ADd MLPOpts

model/mlp.go

@@ -3,6 +3,7 @@ package model
 import (
 	"math/rand"
 
+	F "github.com/itsubaki/autograd/function"
 	L "github.com/itsubaki/autograd/layer"
 	"github.com/itsubaki/autograd/variable"
 )
@@ -12,7 +13,22 @@ type MLP struct {
 	Activation Activation
 }
 
-func NewMLP(outSize []int, activation Activation, s ...rand.Source) *MLP {
+type MLPOpts struct {
+	Activation Activation
+	Source     rand.Source
+}
+
+func NewMLP(outSize []int, opts ...MLPOpts) *MLP {
+	activation := F.Sigmoid
+	if len(opts) > 0 && opts[0].Activation != nil {
+		activation = opts[0].Activation
+	}
+
+	s := make([]rand.Source, 0)
+	if len(opts) > 0 && opts[0].Source != nil {
+		s = append(s, opts[0].Source)
+	}
+
 	layers := make([]*L.Layer, len(outSize))
 	for i := 0; i < len(outSize); i++ {
 		layers[i] = L.Linear(outSize[i], s...)

model/mlp_test.go

@@ -10,8 +10,10 @@ import (
 )
 
 func ExampleMLP() {
-	s := rand.NewSource(1)
-	mlp := model.NewMLP([]int{5, 1}, F.ReLU, s)
+	mlp := model.NewMLP([]int{5, 1}, model.MLPOpts{
+		Activation: F.ReLU,
+		Source:     rand.NewSource(1),
+	})
 
 	x := variable.New(1, 2)
 	y := mlp.Forward(x)
@@ -22,8 +24,10 @@ func ExampleMLP() {
 }
 
 func ExampleMLP_backward() {
-	s := rand.NewSource(1)
-	mlp := model.NewMLP([]int{5, 1}, F.ReLU, s)
+	mlp := model.NewMLP([]int{5, 1}, model.MLPOpts{
+		Activation: F.ReLU,
+		Source:     rand.NewSource(1),
+	})
 
 	x := variable.New(1, 2)
 	y := mlp.Forward(x)