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This library builds a general neural network solver on top the MXNet raw c-apis and operators.
Symbol API of MXNet synthesize a symbolic graph of the neural network. To solve such a graph, it is necessary to back every
Symbol with two
NDArray, one for forward propagation and one for backward propagation. By calling the API
mxExecutorBind, the symbolic graph and backing NDArrays are bind together, producing an
Executor. And with this executor,
mxExecutorBackward can run. By optimization, the the backing NDArrays of the neural network is updated in each iteration.
TrainM is simply a
StateT monad, where the internal state is a store of all the backing
NDArrays together with a
Context (CPU/GPU). Both
forwardOnly must be run inside this monad.
initialize :: DType a => Symbol a -> Config a -> IO (Session a)
It takes the symbolic graph, and a configuration of 1) shapes of the placeholder in the training phase. 2) how to initialize the NDArrays
It returns a initial session, with it to run the training in
fit :: (DType a, MonadIO m, MonadThrow m, Optimizer opt) => opt a -> Symbol a -> M.HashMap String (NDArray a) -> TrainM a m ()
Given a optimizer, the symbolic graph, and feeding the placeholders,
fit carries out a complete forward/backward phase, updating the NDArrays.
fitAndEval :: (DType a, MonadIO m, MonadThrow m, Optimizer opt, EvalMetricMethod mtr) => opt a -> Symbol a -> M.HashMap String (NDArray a) -> mtr a -> TrainM a m ()
Fit the neural network, and also record the evaluation.
forwardOnly :: (DType a, MonadIO m, MonadThrow m) => Symbol a -> M.HashMap String (Maybe (NDArray a)) -> TrainM a m [NDArray a]
Given a the symbolic graph, and feeding the placeholders (data with
Just xx, and label with
forwardOnly carries out a forward phase only, returning the output of the neural network.
- Please see the example in the
- Also see the examples of mxnet-examples repository.