Model

The model API provides convenient high-level interface to do training and predicting on a network described using the symbolic API.

# MXNet.mx.AbstractModel — Type.

AbstractModel

The abstract super type of all models in MXNet.jl.

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# MXNet.mx.FeedForward — Type.

FeedForward

The feedforward model provides convenient interface to train and predict on feedforward architectures like multi-layer MLP, ConvNets, etc. There is no explicitly handling of time index, but it is relatively easy to implement unrolled RNN / LSTM under this framework (TODO: add example). For models that handles sequential data explicitly, please use TODO...

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# MXNet.mx.FeedForward — Method.

FeedForward(arch :: SymbolicNode, ctx)

Arguments:

• arch: the architecture of the network constructed using the symbolic API.
• ctx: the devices on which this model should do computation. It could be a single Context or a list of Context objects. In the latter case, data parallelization will be used for training. If no context is provided, the default context cpu() will be used.

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# MXNet.mx.predict — Method.

predict(self, data; overwrite=false, callback=nothing)

Predict using an existing model. The model should be already initialized, or trained or loaded from a checkpoint. There is an overloaded function that allows to pass the callback as the first argument, so it is possible to do

predict(model, data) do batch_output
  # consume or write batch_output to file
end

Arguments:

• self::FeedForward: the model.
• data::AbstractDataProvider: the data to perform prediction on.
• overwrite::Bool: an Executor is initialized the first time predict is called. The memory allocation of the Executor depends on the mini-batch size of the test data provider. If you call predict twice with data provider of the same batch-size, then the executor can be potentially be re-used. So, if overwrite is false, we will try to re-use, and raise an error if batch-size changed. If overwrite is true (the default), a new Executor will be created to replace the old one.
• verbosity::Integer: Determines the verbosity of the print messages. Higher numbers leads to more verbose printing. Acceptable values are - 0: Do not print anything during prediction - 1: Print allocation information during prediction

See also train, fit, init_model, and load_checkpoint

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# MXNet.mx._split_inputs — Method.

Get a split of batch_size into n_split pieces for data parallelization. Returns a vector of length n_split, with each entry a UnitRange{Int} indicating the slice index for that piece.

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# MXNet.mx.fit — Method.

fit(model :: FeedForward, optimizer, data; kwargs...)

Train the model on data with the optimizer.

• model::FeedForward: the model to be trained.
• optimizer::AbstractOptimizer: the optimization algorithm to use.
• data::AbstractDataProvider: the training data provider.
• n_epoch::Int: default 10, the number of full data-passes to run.
• eval_data::AbstractDataProvider: keyword argument, default nothing. The data provider for the validation set.
• eval_metric::AbstractEvalMetric: keyword argument, default Accuracy(). The metric used to evaluate the training performance. If eval_data is provided, the same metric is also calculated on the validation set.
• kvstore: keyword argument, default :local. The key-value store used to synchronize gradients and parameters when multiple devices are used for training. :type kvstore: KVStore or Symbol
• initializer::AbstractInitializer: keyword argument, default UniformInitializer(0.01).
• force_init::Bool: keyword argument, default false. By default, the random initialization using the provided initializer will be skipped if the model weights already exists, maybe from a previous call to train or an explicit call to init_model or load_checkpoint. When this option is set, it will always do random initialization at the begining of training.
• callbacks::Vector{AbstractCallback}: keyword argument, default []. Callbacks to be invoked at each epoch or mini-batch, see AbstractCallback.
• verbosity::Int: Determines the verbosity of the print messages. Higher numbers leads to more verbose printing. Acceptable values are - 0: Do not print anything during training - 1: Print starting and final messages - 2: Print one time messages and a message at the start of each epoch - 3: Print a summary of the training and validation accuracy for each epoch

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# MXNet.mx.init_model — Method.

init_model(self, initializer; overwrite=false, input_shapes...)

Initialize the weights in the model.

This method will be called automatically when training a model. So there is usually no need to call this method unless one needs to inspect a model with only randomly initialized weights.

Arguments:

• self::FeedForward: the model to be initialized.
• initializer::AbstractInitializer: an initializer describing how the weights should be initialized.
• overwrite::Bool: keyword argument, force initialization even when weights already exists.
• input_shapes: the shape of all data and label inputs to this model, given as keyword arguments. For example, data=(28,28,1,100), label=(100,).

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# MXNet.mx.load_checkpoint — Method.

load_checkpoint(prefix, epoch, ::mx.FeedForward; context)

Load a mx.FeedForward model from the checkpoint prefix, epoch and optionally provide a context.

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# MXNet.mx.train — Method.

train(model :: FeedForward, ...)

Alias to fit.

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