小行星

热爱平淡，向往未知

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\usepackage{CJK}
\usepackage{ifthen}

\newcommand{\CJKfontsize}[4]{%
\fontsize{#1}{#2 plus#3 minus #4}\selectfont}
\newcommand\zihao[1]{%
\ifthenelse{\equal{#1}{0}}{%
\CJKfontsize{42bp}{50.4pt}{.5pt}{.3pt}}{}%
\ifthenelse{\equal{#1}{0-}}{%
\CJKfontsize{36bp}{43.2pt}{.5pt}{.3pt}}{}%
\ifthenelse{\equal{#1}{1}}{%
\CJKfontsize{26bp}{31.2pt}{.5pt}{.3pt}}{}%
\ifthenelse{\equal{#1}{1-}}{%
\CJKfontsize{24bp}{28.8pt}{.5pt}{.3pt}}{}%
\ifthenelse{\equal{#1}{2}}{%
\CJKfontsize{22bp}{26.4pt}{.5pt}{.3pt}}{}%
\ifthenelse{\equal{#1}{2-}}{%
\CJKfontsize{18bp}{21.6pt}{.3pt}{.2pt}}{}%
\ifthenelse{\equal{#1}{3}}{%
\CJKfontsize{16bp}{19.3pt}{.3pt}{.2pt}}{}%
\ifthenelse{\equal{#1}{3-}}{%
\CJKfontsize{15bp}{18pt}{.3pt}{.2pt}}{}%
\ifthenelse{\equal{#1}{4}}{%
\CJKfontsize{14bp}{16.8pt}{.3pt}{.2pt}}{}%
\ifthenelse{\equal{#1}{4-}}{%
\CJKfontsize{12bp}{14.4pt}{.3pt}{.2pt}}{}%
\ifthenelse{\equal{#1}{5}}{%
\CJKfontsize{10.5bp}{12.6pt}{.3pt}{.2pt}}{}%
\ifthenelse{\equal{#1}{5-}}{%
\CJKfontsize{9bp}{10.8pt}{.2pt}{.1pt}}{}%
\ifthenelse{\equal{#1}{6}}{%
\CJKfontsize{7.5bp}{9pt}{.2pt}{.1pt}}{}%
\ifthenelse{\equal{#1}{6-}}{%
\CJKfontsize{6.5bp}{7.8pt}{.2pt}{.1pt}}{}%
\ifthenelse{\equal{#1}{7}}{%
\CJKfontsize{5.5bp}{6.6pt}{.1pt}{.1pt}}{}%
\ifthenelse{\equal{#1}{8}}{%
\CJKfontsize{5bp}{6pt}{.1pt}{.1pt}}{}}

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下载MacTex并安装

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http://www.tug.org/mactex/

Latex编译器TexShop的设置

安装完成后，会有TexShop编译器。

完成如下两个中文的环境设置：

  1. 设置默认的编译为Xelatex：Preferences—Typesetting—Default Command—Command Listed Below—XeLaTex；
  2. 设置语言编码为UTF-8：Preferences—Source—Encoding—Unicode(UTF-8)；当然Text and Background Colors也可以设置为白字黑底；

测试中英文混搭

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\documentclass{article}
\usepackage{fontspec}
\setmainfont{SimSun}

\begin{document}

Hello, \LaTeX3, 中文混搭效果。

\end{document}

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在py文件里面写的

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import matplot

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>>> # an Embedding module containing 10 tensors of size 3
>>> embedding = nn.Embedding(10, 3)
>>> # a batch of 2 samples of 4 indices each
>>> input = Variable(torch.LongTensor([[1,2,4,5],[4,3,2,9]]))
>>> embedding(input)

Variable containing:
(0 ,.,.) =
 -1.0822  1.2522  0.2434
  0.8393 -0.6062 -0.3348
  0.6597  0.0350  0.0837
  0.5521  0.9447  0.0498

(1 ,.,.) =
  0.6597  0.0350  0.0837
 -0.1527  0.0877  0.4260
  0.8393 -0.6062 -0.3348
 -0.8738 -0.9054  0.4281
[torch.FloatTensor of size 2x4x3]

>>> # example with padding_idx
>>> embedding = nn.Embedding(10, 3, padding_idx=0)
>>> input = Variable(torch.LongTensor([[0,2,0,5]]))
>>> embedding(input)

Variable containing:
(0 ,.,.) =
  0.0000  0.0000  0.0000
  0.3452  0.4937 -0.9361
  0.0000  0.0000  0.0000
  0.0706 -2.1962 -0.6276
[torch.FloatTensor of size 1x4x3]

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MobileNetV2(
  (features): Sequential(
    (0): Sequential(
      (0): Conv2d(5, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
      (2): ReLU(inplace)
    )
    (1): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (2): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)
        (4): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (3): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
        (4): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (4): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)
        (4): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (5): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
        (4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (6): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
        (4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (7): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)
        (4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (8): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
        (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (9): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
        (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (10): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
        (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (11): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
        (4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (12): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (4): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (13): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (4): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (14): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)
        (4): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (15): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
        (4): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (16): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
        (4): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (17): InvertedResidual(
      (conv): Sequential(
        (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (2): ReLU6(inplace)
        (3): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
        (4): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)
        (5): ReLU6(inplace)
        (6): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (7): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True)
      )
    )
    (18): Sequential(
      (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True)
      (2): ReLU(inplace)
    )
    (19): AvgPool2d(kernel_size=7, stride=7, padding=0, ceil_mode=False, count_include_pad=True)
  )
  (classifier): Sequential(
    (0): Dropout(p=0.5)
    (1): Linear(in_features=1280, out_features=32, bias=True)
  )
)

Posted on Views:

第一种是 先depth-wise卷积，后升维
第二种是 先降维，然后卷积，然后升维

Posted on Views: 
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import matplotlib as mpl
mpl.rcParams['figure.dpi']= 230

Posted on Edited on Views: