func main() {
const num_layers = 3
const num_neurons_hidden = 96
const desired_error = 0.001
train_data := fann.ReadTrainFromFile("../../datasets/robot.train")
test_data := fann.ReadTrainFromFile("../../datasets/robot.test")
var momentum float32
for momentum = 0.0; momentum < 0.7; momentum += 0.1 {
fmt.Printf("============= momentum = %f =============\n", momentum)
ann := fann.CreateStandart(num_layers, []uint32{train_data.GetNumInput(), num_neurons_hidden, train_data.GetNumOutput()})
ann.SetTrainingAlgorithm(fann.TRAIN_INCREMENTAL)
ann.SetLearningMomentum(momentum)
ann.TrainOnData(train_data, 2000, 500, desired_error)
fmt.Printf("MSE error on train data: %f\n", ann.TestData(train_data))
fmt.Printf("MSE error on test data : %f\n", ann.TestData(test_data))
ann.Destroy()
}
train_data.Destroy()
test_data.Destroy()
}
func main() {
const numLayers = 3
const numNeuronsHidden = 32
const desiredError = 0.0001
const maxEpochs = 300
const epochsBetweenReports = 10
fmt.Println("Creating network.")
trainData := fann.ReadTrainFromFile("datasets/mushroom.train")
ann := fann.CreateStandart(numLayers, []uint32{trainData.GetNumInput(), numNeuronsHidden, 1})
fmt.Println("Training network.")
ann.SetActivationFunctionHidden(fann.SIGMOID_SYMMETRIC_STEPWISE)
ann.SetActivationFunctionOutput(fann.SIGMOID_SYMMETRIC)
ann.TrainOnData(trainData, maxEpochs, epochsBetweenReports, desiredError)
fmt.Println("Testing network")
testData := fann.ReadTrainFromFile("datasets/mushroom.test")
ann.ResetMSE()
var i uint32
for i = 0; i < testData.Lenght(); i++ {
ann.Test(testData.GetInput(i), testData.GetOutput(i))
}
fmt.Printf("MSE error on test data: %f\n", ann.GetMSE())
fmt.Println("Saving network.")
ann.Save("mushroom_float.net")
fmt.Println("Cleaning up.")
trainData.Destroy()
testData.Destroy()
ann.Destroy()
}
func main() {
const num_layers = 3
const num_neurons_hidden = 30
const desired_error = 0.001
fmt.Println("Creating network.")
train_data := fann.ReadTrainFromFile("../../datasets/robot.train")
ann := fann.CreateStandart(num_layers, []uint32{train_data.GetNumInput(), num_neurons_hidden, train_data.GetNumOutput()})
fmt.Println("Training network.")
ann.SetTrainingAlgorithm(fann.TRAIN_INCREMENTAL)
ann.SetLearningMomentum(0.4)
ann.TrainOnData(train_data, 3000, 10, desired_error)
fmt.Println("Testing network")
test_data := fann.ReadTrainFromFile("../../datasets/robot.test")
ann.ResetMSE()
var i uint32
for i = 0; i < test_data.Lenght(); i++ {
ann.Test(test_data.GetInput(i), test_data.GetOutput(i))
}
fmt.Printf("MSE error on test data: %f\n", ann.GetMSE())
fmt.Println("Saving network.")
ann.Save("robot_float.net")
fmt.Println("Cleaning up.")
train_data.Destroy()
test_data.Destroy()
ann.Destroy()
}