File size: 1,259 Bytes
8b76812 1db282d 8b76812 1db282d 8b76812 1db282d 8b76812 ee57c6d 1db282d ee57c6d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
---
license: mit
datasets:
- PolyAI/banking77
language:
- en
tags:
- autoencoder
---
# VAE trained on Banking 77 Open Intent Classification Dataset
This is a Variational Autoencoder (VAE) trained on the [PolyAI/banking77](https://huggingface.co/datasets/PolyAI/banking77) dataset.
### Architecture
- **input_dim**: 768
- **hidden_dim**: 256
- **latent_dim**: 64
#### Encoder
The encoder maps the input to a latent space distribution.
```python
encoder = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.ReLU()
)
mu = nn.Linear(hidden_dim, latent_dim)
logvar = nn.Linear(hidden_dim, latent_dim)
```
#### Decoder
The decoder reconstructs the input from a sample of the latent space.
```python
decoder = nn.Sequential(
nn.Linear(latent_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, input_dim)
)
```
#### Metrics
The model was trained and evaluated using the following metrics:
1. Training set: VAE Loss
* 50% reconstruction loss between original input vs reconstructed output
* 50% KL divergence between Latent Z vs standard normal distribution
2. Validation set: 100% reconstruction loss -> used to find the best model (with the lowest reconstruction loss)
|