public float TestAll()
{
int correct = 0;
for (int i = 0; i < testing.Length; i++)
{
DoodleData data = testing[i];
List <float> inputs = new List <float>();
for (int j = 0; j < data.val.Length; j++)
{
inputs.Add(data.val[j] / 255.0f);
}
eDoodleCategory label = data.label;
float[] guess = nn.predict(inputs).ToArray();
float m = Mathf.Max(guess);
int classification = System.Array.IndexOf(guess, m);
if (classification == (int)label)
{
correct++;
}
}
float percent = 100f * (float)correct / testing.Length;
return(percent);
}
public void TrainEpoch()
{
classifierState = eClassifierState.TRAIN;
training = NNUtils.Shuffle(training);
for (int i = 0; i < training.Length; i++)
{
DoodleData data = training[i];
List <float> inputs = new List <float>();
for (int j = 0; j < data.val.Length; j++)
{
inputs.Add(data.val[j] / 255.0f);
}
eDoodleCategory label = data.label;
List <float> targets = new List <float>()
{
0f, 0f, 0f
};
targets[(int)label] = 1;
nn.train(inputs, targets);
}
classifierState = eClassifierState.NONE;
}
