Ejemplo n.º 1
0
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()
}
Ejemplo n.º 2
0
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()
}
Ejemplo n.º 3
0
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()

}