cskarthik7 / Vanilla-VAE-PyTorch

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Simplified Vanilla-Variational Autoencoders in PyTorch

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Data Used Generic badge :

Chest XRAY Images(Source - Kaggle)

This dataset contains Chest XRAY images with 2 classes - healthy Chest XRAY Images , Pneumonia Chest XRAY Images

Sample Image :

alt text

Model Architecure Code Climate :

The model is based on Encoder-Decoder networks .

The Encoder downsamples the image with two key differen hidden layers : mu(Learned mean) and sigma(standard deviation)

The decoder Network upsamples the samples reparameterised from the outputs of the encoder(mu and sigma)

Optimizer used : Adam

Loss functions : KL Divergence Loss and Binary Cross Entropy Loss

Training Generic badge:

The model was trained for 50 Epochs with learning rate of 0.003

And if the loss is constant or increasing for 3 iterations then the model is assumed to be trained!

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