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Neural network visualization toolkit for keras
| Date | Stars |
|---|---|
| 2026-07-24 | 2990 |
| 2026-07-25 | 2990 |
| 2026-07-28 | 2990 |
| 2026-07-30 | 2990 |
| 2026-07-31 | 2992 |
| 2026-08-06 | 2992 |
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# Keras Visualization Toolkit
[](https://travis-ci.org/raghakot/keras-vis)
[](https://github.com/raghakot/keras-vis/blob/master/LICENSE)
[](https://keras-vis.herokuapp.com/)
keras-vis is a high-level toolkit for visualizing and debugging your trained keras neural net models. Currently
supported visualizations include:
- Activation maximization
- Saliency maps
- Class activation maps
All visualizations by default support N-dimensional image inputs. i.e., it generalizes to N-dim image inputs
to your model.
The toolkit generalizes all of the above as energy minimization problems with a clean, easy to use,
and extendable interface. Compatible with both theano and tensorflow backends with 'channels_first', 'channels_last'
data format.
## Quick links
* Read the documentation at [https://raghakot.github.io/keras-vis](https://raghakot.github.io/keras-vis).
* The Japanese edition is [https://keisen.github.io/keras-vis-docs-ja](https://keisen.github.io/keras-vis-docs-ja).
* Join the slack [channel](https://keras-vis.herokuapp.com/) for questions/discussions.
* We are tracking new features/tasks in [waffle.io](https://waffle.io/raghakot/keras-vis). Would love it if you lend us
a hand and submit PRs.
## Getting Started
In image backprop problems, the goal is to generate an input image that minimizes some loss function.
Setting up an image backprop problem is easy.
**Define weighted loss function**
Various useful loss functions are defined in [losses](https://raghakot.github.io/keras-vis/vis.losses).
A custom loss function can be defined by implementing [Loss.build_loss](https://raghakot.github.io/keras-vis/vis.losses/#lossbuild_loss).
```python
from vis.losses import ActivationMaximization
from vis.regularizers import TotalVariation, LPNorm
filter_indices = [1, 2, 3]
# Tuple consists of (loss_function, weight)
# Add regularizers as needed.
losses = [
(ActivationMaximization(keras_layer, filter_indices), 1),
(LPNorm(model.input), 10),
(TotalVariation(model.input), 10)
]
```
**Configure optimizer to minimize weighted loss**
In order to generate natural looking images, image search space is constrained using regularization penalties.
Some common regularizers are defined in [regularizers](https://raghakot.github.io/keras-vis/vis.regularizers).
Like loss functions, custom regularizer can be defined by implementing
[Loss.build_loss](https://raghakot.github.io/keras-vis/vis.losses/#lossbuild_loss).
```python
from vis.optimizer import Optimizer
optimizer = Optimizer(model.input, losses)
opt_img, grads, _ = optimizer.minimize()
```
Concrete examples of various supported visualizations can be found in
[examples folder](https://github.com/raghakot/keras-vis/tree/master/examples).
## Installation
1) Install [keras](https://github.com/fchollet/keras/blob/master/README.md#installation)
with theano or tensorflow backend. Note that this library requires Keras > 2.0
2) Install keras-vis
> From sources
```bash
sudo python setup.py install
```
> PyPI package
```bash
sudo pip install keras-vis
```
## Visualizations
**NOTE: The links are currently broken and the entire documentation is being reworked.
Please see examples/ for samples.**
Neural nets are black boxes. In the recent years, several approaches for understanding and visualizing Convolutional
Networks have been developed in the literature. They give us a way to peer into the black boxes,
diagnose mis-classifications, and assess whether the network is over/under fitting.
Guided backprop can also be used to create [trippy art](https://deepdreamgenerator.com/gallery), neural/texture
[style transfer](https://github.com/jcjohnson/neural-style) among the list of other growing applications.
Various visualizations, documented in theExcerpt of 7,107 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e353d1bccf57c80e, topic:deep-learning, topic:tensorflow