前言

最近论文需要画出网络结构图，这里简单记录一下使用PlotNeuralNet画图的API。
其他工具还有如何画出漂亮的神经网络图？

一、API

各层的定义在tikzeng.py文件中，具体参数可以查看代码。

to_head(): 添加LaTeX中的头部标签及引用导入的库
to_cor(): 添加颜色定义
to_begin(): 添加开始标签

to_head()、to_cor()、to_begin()这三个函数是必须要调用的。

to_input( pathfile, to=‘(-3,0,0)’, width=8, height=8, name=“temp” ): 可以向网络中添加图片
to_Conv( name, s_filer=256, n_filer=64, offset=“(0,0,0)”, to=“(0,0,0)”, width=1, height=40, depth=40, caption=" " ): 添加一层卷积层

卷积层详细参数，to_ConvConvRelu()、to_ConvRes()、to_ConvSoftMax()同理，唯二不同的是因为比普通卷积层多了一层激活函数，to_ConvConvRelu()等函数的n_filer和width参数，以元组形式同时包括卷积层和激活层的尺寸，例如n_filer=(64,64), width=(2,2)
to_Conv(name,s_filer=256,n_filer=64,offset=“(0,0,0)”,to=“(0,0,0)”,width=1,height=40, depth=40, caption=" ")
name–名称(显示在当前层的下方)
s_filer–卷积层图像尺寸 # 指卷积层结构的参数,并非制图时的尺寸
n_filer–卷积层图像深度(通道数) # 指卷积层结构的参数,并非制图时的尺寸
offset–与前一层分别在x,y,z方向的距离
to–在x,y,z方向的坐标，
width–制图时的厚度
height、depth–制图时的长宽
width、height、depth指在制图时,卷积层的尺寸
caption–备注信息

to_ConvConvRelu( name, s_filer=256, n_filer=(64,64), offset=“(0,0,0)”, to=“(0,0,0)”, width=(2,2), height=40, depth=40, caption=" " ):添加一层带Relu激活函数的卷积层
to_Pool(name, offset=“(0,0,0)”, to=“(0,0,0)”, width=1, height=32, depth=32, opacity=0.5, caption=" "): 添加一层池化层

池化层详细参数
to_Pool(name,offset=“(0,0,0)”,to=“(0,0,0)”,width=1,height=32,depth=32,opacity=0.5,caption=" ")
部分参数与卷积层相同
opacity–透明度,0-1之间
to=“(conv1-east)”–在con1层的东侧

to_UnPool(): 反池化层
to_ConvRes(): 卷积加残差
to_ConvSoftMax(): 添加一层带softmax的卷积层
to_SoftMax(): 添加一层softmax
to_Sum( name, offset=“(0,0,0)”, to=“(0,0,0)”, radius=2.5, opacity=0.6): 加号
to_connection( of, to): 在两层之间建立连接（就是在两层之间加个箭头） # 水平连接，参数为：起始位置、中止位置
to_skip( of, to, pos=1.25): 添加一层带Relu激活函数的卷积层，跨连接，参数为：起始位置、中止位置，pos默认为1.25
to_end(): 添加添加LaTeX结束标签，这个也是必须要调用的

其他几层效果如下：
otherlayers

二、以VGG-F为例

1. VGG-F网络结构图

下图是使用PlotNeuralNet画出的结构图。
VGG-F

2. 代码

import sys
sys.path.append('../')
from pycore.tikzeng import *
from pycore.blocks  import *

arch = [ 
    # 以VGG-F网络为例
    # conv1
    to_ConvConvRelu( name='conv1', s_filer=55, n_filer=(96,96), offset="(0,0,0)", to="(0,0,0)", width=(2,2), height=40, depth=40, caption='Conv1'  ),
    to_Pool(name="pool1", offset="(0,0,0)", to="(conv1-east)", width=1, height=32, depth=32, opacity=0.5),
    
    #conv2
    to_ConvConvRelu( name='conv2', s_filer=27, n_filer=(256,256), offset="(1,0,0)", to="(pool1-east)", width=(3,3), height=35, depth=35, caption='Conv2'  ),
    to_connection( "pool1", "conv2"), 
    to_Pool(name="pool2", offset="(0,0,0)", to="(conv2-east)", width=1, height=30, depth=30, opacity=0.5),
    
    # conv3
    to_ConvConvRelu( name='conv3', s_filer=13, n_filer=(384,384), offset="(1,0,0)", to="(pool2-east)", width=(5,5), height=30, depth=30, caption='Conv3'  ),
    to_connection( "pool2", "conv3"), 
    # conv4
    to_ConvConvRelu( name='conv4', s_filer=13, n_filer=(384,384), offset="(1,0,0)", to="(conv3-east)", width=(5,5), height=25, depth=25, caption='Conv4'  ),
    to_connection( "conv3", "conv4"), 
    # conv5
    to_ConvConvRelu( name='conv5', s_filer=13, n_filer=(256,256), offset="(1,0,0)", to="(conv4-east)", width=(4,4), height=25, depth=25, caption='Conv5'  ),
    to_Pool(name="pool3", offset="(0,0,0)", to="(conv5-east)", width=1, height=20, depth=20, opacity=0.5),
    to_connection( "conv4", "conv5"), 
     
    # fc1
    to_SoftMax(name='fc1', s_filer=4096, offset="(4,0,0)", to="(pool3-east)", width=1.5, height=1.5, depth=100, opacity=0.8, caption='FC1'),
    to_connection( "pool3", "fc1"), 
    # fc2
    to_SoftMax(name='fc2', s_filer=4096, offset="(1.5,0,0)", to="(fc1-east)", width=1.5, height=1.5, depth=100, opacity=0.8, caption='FC2'),
    to_connection( "fc1", "fc2"), 
    # fc1
    to_SoftMax(name='fc3', s_filer=1000, offset="(1.5,0,0)", to="(fc2-east)", width=1.5, height=1.5, depth=70, opacity=0.8, caption='FC3'),
    to_connection( "fc2", "fc3"), 

    to_end() 
    ]
    
def main():
    namefile = str(sys.argv[0]).split('.')[0]
    to_generate(arch, namefile + '.tex' )

if __name__ == '__main__':
    main()