cavis/docs/deeplearning4j-nn/templates/iterators.md

title	short_title	description	category	weight
Deeplearning4j Iterators	Iterators	Data iteration tools for loading into neural networks.	Models	5

What is an iterator?

A dataset iterator allows for easy loading of data into neural networks and help organize batching, conversion, and masking. The iterators included in Eclipse Deeplearning4j help with either user-provided data, or automatic loading of common benchmarking datasets such as MNIST and IRIS.

Usage

For most use cases, initializing an iterator and passing a reference to a MultiLayerNetwork or ComputationGraph fit() method is all you need to begin a task for training:

MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();

// pass an MNIST data iterator that automatically fetches data
DataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, rngSeed);
net.fit(mnistTrain);

Many other methods also accept iterators for tasks such as evaluation:

// passing directly to the neural network
DataSetIterator mnistTest = new MnistDataSetIterator(batchSize, false, rngSeed);
net.eval(mnistTest);

// using an evaluation class
Evaluation eval = new Evaluation(10); //create an evaluation object with 10 possible classes
while(mnistTest.hasNext()){
    DataSet next = mnistTest.next();
    INDArray output = model.output(next.getFeatureMatrix()); //get the networks prediction
    eval.eval(next.getLabels(), output); //check the prediction against the true class
}

Available iterators

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