Activation Functions

An activation function (sometimes also called a transfer function) specifies how the final output of a layer is computed from the weighted sums of the inputs.

By default, hidden layers in theanets use a rectified linear activation function: \(g(z) = \max(0, z)\).

Output layers in theanets.Regressor and theanets.Autoencoder models use linear activations (i.e., the output is just the weighted sum of the inputs from the previous layer: \(g(z) = z\)), and the output layer in theanets.Classifier models uses a softmax activation: \(g(z) = \exp(z) / \sum\exp(z)\).

To specify a different activation function for a layer, include an activation key chosen from the table below, or create a custom activation. As described in Specifying Layers, the activation key can be included in your model specification either using the activation keyword argument in a layer dictionary, or by including the key in a tuple with the layer size:

net = theanets.Regressor([10, (10, 'tanh'), 10])

The activations that theanets provides are:


Activation functions can also be composed by concatenating multiple function names togather using a +. For example, to create a layer that uses a batch-normalized hyperbolic tangent activation:

net = theanets.Regressor([10, (10, 'tanh+norm:z'), 10])

Just like function composition, the order of the components matters! Unlike the notation for mathematical function composition, the functions will be applied from left-to-right.

Custom Activations

To define a custom activation, create a subclass of theanets.Activation, and implement the __call__ method to make the class instance callable. The callable will be given one argument, the array of layer outputs to activate.

class ThresholdedLinear(theanets.Activation):
    def __call__(self, x):
        return x * (x > 1)

This example activation returns 0 if a layer output is less than 1, or the output value itself otherwise. In effect it is a linear activation for “large” outputs (i.e., greater than 1) and zero otherwise. To use it in a model, give the name of the activation:

net = theanets.Regressor([10, (10, 'thresholdedlinear'), 10])