Add errors to dashboard for the chart

network/network.go

@@ -5,7 +5,6 @@ import (
 	"fmt"
 	"math"
 	"os"
-	"strconv"
 	"time"
 
 	"github.com/gookit/color"
@@ -20,7 +19,7 @@ type Network struct {
 	Biases  []Matrix
 	Output  Matrix
 	Rate    float64
-	Error   float64
+	Errors  []float64
 	Time    float64
 }
 
@@ -173,6 +172,15 @@ func (network *Network) Train(iterations int) {
 		network.FeedForward()
 		network.FeedBackward()
 
+		// Append errors for dashboard data
+		if i%(iterations/20) == 0 {
+			network.Errors = append(
+				network.Errors,
+				// Round the error to two decimals
+				network.ComputeError(),
+			)
+		}
+
 		// Increment the progress bar
 		bar.Increment()
 	}
@@ -181,10 +189,6 @@ func (network *Network) Train(iterations int) {
 	// Print the error
 	arrangedError := fmt.Sprintf("%.5f", network.ComputeError())
 
-	// Set the error inside the network struct
-	errorLoss, _ := strconv.ParseFloat(arrangedError, 5)
-	network.Error = errorLoss
-
 	// Calculate elapsed time
 	elapsed := time.Since(start)
 	// Round the elapsed time at two decimals

server/api.go

@@ -8,21 +8,24 @@ import (
 	"github.com/olivia-ai/olivia/network"
 )
 
+// Dashboard contains the data sent for the dashboard
 type Dashboard struct {
 	Layers   Layers   `json:"layers"`
 	Training Training `json:"training"`
 }
 
+// Layers contains the data of the network's layers
 type Layers struct {
 	InputNodes   int `json:"input"`
 	HiddenLayers int `json:"hidden"`
 	OutputNodes  int `json:"output"`
 }
 
+// Training contains the data related to the training of the network
 type Training struct {
-	Rate  float64 `json:"rate"`
-	Error float64 `json:"error"`
-	Time  float64 `json:"time"`
+	Rate   float64   `json:"rate"`
+	Errors []float64 `json:"errors"`
+	Time   float64   `json:"time"`
 }
 
 // GetDashboardData encodes the json for the dashboard data
@@ -56,8 +59,8 @@ func GetLayers() Layers {
 func GetTraining() Training {
 	// Retrieve the information from the neural network
 	return Training{
-		Rate:  neuralNetwork.Rate,
-		Error: neuralNetwork.Error,
-		Time:  neuralNetwork.Time,
+		Rate:   neuralNetwork.Rate,
+		Errors: neuralNetwork.Errors,
+		Time:   neuralNetwork.Time,
 	}
 }