// Use this for initialization
void Start()
{
ann = new ANN(3, learningRate, learningRateDecay, weightsDecay, neuronSpawner);
ann.AddLayer(5, "LeakyReLU");
ann.AddLayer(10, "LeakyReLU");
ann.AddLayer(10, "LeakyReLU");
ann.AddLayer(5, "LeakyReLU");
ann.AddLayer(2, "Linear");
ann.loadBrain();
/*
* List<double> target = new List<double>();
* target.Add(0.1f); target.Add(-0.1f);
* List<double> input = new List<double>();
* input.Add(0.0f); input.Add(0.5f); input.Add(0.0f);
* ann.Train(input, target);
* input.Clear();
* input.Add(0.0f); input.Add(0.0f); input.Add(0.5f);
* ann.Train(input, target);
* input.Clear();
* input.Add(0.0f); input.Add(0.5f); input.Add(0.5f);
* ann.Train(input, target);
*
* target.Clear();
* target.Add(-0.1f); target.Add(0.1f);
* input.Clear();
* input.Add(0.0f); input.Add(-0.5f); input.Add(0.0f);
* ann.Train(input, target);
* input.Clear();
* input.Add(0.0f); input.Add(0.0f); input.Add(-0.5f);
* ann.Train(input, target);
* input.Clear();
* input.Add(0.0f); input.Add(0.5f); input.Add(-0.5f);
* ann.Train(input, target);
*/
ballStartPos = ball.transform.position;
Time.timeScale = 5.0f;
}
// Use this for initialization
void Start()
{
ann = new ANN(2, 0.1f, 1f, 0.0003f, neuronSpawner);
ann.AddLayer(1, "Sigmoid");
}
// Use this for initialization
void Start()
{
ann = new ANN(2, 0.2f, 0.999f, 0.0003f, neuronSpawner);
ann.AddLayer(2, "LeakyReLU");
ann.AddLayer(1, "Sigmoid");
}
// Use this for initialization
void Start()
{
ann = new ANN(2, 0.8f, 0.999f, 0.0003f);
ann.AddLayer(32, "LeakyReLU");
ann.AddLayer(1, "Sigmoid");
}