# `Soothsayer.Trainer`
[🔗](https://github.com/georgeguimaraes/soothsayer/blob/v0.6.3/lib/soothsayer/trainer.ex#L1)

Training functionality for Soothsayer models.

Handles standard training and custom training with L1 regularization
on specified layer weights (AR, trend, etc.).

# `compute_l1_penalty`

```elixir
@spec compute_l1_penalty(Axon.ModelState.t(any(), any()), [String.t()]) ::
  Nx.Tensor.t()
```

Computes L1 penalty for specified layer kernels.

## Parameters

  * `params` - An `Axon.ModelState` containing the model parameters.
  * `layer_names` - A list of layer names to include in the penalty.

## Returns

  A scalar tensor with the sum of absolute values of all kernel weights
  in the specified layers.

## Examples

    iex> penalty = Soothsayer.Trainer.compute_l1_penalty(params, ["ar_dense_out"])
    #Nx.Tensor<f32 6.0>

# `fit`

```elixir
@spec fit(
  Axon.t(),
  %{required(String.t()) =&gt; Nx.Tensor.t()},
  Nx.Tensor.t(),
  non_neg_integer(),
  map()
) ::
  Axon.ModelState.t(any(), any())
```

Trains a network on the provided data.

## Parameters

  * `network` - An Axon neural network.
  * `x` - A map of input tensors.
  * `y` - A tensor of target values.
  * `epochs` - The number of training epochs.
  * `config` - A map containing training configuration:
    - `:learning_rate` - Learning rate for the optimizer.
    - `:ar` - Optional map with `:regularization` for AR L1 penalty.
    - `:trend` - Optional map with `:regularization` for trend L1 penalty.

## Returns

  The trained `Axon.ModelState`.

## Examples

    iex> config = %{learning_rate: 0.1}
    iex> params = Soothsayer.Trainer.fit(network, x, y, 100, config)
    %Axon.ModelState{...}

---

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