1. VGG block 3x3 Conv, pad 1 (n layers, m channels usually double, ReLU) 2x2 MaxPool, stride 2 (half size per block) It turns out that ‘deeper 3x3 Conv’ is better than ‘5x5 Conv’.
2. Architecture multiple VGG blocks Dense (4096) (Flatten, Linear, ReLU, Dropout) Dense (4096) (Linear, ReLU, Dropout) Dense (1000) 3. Code import torch from torch import nn def vgg_block(num_conv,in_channels,out_channels) ->nn.Sequential: layers: List[nn.Module] = [] for _ in range(num_conv): layers.
1. AlexNet Compared with LeNet, it has some changes.
Add noise and avoid overfitting(data transform). bigger and deeper use dropout (normalization) AvgPooling -> MaxPooling Sigmoid -> ReLU 2. Code net = nn.Sequential( nn.Conv2d(3,96,kernel_size=11,stride=4,padding=2),nn.ReLU(), nn.MaxPool2d(kernel_size=3,stride=2), # nn.Conv2d(96,128*2,kernel_size=5,padding=2),nn.ReLU(), nn.MaxPool2d(kernel_size=3,stride=2), nn.Conv2d(128*2,192*2,kernel_size=3,padding=1),nn.ReLU(), nn.Conv2d(192*2,192*2,kernel_size=3,padding=1),nn.ReLU(), nn.Conv2d(192*2,128*2,kernel_size=3,padding=1),nn.ReLU(), nn.MaxPool2d(kernel_size=3,stride=2), # 6*6*256 nn.Flatten(), nn.Linear(6*6*256,2048*2),nn.ReLU(),nn.Dropout(p=0.5), nn.Linear(2048*2,2048*2),nn.ReLU(),nn.Dropout(p=0.5), nn.Linear(2048*2,1000),nn.ReLU(), ) 3. Q&A CNN提取的特征只是针对最后的分类任务，对于人来说大部分难以理解，因此它的可解释性较差。 Last two 4096 Full Connected Layers is necessary. A good name.
1. Install Hexo npm install hexo-cli -g hexo init blog cd blog npm install hexo server 2. Add Live2d npm install --save hexo-helper-live2d then, insert code into _config.xml
live2d: enable: true scriptFrom: local pluginRootPath: live2dw/ pluginJsPath: lib/ pluginModelPath: assets/ tagMode: false debug: false model: use: wanko display: position: right width: 150 height: 300 mobile: show: true pull your ‘assets’ contents from live2d model directory to ‘./live2d_models/wanko’. At last, let’s run and compile the project
1. Concept Applying ’translation invariance’ and ’locality’ to the MLP, we then get a CNN which can significantly reduce the parameters.
$$h_{i,j} = \sum_{a,b}v_{i,j,a,b}x_{i+a,j+b}$$
$$\Rightarrow h_{i,j} = \sum_{a=-\Delta}^{\Delta}\sum_{b=-\Delta}^{\Delta}v_{a,b}x_{i+a,j+b}$$
2. Conv2d 2.1 Definition Input $$X: (N, C_{in}, H, W)$$
Kernel $$W: (h,w)$$
Bias: $$b$$
Output $$Y: (N, C_{out}, H’, W’)$$
$$Y=X\star W+b$$
2.2 Cross Correlation $$y_{i,j} = \sum_{a=1}^{h}\sum_{b=1}^{w}w_{a,b}x_{i+a,j+b}$$
2.3 Conv2d $$y_{i,j} = \sum_{a=1}^{h}\sum_{b=1}^{w}w_{-a,-b}x_{i+a,j+b}$$
As for implementation, we use ‘Cross Correlation’ but call it ‘Conv2d’.
Homepage add publish time simplfy the layout page support search Navigator navigation link Categories list categories add sidebar cat navigate Live2d baisc function switch model Markdown Mathjax In progress..
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1. Install Docker First, we need to install Docker Engine according to the instructions on the official website. Then, for convenience, we can do the following to avoid typing ‘sudo’ each time.
cat /etc/group | grep docker sudo groupadd docker # if no output sudo gpasswd -a ${USER} docker sudo service docker restart newgrp - docker 2. Image Command 2.1 search We can search Docker Hub to find docker images that we want.