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Custom Activation Function

Rationale

The biggest strength of {kindling} when modelling neural networks is its versatility — it inherits {torch}’s versatility while being human friendly, including the ability to apply custom optimizer functions, loss functions, and per-layer activation functions. Learn more: https://kindling.joshuamarie.com/articles/special-cases.

With act_funs(), you are not limited to the activation functions available in {torch}’s namespace. Use new_act_fn() to wrap any compatible function into a validated custom activation. This feature, however, is only available on version 0.3.0 and above.

Function to use

To do this, use new_act_fn(). It takes a user-supplied function, validates it against a small dummy tensor at definition time (a dry-run probe), and wraps it in a call-time type guard. This means errors surface early — before your model ever starts training.

The function you supply must:

Accept at least one argument (the input tensor).
Return a torch_tensor.

Basic Usage

Currently, nnf_tanh doesn’t exist in the {torch} namespace, so tanh is not a valid argument to act_funs(). With new_act_fn(), you can wrap torch::torch_tanh() to make it usable.

Here’s a basic example that wraps torch::torch_tanh() as a custom activation:

hyper_tan = new_act_fn(\(x) torch::torch_tanh(x))

You can also pass it directly into act_funs(), just like any built-in activation:

act_funs(relu, elu, new_act_fn(\(x) torch::torch_tanh(x)))

Using Custom Activations in a Model

Naturally, functions for modelling, like ffnn(), accept act_funs() into the activations argument. Again, you can pass a custom activation function within new_act_fn(), then pass it through act_funs().

Here’s a basic example:

model = ffnn(
    Sepal.Length ~ .,
    data = iris[, 1:4],
    hidden_neurons = c(64, 32, 16),
    activations = act_funs(
        relu,
        silu,
        new_act_fn(\(x) torch::torch_tanh(x))
    ),
    epochs = 50
)
model

Each element of act_funs() corresponds to one hidden layer, in order. Here, the first hidden layer uses ReLU, the second uses SiLU (Swish), and the third uses Tanh.

You can also use a single custom activation recycled across all layers:

ffnn(
    Sepal.Length ~ .,
    data = iris[, 1:4],
    hidden_neurons = c(64, 32),
    activations = act_funs(new_act_fn(\(x) torch::torch_tanh(x))),
    epochs = 50
)

Skipping the Dry-Run Probe

By default, new_act_fn() runs a quick dry-run with a small dummy tensor to validate your function before training. You can disable this with probe = FALSE, though this is generally not recommended:

my_act = new_act_fn(\(x) torch::torch_tanh(x), probe = FALSE)

Naming Your Custom Activation

You can provide a human-readable name via .name, which is used in print output and diagnostics:

my_act = new_act_fn(\(x) torch::torch_tanh(x), .name = "my_tanh")

Here’s a simple application:

ffnn(
    Sepal.Length ~ .,
    data = iris[, 1:4],
    hidden_neurons = c(64, 32),
    activations = act_funs(
        relu, 
        new_act_fn(\(x) torch::torch_tanh(x), .name = "hyper_tanh")
    ),
    epochs = 50
)

Error Handling

new_act_fn() is designed to fail loudly and early. Common errors include:

Function returns a non-tensor. This will error at definition time:
```
new_act_fn(\(x) as.numeric(x))
```
Function accepts no arguments. This will error immediately:
```
new_act_fn(function() torch::torch_zeros(2))
```

These checks ensure your model’s architecture is valid before any data ever flows through it.

Summary

Feature	Details
Wraps any R function	Must accept a tensor, return a tensor
Dry-run probe	Validates at definition time (`probe = TRUE` by default)
Call-time guard	Type-checks output on every forward pass
Compatible with `act_funs()`	Use alongside built-in activations freely
Closures supported	Parametric activations work naturally

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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