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---
title: Autoencoder General Purpose 2D
emoji: 🧬
colorFrom: blue
colorTo: green
sdk: pytorch
tags:
- transcriptomics
- dimensionality-reduction
- ae
- general
license: mit
---
# Autoencoder (General Purpose, 2D)
This model is part of the TRACERx Datathon 2025 transcriptomics analysis pipeline.
## Model Details
- **Model Type**: Autoencoder
- **Dataset**: General Purpose
- **Latent Dimensions**: 2
- **Compression Mode**: samples
- **Framework**: PyTorch
## Usage
This model is designed to be used with the TRACERx Datathon 2025 analysis pipeline.
It will be automatically downloaded and cached when needed.
## Model Architecture
- Input: Gene expression data
- Hidden layers: [input_size, 512, 256, 128, 2]
- Output: 2-dimensional latent representation
- Activation: ELU with batch normalization
## Training Data
Trained on broader open transcriptomics datasets
## Files
- `autoencoder_2_latent_dims_oos_mode.pt`: Main model weights
- `latent_df.csv`: Example latent representations (if available)
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