ganarchist-0.1.0.0: Switching loss prediction of High-Voltage GaN Half-Bridges

Safe Haskell	Safe-Inferred
Language	Haskell2010

Run

Description

Run Model Training

Synopsis

data Batch = Batch {
- xs :: !Tensor
- ys :: !Tensor
}
data TrainState = TrainState {
- epoch :: !Int
- lossBuffer :: ![Tensor]
- lossBuffer' :: ![Tensor]
- trainLoss :: ![Float]
- validLoss :: ![Float]
- model :: !Net
- optim :: !Optim
- learningRate :: !LearningRate
- batchSize :: !Int
- modelPath :: !FilePath
- xParams :: ![String]
- yParams :: ![String]
}
validStep :: Net -> Batch -> Tensor
validEpoch :: [Batch] -> StateT TrainState IO ()
trainStep :: LearningRate -> Net -> Optim -> Batch -> IO (Net, Optim, Tensor)
trainEpoch :: [Batch] -> StateT TrainState IO ()
shuffleData :: Tensor -> Tensor -> StateT TrainState IO [Batch]
runTraining :: Tensor -> Tensor -> StateT TrainState IO Net
train :: Int -> IO ()
testModel :: [String] -> (Tensor -> Tensor) -> Tensor -> Tensor -> IO ()

Documentation

data Batch Source #

A single batch of Data

Constructors

Batch
Fields xs :: !Tensor Input Data ys :: !Tensor Output Data

Instances

Instances details

Show Batch Source #
Instance details Defined in Run Methods showsPrec :: Int -> Batch -> ShowS # show :: Batch -> String # showList :: [Batch] -> ShowS #
Eq Batch Source #
Instance details Defined in Run Methods (==) :: Batch -> Batch -> Bool # (/=) :: Batch -> Batch -> Bool #

data TrainState Source #

Training State

Constructors

Fields

epoch :: !Int
Current Epoch
lossBuffer :: ![Tensor]
Buffer for batch losses
lossBuffer' :: ![Tensor]
Buffer for batch losses
trainLoss :: ![Float]
Training Loss
validLoss :: ![Float]
Validation Loss
model :: !Net
Surrogate Model
optim :: !Optim
Optimizier
learningRate :: !LearningRate
Learning Rate
batchSize :: !Int
Batch Size
modelPath :: !FilePath
Save Path
xParams :: ![String]
X Columns
yParams :: ![String]
Y Columns

validStep :: Net -> Batch -> Tensor Source #

Validation Step without gradient

validEpoch :: [Batch] -> StateT TrainState IO () Source #

Validation Epoch

trainStep :: LearningRate -> Net -> Optim -> Batch -> IO (Net, Optim, Tensor) Source #

Training Step with Gradient

trainEpoch :: [Batch] -> StateT TrainState IO () Source #

Training Epoch

shuffleData :: Tensor -> Tensor -> StateT TrainState IO [Batch] Source #

Data Shuffler

runTraining :: Tensor -> Tensor -> StateT TrainState IO Net Source #

Training in State Monad

train :: Int -> IO () Source #

Main Training Function

testModel :: [String] -> (Tensor -> Tensor) -> Tensor -> Tensor -> IO () Source #