public Vol Clone()
{
var V = new Vol(this.sx, this.sy, this.depth, 0.0f);
var n = this.w.Length;
for (var i = 0; i < n; i++)
{
V.w[i] = this.w[i];
}
return(V);
}
public Vol Forward(Vol V, bool is_training)
{
this.in_act = V;
var V2 = V.CloneAndZero();
var N = V.w.Length;
for (var i = 0; i < N; i++)
{
V2.w[i] = Util.Tanh(V.w[i]);
}
this.out_act = V2;
return(this.out_act);
}
public Vol Forward(Vol V, bool is_training)
{
this.in_act = V;
var V2 = V.CloneAndZero();
var N = V.w.Length;
var V2w = V2.w;
var Vw = V.w;
for (var i = 0; i < N; i++)
{
V2w[i] = 1.0f / (1.0f + (float)Math.Exp(-Vw[i]));
}
this.out_act = V2;
return(this.out_act);
}
public Vol Forward(Vol V, bool is_training)
{
this.in_act = V;
var V2 = V.Clone();
var N = V.w.Length;
var V2w = V2.w;
for (var i = 0; i < N; i++)
{
if (V2w[i] < 0)
{
V2w[i] = 0; // threshold at 0
}
}
this.out_act = V2;
return(this.out_act);
}
void Update()
{
for (var i = 0; i < iteration; ++i)
{
foreach (Vector3 v in data)
{
trainer.Train(new Vol(new float[] { v.x, v.y }), v.z);
}
}
if (Input.GetMouseButtonDown(0))
{
Ray ray = Camera.main.ScreenPointToRay(Input.mousePosition);
var pos = ray.origin;
pos /= 10.0f;
pos.z = 0;
data.Add(pos);
}
if (Input.GetMouseButtonDown(1))
{
Ray ray = Camera.main.ScreenPointToRay(Input.mousePosition);
var pos = ray.origin;
pos /= 10.0f;
pos.z = 1;
data.Add(pos);
}
for (var i = 0; i < pixels.GetLength(0); ++i)
{
for (var j = 0; j < pixels.GetLength(1); ++j)
{
Vol action = net.Forward(new Vol(new float[] { i / 10f, j / 10f }));
float output = action.w[0];
if (output > 0.5)
{
pixels [i, j].GetComponent <MeshRenderer> ().material.color = new Color(1, 0, 0);
}
else
{
pixels [i, j].GetComponent <MeshRenderer> ().material.color = new Color(0, 0, 1);
}
}
}
}
public FullyConnLayer(int depth, int numOfOutput, float bias_pref = 0.0f)
{
this.out_depth = numOfOutput;
this.num_inputs = 1 * 1 * depth;
this.out_sx = 1;
this.out_sy = 1;
this.l1_decay_mul = 0.0f;
this.l2_decay_mul = 1.0f;
var bias = bias_pref;
this.filters = new Vol[this.out_depth];
for (var i = 0; i < this.out_depth; i++)
{
filters [i] = new Vol(1, 1, this.num_inputs);
}
this.biases = new Vol(1, 1, this.out_depth, bias);
}
public Vol Forward(Vol V, bool is_training)
{
this.in_act = V;
var A = new Vol(1, 1, this.out_depth, 0.0f);
var Vw = V.w;
for (var i = 0; i < this.out_depth; i++)
{
var a = 0.0f;
var wi = this.filters[i].w;
for (var d = 0; d < this.num_inputs; d++)
{
a += Vw[d] * wi[d];
}
a += this.biases.w[i];
A.w[i] = a;
}
this.out_act = A;
return(this.out_act);
}
public void Train(Vol x, object y)
{
net.Forward(x, true);
var cost_loss = net.Backward(y);
//Debug.Log ("loss:" + cost_loss);
var l2_decay_loss = 0.0f;
var l1_decay_loss = 0.0f;
List <ParamsAndGrads> pglist = new List <ParamsAndGrads> ();
net.GetParamsAndGrads(pglist);
for (var i = 0; i < pglist.Count; i++)
{
var pg = pglist[i];
var p = pg.param;
var g = pg.grads;
var l2_decay_mul = pg.l2_decay_mul;
var l1_decay_mul = pg.l1_decay_mul;
var l2_decay = this.l2_decay * l2_decay_mul;
var l1_decay = this.l1_decay * l1_decay_mul;
var plen = p.Length;
for (var j = 0; j < plen; j++)
{
l2_decay_loss += l2_decay * p[j] * p[j] / 2;
l1_decay_loss += l1_decay * Math.Abs(p[j]);
var l1grad = l1_decay * (p[j] > 0 ? 1 : -1);
var l2grad = l2_decay * (p[j]);
var gij = (l2grad + l1grad + g [j]);
//p[j] += - this.learning_rate * gij;
p[j] += -this.learning_rate * g[j];
// 記得要歸0
g[j] = 0.0f;
}
}
}
public Vol Forward(Vol V, bool is_training = false)
{
var act = this.layers[0].Forward(V, is_training);
for (var i = 1; i < this.layers.Count; i++)
{
/*
* for (var j = 0; j < act.w.Length; ++j) {
* Debug.Log (act.w [j]);
* }
* Debug.Log ("======="+i+"========");
*/
act = this.layers[i].Forward(act, is_training);
}
/*
* for (var j = 0; j < act.w.Length; ++j) {
* Debug.Log (act.w [j]);
* }
* Debug.Log ("======="+(this.layers.Count)+"========");
*/
return(act);
}
public Vol Forward(Vol V, bool is_training)
{
this.in_act = V;
this.out_act = V;
return(this.out_act);
}
