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README
mxnet-nn
This library builds a general neural network solver on top the MXNet raw c-apis and operators.
Background
The 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, mxExecutorForward and mxExecutorBackward can run. By optimization, the the backing NDArrays of the neural network is updated in each iteration.
DataIter
MXNet provides data iterators. And it can be wrapped in a Stream or Conduit. The mxnet-dataiter provides a implementation.
Some explanation
TrainM Monad
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 fit and forwardOnly must be run inside this monad.
initialize
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 TrainM Monad.
fit
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
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
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 Nothing). forwardOnly carries out a forward phase only, returning the output of the neural network.
Usage
- Please see the example in the
examplesdirectory. - Also see the examples of mxnet-examples repository.