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aifig-0.1.7
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مشاهده بیشترتوضیحات
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