NeuralNetwork/neuralnetwork/main.go

package main

import (
	"fmt"

	neuralnetwork "./neuralnetworkbase"
	mat "gonum.org/v1/gonum/mat"
)

func main() {
	sizes := []int{4, 8, 8, 3}
	nn := neuralnetwork.NewNeuralNetwork(sizes, 0.1, 481)

	for i := 0; i < nn.Count; i++ {
		if i > 0 {
			fmt.Printf("Weights before:\n%v\n\n", mat.Formatted(nn.Weights[i], mat.Prefix(""), mat.Excerpt(0)))
			fmt.Printf("Biases before:\n%v\n\n", mat.Formatted(nn.Biases[i], mat.Prefix(""), mat.Excerpt(0)))
			fmt.Printf("Z before:\n%v\n\n", mat.Formatted(nn.Z[i], mat.Prefix(""), mat.Excerpt(0)))
		}
		fmt.Printf("A before:\n%v\n\n", mat.Formatted(nn.A[i], mat.Prefix(""), mat.Excerpt(0)))
	}

	dataSet, result := readData("./iris.data")
	nn.Train(dataSet, result)

	for i := 0; i < nn.Count; i++ {
		if i > 0 {
			fmt.Printf("Weights after:\n%v\n\n", mat.Formatted(nn.Weights[i], mat.Prefix(""), mat.Excerpt(0)))
			fmt.Printf("Biases after:\n%v\n\n", mat.Formatted(nn.Biases[i], mat.Prefix(""), mat.Excerpt(0)))
			fmt.Printf("Z after:\n%v\n\n", mat.Formatted(nn.Z[i], mat.Prefix(""), mat.Excerpt(0)))
		}
		fmt.Printf("A after:\n%v\n\n", mat.Formatted(nn.A[i], mat.Prefix(""), mat.Excerpt(0)))
	}
	// data := make([]float64, sizes[0])
	// for i := range data {
	// 	data[i] = rand.Float64()
	// }
	// aIn := mat.NewDense(sizes[0], 1, data)

	failCount := 0
	for i := 0; i < len(dataSet); i++ {
		index, _ := nn.Predict(dataSet[i])
		if result[i].At(index, 0) != 1.0 {
			failCount++
			fmt.Printf("Fail: %v, %v\n\n", i, result[i].At(index, 0))
		}
	}

	fmt.Printf("Fail count: %v\n\n", failCount)
}