Safe Haskell	None
Language	Haskell2010

Net

Description

Neural Network Definition and Training

Synopsis

data OpNetSpec = OpNetSpec {
- numX :: !Int
- numY :: !Int
}
data OpNet = OpNet {
- fc0 :: !Linear
- fc1 :: !Linear
- fc2 :: !Linear
- fc3 :: !Linear
- fc4 :: !Linear
- fc5 :: !Linear
- fc6 :: !Linear
}
forward :: OpNet -> Tensor -> Tensor
noGrad :: Parameterized f => f -> IO f
saveCheckPoint :: FilePath -> OpNet -> Adam -> IO ()
loadCheckPoint :: FilePath -> OpNetSpec -> Int -> IO (OpNet, Adam)
traceModel :: Device -> PDK -> Int -> [String] -> [String] -> (Tensor -> Tensor) -> IO ScriptModule
unTraceModel :: ScriptModule -> Tensor -> Tensor
saveInferenceModel :: FilePath -> ScriptModule -> IO ()
loadInferenceModel :: FilePath -> IO ScriptModule

Documentation

data OpNetSpec Source #

Neural Network Specification

Constructors

OpNetSpec
Fields numX :: !Int Number of input neurons numY :: !Int Number of output neurons

Instances

Instances details

Eq OpNetSpec Source #
Instance details Defined in Net Methods (==) :: OpNetSpec -> OpNetSpec -> Bool # (/=) :: OpNetSpec -> OpNetSpec -> Bool #
Show OpNetSpec Source #
Instance details Defined in Net Methods showsPrec :: Int -> OpNetSpec -> ShowS # show :: OpNetSpec -> String # showList :: [OpNetSpec] -> ShowS #
Randomizable OpNetSpec OpNet Source #	Neural Network Weight initialization
Instance details Defined in Net Methods sample :: OpNetSpec -> IO OpNet

data OpNet Source #

Network Architecture

Constructors

OpNet
Fields fc0 :: !Linear fc1 :: !Linear fc2 :: !Linear fc3 :: !Linear fc4 :: !Linear fc5 :: !Linear fc6 :: !Linear

Instances

Instances details

Show OpNet Source #
Instance details Defined in Net Methods showsPrec :: Int -> OpNet -> ShowS # show :: OpNet -> String # showList :: [OpNet] -> ShowS #
Generic OpNet Source #
Instance details Defined in Net Associated Types type Rep OpNet :: Type -> Type # Methods from :: OpNet -> Rep OpNet x # to :: Rep OpNet x -> OpNet #
Parameterized OpNet Source #
Instance details Defined in Net Methods flattenParameters :: OpNet -> [Parameter] _replaceParameters :: OpNet -> ParamStream OpNet
Randomizable OpNetSpec OpNet Source #	Neural Network Weight initialization
Instance details Defined in Net Methods sample :: OpNetSpec -> IO OpNet
type Rep OpNet Source #
Instance details Defined in Net type Rep OpNet = D1 ('MetaData "OpNet" "Net" "prehsept-0.6.6.6-JF5ShOGDAAmDmsWs2Fkoer" 'False) (C1 ('MetaCons "OpNet" 'PrefixI 'True) ((S1 ('MetaSel ('Just "fc0") 'NoSourceUnpackedness 'SourceStrict 'DecidedStrict) (Rec0 Linear) :: (S1 ('MetaSel ('Just "fc1") 'NoSourceUnpackedness 'SourceStrict 'DecidedStrict) (Rec0 Linear) :: S1 ('MetaSel ('Just "fc2") 'NoSourceUnpackedness 'SourceStrict 'DecidedStrict) (Rec0 Linear))) :: ((S1 ('MetaSel ('Just "fc3") 'NoSourceUnpackedness 'SourceStrict 'DecidedStrict) (Rec0 Linear) :: S1 ('MetaSel ('Just "fc4") 'NoSourceUnpackedness 'SourceStrict 'DecidedStrict) (Rec0 Linear)) :: (S1 ('MetaSel ('Just "fc5") 'NoSourceUnpackedness 'SourceStrict 'DecidedStrict) (Rec0 Linear) :: S1 ('MetaSel ('Just "fc6") 'NoSourceUnpackedness 'SourceStrict 'DecidedStrict) (Rec0 Linear)))))

forward :: OpNet -> Tensor -> Tensor Source #

Neural Network Forward Pass with scaled Data

noGrad :: Parameterized f => f -> IO f Source #

Remove Gradient for tracing / scripting

saveCheckPoint :: FilePath -> OpNet -> Adam -> IO () Source #

Save Model and Optimizer Checkpoint

loadCheckPoint :: FilePath -> OpNetSpec -> Int -> IO (OpNet, Adam) Source #

Load a Saved Model and Optimizer CheckPoint

traceModel :: Device -> PDK -> Int -> [String] -> [String] -> (Tensor -> Tensor) -> IO ScriptModule Source #

Trace and Return a Script Module

unTraceModel :: ScriptModule -> Tensor -> Tensor Source #

Trace to Function

saveInferenceModel :: FilePath -> ScriptModule -> IO () Source #

Save a Traced ScriptModule

loadInferenceModel :: FilePath -> IO ScriptModule Source #

Load a Traced ScriptModule