public static void Run()
{
Stopwatch sw = new Stopwatch();
RILogManager.Default?.SendDebug("CIFAR Data Loading...");
CifarData cifarData = new CifarData();
FunctionStack nn = new FunctionStack("Test18",
new Convolution2D(true, 3, 32, 3, name: "l1 Conv2D", gpuEnable: true),
new ReLU(name: "l1 ReLU"),
new MaxPooling(2, name: "l1 MaxPooling", gpuEnable: false),
new Dropout(0.25, name: "l1 DropOut"),
new Convolution2D(true, 32, 64, 3, name: "l2 Conv2D", gpuEnable: false),
new ReLU(name: "l2 ReLU"),
new MaxPooling(2, 2, name: "l2 MaxPooling", gpuEnable: false),
new Dropout(0.25, name: "l2 DropOut"),
new Linear(true, 13 * 13 * 64, 512, name: "l3 Linear", gpuEnable: false),
new ReLU(name: "l3 ReLU"),
new Dropout(name: "l3 DropOut"),
new Linear(true, 512, 10, name: "l4 Linear", gpuEnable: false)
);
nn.SetOptimizer(new AdaDelta());
RILogManager.Default?.SendDebug("Training Start...");
for (int epoch = 1; epoch < 3; epoch++)
{
RILogManager.Default?.SendDebug("epoch " + epoch);
Real totalLoss = 0;
long totalLossCount = 0;
for (int i = 1; i < TRAIN_DATA_COUNT + 1; i++)
{
sw.Restart();
RILogManager.Default?.SendDebug("\nbatch count " + i + "/" + TRAIN_DATA_COUNT);
TestData.TestDataSet datasetX = cifarData.GetRandomXSet(BATCH_DATA_COUNT);
Real sumLoss = Trainer.Train(nn, datasetX.Data, datasetX.Label, new SoftmaxCrossEntropy());
totalLoss += sumLoss;
totalLossCount++;
RILogManager.Default?.SendDebug("total loss " + totalLoss / totalLossCount);
RILogManager.Default?.SendDebug("local loss " + sumLoss);
sw.Stop();
RILogManager.Default?.SendDebug("time " + sw.Elapsed.TotalMilliseconds);
if (i % 20 == 0)
{
RILogManager.Default?.SendDebug("\nTesting...");
TestDataSet datasetY = cifarData.GetRandomYSet(TEACH_DATA_COUNT);
Real accuracy = Trainer.Accuracy(nn, datasetY.Data, datasetY.Label);
RILogManager.Default?.SendDebug("accuracy " + accuracy);
}
}
}
}
public static void Run()
{
Stopwatch sw = new Stopwatch();
//MNISTのデータを用意する
Console.WriteLine("CIFAR Data Loading...");
CifarData cifarData = new CifarData();
//ネットワークの構成を FunctionStack に書き連ねる
FunctionStack nn = new FunctionStack(
new Convolution2D(3, 32, 3, name: "l1 Conv2D", gpuEnable: true),
new ReLU(name: "l1 ReLU"),
new MaxPooling(2, name: "l1 MaxPooling", gpuEnable: true),
new Dropout(0.25, name: "l1 DropOut"),
new Convolution2D(32, 64, 3, name: "l2 Conv2D", gpuEnable: true),
new ReLU(name: "l2 ReLU"),
new MaxPooling(2, 2, name: "l2 MaxPooling", gpuEnable: true),
new Dropout(0.25, name: "l2 DropOut"),
new Linear(13 * 13 * 64, 512, name: "l3 Linear", gpuEnable: true),
new ReLU(name: "l3 ReLU"),
new Dropout(name: "l3 DropOut"),
new Linear(512, 10, name: "l4 Linear", gpuEnable: true)
);
//optimizerを宣言
nn.SetOptimizer(new Adam());
Console.WriteLine("Training Start...");
//三世代学習
for (int epoch = 1; epoch < 3; epoch++)
{
Console.WriteLine("epoch " + epoch);
//全体での誤差を集計
Real totalLoss = 0;
long totalLossCount = 0;
//何回バッチを実行するか
for (int i = 1; i < TRAIN_DATA_COUNT + 1; i++)
{
sw.Restart();
Console.WriteLine("\nbatch count " + i + "/" + TRAIN_DATA_COUNT);
//訓練データからランダムにデータを取得
TestData.TestDataSet datasetX = cifarData.GetRandomXSet(BATCH_DATA_COUNT);
//バッチ学習を並列実行する
Real sumLoss = Trainer.Train(nn, datasetX.Data, datasetX.Label, new SoftmaxCrossEntropy());
totalLoss += sumLoss;
totalLossCount++;
//結果出力
Console.WriteLine("total loss " + totalLoss / totalLossCount);
Console.WriteLine("local loss " + sumLoss);
sw.Stop();
Console.WriteLine("time" + sw.Elapsed.TotalMilliseconds);
//20回バッチを動かしたら精度をテストする
if (i % 20 == 0)
{
Console.WriteLine("\nTesting...");
//テストデータからランダムにデータを取得
TestData.TestDataSet datasetY = cifarData.GetRandomYSet(TEACH_DATA_COUNT);
//テストを実行
Real accuracy = Trainer.Accuracy(nn, datasetY.Data, datasetY.Label);
Console.WriteLine("accuracy " + accuracy);
}
}
}
}
