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aifig-0.1.7


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توضیحات

A machine learning figure generation library
ویژگی مقدار
سیستم عامل -
نام فایل aifig-0.1.7
نام aifig
نسخه کتابخانه 0.1.7
نگهدارنده []
ایمیل نگهدارنده []
نویسنده Sigve Rokenes
ایمیل نویسنده me@evgiz.net
آدرس صفحه اصلی https://github.com/evgiz/aifig
آدرس اینترنتی https://pypi.org/project/aifig/
مجوز -
# AI Figures ## Purpose AIFIG is a python library for generating figures of machine learning models. The libary allows you to generate figures such as the following, which may be useful for use in presentations, papers etc. <img src="https://raw.githubusercontent.com/evgiz/aifig/master/img/fig_gan.png"> **AIFIG** is a refactored version of some of my personal code. Functionality will naturally be limited and not suited for every use. I encourage anyone who is interested to contribute with additional features. If you use AIFIG in a paper, you can cite the library like this (bibtex): ```latex @misc{aifig, author = {Sigve Rokenes}, title = {AI-FIG}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/evgiz/aifig}} } ``` ## Install AI-FIG library with svg export: ```bash pip install aifig ``` If you need to export as png or pdf: ```bash pip install svglib ``` ## Usage #### Simple example ```python # Import library import aifig # Create new figure, title and author is optional my_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") ``` *The above code generates this figure:* <img src="https://raw.githubusercontent.com/evgiz/aifig/master/img/fig_simple.png"> #### Multi-graph example (GAN model) ```python import aifig figure = aifig.figure() # Define generator network generator_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 network discriminator_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 elements gen_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 figure figure.add(gen_graph, 0, 0) figure.add(dat_graph, 1, 0) figure.add(dsc_graph, 0, 1) # Connect inputs to discriminator network figure.connect("gen", "dsc") figure.connect("dat", "dsc") # Save figure as png figure.save_png("gan.png") ``` *This code generates the following figure:* <img src="https://raw.githubusercontent.com/evgiz/aifig/master/img/fig_gan.png"> ## API A figure consists of one or more graphs. These graphs are placed in a grid using `figure.add(graph, x, y)`. You can add elements to graphs using `mygraph.add(element)`, and you can connect graphs with arrows using `figure.connect("graph_name1", "graph_name2")`. Finally, to save a figure, use `my_figure.save_svg("fig.svg")` or variants for different formats. ```python # ===================== # # Figure # # ===================== # # title figure title # author figure author my_figure = aifig.figure() # figure.add # graph graph to add # x x position in grid # y y position in grid my_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 position my_figure.connect("graph1", "graph2") # figure.save (path) # path file path to save to # scale upscale (png only) # debug enable debug draw mode my_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 rectangle dense = aifig.dense() # Dense (fully connected) conv = aifig.conv() # Convolutional layer # ===================== # # Simple elements # # ===================== # # label text label, use None to hide # comment additional comment text pool = aifig.pool() # Pooling layer image = aifig.image() # Image (usually input) arrow = aifig.arrow() # Arrow # ===================== # # Special elements # # ===================== # # width width of padding (use negative to reduce) padding = aifig.padding(10) ``` ### Dependencies - svgwrite - svglib (only to save as pdf/png) - reportlab (only to save as pdf/png)


نیازمندی

مقدار نام
- svgwrite


نحوه نصب


نصب پکیج whl aifig-0.1.7:

    pip install aifig-0.1.7.whl


نصب پکیج tar.gz aifig-0.1.7:

    pip install aifig-0.1.7.tar.gz