public void Run()
{
// 入力レイヤ
var inputLayer = new InputLayer(3);
// 隠れレイヤ
var layer00 = new FullyConnectedLayer(inputLayer, 20, DLF.ReLU, DLF.UpdateWeight(), DLF.GetRandomWeight);
// 隠れレイヤ
var layer01 = new FullyConnectedLayer(layer00, 10, DLF.ReLU, DLF.UpdateWeight(), DLF.GetRandomWeight);
// 出力レイヤ
var layer02 = new SoftmaxLayer(layer01, 3);
var batchSize = 8;
var epoch = 1000;
var learningRate = 0.01;
Func <IEnumerable <Tuple <double, double> >, double> errorFunction = DLF.ErrorFunctionCrossEntropy;
var machine = new Machine(learningRate, epoch, batchSize, new Validator(3)
, x => errorFunction(x) * (1.0 / batchSize)
, inputLayer
, layer00
, layer01
, layer02);
// 学習データを生成
var testData = DLF.Shuffle(
from x in Enumerable.Range(1, 8)
from y in Enumerable.Range(1, 8)
from z in Enumerable.Range(1, 8)
let v = x + (y * 2) + z
let expect = v < 15 ? new[] { 1.0, 0.0, 0.0 }
: v < 20 ? new[] { 0.0, 1.0, 0.0 }
: new[] { 0.0, 0.0, 1.0 }
select LearningData.New(expect.ToString(), new double[] { x, y, z }, expect))
.ToArray();
var validData = testData.Skip(testData.Length / 2).ToArray();
testData = testData.Take(testData.Length / 2).ToArray();
machine.Learn(testData.ToArray(), validData.ToArray());
}
public void Run()
{
var batchSize = 10;
var epoch = 50;
var learningRate = 0.005;
var outputSize = 10;
// 入力レイヤ
var inputLayer = new InputLayer(28, 28);
// 畳み込みレイヤ
// プーリングレイヤ
var layer00 = new ConvolutionLayer(inputLayer, (3, 1, 20), DLF.ReLU, u => u < 0);
var layer01 = new PoolingLayer(layer00, (2, 2));
// 畳み込みレイヤ
// プーリングレイヤ
var layer02 = new ConvolutionLayer(layer01, (3, 2, 50), DLF.ReLU, u => u < 0);
var layer03 = new PoolingLayer(layer02, (2, 2));
// 出力レイヤ
var layer04 = new SoftmaxLayer(layer03, outputSize);
Func <IEnumerable <Tuple <double, double> >, double> errorFunction = DLF.ErrorFunctionCrossEntropy;
var machine = new Machine(learningRate, epoch, batchSize, new Validator(outputSize)
, x => errorFunction(x) * (1.0 / batchSize)
, inputLayer
, layer00
, layer01
, layer02
, layer03
, layer04);
// 学習データを生成
var testData = DLF.Shuffle(new MNISTLoader().Load()).ToArray();
// 0-9を均等にピックアップ
var pickNum = 20;
var a = new[] {
testData.Take(10000).Where(x => x.Name == "0").Take(pickNum),
testData.Take(10000).Where(x => x.Name == "1").Take(pickNum),
testData.Take(10000).Where(x => x.Name == "2").Take(pickNum),
testData.Take(10000).Where(x => x.Name == "3").Take(pickNum),
testData.Take(10000).Where(x => x.Name == "4").Take(pickNum),
testData.Take(10000).Where(x => x.Name == "5").Take(pickNum),
testData.Take(10000).Where(x => x.Name == "6").Take(pickNum),
testData.Take(10000).Where(x => x.Name == "7").Take(pickNum),
testData.Take(10000).Where(x => x.Name == "8").Take(pickNum),
testData.Take(10000).Where(x => x.Name == "9").Take(pickNum),
}.SelectMany(x => x).ToArray();
var b = new[] {
testData.Skip(10000).Where(x => x.Name == "0").Take(pickNum),
testData.Skip(10000).Where(x => x.Name == "1").Take(pickNum),
testData.Skip(10000).Where(x => x.Name == "2").Take(pickNum),
testData.Skip(10000).Where(x => x.Name == "3").Take(pickNum),
testData.Skip(10000).Where(x => x.Name == "4").Take(pickNum),
testData.Skip(10000).Where(x => x.Name == "5").Take(pickNum),
testData.Skip(10000).Where(x => x.Name == "6").Take(pickNum),
testData.Skip(10000).Where(x => x.Name == "7").Take(pickNum),
testData.Skip(10000).Where(x => x.Name == "8").Take(pickNum),
testData.Skip(10000).Where(x => x.Name == "9").Take(pickNum),
}.SelectMany(x => x).ToArray();
machine.Learn(a, b);
}
