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Neural network visualization toolkit for tf.keras
| Date | Stars |
|---|---|
| 2026-07-24 | 337 |
| 2026-07-25 | 337 |
| 2026-07-28 | 337 |
| 2026-07-30 | 337 |
| 2026-07-31 | 337 |
| 2026-08-04 | 337 |
| 2026-08-06 | 337 |
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# [tf-keras-vis](https://keisen.github.io/tf-keras-vis-docs/) <!-- sec.0 --> [](https://pepy.tech/project/tf-keras-vis) [](https://badge.fury.io/py/tf-keras-vis) [](https://badge.fury.io/py/tf-keras-vis) [](https://github.com/keisen/tf-keras-vis/actions/workflows/python-package.yml) [](https://opensource.org/licenses/MIT) [](https://keisen.github.io/tf-keras-vis-docs/) <!-- ./sec.0 --> <!-- sec.1 --> <!-- ./sec.1 --> ## Web documents https://keisen.github.io/tf-keras-vis-docs/ ## Overview <!-- sec.2 --> tf-keras-vis is a visualization toolkit for debugging `keras.Model` in Tensorflow2.0+. Currently supported methods for visualization include: * Feature Visualization - ActivationMaximization ([web](https://distill.pub/2017/feature-visualization/), [github](https://github.com/raghakot/keras-vis)) * Class Activation Maps - GradCAM ([paper](https://arxiv.org/pdf/1610.02391v1.pdf)) - GradCAM++ ([paper](https://arxiv.org/pdf/1710.11063.pdf)) - ScoreCAM ([paper](https://arxiv.org/pdf/1910.01279.pdf), [github](https://github.com/haofanwang/Score-CAM)) - Faster-ScoreCAM ([github](https://github.com/tabayashi0117/Score-CAM/blob/master/README.md#faster-score-cam)) - LayerCAM ([paper](http://mftp.mmcheng.net/Papers/21TIP_LayerCAM.pdf), [github](https://github.com/PengtaoJiang/LayerCAM)) :new::zap: * Saliency Maps - Vanilla Saliency ([paper](https://arxiv.org/pdf/1312.6034.pdf)) - SmoothGrad ([paper](https://arxiv.org/pdf/1706.03825.pdf)) tf-keras-vis is designed to be light-weight, flexible and ease of use. All visualizations have the features as follows: * Support **N-dim image inputs**, that's, not only support pictures but also such as 3D images. * Support **batch wise** processing, so, be able to efficiently process multiple input images. * Support the model that have either **multiple inputs** or **multiple outputs**, or both. * Support the **mixed-precision** model. And in ActivationMaximization, * Support Optimizers that are built to keras. <!-- ./sec.2 --> ### Visualizations <!-- sec.3 --> #### Dense Unit <img src='https://github.com/keisen/tf-keras-vis/raw/master/docs/examples/images/visualize-dense-layer.png' width='600px' /> #### Convolutional Filter <img src='https://github.com/keisen/tf-keras-vis/raw/master/docs/examples/images/visualize-filters.png' width='600px' /> #### Class Activation Map <img src='https://github.com/keisen/tf-keras-vis/raw/master/docs/examples/images/gradcam_plus_plus.png' width='600px' /> The images above are generated by `GradCAM++`. #### Saliency Map <img src='https://github.com/keisen/tf-keras-vis/raw/master/docs/examples/images/smoothgrad.png' width='600px' /> The images above are generated by `SmoothGrad`. <!-- ./sec.3 --> ## Usage ### ActivationMaximization (Visualizing Convolutional Filter) <!-- ActivationMaximization --> ```python import tensorflow as tf from keras.applications import VGG16 from matplotlib import pyplot as plt from tf_keras_vis.activation_maximization import ActivationMaximization from tf_keras_vis.activation_maximization.callbacks import Progress from tf_keras_vis.activation_maximization.input_modifiers import Jitter, Rotate2D from tf_keras_vis.activation_maximization.regularizers import TotalVariation2D, Norm from tf_keras_vis.utils.model_modifiers import ExtractIntermediateLayer, ReplaceToLinear from tf_keras_vis.utils.scores import CategoricalScore # Create the visualization instance. # All visualization classes accept a model and model-modifier, which, for example, # replaces the activation
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d1e62e5f9835bd01, topic:deep-learning, topic:tensorflow
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