Study activation functions | Let’s learn – Neural networks from scratch in Go – 8

Welcome to the eighth part of our series on creating neural networks from scratch in Go! In this installment, we will be focusing on activation functions, an important component of neural networks.

What are activation functions?

Activation functions are what introduce non-linearity to neural networks. They determine the output of a neural network node given its input. Without activation functions, the neural network would only be able to output linear combinations of its inputs, making it less powerful in representing complex patterns in data.

Types of activation functions

There are several types of activation functions that are commonly used in neural networks:

• Sigmoid
• Tanh
• ReLU (Rectified Linear Unit)
• Leaky ReLU
• Softmax

Implementing activation functions in Go

Let’s take a look at how we can implement some of these activation functions in our neural network in Go:

// Sigmoid activation function
func Sigmoid(x float64) float64 {
    return 1.0 / (1.0 + math.Exp(-x))
}

// Tanh activation function
func Tanh(x float64) float64 {
    return math.Tanh(x)
}

// ReLU activation function
func ReLU(x float64) float64 {
    return math.Max(0, x)
}

Conclusion

Activation functions play a crucial role in the performance of neural networks. By introducing non-linearity, they allow neural networks to learn complex patterns in data. In the next part of our series, we will continue to explore different components of neural networks. Stay tuned!