Refactoring and implementing Dense layers | Let’s learn – Neural networks from scratch in Go – 7

In this tutorial, we will be discussing how to refactor and implement Dense layers in a neural network from scratch using Go programming language.

Refactoring the neural network code

Before implementing Dense layers, let’s refactor our existing neural network code to make it more modular and reusable. We can create a new package called “nn” and move our neural network related code into it. This will help us organize our code better and make it easier to add new layers in the future.

Implementing Dense layers

Now that we have refactored our code, let’s implement the Dense layers. Dense layers are the most common type of layers used in neural networks. Each neuron in a dense layer is connected to every neuron in the previous layer.

Here’s a basic implementation of a Dense layer in Go:

“`go
type Dense struct {
InputSize int
OutputSize int
Weights [][]float64
Bias []float64
}

func NewDense(inputSize, outputSize int) *Dense {
weights := make([][]float64, inputSize)
for i := range weights {
weights[i] = make([]float64, outputSize)
}

bias := make([]float64, outputSize)

return &Dense{
InputSize: inputSize,
OutputSize: outputSize,
Weights: weights,
Bias: bias,
}
}
“`

We have defined a struct called Dense that stores the input size, output size, weights, and bias of the layer. We also have a constructor function NewDense that initializes the weights and bias with random values.

Next, we need to implement the forward pass method for the Dense layer:

“`go
func (d *Dense) Forward(input []float64) []float64 {
output := make([]float64, d.OutputSize)

for i := 0; i < d.OutputSize; i++ {
for j := 0; j < d.InputSize; j++ {
output[i] += input[j] * d.Weights[j][i]
}
output[i] += d.Bias[i]
}

return output
}
“`

The Forward method takes an input vector and performs the matrix multiplication operation to calculate the output of the layer. We iterate over each neuron in the layer, calculate the weighted sum of inputs, and add the bias to get the final output.

With the Dense layer implementation in place, we can now add it to our neural network and train it on a dataset to see how well it performs.

That’s it for this tutorial on refactoring and implementing Dense layers in a neural network from scratch in Go. Stay tuned for more articles on building neural networks and machine learning models.