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Release 0.6.0
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@keisen keisen released this 22 May 06:11
· 192 commits to master since this release
v0.6.0
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Learn about vigilant mode.

Change a terminology

  • The Loss is changed to Score. Because visualizations does NOT need to calculate any loss between labels and the model outputs, and the calculated values is used as just scores, we thought that the former is proper than latter.

Add Features and Improvements

  • Support Python 3.9
  • Support Tensorflow 2.4 and 2.5
  • Support mixed-precision
    • Issues / #43 , #45 and #47
    • only tensorflow 2.4.0+
  • Add unconnected_gradients option to __call__() of ActivationMaximization, Saliency, GradCAM, GradCAM++.
  • Add standardize_cam option to __call__() of GradCAM, GradCAM++ and ScoreCAM.
  • Add normalize_saliency option to __call__() of Saliency.

Breaking Changes

  • In all visualization class constructor, the model passed as a argument is NOT cloned when model_modifier is None.
  • Deprecates and Disable normalize_gradient option in ActivationMaximizaion and GradCAM.
  • Deprecates tf_keras_vis.utils.callback module. Use tf_keras_vis.activation_maximization.callbacks module instead.
    • Deprecates and Rename Print to PrintLogger.
    • Deprecates and Rename GifGenerator to GifGenerator2D.
  • Deprecates tf_keras_vis.utils.regularizers.TotalVariation. Use tf_keras_vis.utils.regularizers.TotalVariation2D instead.
  • Deprecates tf_keras_vis.utils.regularizers.L2Norm. Use tf_keras_vis.utils.regularizers.Norm instead.
  • Deprecates and Rename tf_keras_vis.utils.normalize to tf_keras_vis.utils.standardize.
  • Don't need to use tf_keras_vis.utils.normalize to visualize CAM or Saliency. Use standardize_cam and standardize_saliency option instead respectively.
  • Don't need to cast activations maximized by ActivationMaximization to visualize.

BugFix and Other Changes

  • Fixes a problem in Rotate input-modifier that it can't work correctly when input tensors is not 2D images.
  • Add a test utility and testcases.
  • Update dockerfiles and example notebooks.

Known Issues

  • With a mixed-precision model, Regurarization values that is calculated by ActivationMaximization may be NaN.
  • With a mixed-precision model that has a layer which are set float32 dtype exlicitly, ActivationMaximization may raise a error.
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