Shypd.aiThe largest open index of AI toolskeras-vis is a high-level toolkit for visualizing and debugging trained Keras neural network models. It supports activation maximization, saliency maps, and class activation maps to understand model behavior.
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keras-vis is a high-level toolkit designed for visualizing and debugging trained Keras neural network models. It offers various visualization techniques, including activation maximization, saliency maps, and class activation maps, all of which support N-dimensional image inputs. The toolkit generalizes these visualizations as energy minimization problems, providing a clean, easy-to-use, and extendable interface. It is compatible with both Theano and TensorFlow backends, supporting 'channels_first' and 'channels_last' data formats. keras-vis helps users peer into the 'black boxes' of neural networks, diagnose mis-classifications, and assess whether models are over/under-fitting, offering insights into model decision-making processes.
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