MXNet.NN

Documentation

data Parameter a

A parameter is two NDArray to back a Symbol

Constructors

Parameter

Fields

• _param_in :: NDArray a
• _param_grad :: NDArray a

Instances

(Pretty a, DType a) => Show (Parameter a)

Methods

showsPrec :: Int -> Parameter a -> ShowS

show :: Parameter a -> String

showList :: [Parameter a] -> ShowS

data Config a

For every symbol in the neural network, it can be placeholder or a variable. therefore, a Config is to specify the shape of the placeholder and the method to initialize the variables.

Note that it is not right to specify a symbol as both placeholder and initializer, although it is tolerated and such a symbol is considered as a variable.

Note that any symbol not specified will be initialized with the _cfg_default_initializer.

Constructors

Config

Fields

• _cfg_placeholders :: HashMap String [Int]
• _cfg_initializers :: HashMap String (Initializer a)
• _cfg_default_initializer :: Initializer a
• _cfg_context :: Context

data Session a

Session is all the Parameters and a Context type Session a = (M.HashMap String (Parameter a), Context)

Constructors

Session

Fields

• _sess_param :: !(HashMap String (Parameter a))
• _sess_context :: !Context

data Exc

Possible exception in TrainM

Constructors

MismatchedShape String

Instances

Show Exc

Methods

showsPrec :: Int -> Exc -> ShowS

show :: Exc -> String

showList :: [Exc] -> ShowS

Exception Exc

Methods

toException :: Exc -> SomeException

fromException :: SomeException -> Maybe Exc

displayException :: Exc -> String

type Initializer a = Context -> [Int] -> IO (NDArray a)

Initializer is about how to create a NDArray from a given shape.

Usually, it can be a wrapper of MXNet operators, such as random_uniform, random_normal, random_gamma, etc..

type TrainM a m = StateT (Session a) m

TrainM is a StateT monad

train :: (DType a, Monad m) => Session a -> TrainM a m r -> m r

Execute the TrainM monad

inferShape :: DType a => Symbol a -> HashMap String (NDArray a) -> IO (HashMap String [Int])

infer the shapes of all the symbols in a symbolic neural network

initialize :: DType a => Symbol a -> Config a -> IO (Session a)

initialize all parameters

fit :: (DType a, MonadIO m, MonadThrow m, Optimizer opt, OptArgsCst opt g) => opt a g -> Symbol a -> HashMap String (NDArray a) -> TrainM a m ()

single step train. Must provide all the placeholders.

fitAndEval :: (DType a, MonadIO m, MonadThrow m, Optimizer opt, OptArgsCst opt g, EvalMetricMethod mth) => opt a g -> Symbol a -> HashMap String (NDArray a) -> Metric a mth -> TrainM a m ()

single step train. Must provide all the placeholders. After fitting, it also update the evaluation metric.

forwardOnly :: (DType a, MonadIO m, MonadThrow m) => Symbol a -> HashMap String (Maybe (NDArray a)) -> TrainM a m [NDArray a]

forward only. Must provide all the placeholders, setting the data to Just xx, and set label to Nothing.

Note that the batch size here can be different from that in the training phase.

getContext :: Monad m => TrainM a m Context

sess_param :: forall a a. Lens (Session a) (Session a) (HashMap String (Parameter a)) (HashMap String (Parameter a))

sess_context :: forall a. Lens' (Session a) Context

module MXNet.NN.Optimizer