bool TestRandomImage(bool generateImage = false)
{
int random = Random.Range(0, (int)testImages.imageCount);
Tensor input = new Tensor(1, 0, 28 * 28, 1);
for (int j = 0; j < testImages.pixelValueArrayOffset; j++)
{
input[0, 0, j, 0] =
testImages.pixelValueArray[random * testImages.pixelValueArrayOffset + j];
//if (j % 28 == 0)
//{
// s += '\n';
//}
//s += testImages.pixelValueArray[random * testImages.pixelValueArrayOffset + j] < 0.01 ? '_' : '#' ;
}
if (generateImage)
{
image.sprite = Sprite.Create(testImages.GetImage(random), new Rect(0, 0, 28, 28), Vector2.zero);
}
Tensor output = worker.Execute(input).PeekOutput("Y");
int value = GetResult(output);
guessText.text = value.ToString();
return(value == testImages.labelValueArray[random]);
}
//public void TestNeuralNetwork()
//{
// int[] fails = new int[10];
// int failsTotal = 0;
// for (int i = 0; i < testImages.imageCount; i++)
// {
// float[] result = new float[testImages.pixelValueArrayOffset];
// for (int resultIndex = 0; resultIndex < testImages.pixelValueArrayOffset; resultIndex++)
// {
// result[resultIndex] = testImages.PixelValueArray[i * testImages.pixelValueArrayOffset + resultIndex];
// }
// discriminator.Predict(testImages.PixelValueArray[i * testImages.pixelValueArrayOffset]);
// if (!IsResultCorrect(result, testImages.LabelValueArray[i]))
// {
// fails[testImages.LabelValueArray[i]]++;
// failsTotal++;
// }
// }
// Debug.Log($"{failsTotal} fails:");
// Debug.Log((1 - (float)failsTotal / (float)(testImages.imageCount)) * 100 + " % accurate");
//}
public void TestRandomImage()
{
float certainty = 0;
int random = Random.Range(0, (int)testImages.imageCount);
//float[] result = discriminator.Predict(testImages.PixelValueArray[random]);
image.sprite = Sprite.Create(testImages.GetImage(random), new Rect(0, 0, 28, 28), Vector2.zero);
//guessText.text = GetGuessedValue(result, ref certainty).ToString();
certaintyText.text = (certainty * 100f).ToString("F2") + '%';
}
public void TestRandomImage()
{
float certainty = 0;
int random = Random.Range(0, (int)testImages.imageCount);
float[] input = new float[testImages.pixelValueArrayOffset];
for (int j = 0; j < testImages.pixelValueArrayOffset; j++)
{
input[j] = testImages.pixelValueArray[random * testImages.pixelValueArrayOffset + j];
}
float[] result = neuralNetwork.Guess(input);
image.sprite = Sprite.Create(testImages.GetImage(random), new Rect(0, 0, 28, 28), Vector2.zero);
guessText.text = GetGuessedValue(result, ref certainty).ToString();
certaintyText.text = (certainty * 100f).ToString("F2") + '%';
}