转载请注明出处：

https://www.cnblogs.com/darkknightzh/p/9985027.html

论文网址：

第三方实现：

Pytorch：https://github.com/Randl/DropBlock-pytorch

Tensorflow：https://github.com/DHZS/tf-dropblock

修改后的pytorch实现：https://github.com/darkknightzh/DropBlock_pytorch

一总体介绍

传统的dropout对FC层效果更好，对conv层效果较差，原因可能是conv层的特征单元是空间相关的，使用dropout随机丢弃一些特征，网络仍旧能从相邻区域获取信息，因而输入的信息仍旧能够被送到下一层，导致网络过拟合。而论文提出的DropBlock则是将在特征图上连续的信息一起丢弃。

下图是一个简单示例。a为输入图像，狗的头、脚等区域具有相关性。b为dropout的方式直接丢弃信息，此时能从临近区域获取相关信息（带x的为丢弃信息的mask）。c为DropBlock的方式，将一定区域内的特征全部丢弃。

二算法框架

由于要使用mask，DropBlock有两种实现方式：① 不同特征层共享相同的DropBlock mask，在相同位置丢弃信息；② 每个特征层使用各自的DropBlock mask。文中发现第二种效果更好，下图是方式②的流程（DropBlock有两个参数，block_size和γ。block_size为块的大小，γ为伯努利分布的参数）：

说明：

3 如果处于推断模式，则不丢弃任何特征

5 生成mask M，每个点均服从参数为γ的伯努利分布（伯努利分布，随机变量x以概率γ和1-γ取1和0）。需要注意的是，只有mask的绿色有效区域内的点服从该分布，如下图a所示，这样确保步骤6不会处理边缘区域。

6 对于M中为0的点，以该点为中心，创建一个长宽均为block_size的矩形，该矩形内所有值均置0。如上图b所示。

7 将mask应用于特征图上：A=A*M

8 对特征进行归一化：A=A*count(M)/count_ones(M)。此处count指M的像素数（即特征图的宽*高），count_ones指M中值为1的像素数。其他文献看得少，此处不知道怎么解释了。反正是对特征值放大一点点。。。

三具体细节

block_size：所有特征图的block_size均相同，文中为7时大部分情况下最好。

γ：并未直接设置γ，而是从keep_prob来推断γ，这两者关系如下（feat_size为特征图的大小）：

$\gamma =\frac{1-keep\_prob}{block\_siz{{e}^{2}}}\frac{feat\_siz{{e}^{2}}}{{{(feat\_size-block\_size+1)}^{2}}}$

keep_prob：固定keep_prob时DropBlock效果不好，而在训练初期使用过小的keep_prob会又会影响学习到的参数。因而随着训练的进行DropBlock将keep_prob从1线性降低到目标值（如0.9）

四实验结果

具体见论文吧。

只说一下，在resnet 50上测试时，将训练epochs从90轮增加到270轮，并且在125、200、250轮分别将学习率降低到原来的1/10。另一方面，由于训练次数太多，因而进行比较的baseline，不是使用最后的结果（训练次数过多，模型可能过拟合，验证集上性能可能会降低），而是每次训练都会计算在验证集上的性能，并取最好的作为baseline的性能。

五 pytorch代码及修改

1. 使用conv2D来得到mask

参考网址中给出了pytorch的代码，缺点是需要手动降低keep_prob（如下）：

 class DropBlock2D(nn.Module):

     r"""Randomly zeroes spatial blocks of the input tensor.

     As described in the paper

     `DropBlock: A regularization method for convolutional networks`_ ,

     dropping whole blocks of feature map allows to remove semantic

     information as compared to regular dropout.

     Args:

         keep_prob (float, optional): probability of an element to be kept.

         Authors recommend to linearly decrease this value from 1 to desired

         value.

         block_size (int, optional): size of the block. Block size in paper

         usually equals last feature map dimensions.

     Shape:

         - Input: :math:`(N, C, H, W)`

         - Output: :math:`(N, C, H, W)` (same shape as input)

     .. _DropBlock: A regularization method for convolutional networks:

        https://arxiv.org/abs/1810.12890

     """

     def __init__(self, keep_prob=0.9, block_size=7):

         super(DropBlock2D, self).__init__()

         self.keep_prob = keep_prob

         self.block_size = block_size

     def forward(self, input):

         if not self.training or self.keep_prob == 1:

             return input

         gamma = (1. - self.keep_prob) / self.block_size ** 2

         for sh in input.shape[2:]:

             gamma *= sh / (sh - self.block_size + 1)

         M = torch.bernoulli(torch.ones_like(input) * gamma)

         Msum = F.conv2d(M, torch.ones((input.shape[1], 1, self.block_size, self.block_size)).to(device=input.device, dtype=input.dtype),

                         padding=self.block_size // 2, groups=input.shape[1])

         torch.set_printoptions(threshold=5000)

         mask = (Msum < 1).to(device=input.device, dtype=input.dtype)

         return input * mask * mask.numel() /mask.sum() #TODO input * mask * self.keep_prob ?

代码中使用conv2D来得到mask。

33-35行得到gamma。

36行得到和输入尺寸相同的，且符合伯努利分布的矩阵M

37，38，41行通过conv2d得到mask。

2. 使用max_pool2d来得到mask

可以结合tensorflow的代码，使用max_pool2d来得到mask，如下面代码所示：

 class DropBlock2DV2(nn.Module):

     def __init__(self, keep_prob=0.9, block_size=7):

         super(DropBlock2DV2, self).__init__()

         self.keep_prob = keep_prob

         self.block_size = block_size

     def forward(self, input):

         if not self.training or self.keep_prob == 1:

             return input

         gamma = (1. - self.keep_prob) / self.block_size ** 2

         for sh in input.shape[2:]:

             gamma *= sh / (sh - self.block_size + 1)

         M = torch.bernoulli(torch.ones_like(input) * gamma)

         Msum = F.max_pool2d(M, kernel_size=[self.block_size, self.block_size], stride=1, padding=self.block_size // 2)

         mask = (1 - Msum).to(device=input.device, dtype=input.dtype)

         return input * mask * mask.numel() /mask.sum()

3. 结果对比

下图为直接对mnist图像使用DropBlock2D得到的伯努利分布M（两种代码结果一致）

下图为得到的Msum（两种代码结果一致）

上面结果均相同。

当对中间层使用DropBlock2D时，第一组为M，第二组为Msum，第三组为mask：

当对中间层使用DropBlock2DV2时，第一组为M，第二组为Msum，第三组为mask，

可见，虽然Msum不一样，但是mask结果一样。

下面为使用DropBlock2D训练结果：

Train Epoch: 1 [0/60000 (0%)]	Loss: 2.361322

Train Epoch: 1 [2560/60000 (4%)]	Loss: 2.341034

Train Epoch: 1 [5120/60000 (9%)]	Loss: 2.287267

Train Epoch: 1 [7680/60000 (13%)]	Loss: 2.274684

Train Epoch: 1 [10240/60000 (17%)]	Loss: 2.260440

Train Epoch: 1 [12800/60000 (21%)]	Loss: 2.259492

Train Epoch: 1 [15360/60000 (26%)]	Loss: 2.240603

Train Epoch: 1 [17920/60000 (30%)]	Loss: 2.207781

Train Epoch: 1 [20480/60000 (34%)]	Loss: 2.177025

Train Epoch: 1 [23040/60000 (38%)]	Loss: 2.137965

Train Epoch: 1 [25600/60000 (43%)]	Loss: 2.029636

Train Epoch: 1 [28160/60000 (47%)]	Loss: 1.967242

Train Epoch: 1 [30720/60000 (51%)]	Loss: 1.948034

Train Epoch: 1 [33280/60000 (55%)]	Loss: 1.856952

Train Epoch: 1 [35840/60000 (60%)]	Loss: 1.786148

Train Epoch: 1 [38400/60000 (64%)]	Loss: 1.657498

Train Epoch: 1 [40960/60000 (68%)]	Loss: 1.604034

Train Epoch: 1 [43520/60000 (72%)]	Loss: 1.550600

Train Epoch: 1 [46080/60000 (77%)]	Loss: 1.425241

Train Epoch: 1 [48640/60000 (81%)]	Loss: 1.455231

Train Epoch: 1 [51200/60000 (85%)]	Loss: 1.263960

Train Epoch: 1 [53760/60000 (89%)]	Loss: 1.255704

Train Epoch: 1 [56320/60000 (94%)]	Loss: 1.156345

Train Epoch: 1 [58880/60000 (98%)]	Loss: 1.272000

Test set: Average loss: 0.7381, Accuracy: 8467/10000 (85%)

Train Epoch: 2 [0/60000 (0%)]	Loss: 1.198182

Train Epoch: 2 [2560/60000 (4%)]	Loss: 1.133391

Train Epoch: 2 [5120/60000 (9%)]	Loss: 0.958502

Train Epoch: 2 [7680/60000 (13%)]	Loss: 1.068815

Train Epoch: 2 [10240/60000 (17%)]	Loss: 1.103716

Train Epoch: 2 [12800/60000 (21%)]	Loss: 0.949479

Train Epoch: 2 [15360/60000 (26%)]	Loss: 0.931306

Train Epoch: 2 [17920/60000 (30%)]	Loss: 0.922673

Train Epoch: 2 [20480/60000 (34%)]	Loss: 0.954674

Train Epoch: 2 [23040/60000 (38%)]	Loss: 0.875961

Train Epoch: 2 [25600/60000 (43%)]	Loss: 0.892115

Train Epoch: 2 [28160/60000 (47%)]	Loss: 0.835847

Train Epoch: 2 [30720/60000 (51%)]	Loss: 0.847458

Train Epoch: 2 [33280/60000 (55%)]	Loss: 0.791372

Train Epoch: 2 [35840/60000 (60%)]	Loss: 0.856625

Train Epoch: 2 [38400/60000 (64%)]	Loss: 0.717881

Train Epoch: 2 [40960/60000 (68%)]	Loss: 0.752228

Train Epoch: 2 [43520/60000 (72%)]	Loss: 0.803537

Train Epoch: 2 [46080/60000 (77%)]	Loss: 0.756578

Train Epoch: 2 [48640/60000 (81%)]	Loss: 0.751846

Train Epoch: 2 [51200/60000 (85%)]	Loss: 0.717995

Train Epoch: 2 [53760/60000 (89%)]	Loss: 0.780597

Train Epoch: 2 [56320/60000 (94%)]	Loss: 0.731817

Train Epoch: 2 [58880/60000 (98%)]	Loss: 0.689091

Test set: Average loss: 0.3513, Accuracy: 9160/10000 (92%)

Train Epoch: 3 [0/60000 (0%)]	Loss: 0.637277

Train Epoch: 3 [2560/60000 (4%)]	Loss: 0.669868

Train Epoch: 3 [5120/60000 (9%)]	Loss: 0.628944

Train Epoch: 3 [7680/60000 (13%)]	Loss: 0.577496

Train Epoch: 3 [10240/60000 (17%)]	Loss: 0.592267

Train Epoch: 3 [12800/60000 (21%)]	Loss: 0.653577

Train Epoch: 3 [15360/60000 (26%)]	Loss: 0.665416

Train Epoch: 3 [17920/60000 (30%)]	Loss: 0.586191

Train Epoch: 3 [20480/60000 (34%)]	Loss: 0.608021

Train Epoch: 3 [23040/60000 (38%)]	Loss: 0.687553

Train Epoch: 3 [25600/60000 (43%)]	Loss: 0.543245

Train Epoch: 3 [28160/60000 (47%)]	Loss: 0.709381

Train Epoch: 3 [30720/60000 (51%)]	Loss: 0.593673

Train Epoch: 3 [33280/60000 (55%)]	Loss: 0.578504

Train Epoch: 3 [35840/60000 (60%)]	Loss: 0.549813

Train Epoch: 3 [38400/60000 (64%)]	Loss: 0.525090

Train Epoch: 3 [40960/60000 (68%)]	Loss: 0.660547

Train Epoch: 3 [43520/60000 (72%)]	Loss: 0.453336

Train Epoch: 3 [46080/60000 (77%)]	Loss: 0.564345

Train Epoch: 3 [48640/60000 (81%)]	Loss: 0.553669

Train Epoch: 3 [51200/60000 (85%)]	Loss: 0.553587

Train Epoch: 3 [53760/60000 (89%)]	Loss: 0.606980

Train Epoch: 3 [56320/60000 (94%)]	Loss: 0.595271

Train Epoch: 3 [58880/60000 (98%)]	Loss: 0.544352

Test set: Average loss: 0.2539, Accuracy: 9336/10000 (93%)

Train Epoch: 4 [0/60000 (0%)]	Loss: 0.620331

Train Epoch: 4 [2560/60000 (4%)]	Loss: 0.567480

Train Epoch: 4 [5120/60000 (9%)]	Loss: 0.508343

Train Epoch: 4 [7680/60000 (13%)]	Loss: 0.494243

Train Epoch: 4 [10240/60000 (17%)]	Loss: 0.520037

Train Epoch: 4 [12800/60000 (21%)]	Loss: 0.495483

Train Epoch: 4 [15360/60000 (26%)]	Loss: 0.469755

Train Epoch: 4 [17920/60000 (30%)]	Loss: 0.593844

Train Epoch: 4 [20480/60000 (34%)]	Loss: 0.403337

Train Epoch: 4 [23040/60000 (38%)]	Loss: 0.546628

Train Epoch: 4 [25600/60000 (43%)]	Loss: 0.521818

Train Epoch: 4 [28160/60000 (47%)]	Loss: 0.461603

Train Epoch: 4 [30720/60000 (51%)]	Loss: 0.451833

Train Epoch: 4 [33280/60000 (55%)]	Loss: 0.439977

Train Epoch: 4 [35840/60000 (60%)]	Loss: 0.529679

Train Epoch: 4 [38400/60000 (64%)]	Loss: 0.489150

Train Epoch: 4 [40960/60000 (68%)]	Loss: 0.542619

Train Epoch: 4 [43520/60000 (72%)]	Loss: 0.471994

Train Epoch: 4 [46080/60000 (77%)]	Loss: 0.438699

Train Epoch: 4 [48640/60000 (81%)]	Loss: 0.415263

Train Epoch: 4 [51200/60000 (85%)]	Loss: 0.391874

Train Epoch: 4 [53760/60000 (89%)]	Loss: 0.521654

Train Epoch: 4 [56320/60000 (94%)]	Loss: 0.433007

Train Epoch: 4 [58880/60000 (98%)]	Loss: 0.388784

Test set: Average loss: 0.2137, Accuracy: 9432/10000 (94%)

Train Epoch: 5 [0/60000 (0%)]	Loss: 0.531463

Train Epoch: 5 [2560/60000 (4%)]	Loss: 0.501781

Train Epoch: 5 [5120/60000 (9%)]	Loss: 0.392028

Train Epoch: 5 [7680/60000 (13%)]	Loss: 0.506029

Train Epoch: 5 [10240/60000 (17%)]	Loss: 0.437021

Train Epoch: 5 [12800/60000 (21%)]	Loss: 0.447683

Train Epoch: 5 [15360/60000 (26%)]	Loss: 0.392386

Train Epoch: 5 [17920/60000 (30%)]	Loss: 0.416837

Train Epoch: 5 [20480/60000 (34%)]	Loss: 0.451183

Train Epoch: 5 [23040/60000 (38%)]	Loss: 0.433644

Train Epoch: 5 [25600/60000 (43%)]	Loss: 0.470782

Train Epoch: 5 [28160/60000 (47%)]	Loss: 0.421898

Train Epoch: 5 [30720/60000 (51%)]	Loss: 0.445305

Train Epoch: 5 [33280/60000 (55%)]	Loss: 0.479172

Train Epoch: 5 [35840/60000 (60%)]	Loss: 0.375300

Train Epoch: 5 [38400/60000 (64%)]	Loss: 0.370561

Train Epoch: 5 [40960/60000 (68%)]	Loss: 0.532634

Train Epoch: 5 [43520/60000 (72%)]	Loss: 0.383319

Train Epoch: 5 [46080/60000 (77%)]	Loss: 0.506851

Train Epoch: 5 [48640/60000 (81%)]	Loss: 0.408551

Train Epoch: 5 [51200/60000 (85%)]	Loss: 0.392309

Train Epoch: 5 [53760/60000 (89%)]	Loss: 0.477996

Train Epoch: 5 [56320/60000 (94%)]	Loss: 0.414231

Train Epoch: 5 [58880/60000 (98%)]	Loss: 0.430027

Test set: Average loss: 0.1803, Accuracy: 9499/10000 (95%)

Train Epoch: 6 [0/60000 (0%)]	Loss: 0.415102

Train Epoch: 6 [2560/60000 (4%)]	Loss: 0.281096

Train Epoch: 6 [5120/60000 (9%)]	Loss: 0.363706

Train Epoch: 6 [7680/60000 (13%)]	Loss: 0.390821

Train Epoch: 6 [10240/60000 (17%)]	Loss: 0.408141

Train Epoch: 6 [12800/60000 (21%)]	Loss: 0.422376

Train Epoch: 6 [15360/60000 (26%)]	Loss: 0.394143

Train Epoch: 6 [17920/60000 (30%)]	Loss: 0.375477

Train Epoch: 6 [20480/60000 (34%)]	Loss: 0.525811

Train Epoch: 6 [23040/60000 (38%)]	Loss: 0.497835

Train Epoch: 6 [25600/60000 (43%)]	Loss: 0.407303

Train Epoch: 6 [28160/60000 (47%)]	Loss: 0.464176

Train Epoch: 6 [30720/60000 (51%)]	Loss: 0.538057

Train Epoch: 6 [33280/60000 (55%)]	Loss: 0.390377

Train Epoch: 6 [35840/60000 (60%)]	Loss: 0.403131

Train Epoch: 6 [38400/60000 (64%)]	Loss: 0.512176

Train Epoch: 6 [40960/60000 (68%)]	Loss: 0.378995

Train Epoch: 6 [43520/60000 (72%)]	Loss: 0.485860

Train Epoch: 6 [46080/60000 (77%)]	Loss: 0.388245

Train Epoch: 6 [48640/60000 (81%)]	Loss: 0.388625

Train Epoch: 6 [51200/60000 (85%)]	Loss: 0.379450

Train Epoch: 6 [53760/60000 (89%)]	Loss: 0.407995

Train Epoch: 6 [56320/60000 (94%)]	Loss: 0.398069

Train Epoch: 6 [58880/60000 (98%)]	Loss: 0.372017

Test set: Average loss: 0.1640, Accuracy: 9545/10000 (95%)

Train Epoch: 7 [0/60000 (0%)]	Loss: 0.357198

Train Epoch: 7 [2560/60000 (4%)]	Loss: 0.393828

Train Epoch: 7 [5120/60000 (9%)]	Loss: 0.400493

Train Epoch: 7 [7680/60000 (13%)]	Loss: 0.352309

Train Epoch: 7 [10240/60000 (17%)]	Loss: 0.357330

Train Epoch: 7 [12800/60000 (21%)]	Loss: 0.320281

Train Epoch: 7 [15360/60000 (26%)]	Loss: 0.440437

Train Epoch: 7 [17920/60000 (30%)]	Loss: 0.388508

Train Epoch: 7 [20480/60000 (34%)]	Loss: 0.403732

Train Epoch: 7 [23040/60000 (38%)]	Loss: 0.322718

Train Epoch: 7 [25600/60000 (43%)]	Loss: 0.372186

Train Epoch: 7 [28160/60000 (47%)]	Loss: 0.383310

Train Epoch: 7 [30720/60000 (51%)]	Loss: 0.390324

Train Epoch: 7 [33280/60000 (55%)]	Loss: 0.397030

Train Epoch: 7 [35840/60000 (60%)]	Loss: 0.285954

Train Epoch: 7 [38400/60000 (64%)]	Loss: 0.353880

Train Epoch: 7 [40960/60000 (68%)]	Loss: 0.240818

Train Epoch: 7 [43520/60000 (72%)]	Loss: 0.425822

Train Epoch: 7 [46080/60000 (77%)]	Loss: 0.457441

Train Epoch: 7 [48640/60000 (81%)]	Loss: 0.375786

Train Epoch: 7 [51200/60000 (85%)]	Loss: 0.365595

Train Epoch: 7 [53760/60000 (89%)]	Loss: 0.364900

Train Epoch: 7 [56320/60000 (94%)]	Loss: 0.266271

Train Epoch: 7 [58880/60000 (98%)]	Loss: 0.442880

Test set: Average loss: 0.1536, Accuracy: 9577/10000 (96%)

Train Epoch: 8 [0/60000 (0%)]	Loss: 0.371176

Train Epoch: 8 [2560/60000 (4%)]	Loss: 0.291452

Train Epoch: 8 [5120/60000 (9%)]	Loss: 0.416313

Train Epoch: 8 [7680/60000 (13%)]	Loss: 0.366298

Train Epoch: 8 [10240/60000 (17%)]	Loss: 0.296640

Train Epoch: 8 [12800/60000 (21%)]	Loss: 0.348865

Train Epoch: 8 [15360/60000 (26%)]	Loss: 0.417321

Train Epoch: 8 [17920/60000 (30%)]	Loss: 0.367058

Train Epoch: 8 [20480/60000 (34%)]	Loss: 0.336347

Train Epoch: 8 [23040/60000 (38%)]	Loss: 0.362581

Train Epoch: 8 [25600/60000 (43%)]	Loss: 0.288644

Train Epoch: 8 [28160/60000 (47%)]	Loss: 0.330283

Train Epoch: 8 [30720/60000 (51%)]	Loss: 0.298797

Train Epoch: 8 [33280/60000 (55%)]	Loss: 0.344115

Train Epoch: 8 [35840/60000 (60%)]	Loss: 0.356521

Train Epoch: 8 [38400/60000 (64%)]	Loss: 0.297551

Train Epoch: 8 [40960/60000 (68%)]	Loss: 0.440755

Train Epoch: 8 [43520/60000 (72%)]	Loss: 0.364442

Train Epoch: 8 [46080/60000 (77%)]	Loss: 0.272985

Train Epoch: 8 [48640/60000 (81%)]	Loss: 0.342815

Train Epoch: 8 [51200/60000 (85%)]	Loss: 0.329443

Train Epoch: 8 [53760/60000 (89%)]	Loss: 0.257021

Train Epoch: 8 [56320/60000 (94%)]	Loss: 0.350975

Train Epoch: 8 [58880/60000 (98%)]	Loss: 0.252117

Test set: Average loss: 0.1425, Accuracy: 9592/10000 (96%)

Train Epoch: 9 [0/60000 (0%)]	Loss: 0.308571

Train Epoch: 9 [2560/60000 (4%)]	Loss: 0.354496

Train Epoch: 9 [5120/60000 (9%)]	Loss: 0.454645

Train Epoch: 9 [7680/60000 (13%)]	Loss: 0.340073

Train Epoch: 9 [10240/60000 (17%)]	Loss: 0.304953

Train Epoch: 9 [12800/60000 (21%)]	Loss: 0.266210

Train Epoch: 9 [15360/60000 (26%)]	Loss: 0.414784

Train Epoch: 9 [17920/60000 (30%)]	Loss: 0.324893

Train Epoch: 9 [20480/60000 (34%)]	Loss: 0.367169

Train Epoch: 9 [23040/60000 (38%)]	Loss: 0.346932

Train Epoch: 9 [25600/60000 (43%)]	Loss: 0.382222

Train Epoch: 9 [28160/60000 (47%)]	Loss: 0.356705

Train Epoch: 9 [30720/60000 (51%)]	Loss: 0.287982

Train Epoch: 9 [33280/60000 (55%)]	Loss: 0.280525

Train Epoch: 9 [35840/60000 (60%)]	Loss: 0.244508

Train Epoch: 9 [38400/60000 (64%)]	Loss: 0.290698

Train Epoch: 9 [40960/60000 (68%)]	Loss: 0.352147

Train Epoch: 9 [43520/60000 (72%)]	Loss: 0.352036

Train Epoch: 9 [46080/60000 (77%)]	Loss: 0.398510

Train Epoch: 9 [48640/60000 (81%)]	Loss: 0.291793

Train Epoch: 9 [51200/60000 (85%)]	Loss: 0.276297

Train Epoch: 9 [53760/60000 (89%)]	Loss: 0.345035

Train Epoch: 9 [56320/60000 (94%)]	Loss: 0.246514

Train Epoch: 9 [58880/60000 (98%)]	Loss: 0.306455

Test set: Average loss: 0.1283, Accuracy: 9633/10000 (96%)

Train Epoch: 10 [0/60000 (0%)]	Loss: 0.217172

Train Epoch: 10 [2560/60000 (4%)]	Loss: 0.325854

Train Epoch: 10 [5120/60000 (9%)]	Loss: 0.344469

Train Epoch: 10 [7680/60000 (13%)]	Loss: 0.270222

Train Epoch: 10 [10240/60000 (17%)]	Loss: 0.369047

Train Epoch: 10 [12800/60000 (21%)]	Loss: 0.422427

Train Epoch: 10 [15360/60000 (26%)]	Loss: 0.279517

Train Epoch: 10 [17920/60000 (30%)]	Loss: 0.290899

Train Epoch: 10 [20480/60000 (34%)]	Loss: 0.312549

Train Epoch: 10 [23040/60000 (38%)]	Loss: 0.253973

Train Epoch: 10 [25600/60000 (43%)]	Loss: 0.304302

Train Epoch: 10 [28160/60000 (47%)]	Loss: 0.287465

Train Epoch: 10 [30720/60000 (51%)]	Loss: 0.238241

Train Epoch: 10 [33280/60000 (55%)]	Loss: 0.431481

Train Epoch: 10 [35840/60000 (60%)]	Loss: 0.208366

Train Epoch: 10 [38400/60000 (64%)]	Loss: 0.290634

Train Epoch: 10 [40960/60000 (68%)]	Loss: 0.279229

Train Epoch: 10 [43520/60000 (72%)]	Loss: 0.297195

Train Epoch: 10 [46080/60000 (77%)]	Loss: 0.251031

Train Epoch: 10 [48640/60000 (81%)]	Loss: 0.311252

Train Epoch: 10 [51200/60000 (85%)]	Loss: 0.391167

Train Epoch: 10 [53760/60000 (89%)]	Loss: 0.389775

Train Epoch: 10 [56320/60000 (94%)]	Loss: 0.315159

Train Epoch: 10 [58880/60000 (98%)]	Loss: 0.249528

Test set: Average loss: 0.1238, Accuracy: 9652/10000 (97%)

下面为使用DropBlock2DV2训练结果：

Train Epoch: 1 [0/60000 (0%)]	Loss: 2.361322

Train Epoch: 1 [2560/60000 (4%)]	Loss: 2.341034

Train Epoch: 1 [5120/60000 (9%)]	Loss: 2.287267

Train Epoch: 1 [7680/60000 (13%)]	Loss: 2.274684

Train Epoch: 1 [10240/60000 (17%)]	Loss: 2.260440

Train Epoch: 1 [12800/60000 (21%)]	Loss: 2.259492

Train Epoch: 1 [15360/60000 (26%)]	Loss: 2.240603

Train Epoch: 1 [17920/60000 (30%)]	Loss: 2.207781

Train Epoch: 1 [20480/60000 (34%)]	Loss: 2.177025

Train Epoch: 1 [23040/60000 (38%)]	Loss: 2.137965

Train Epoch: 1 [25600/60000 (43%)]	Loss: 2.029636

Train Epoch: 1 [28160/60000 (47%)]	Loss: 1.967242

Train Epoch: 1 [30720/60000 (51%)]	Loss: 1.948036

Train Epoch: 1 [33280/60000 (55%)]	Loss: 1.856993

Train Epoch: 1 [35840/60000 (60%)]	Loss: 1.786044

Train Epoch: 1 [38400/60000 (64%)]	Loss: 1.657677

Train Epoch: 1 [40960/60000 (68%)]	Loss: 1.603945

Train Epoch: 1 [43520/60000 (72%)]	Loss: 1.550625

Train Epoch: 1 [46080/60000 (77%)]	Loss: 1.424808

Train Epoch: 1 [48640/60000 (81%)]	Loss: 1.454958

Train Epoch: 1 [51200/60000 (85%)]	Loss: 1.263500

Train Epoch: 1 [53760/60000 (89%)]	Loss: 1.255482

Train Epoch: 1 [56320/60000 (94%)]	Loss: 1.157445

Train Epoch: 1 [58880/60000 (98%)]	Loss: 1.271838

Test set: Average loss: 0.7383, Accuracy: 8467/10000 (85%)

Train Epoch: 2 [0/60000 (0%)]	Loss: 1.198318

Train Epoch: 2 [2560/60000 (4%)]	Loss: 1.133464

Train Epoch: 2 [5120/60000 (9%)]	Loss: 0.957895

Train Epoch: 2 [7680/60000 (13%)]	Loss: 1.068100

Train Epoch: 2 [10240/60000 (17%)]	Loss: 1.103465

Train Epoch: 2 [12800/60000 (21%)]	Loss: 0.948980

Train Epoch: 2 [15360/60000 (26%)]	Loss: 0.931363

Train Epoch: 2 [17920/60000 (30%)]	Loss: 0.922501

Train Epoch: 2 [20480/60000 (34%)]	Loss: 0.954827

Train Epoch: 2 [23040/60000 (38%)]	Loss: 0.876058

Train Epoch: 2 [25600/60000 (43%)]	Loss: 0.891441

Train Epoch: 2 [28160/60000 (47%)]	Loss: 0.835737

Train Epoch: 2 [30720/60000 (51%)]	Loss: 0.847362

Train Epoch: 2 [33280/60000 (55%)]	Loss: 0.790432

Train Epoch: 2 [35840/60000 (60%)]	Loss: 0.857441

Train Epoch: 2 [38400/60000 (64%)]	Loss: 0.718644

Train Epoch: 2 [40960/60000 (68%)]	Loss: 0.751785

Train Epoch: 2 [43520/60000 (72%)]	Loss: 0.803771

Train Epoch: 2 [46080/60000 (77%)]	Loss: 0.754844

Train Epoch: 2 [48640/60000 (81%)]	Loss: 0.751976

Train Epoch: 2 [51200/60000 (85%)]	Loss: 0.717965

Train Epoch: 2 [53760/60000 (89%)]	Loss: 0.781195

Train Epoch: 2 [56320/60000 (94%)]	Loss: 0.730536

Train Epoch: 2 [58880/60000 (98%)]	Loss: 0.689717

Test set: Average loss: 0.3512, Accuracy: 9149/10000 (91%)

Train Epoch: 3 [0/60000 (0%)]	Loss: 0.637135

Train Epoch: 3 [2560/60000 (4%)]	Loss: 0.669247

Train Epoch: 3 [5120/60000 (9%)]	Loss: 0.628941

Train Epoch: 3 [7680/60000 (13%)]	Loss: 0.577849

Train Epoch: 3 [10240/60000 (17%)]	Loss: 0.592452

Train Epoch: 3 [12800/60000 (21%)]	Loss: 0.653468

Train Epoch: 3 [15360/60000 (26%)]	Loss: 0.664662

Train Epoch: 3 [17920/60000 (30%)]	Loss: 0.587736

Train Epoch: 3 [20480/60000 (34%)]	Loss: 0.608644

Train Epoch: 3 [23040/60000 (38%)]	Loss: 0.687277

Train Epoch: 3 [25600/60000 (43%)]	Loss: 0.544342

Train Epoch: 3 [28160/60000 (47%)]	Loss: 0.708621

Train Epoch: 3 [30720/60000 (51%)]	Loss: 0.593251

Train Epoch: 3 [33280/60000 (55%)]	Loss: 0.579146

Train Epoch: 3 [35840/60000 (60%)]	Loss: 0.549925

Train Epoch: 3 [38400/60000 (64%)]	Loss: 0.525394

Train Epoch: 3 [40960/60000 (68%)]	Loss: 0.661625

Train Epoch: 3 [43520/60000 (72%)]	Loss: 0.455882

Train Epoch: 3 [46080/60000 (77%)]	Loss: 0.563778

Train Epoch: 3 [48640/60000 (81%)]	Loss: 0.553182

Train Epoch: 3 [51200/60000 (85%)]	Loss: 0.553937

Train Epoch: 3 [53760/60000 (89%)]	Loss: 0.606809

Train Epoch: 3 [56320/60000 (94%)]	Loss: 0.594416

Train Epoch: 3 [58880/60000 (98%)]	Loss: 0.544529

Test set: Average loss: 0.2543, Accuracy: 9338/10000 (93%)

Train Epoch: 4 [0/60000 (0%)]	Loss: 0.619532

Train Epoch: 4 [2560/60000 (4%)]	Loss: 0.567222

Train Epoch: 4 [5120/60000 (9%)]	Loss: 0.508649

Train Epoch: 4 [7680/60000 (13%)]	Loss: 0.494902

Train Epoch: 4 [10240/60000 (17%)]	Loss: 0.521278

Train Epoch: 4 [12800/60000 (21%)]	Loss: 0.495832

Train Epoch: 4 [15360/60000 (26%)]	Loss: 0.468417

Train Epoch: 4 [17920/60000 (30%)]	Loss: 0.595662

Train Epoch: 4 [20480/60000 (34%)]	Loss: 0.403730

Train Epoch: 4 [23040/60000 (38%)]	Loss: 0.547263

Train Epoch: 4 [25600/60000 (43%)]	Loss: 0.523064

Train Epoch: 4 [28160/60000 (47%)]	Loss: 0.460831

Train Epoch: 4 [30720/60000 (51%)]	Loss: 0.452652

Train Epoch: 4 [33280/60000 (55%)]	Loss: 0.439493

Train Epoch: 4 [35840/60000 (60%)]	Loss: 0.528650

Train Epoch: 4 [38400/60000 (64%)]	Loss: 0.487770

Train Epoch: 4 [40960/60000 (68%)]	Loss: 0.540879

Train Epoch: 4 [43520/60000 (72%)]	Loss: 0.470456

Train Epoch: 4 [46080/60000 (77%)]	Loss: 0.437475

Train Epoch: 4 [48640/60000 (81%)]	Loss: 0.415573

Train Epoch: 4 [51200/60000 (85%)]	Loss: 0.392564

Train Epoch: 4 [53760/60000 (89%)]	Loss: 0.521458

Train Epoch: 4 [56320/60000 (94%)]	Loss: 0.433528

Train Epoch: 4 [58880/60000 (98%)]	Loss: 0.389609

Test set: Average loss: 0.2143, Accuracy: 9432/10000 (94%)

Train Epoch: 5 [0/60000 (0%)]	Loss: 0.531064

Train Epoch: 5 [2560/60000 (4%)]	Loss: 0.501403

Train Epoch: 5 [5120/60000 (9%)]	Loss: 0.393153

Train Epoch: 5 [7680/60000 (13%)]	Loss: 0.507973

Train Epoch: 5 [10240/60000 (17%)]	Loss: 0.437351

Train Epoch: 5 [12800/60000 (21%)]	Loss: 0.449640

Train Epoch: 5 [15360/60000 (26%)]	Loss: 0.393346

Train Epoch: 5 [17920/60000 (30%)]	Loss: 0.416081

Train Epoch: 5 [20480/60000 (34%)]	Loss: 0.449489

Train Epoch: 5 [23040/60000 (38%)]	Loss: 0.432535

Train Epoch: 5 [25600/60000 (43%)]	Loss: 0.470373

Train Epoch: 5 [28160/60000 (47%)]	Loss: 0.421085

Train Epoch: 5 [30720/60000 (51%)]	Loss: 0.445716

Train Epoch: 5 [33280/60000 (55%)]	Loss: 0.478192

Train Epoch: 5 [35840/60000 (60%)]	Loss: 0.374121

Train Epoch: 5 [38400/60000 (64%)]	Loss: 0.369466

Train Epoch: 5 [40960/60000 (68%)]	Loss: 0.533549

Train Epoch: 5 [43520/60000 (72%)]	Loss: 0.383733

Train Epoch: 5 [46080/60000 (77%)]	Loss: 0.507195

Train Epoch: 5 [48640/60000 (81%)]	Loss: 0.407680

Train Epoch: 5 [51200/60000 (85%)]	Loss: 0.392881

Train Epoch: 5 [53760/60000 (89%)]	Loss: 0.479417

Train Epoch: 5 [56320/60000 (94%)]	Loss: 0.414116

Train Epoch: 5 [58880/60000 (98%)]	Loss: 0.432079

Test set: Average loss: 0.1801, Accuracy: 9501/10000 (95%)

Train Epoch: 6 [0/60000 (0%)]	Loss: 0.415744

Train Epoch: 6 [2560/60000 (4%)]	Loss: 0.280737

Train Epoch: 6 [5120/60000 (9%)]	Loss: 0.364816

Train Epoch: 6 [7680/60000 (13%)]	Loss: 0.390640

Train Epoch: 6 [10240/60000 (17%)]	Loss: 0.410318

Train Epoch: 6 [12800/60000 (21%)]	Loss: 0.423457

Train Epoch: 6 [15360/60000 (26%)]	Loss: 0.392294

Train Epoch: 6 [17920/60000 (30%)]	Loss: 0.373533

Train Epoch: 6 [20480/60000 (34%)]	Loss: 0.528408

Train Epoch: 6 [23040/60000 (38%)]	Loss: 0.498351

Train Epoch: 6 [25600/60000 (43%)]	Loss: 0.406549

Train Epoch: 6 [28160/60000 (47%)]	Loss: 0.462406

Train Epoch: 6 [30720/60000 (51%)]	Loss: 0.534846

Train Epoch: 6 [33280/60000 (55%)]	Loss: 0.390974

Train Epoch: 6 [35840/60000 (60%)]	Loss: 0.403040

Train Epoch: 6 [38400/60000 (64%)]	Loss: 0.513974

Train Epoch: 6 [40960/60000 (68%)]	Loss: 0.380744

Train Epoch: 6 [43520/60000 (72%)]	Loss: 0.485197

Train Epoch: 6 [46080/60000 (77%)]	Loss: 0.387865

Train Epoch: 6 [48640/60000 (81%)]	Loss: 0.387096

Train Epoch: 6 [51200/60000 (85%)]	Loss: 0.380673

Train Epoch: 6 [53760/60000 (89%)]	Loss: 0.407134

Train Epoch: 6 [56320/60000 (94%)]	Loss: 0.398302

Train Epoch: 6 [58880/60000 (98%)]	Loss: 0.372190

Test set: Average loss: 0.1641, Accuracy: 9549/10000 (95%)

Train Epoch: 7 [0/60000 (0%)]	Loss: 0.356512

Train Epoch: 7 [2560/60000 (4%)]	Loss: 0.393870

Train Epoch: 7 [5120/60000 (9%)]	Loss: 0.402270

Train Epoch: 7 [7680/60000 (13%)]	Loss: 0.353308

Train Epoch: 7 [10240/60000 (17%)]	Loss: 0.358547

Train Epoch: 7 [12800/60000 (21%)]	Loss: 0.319642

Train Epoch: 7 [15360/60000 (26%)]	Loss: 0.438179

Train Epoch: 7 [17920/60000 (30%)]	Loss: 0.386755

Train Epoch: 7 [20480/60000 (34%)]	Loss: 0.404731

Train Epoch: 7 [23040/60000 (38%)]	Loss: 0.322265

Train Epoch: 7 [25600/60000 (43%)]	Loss: 0.372252

Train Epoch: 7 [28160/60000 (47%)]	Loss: 0.381766

Train Epoch: 7 [30720/60000 (51%)]	Loss: 0.390532

Train Epoch: 7 [33280/60000 (55%)]	Loss: 0.396237

Train Epoch: 7 [35840/60000 (60%)]	Loss: 0.285679

Train Epoch: 7 [38400/60000 (64%)]	Loss: 0.355077

Train Epoch: 7 [40960/60000 (68%)]	Loss: 0.241128

Train Epoch: 7 [43520/60000 (72%)]	Loss: 0.426708

Train Epoch: 7 [46080/60000 (77%)]	Loss: 0.456212

Train Epoch: 7 [48640/60000 (81%)]	Loss: 0.376701

Train Epoch: 7 [51200/60000 (85%)]	Loss: 0.365228

Train Epoch: 7 [53760/60000 (89%)]	Loss: 0.365721

Train Epoch: 7 [56320/60000 (94%)]	Loss: 0.266916

Train Epoch: 7 [58880/60000 (98%)]	Loss: 0.443687

Test set: Average loss: 0.1535, Accuracy: 9579/10000 (96%)

Train Epoch: 8 [0/60000 (0%)]	Loss: 0.370897

Train Epoch: 8 [2560/60000 (4%)]	Loss: 0.289111

Train Epoch: 8 [5120/60000 (9%)]	Loss: 0.414931

Train Epoch: 8 [7680/60000 (13%)]	Loss: 0.367614

Train Epoch: 8 [10240/60000 (17%)]	Loss: 0.296232

Train Epoch: 8 [12800/60000 (21%)]	Loss: 0.348647

Train Epoch: 8 [15360/60000 (26%)]	Loss: 0.418467

Train Epoch: 8 [17920/60000 (30%)]	Loss: 0.364306

Train Epoch: 8 [20480/60000 (34%)]	Loss: 0.336696

Train Epoch: 8 [23040/60000 (38%)]	Loss: 0.364676

Train Epoch: 8 [25600/60000 (43%)]	Loss: 0.286585

Train Epoch: 8 [28160/60000 (47%)]	Loss: 0.332353

Train Epoch: 8 [30720/60000 (51%)]	Loss: 0.295880

Train Epoch: 8 [33280/60000 (55%)]	Loss: 0.344958

Train Epoch: 8 [35840/60000 (60%)]	Loss: 0.355939

Train Epoch: 8 [38400/60000 (64%)]	Loss: 0.297799

Train Epoch: 8 [40960/60000 (68%)]	Loss: 0.443442

Train Epoch: 8 [43520/60000 (72%)]	Loss: 0.366912

Train Epoch: 8 [46080/60000 (77%)]	Loss: 0.272624

Train Epoch: 8 [48640/60000 (81%)]	Loss: 0.340495

Train Epoch: 8 [51200/60000 (85%)]	Loss: 0.332022

Train Epoch: 8 [53760/60000 (89%)]	Loss: 0.256738

Train Epoch: 8 [56320/60000 (94%)]	Loss: 0.351117

Train Epoch: 8 [58880/60000 (98%)]	Loss: 0.253743

Test set: Average loss: 0.1426, Accuracy: 9593/10000 (96%)

Train Epoch: 9 [0/60000 (0%)]	Loss: 0.307950

Train Epoch: 9 [2560/60000 (4%)]	Loss: 0.354884

Train Epoch: 9 [5120/60000 (9%)]	Loss: 0.455334

Train Epoch: 9 [7680/60000 (13%)]	Loss: 0.339795

Train Epoch: 9 [10240/60000 (17%)]	Loss: 0.302723

Train Epoch: 9 [12800/60000 (21%)]	Loss: 0.262783

Train Epoch: 9 [15360/60000 (26%)]	Loss: 0.413777

Train Epoch: 9 [17920/60000 (30%)]	Loss: 0.325851

Train Epoch: 9 [20480/60000 (34%)]	Loss: 0.367753

Train Epoch: 9 [23040/60000 (38%)]	Loss: 0.348576

Train Epoch: 9 [25600/60000 (43%)]	Loss: 0.379523

Train Epoch: 9 [28160/60000 (47%)]	Loss: 0.357496

Train Epoch: 9 [30720/60000 (51%)]	Loss: 0.287231

Train Epoch: 9 [33280/60000 (55%)]	Loss: 0.282984

Train Epoch: 9 [35840/60000 (60%)]	Loss: 0.244869

Train Epoch: 9 [38400/60000 (64%)]	Loss: 0.289696

Train Epoch: 9 [40960/60000 (68%)]	Loss: 0.353052

Train Epoch: 9 [43520/60000 (72%)]	Loss: 0.352727

Train Epoch: 9 [46080/60000 (77%)]	Loss: 0.398184

Train Epoch: 9 [48640/60000 (81%)]	Loss: 0.291148

Train Epoch: 9 [51200/60000 (85%)]	Loss: 0.276232

Train Epoch: 9 [53760/60000 (89%)]	Loss: 0.342424

Train Epoch: 9 [56320/60000 (94%)]	Loss: 0.245837

Train Epoch: 9 [58880/60000 (98%)]	Loss: 0.305476

Test set: Average loss: 0.1284, Accuracy: 9632/10000 (96%)

Train Epoch: 10 [0/60000 (0%)]	Loss: 0.216860

Train Epoch: 10 [2560/60000 (4%)]	Loss: 0.325897

Train Epoch: 10 [5120/60000 (9%)]	Loss: 0.341949

Train Epoch: 10 [7680/60000 (13%)]	Loss: 0.270563

Train Epoch: 10 [10240/60000 (17%)]	Loss: 0.369292

Train Epoch: 10 [12800/60000 (21%)]	Loss: 0.424985

Train Epoch: 10 [15360/60000 (26%)]	Loss: 0.280446

Train Epoch: 10 [17920/60000 (30%)]	Loss: 0.291100

Train Epoch: 10 [20480/60000 (34%)]	Loss: 0.314208

Train Epoch: 10 [23040/60000 (38%)]	Loss: 0.253791

Train Epoch: 10 [25600/60000 (43%)]	Loss: 0.304992

Train Epoch: 10 [28160/60000 (47%)]	Loss: 0.288408

Train Epoch: 10 [30720/60000 (51%)]	Loss: 0.239414

Train Epoch: 10 [33280/60000 (55%)]	Loss: 0.431122

Train Epoch: 10 [35840/60000 (60%)]	Loss: 0.208240

Train Epoch: 10 [38400/60000 (64%)]	Loss: 0.291883

Train Epoch: 10 [40960/60000 (68%)]	Loss: 0.281189

Train Epoch: 10 [43520/60000 (72%)]	Loss: 0.297465

Train Epoch: 10 [46080/60000 (77%)]	Loss: 0.253582

Train Epoch: 10 [48640/60000 (81%)]	Loss: 0.311440

Train Epoch: 10 [51200/60000 (85%)]	Loss: 0.389858

Train Epoch: 10 [53760/60000 (89%)]	Loss: 0.389085

Train Epoch: 10 [56320/60000 (94%)]	Loss: 0.314335

Train Epoch: 10 [58880/60000 (98%)]	Loss: 0.248064

Test set: Average loss: 0.1241, Accuracy: 9655/10000 (97%)

可见总体上一样。

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（原）DropBlock A regularization method for convolutional networks

一 总体介绍

二 算法框架

三 具体细节

四 实验结果