机器学习图形生成库
aifig的Python项目详细描述
人工智能图
目的
aifig是一个python库，用于生成机器学习模型的图形。
libary允许您生成以下图形，这些图形可能对演示文稿、论文等有用。
aifig是我的一些个人代码的重构版本。功能自然会受到限制，不适合每次使用。我鼓励任何有兴趣贡献额外功能的人。
如果你在论文中使用aifig，你可以这样引用库（bibtex）：
@misc{aifig, author = {Sigve Rokenes}, title = {AI-FIG}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/evgiz/aifig}}}
安装
带有SVG导出的AI-FIG库：
pip install aifig
如果需要导出为PNG或PDF格式：
pip install svglib
用法
简单示例
# Import libraryimportaifig# Create new figure, title and author is optionalmy_figure=aifig.figure("Figure 1","Sigve Rokenes")# Figures consist of graphs (eg. each network in a model)my_graph=aifig.graph("gen")# Graphs contain elements (inputs, outputs, layers)my_graph.add(aifig.dense("input",16))my_graph.add(aifig.dense("hidden_1",64))my_graph.add(aifig.dense("hidden_2",128))my_graph.add(aifig.dense("hidden_3",64))my_graph.add(aifig.dense("output",1))my_graph.add(aifig.arrow("prediction"))# Add the graph to the figure at position (0,0)my_figure.add(graph,0,0)# Save the figure my_figure.save_png("my_figure.png",scale=1)my_figure.save_svg("my_figure.svg")my_figure.save_pdf("my_figure.pdf")
以上代码生成此图：
多图示例（gan模型）
importaifigfigure=aifig.figure()# Define generator networkgenerator_elements=[aifig.dense("noise_vector",128,comment="norm_dist",simple=True),aifig.conv("tconv_1",48,comment="5x5"),aifig.conv("tconv_2",32,comment="5x5"),aifig.conv("tconv_3",8,comment="5x5"),aifig.conv("tconv_4",3,comment="5x5"),aifig.image("gen_result",comment="(fake image)")]# Define discriminator networkdiscriminator_elements=[aifig.image("image_input",comment="real/fake"),aifig.conv("conv_1",16,comment="5x5"),aifig.pool("max_pool")aifig.conv("conv_2",32,comment="5x5"),aifig.pool("max_pool"),aifig.conv("conv_3",48,comment="5x5"),aifig.dense("dense_1",64),aifig.dense("output",1),aifig.arrow("prediction",comment="log prob")]# Create graphs with elementsgen_graph=aifig.graph("gen",generator_elements)dsc_graph=aifig.graph("dsc",discriminator_elements)dat_graph=aifig.graph("dat",[aifig.image("real_image",comment="(dataset)")])# Add graphs to figurefigure.add(gen_graph,0,0)figure.add(dat_graph,1,0)figure.add(dsc_graph,0,1)# Connect inputs to discriminator networkfigure.connect("gen","dsc")figure.connect("dat","dsc")# Save figure as pngfigure.save_png("gan.png")
此代码生成下图：
API
图形由一个或多个图形组成。这些图使用figure.add(graph, x, y)放置在网格中。可以使用mygraph.add(element)将元素添加到图中，也可以使用figure.connect("graph_name1", "graph_name2")将图与箭头连接。最后，要保存一个图形，使用不同格式的my_figure.save_svg("fig.svg")或变体。
# ===================== ## Figure ## ===================== ## title figure title# author figure authormy_figure=aifig.figure()# figure.add# graph graph to add# x x position in grid# y y position in gridmy_figure.add(graph,0,0)# figure.connect# from name of first graph# to name of second graph# position grid position of arrow, use this if # different arrows overlap# offset arrow offset in units, useful to# distinguish different arrows at same positionmy_figure.connect("graph1","graph2")# figure.save (path)# path file path to save to# scale upscale (png only)# debug enable debug draw modemy_figure.save_png("my_figure.png",scale=1)my_figure.save_svg("my_figure.svg")my_figure.save_pdf("my_figure.pdf")# ===================== ## Graph ## ===================== ## name (required)# elements [list of elements]# spacing (between elements, default 32)my_graph=aifig.graph("graph_name")my_graph.add(element)# ===================== ## Layer elements ## ===================== ## label text label, use None to hide# size size of layer (nodes, filters)# comment additional comment text# size_label set to False to hide size label# simple (dense only) set True to render as simple rectangledense=aifig.dense()# Dense (fully connected)conv=aifig.conv()# Convolutional layer# ===================== ## Simple elements ## ===================== ## label text label, use None to hide# comment additional comment textpool=aifig.pool()# Pooling layerimage=aifig.image()# Image (usually input)arrow=aifig.arrow()# Arrow# ===================== ## Special elements ## ===================== ## width width of padding (use negative to reduce)padding=aifig.padding(10)
依赖性
- svgwrite
- svglib（仅保存为pdf/png）
- ReportLab（仅保存为PDF/PNG）
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