Autoencoders-Implementation

This repository contains the implementation and visualisation of some autoencodrs for latent space pattern-learning

Convolutional Autoencoder

Trained a three layered convolution encoder and corresponding decoder on MNIST dataset for 50 EPOCHS and reduced the latent image representation to 49 neurons and still got good results.

A brief Summary of it -

• Trained for - 50 epochs
• train:val:test dataset ratio = 8:4:3
• train_loss = 0.0877
• val_loss = 0.0875
• test_loss = 0.0866
• lr = 0.001
• batch_size = 64
• optimizers = Adam
• loss = BinaryCrossEntropy

Some visualisations of both original and reconstructed images at different instances

Epoch -1 ( Original above, Reconstructed below)

Epoch -11

Epoch -21

Epoch -31

Epoch -41

Epoch -50

It's good that with the latent feature represenation of 49 dimensions we able to generate good reconstructed images