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PyTorchPractice

Pytorch implementation of visulaization of Convolutional Neural Nework weights

Variational Encoder on MNIST

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
VAE_Iter1 VAE_Iter24 VAE_Iter50

CNN Layer weight and output visualization

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

Filter Visualization of Alex Net

Layer 0 (Multi Channel) Layer 3 (Single Channel) Layer 6 (single Channel)
AlexNet_Layer0 AlexNet_Layer3 AlexNet_Layer6

First layer weight of ResNet

Test Image for ResNet output

Layer 0 (Multi Channel) Layer 3 (Single Channel) Layer 6 (single Channel)

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Practicing pytorch by understanding others code

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