| # 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](https://github.com/hussamalafandi/Generative_AI). | |
| ## 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](https://wandb.ai/hussam-alafandi/mnist-autoencoder/runs/f81c7dgf?nw=nwuserhussamalafandi). | |
| ## Load Model | |
| ```python | |
| 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. | |