
GoogLeNetv3	99.24	89.46	150左右
ResNet18	99.90	92.70	150左右

从训练过程看，VGG16和VGG16-big的模型复杂度较低，训练过程收敛速度较快。但采用较大感受野后的模型精度下降了好7个点左右，估计层数降低之后，模型的表达能力还是降低了不少。博主有时间的时候会进一步在不同的数据集上进行测试。
GoogLeNet和ResNet模型的复杂度较大，模型训练的时间相对较长，模型的精度也有所提高。但GoogLeNet的模型出现了比较大的过拟合，在训练集上达到了99.24%，但验证集上只能到89.46%。而ResNet的网络在训练集上达到99.9%，而验证集上的分类精度为：92.7%。
笔者在执行的过程中，没有加入参数的正则化。下一篇博客，笔者将详细讨论参数正则化对模型精度和训练过程的影响。

附录A. keras踩坑记录 A.1. 函数类 A.1.1 categorical_crossentropy 和 sparse_categorical_crossentropy 的区别在哪？

如果你的 targets 是 one-hot 编码，用 categorical_crossentropy
　　one-hot 编码：[0, 0, 1], [1, 0, 0], [0, 1, 0]
如果你的 tagets 是数字编码，用 sparse_categorical_crossentropy
　　数字编码：2, 0, 1

A.1.2 keras fit、fit_generator和train_on_batch的区分

简单来说，就是fit支持能放进RAM的小规模的数据集训练，fit_generator支持大规模的数据集，且支持一个数据增强的训练数据生成器，而train_on_batch则支持任意大小的（不一定要满足batch_size）的数据的训练。
详细内容可参考博客如何使用Keras fit和fit_generator（动手教程）

A.2. BUG类

问题：完成第一遍设计的时候，一个VGG16在CIFAR10上跑了三天的训练，受不了了，赶紧想办法。后来发现是keras没有调用GPU进行训练。
解决方法：把tensorflow卸载，重新装成tensorflow-gpu版本，解决了。

作者：AngelinaRan

网络结构卷积神经网络神经网络卷积

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