University of Michigan - Ann Arbor

Modern neural network models use non-linear activation functions. They allow the model to create complex mappings between the network’s inputs and outputs, which are essential for learning and modeling complex data, such as images, video, audio, and data sets which are non-linear or have high dimensionality.

There are 8 common non-linear activation functions:
 - Sigmoid / Logistic
 - TanH / Hyperbolic Tangent
 - ELU (Exponential Linear Unit)
 - ReLU (Rectified Linear Unit)
 - Leaky ReLU
 - Parametric ReLU
 - Softmax
 - Swish

Non-Linear Activation Functions

Developers need to know what works and how to use it. This site uses less math and more tutorials with working code.
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Non-linear functions address the problems of a linear activation function:

 - They allow backpropagation because they have a derivative function which is related to the inputs.
 - They allow “stacking” of multiple layers of neurons to create a deep neural network. Multiple hidden layers of neurons are needed to learn complex data sets with high levels of accuracy.

Linear vs. Non-Linear Activation Functions

$S(x) = \frac{1}{1 +e^{-x}} = \frac{e^x}{e^x + 1}$ 


Sigmoid/Logistic Function

$S(x)  = tanh(x) = \frac{e^x-e^{-x}}{e^x + e^{-x}}$


TanH/Hyperbolic Tangent Function

The Swish function, introduced by Ramachandran et al. in 2017, is a mathematical function defined as the product of its input and the sigmoid function applied to a scaled version of the input. For a scalar input $x$, it is expressed as:

$ \operatorname{swish}(x) = x \cdot \operatorname{sigmoid}(\beta x) = \frac{x}{1+e^{-\beta x}}$

where $\beta$ is a constant or a trainable parameter. When applied element-wise to a vector $\mathbf{h}$ in neural networks, the formula is written as:

$\sigma_{\text{swish}}(\mathbf{h}) = \mathbf{h} \odot \text{Sigmoid}(\beta \mathbf{h})$

where $\odot$ denotes the element-wise product.

Swish Function

The Rectified Linear Unit (ReLU) is a common choice for the activation function $$\sigma(\cdot)$$ within the hidden layers of neural networks. It is defined to output the positive portion of its argument. When applied to an input vector $$\mathbf{h}$$, the ReLU function is given by the formula: $$\sigma_{\mathrm{relu}}(\mathbf{h}) = \mathrm{max}(0, \mathbf{h})$$.

ReLU (Rectified Linear Unit)

 If x > 0:
 $f(x)=x$
 If x $<=$  1:
$f(x) = α(e^x - 1)$

ELU (Exponential Linear Unit)

Which of the following introduces nonlinearity into neural networks?

To convert raw, unnormalized outputs $$\mathbf{o}$$ into valid probabilities, the softmax function applies an exponential function to each component and then normalizes them by their sum. The exponentiation ensures that all probabilities are non-negative, while the division ensures that they sum to $$1$$. Mathematically, the predicted probability distribution $$\hat{\mathbf{y}}$$ is defined as:

$$\hat{\mathbf{y}} = \mathrm{softmax}(\mathbf{o}) \quad 	extrm{where}\quad \hat{y}_i = \frac{\exp(o_i)}{\sum_j \exp(o_j)}$$

This guarantees that $$0 \le \hat{y}_i \le 1$$ and $$\sum_j \hat{y}_j = 1$$. Unlike other normalizations or the probit model, the softmax function preserves order and leads to a more well-behaved optimization problem.

Softmax Function

Comparing the advantages and disadvantages of Sigmoid, Hyperbolic Tangent (Tanh), and Rectified Linear Unit (ReLU) activation functions in neural networks:

**Sigmoid**
- **Pros:** Useful for binary classification tasks, typically in the output layer, as it maps values to a range between 0 and 1.
- **Cons:** Not zero-centered, which can make optimization harder, and is susceptible to the vanishing gradient problem.

**Hyperbolic Tangent (Tanh)**
- **Pros:** Zero-centered, mapping values between -1 and 1, which helps model negative, neutral, and positive inputs and makes backpropagation more stable.
- **Cons:** Computationally more expensive than ReLU and still suffers from vanishing gradients for very large or small inputs.

**Rectified Linear Unit (ReLU)**
- **Pros:** Extremely simple and computationally efficient, which accelerates model convergence and makes it the default choice for hidden layers.
- **Cons:** Suffers from the dying ReLU problem, where the derivative becomes 0 when the input $$z < 0$$, preventing backpropagation for those neurons.

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