Pytorch implementation of visulaization of Convolutional Neural Nework weights
Training and visulization of results of a Variational Auto Encoder based on the example in https://github.com/pytorch/examples/tree/master/vae
Iteration 1 | Iteration 24 | Iteration 50 |
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Plotted filter weights and outputs for a test image of popular pretrained convolutional neural network models ( ResNet, AlexNet) based on example in https://debuggercafe.com/visualizing-filters-and-feature-maps-in-convolutional-neural-networks-using-pytorch/ and https://towardsdatascience.com/visualizing-convolution-neural-networks-using-pytorch-3dfa8443e74e
Layer 0 (Multi Channel) | Layer 3 (Single Channel) | Layer 6 (single Channel) |
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Layer 0 (Multi Channel) | Layer 3 (Single Channel) | Layer 6 (single Channel) |
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