public static void Run()
{
DataMaker dataMaker = new DataMaker(STEPS_PER_CYCLE, NUMBER_OF_CYCLES);
NdArray trainData = dataMaker.Make();
FunctionStack model = new FunctionStack("Test8",
new Linear(true, 1, 5, name: "Linear l1"),
new LSTM(true, 5, 5, name: "LSTM l2"),
new Linear(true, 5, 1, name: "Linear l3")
);
model.SetOptimizer(new Adam());
RILogManager.Default?.SendDebug("Training...");
for (int epoch = 0; epoch < TRAINING_EPOCHS; epoch++)
{
NdArray[] sequences = dataMaker.MakeMiniBatch(trainData, MINI_BATCH_SIZE, LENGTH_OF_SEQUENCE);
Real loss = ComputeLoss(model, sequences);
model.Update();
model.ResetState();
if (epoch != 0 && epoch % DISPLAY_EPOCH == 0)
{
RILogManager.Default?.SendDebug("[{0}]training loss:\t{1}", epoch, loss);
}
}
RILogManager.Default?.SendDebug("Testing...");
NdArray[] testSequences = dataMaker.MakeMiniBatch(trainData, MINI_BATCH_SIZE, LENGTH_OF_SEQUENCE);
int sample_index = 45;
predict(testSequences[sample_index], model, PREDICTION_LENGTH);
}
public static void Run()
{
DataMaker dataMaker = new DataMaker(STEPS_PER_CYCLE, NUMBER_OF_CYCLES);
NdArray <Real> trainData = dataMaker.Make();
//ネットワークの構成は FunctionStack に書き連ねる
FunctionStack <Real> model = new FunctionStack <Real>(
new Linear <Real>(1, 5, name: "Linear l1"),
new LSTM <Real>(5, 5, name: "LSTM l2"),
new Linear <Real>(5, 1, name: "Linear l3")
);
//optimizerを宣言
Adam <Real> adam = new Adam <Real>();
adam.SetUp(model);
//訓練ループ
Console.WriteLine("Training...");
for (int epoch = 0; epoch < TRAINING_EPOCHS; epoch++)
{
NdArray <Real>[] sequences = dataMaker.MakeMiniBatch(trainData, MINI_BATCH_SIZE, LENGTH_OF_SEQUENCE);
Real loss = ComputeLoss(model, sequences);
adam.Update();
model.ResetState();
if (epoch != 0 && epoch % DISPLAY_EPOCH == 0)
{
Console.WriteLine("[{0}]training loss:\t{1}", epoch, loss);
}
}
Console.WriteLine("Testing...");
NdArray <Real>[] testSequences = dataMaker.MakeMiniBatch(trainData, MINI_BATCH_SIZE, LENGTH_OF_SEQUENCE);
int sample_index = 45;
predict(testSequences[sample_index], model, PREDICTION_LENGTH);
}
public static void Run()
{
DataMaker dataMaker = new DataMaker(STEPS_PER_CYCLE, NUMBER_OF_CYCLES);
NdArray trainData = dataMaker.Make();
//Network configuration is written in FunctionStack
FunctionStack model = new FunctionStack(
new Linear(1, 5, name: "Linear l1"),
new LSTM(5, 5, name: "LSTM l2"),
new Linear(5, 1, name: "Linear l3")
);
//Declare optimizer
model.SetOptimizer(new Adam());
//Training loop
Console.WriteLine("Training...");
for (int epoch = 0; epoch < TRAINING_EPOCHS; epoch++)
{
NdArray[] sequences = dataMaker.MakeMiniBatch(trainData, MINI_BATCH_SIZE, LENGTH_OF_SEQUENCE);
Real loss = ComputeLoss(model, sequences);
model.Update();
model.ResetState();
if (epoch != 0 && epoch % DISPLAY_EPOCH == 0)
{
Console.WriteLine("[{0}]training loss:\t{1}", epoch, loss);
}
}
Console.WriteLine("Testing...");
NdArray[] testSequences = dataMaker.MakeMiniBatch(trainData, MINI_BATCH_SIZE, LENGTH_OF_SEQUENCE);
int sample_index = 45;
predict(testSequences[sample_index], model, PREDICTION_LENGTH);
}
