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--- |
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tags: |
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- pytorch |
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- autoencoder |
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- generative-ai |
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- mnist |
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license: mit |
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datasets: |
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- mnist |
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metrics: |
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- mse |
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language: |
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- en |
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--- |
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# AutoEncoder |
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A simple autoencoder trained on MNIST. |
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This model is part of the "Introduction to Generative AI" course. |
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For more details, visit the [GitHub repository](https://github.com/hussamalafandi/Generative_AI). |
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## Model Description |
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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. |
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## Training Details |
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- **Dataset**: MNIST (handwritten digits) |
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- **Loss Function**: Mean Squared Error (MSE) |
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- **Optimizer**: Adam |
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- **Learning Rate**: 0.001 |
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- **Epochs**: 40 |
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- **Latent dim**: 10 |
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## Tracking |
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For detailed training logs and metrics, visit the [Weights & Biases run](https://wandb.ai/hussam-alafandi/mnist-autoencoder/runs/f81c7dgf?nw=nwuserhussamalafandi). |
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## Load Model |
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```python |
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from model import AutoEncoder |
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import torch |
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model = AutoEncoder() |
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model.load_state_dict(torch.load("model.pth")) |
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model.eval() |
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``` |
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## License |
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This project is licensed under the MIT License. See the LICENSE file for details. |
