CNN网络结构-ResNet

背景

2015年，残差网络在ImageNet测试集上的错误率仅为 3.57%。

在ImageNet检测、ImageNet定位、COCO检测以及COCO分割上均获得了第一名的成绩。

结构

深度在神经网络中有及其重要的作用，但越深的网络越难训练。

随着深度的增加，从训练一开始，梯度消失或梯度爆炸就会阻止收敛，标准初始化和中间归一化能够一定程度解决这个问题，但依旧会出现退化问题——随着深度的增加，准确率会达到饱和，再持续增加深度则会导致准确率下降。

这个问题不是由于过拟合造成的，因为训练误差也会随着深度增加而增大。

深度残差网络则能解决这个问题。

残差网络基本结构如下：

 

实现

 以Imagenet为例，实现Resnet 34，简化部分mxnet源码。

 units=[3, 4, 6, 3]和上面结构图的颜色块一致。[64, 64, 128, 256, 512]是每块的输出大小（dim=1）。

 其中每次调用residual_unit，当bottle_neck = True （超过50层时），会创建4个卷积层，当 bottle_neck = False时，创建3个卷积层，对比如下。

 详细网络可以使用ProtoFiles结合http://ethereon.github.io/netscope/#/editor理解。

                     

 

 

def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck=True, bn_mom=0.9, workspace=256, memonger=False):
    """Return ResNet Unit symbol for building ResNet
    Parameters
    ----------
    data : str
        Input data
    num_filter : int
        Number of output channels
    bnf : int
        Bottle neck channels factor with regard to num_filter
    stride : tupe
        Stride used in convolution
    dim_match : Boolen
        True means channel number between input and output is the same, otherwise means differ
    name : str
        Base name of the operators
    workspace : int
        Workspace used in convolution operator
    """
    if bottle_neck:
        # the same as https://github.com/facebook/fb.resnet.torch#notes, a bit difference with origin paper
        bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
        act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
        conv1 = mx.sym.Convolution(data=act1, num_filter=int(num_filter*0.25), kernel=(1,1), stride=(1,1), pad=(0,0),
                                   no_bias=True, workspace=workspace, name=name + '_conv1')
        bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')
        act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name + '_relu2')
        conv2 = mx.sym.Convolution(data=act2, num_filter=int(num_filter*0.25), kernel=(3,3), stride=stride, pad=(1,1),
                                   no_bias=True, workspace=workspace, name=name + '_conv2')
        bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')
        act3 = mx.sym.Activation(data=bn3, act_type='relu', name=name + '_relu3')
        conv3 = mx.sym.Convolution(data=act3, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True,
                                   workspace=workspace, name=name + '_conv3')
        if dim_match:
            shortcut = data
        else:
            shortcut = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
                                            workspace=workspace, name=name+'_sc')
        if memonger:
            shortcut._set_attr(mirror_stage='True')
        return conv3 + shortcut
    else:
        bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1')
        act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1')
        conv1 = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),
                                      no_bias=True, workspace=workspace, name=name + '_conv1')
        bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2')
        act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name + '_relu2')
        conv2 = mx.sym.Convolution(data=act2, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),
                                      no_bias=True, workspace=workspace, name=name + '_conv2')
        if dim_match:
            shortcut = data
        else:
            shortcut = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
                                            workspace=workspace, name=name+'_sc')
        if memonger:
            shortcut._set_attr(mirror_stage='True')
        return conv2 + shortcut

def resnet(units=[3, 4, 6, 3], num_stages=4, filter_list=[64, 64, 128, 256, 512], num_classes=1000,  bottle_neck=False, bn_mom=0.9, workspace=256, memonger=False):
    """Return ResNet symbol of
    Parameters
    ----------
    units : list
        Number of units in each stage
    num_stages : int
        Number of stage
    filter_list : list
        Channel size of each stage
    num_classes : int
        Ouput size of symbol
    dataset : str
        Dataset type, only cifar10 and imagenet supports
    workspace : int
        Workspace used in convolution operator
    """
    num_unit = len(units)
    assert(num_unit == num_stages)
    data = mx.sym.Variable(name='data')
    data = mx.sym.identity(data=data, name='id')
    data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data')
    body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3),
                              no_bias=True, name="conv0", workspace=workspace)
    body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')
    body = mx.sym.Activation(data=body, act_type='relu', name='relu0')
    body = mx.symbol.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')

    for i in range(num_stages):
        body = residual_unit(body, filter_list[i+1], (1 if i==0 else 2, 1 if i==0 else 2), False,
                             name='stage%d_unit%d' % (i + 1, 1), 
, workspace=workspace,
                             memonger=memonger)
        for j in range(units[i]-1):
            body = residual_unit(body, filter_list[i+1], (1,1), True, name='stage%d_unit%d' % (i + 1, j + 2),
                                 bottle_neck=bottle_neck, workspace=workspace, memonger=memonger)
    bn1 = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')
    relu1 = mx.sym.Activation(data=bn1, act_type='relu', name='relu1')
    # Although kernel is not used here when global_pool=True, we should put one
    pool1 = mx.symbol.Pooling(data=relu1, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
    flat = mx.symbol.Flatten(data=pool1)
    fc1 = mx.symbol.FullyConnected(data=flat, num_hidden=num_classes, name='fc1')
    return mx.symbol.SoftmaxOutput(data=fc1, name='softmax')