visualkeras 0.0.1

Architecture visualization of Keras models

visualkeras

Introduction

Visualkeras is a Python package to help visualize Keras neural network architectures. It allows easy styling to fit most needs. As of now it only supports layered style architecture generation which is great for CNNs (Convolutional Neural Networks).

Installation

To install the current release:

pip install visualkeras

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

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

Default

visualkeras.layered_view(model)

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.

Planned features

• Translucent layers
• Graph view for dense networks