# `Edifice.Feedforward.TabNet`
[🔗](https://github.com/blasphemetheus/edifice/blob/main/lib/edifice/feedforward/tabnet.ex#L1)

TabNet - Attentive Interpretable Tabular Learning.

TabNet uses sequential attention to select which features to reason about
at each decision step. This provides instance-wise feature selection,
making the model inherently interpretable while maintaining high performance
on tabular data.

## Architecture

```
Input [batch, input_size]
      |
      v
+--------------------------------------+
| Initial BN                           |
+--------------------------------------+
      |
      v
+--------------------------------------+
| Step 1:                              |
|   Attention: select features via     |
|     sparse mask M = sparsemax(...)   |
|   Transform: process selected feats  |
|   Split: h_step -> decision + next   |
+--------------------------------------+
      |  (repeat num_steps)
      v
+--------------------------------------+
| Aggregate: sum decision outputs      |
+--------------------------------------+
      |
      v
Output [batch, hidden_size or num_classes]
```

## Feature Selection

At each step, TabNet uses an attention transformer to produce a mask
that selects relevant input features. The relaxation factor gamma
controls how much previously attended features can be reused.

## Usage

    model = TabNet.build(
      input_size: 128,
      hidden_size: 64,
      num_steps: 3,
      relaxation_factor: 1.5,
      num_classes: 10
    )

## References

- Arik & Pfister, "TabNet: Attentive Interpretable Tabular Learning"
  (AAAI 2021)
- https://arxiv.org/abs/1908.07442

# `build_opt`

```elixir
@type build_opt() ::
  {:dropout, float()}
  | {:hidden_size, pos_integer()}
  | {:input_size, pos_integer()}
  | {:num_classes, pos_integer() | nil}
  | {:num_steps, pos_integer()}
  | {:relaxation_factor, float()}
```

Options for `build/1`.

# `build`

```elixir
@spec build([build_opt()]) :: Axon.t()
```

Build a TabNet model.

## Options

- `:input_size` - Input feature dimension (required)
- `:hidden_size` - Hidden dimension for processing (default: 64)
- `:num_steps` - Number of sequential attention steps (default: 3)
- `:relaxation_factor` - Controls feature reuse across steps (default: 1.5)
- `:num_classes` - If provided, adds classification head (default: nil)
- `:dropout` - Dropout rate (default: 0.0)

## Returns

An Axon model: `[batch, input_size]` -> `[batch, hidden_size or num_classes]`

# `output_size`

```elixir
@spec output_size(keyword()) :: pos_integer()
```

Get the output size of a TabNet model.

---

*Consult [api-reference.md](api-reference.md) for complete listing*