How can you implement a sparse autoencoder in PyTorch for dimensionality reduction

Question

Can you tell me how can you implement a sparse autoencoder in PyTorch for dimensionality reduction?

Answer 1

You can implement a sparse autoencoder in PyTorch for dimensionality reduction:

In the above code, we are using the following:

• Sparse Autoencoder: Combines a reconstruction loss (MSELoss) with a sparsity loss (KL divergence) to enforce sparsity on the hidden layer activations.
• Training Loop: Optimizes the model to reconstruct input data while encouraging sparse representations in the hidden layer.

Hence, By referring to above you can implement a sparse autoencoder in PyTorch for dimensionality reduction.