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keravis is a high-level API for ConvNet visualizations in Keras. As of v1.0, it supports visualizations of
with support for nested pretrained models.
This is a hobby project that was inspired by lecture 14 of Stanford's CS231n: Convolutional Neural Networks for Visual Recognition http://cs231n.stanford.edu/. It is not yet optimized for serious use (see keras-vis instead).
You can install keravis using pip
pip install keravis
Read the documentation
Below are sample visualizations from a small convolutional network trained on MNIST
from keravis import feature_space
feature_space(model,X=x_test[:5000],y=y_test[:5000],kind='tsne')
from keravis import saliency_backprop
saliency_backprop(model,test_img,class_idx=7)
from keravis import saliency_guided_backprop
saliency_guided_backprop(model,test_img,class_idx=7)
from keravis import maximal_class_score_input
maximal_class_score_input(model,class_idx=5,dim=(28,28,1))
from keravis import maximally_activating_patches
maximally_activating_patches(model,'conv2d_1',X=x_test)
FAQs
A high-level API for ConvNet visualizations in Keras
We found that keravis demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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