Exemplo n.º 1
0
func TrainRBM() {
	train_file := "./data_0.dat"
	validation_file := "./data_1.dat"
	class_sizes := []int{4, 2, 5, 1}
	hidden_layer_size := 2

	train_data_accessor := rbm.NewInstanceLoader(train_file, len(class_sizes))
	defer train_data_accessor.Close()

	validation_data_accessor := rbm.NewInstanceLoader(validation_file, len(class_sizes))
	defer validation_data_accessor.Close()

	class_biases, y_bias := rbm.GetBiases(class_sizes, train_data_accessor)
	var rbm_m rbm.SparseClassRBM
	(&rbm_m).Initialize(class_sizes, class_biases, hidden_layer_size, y_bias)

	var trainer rbm.RBMTrainer

	learning_rate := rbm.WeightT(0.001)
	regularization := rbm.WeightT(0.0)
	momentum := rbm.WeightT(0.0)
	gen_learning_imp := rbm.WeightT(0.0)
	gibs_chain_len := 1

	trainer.Initialize(&rbm_m, train_data_accessor, validation_data_accessor,
		learning_rate, regularization, momentum, gen_learning_imp, gibs_chain_len)

	trainer.Train()

	//Evaluate RBM
	//	test_file := "./data_2.dat"
}
Exemplo n.º 2
0
func GenTrainData() {
	rand.Seed(time.Now().Unix())

	model := SimpleModel{
		[][]rbm.WeightT{
			{
				0.1, 0.2, 0.3, 0.4,
			}, {
				0.5, 0.5,
			}, {
				0.7, 0.01, 0.09, 0.1, 0.1,
			}, {
				1.0,
			},
		},
		[][]rbm.WeightT{
			{
				0.2, -0.3, 1.2, 3.5,
			}, {
				0.2, 0.3,
			}, {
				-0.01, 0.39, 0.2, -1.0, 0.09,
			}, {
				1.0,
			},
		},
		0.8,
		0.0,
	}

	instance_cnt := []int{100000, 20000, 40000}
	prefix := "./data"

	for i, c := range instance_cnt {
		out_fd, err := os.Create(fmt.Sprintf("%s_%d.dat", prefix, i))
		if err != nil {
			fmt.Printf("Failed to create file: %s, %s.\n", out_fd, err)
			return
		}
		defer out_fd.Close()
		for j := 0; j < c; j++ {
			x, y := model.GenerateInstance()
			out_fd.WriteString(InstanceToString(x, y) + "\n")
		}
	}

	validation_file := "./data_1.dat"
	validation_data_accessor := rbm.NewInstanceLoader(validation_file, len(model.feature_distribution))
	defer validation_data_accessor.Close()

	validation_auc := rbm.ROCAuc(model, validation_data_accessor)
	log_likelihood := rbm.LogLikelihood(model, validation_data_accessor)
	rmse := rbm.RMSE(model, validation_data_accessor)
	fmt.Printf("Max Validation AUC: %f\n", validation_auc)
	fmt.Printf("Max Validation LogLikelihood: %f\n", log_likelihood)
	fmt.Printf("max RMSE: %f\n", rmse)
}