metadata
tags:
- pytorch
- autoencoder
- generative-ai
- mnist
license: mit
datasets:
- mnist
metrics:
- mse
language:
- en
AutoEncoder
A simple autoencoder trained on MNIST.
This model is part of the "Introduction to Generative AI" course.
For more details, visit the GitHub repository.
Model Description
The AutoEncoder is a neural network designed to compress and reconstruct input data. It consists of an encoder that compresses the input into a latent space and a decoder that reconstructs the input from the latent representation.
Training Details
- Dataset: MNIST (handwritten digits)
- Loss Function: Mean Squared Error (MSE)
- Optimizer: Adam
- Learning Rate: 0.001
- Epochs: 40
- Latent dim: 10
Tracking
For detailed training logs and metrics, visit the Weights & Biases run.
Load Model
from model import AutoEncoder
import torch
model = AutoEncoder()
model.load_state_dict(torch.load("model.pth"))
model.eval()
License
This project is licensed under the MIT License. See the LICENSE file for details.