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| license: bsd-3-clause |
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| # DANN & GAIN Hybrid Model |
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| This repository contains the GAIN-DANN hybrid model, which is a hybrid model that combines: |
| - **Generative Adversarial Imputation Networks (GAIN)** for missing values imputation |
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| - **Domain-Adversarial Neural Networks (DANN)** for domain-aware learning and classification. |
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| The model is designed for proteomics data and has been pre-trained on a subset of the HeLa dataset. |
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| ## Model Architecture |
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| Key components: |
| - `Encoder`: Maps input to latent space |
| - `Generator` & `Discriminator`: For adversarial imputation |
| - `Gradient Reversal Layer`: Enables domain adversarial training |
| - `Domain Classifier`: Predicts domain labels |
| - `Decoder`: Reconstructs inputs after imputation |
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| ## Model Configuration |
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| The pre-trained model (`pytorch_model.bin`) has been trained on the HeLa dataset with the following configuration: |
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| - Input Dimension: 3013 |
| - Latent Dimension: 3013 |
| - Number of Classes: 17 |
| - Hidden Dimension: 128 |
| - Dropout: 0.3 |
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| The model weights are stored in `pytorch_model.bin`, and the configuration is provided in `config.json`. |
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| ## Usage |
| To use the pre-trained model for inference: |
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| 1. Install the required dependencies: |
| ```bash |
| pip install torch numpy |
| |
