Machine learning 问题是，我建议您在输出层中使用softmax激活函数。LeakyRELU的性能优于sigmoid或tanh，这是因为这些值太大。从某种意义上说，它们被tanh和sigmoid虐待，并被计算机四舍五入为整数。softmax激活函数将给出每个节点的百分比（1-

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问题是，我建议您在输出层中使用softmax激活函数。LeakyRELU的性能优于sigmoid或tanh，这是因为这些值太大。从某种意义上说，它们被tanh和sigmoid虐待，并被计算机四舍五入为整数。softmax激活函数将给出每个节点的百分比（1-0）。其中所有百分比总和为1。这会让你比SigmoidHanks更有意义谢谢你的帮助。我的下一步将是研究卷积网络。

public static float ACTIVE_VALUE = 1;
public static float INACTIVE_VALUE = -1;

// This is specifically designed for a algorithm that will detect a number between 0 - 9
public static float[] valueToArray(int value)
{

    switch (value)
    {
        case 0:
            return new float[] { ACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE,
                                INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE };
        case 1:
            return new float[] { INACTIVE_VALUE, ACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE,
                                INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE };
        case 2:
            return new float[] { INACTIVE_VALUE, INACTIVE_VALUE, ACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE,
                                INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE };
        case 3:
            return new float[] { INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, ACTIVE_VALUE, INACTIVE_VALUE,
                                INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE };
        case 4:
            return new float[] { INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, ACTIVE_VALUE,
                                INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE };
        case 5:
            return new float[] { INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, 
                                ACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE };
        case 6:
            return new float[] { INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE,
                                INACTIVE_VALUE, ACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE };
        case 7:
            return new float[] { INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE,
                                INACTIVE_VALUE, INACTIVE_VALUE, ACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE };
        case 8:
            return new float[] { INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE,
                                INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, ACTIVE_VALUE, INACTIVE_VALUE };
        case 9:
            return new float[] { INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE,
                                INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, ACTIVE_VALUE };
        default:
            return new float[] { INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE,
                                INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE, INACTIVE_VALUE };


    }
}

    public static void singleThread()
{

    int batchSize = 10000;
    int rangeLow = 0;
    int rangeHi = 9;

    int hits = 0;


    while (true)
    {

        // alternates between training and testing
        //Console.WriteLine("Training...  ");



        for (int i = 0; i < batchSize; i++)
        {

            // Give a training progress report every 100 iterations, this should increase performance
            if (i % 100 == 0)
            {
                Console.SetCursorPosition(0, Console.CursorTop);
                Console.Write("Training: ");
                Console.Write("(" + (((float)i / (float)batchSize) * 100) + "%)");
                Console.Write("                    ");
            }


            // randomly select an image from the list
            int number = rng.Next(rangeLow, rangeHi);
            int index = rng.Next(1, 20);

            Bitmap loadedImage = (Bitmap)Image.FromFile("Train/" + number + "/" +
                                 index + ".png", true);


            int indexLocation = 0;
            // Convert the image into a grayScale value
            for (int x = 0; x < loadedImage.Width; x++)
            {
                for (int y = 0; y < loadedImage.Height; y++)
                {
                    Color pixel = loadedImage.GetPixel(x, y);
                    int grayValue = (int)((pixel.R * 0.3) + (pixel.G * 0.59) + (pixel.B * 0.11));
                    //Console.WriteLine(grayValue);
                    networkInputs[indexLocation] = grayValue;
                    indexLocation++;
                }
            }

            // The network will guess what the image is, and return the guess as a float array

            float[] guess = currentNetwork.BackPropagate(networkInputs, Interface.valueToArray(number));

            // This if statement checks if the guess was correct
            if (Interface.guessToValue(guess) == number)
            {
                hits++;
            }

        }

        currentNetwork.Performance = ((float) hits / (float) batchSize);
        hits = 0;

        Console.WriteLine("Score: " + (currentNetwork.Performance * 100) + "%");
    }
}