visualkeras for Keras / TensorFlow

⚠ Maintainer Wanted ⚠

When I released this package a few years ago just before I started my PhD I was almost certain that I'd continue to use TensorFlow and would require this library daily. However, nowadays I never use TensorFlow... As such maintaining a package for a framework that I do not use while doing a PhD has become very cumbersome. That's why I need you! Yes, you! If you have at least some experience with OSS and know how to create good code please contact me to become a maintainer. There is a lot of potential in this framwork and it has gained quite some traction!

Introduction

Visualkeras is a Python package to help visualize Keras (either standalone or included in tensorflow) neural network architectures. It allows easy styling to fit most needs. This module supports layered style architecture generation which is great for CNNs (Convolutional Neural Networks), and a graph style architecture, which works great for most models including plain feed-forward networks.

Model Support

Mode	Sequential	Functional	Subclassed models
`visualkeras.layered_view()`	yes⁽¹⁾	partially^(1,2)	not tested
`visualkeras.graph_view()`	yes	yes	not tested

¹: Any tensor with more than 3 dimensions will be rendered as 3D tensor with elongated z-axis.

²: Only linear models where each layer has no more than one in or output. Non-linear models will be shown in sequential order.

Installation

To install published releases from PyPi execute:

pip install visualkeras

To update visualkeras to the latest version, add the --upgrade flag to the above commands.

If you want the latest (potentially unstable) features you can also directly install from the github master branch:

pip install git+https://github.com/paulgavrikov/visualkeras

Usage

Generating neural network architectures is easy:

import visualkeras

model = ...

visualkeras.layered_view(model).show() # display using your system viewer
visualkeras.layered_view(model, to_file='output.png') # write to disk
visualkeras.layered_view(model, to_file='output.png').show() # write and show

To help understand some of the most important parameters we are going to use a VGG16 CNN architecture (see example.py).

Default

visualkeras.layered_view(model)

Legend

You can set the legend parameter to describe the relationship between color and layer types. It is also possible to pass a custom PIL.ImageFont to use (or just leave it out and visualkeras will use the default PIL font). Please note that you may need to provide the full path of the desired font depending on your OS.

from PIL import ImageFont

font = ImageFont.truetype("arial.ttf", 32)  # using comic sans is strictly prohibited!
visualkeras.layered_view(model, legend=True, font=font)  # font is optional!

Flat Style

visualkeras.layered_view(model, draw_volume=False)

Spacing and logic grouping

The global distance between two layers can be controlled with spacing. To generate logical groups a special dummy keras layer visualkeras.SpacingDummyLayer() can be added.

model = ...
...
model.add(visualkeras.SpacingDummyLayer(spacing=100))
...

visualkeras.layered_view(model, spacing=0)

Custom color map

It is possible to provide a custom color map for fill and outline per layer type.

from tensorflow.python.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D, ZeroPadding2D
from collections import defaultdict

color_map = defaultdict(dict)
color_map[Conv2D]['fill'] = 'orange'
color_map[ZeroPadding2D]['fill'] = 'gray'
color_map[Dropout]['fill'] = 'pink'
color_map[MaxPooling2D]['fill'] = 'red'
color_map[Dense]['fill'] = 'green'
color_map[Flatten]['fill'] = 'teal'

visualkeras.layered_view(model, color_map=color_map)

Hiding layers

Some models may consist of too many layers to visualize or to comprehend the model. In this case it can be helpful to hide (ignore) certain layers of the keras model without modifying it. Visualkeras allows ignoring layers by their type (type_ignore) or index in the keras layer sequence (index_ignore).

visualkeras.layered_view(model, type_ignore=[ZeroPadding2D, Dropout, Flatten])

Scaling dimensions

Visualkeras computes the size of each layer by the output shape. Values are transformed into pixels. Then, scaling is applied. By default visualkeras will enlarge the x and y dimension and reduce the size of the z dimensions as this has deemed visually most appealing. However, it is possible to control scaling using scale_xy and scale_z. Additionally, to prevent to small or large options minimum and maximum values can be set (min_xy, min_z, max_xy, max_z).

visualkeras.layered_view(model, scale_xy=1, scale_z=1, max_z=1000)

Note: Scaled models may hide the true complexity of a layer, but are visually more appealing.

FAQ

Feature X documented here does not work

The main branch may be ahead of pypi. Consider upgrading to the latest (perhaps unstable) build as discussed in Installation.

Installing aggdraw fails

This is most likely due to missing gcc / g++ components (e.g. on Elementary OS). Try installing them via your package manager, e.g.:

sudo apt-get install gcc
sudo apt-get install g++

.show() doesn't open a window

You have probably not configured your default image viewer. You can install imagemagick via most package managers:

sudo apt-get install imagemagick

Citation

If you find this project helpful for your research please consider citing it in your publication as follows.

@misc{Gavrikov2020VisualKeras,
  author = {Gavrikov, Paul},
  title = {visualkeras},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/paulgavrikov/visualkeras}},
}

YonatanRA/visualkeras