public void SharpLearning_Example()
{
// Load data
var(observations, targets) = DataSetUtilities.LoadWinequalityWhite();
// transform data for neural net
var transform = new MinMaxTransformer(0.0, 1.0);
transform.Transform(observations, observations);
var featureCount = observations.ColumnCount;
// define the neural net.
var net = new NeuralNet();
net.Add(new InputLayer(inputUnits: featureCount));
net.Add(new DenseLayer(32, Activation.Relu));
net.Add(new DenseLayer(32, Activation.Relu));
net.Add(new SquaredErrorRegressionLayer());
// using only 10 iteration to make the example run faster.
// using square error as error metric. This is only used for reporting progress.
var learner = new RegressionNeuralNetLearner(net, iterations: 10, loss: new SquareLoss(),
optimizerMethod: OptimizerMethod.Adam);
var cv = new RandomCrossValidation <double>(10, seed: 232);
var predictions = cv.CrossValidate(learner, observations, targets);
Trace.WriteLine(FormatErrorString(targets, predictions));
}
public void SharpLearning_With_Cntk_Example()
{
// Load data
var(observations, targets) = DataSetUtilities.LoadWinequalityWhite();
// transform data for neural net
var transform = new MinMaxTransformer(0.0, 1.0);
transform.Transform(observations, observations);
var featureCount = observations.ColumnCount;
var observationCount = observations.RowCount;
var targetCount = 1;
var inputShape = new int[] { featureCount, 1 };
var outputShape = new int[] { targetCount };
// Convert data to float, and wrap as minibatch data.
var observationsFloat = observations.Data().Select(v => (float)v).ToArray();
var observationsData = new MemoryMinibatchData(observationsFloat, inputShape, observationCount);
var targetsFloat = targets.Select(v => (float)v).ToArray();
var targetsData = new MemoryMinibatchData(targetsFloat, outputShape, observationCount);
var dataType = DataType.Float;
var device = DeviceDescriptor.CPUDevice;
// setup input and target variables.
var inputVariable = Layers.Input(inputShape, dataType);
var targetVariable = Variable.InputVariable(outputShape, dataType);
// setup name to variable
var nameToVariable = new Dictionary <string, Variable>
{
{ "observations", inputVariable },
{ "targets", targetVariable },
};
// Get cross validation folds.
var sampler = new RandomIndexSampler <double>(seed: 24);
var crossValidationIndexSets = CrossValidationUtilities
.GetKFoldCrossValidationIndexSets(sampler, foldCount: 10, targets: targets);
var predictions = new double[observationCount];
// Run cross validation loop.
foreach (var set in crossValidationIndexSets)
{
// setup data.
var trainingNameToData = new Dictionary <string, MemoryMinibatchData>
{
{ "observations", observationsData.GetSamples(set.trainingIndices) },
{ "targets", targetsData.GetSamples(set.trainingIndices) }
};
var validationNameToData = new Dictionary <string, MemoryMinibatchData>
{
{ "observations", observationsData.GetSamples(set.validationIndices) },
{ "targets", targetsData.GetSamples(set.validationIndices) }
};
var trainSource = new MemoryMinibatchSource(nameToVariable, trainingNameToData, seed: 232, randomize: true);
var validationSource = new MemoryMinibatchSource(nameToVariable, validationNameToData, seed: 232, randomize: false);
// Create model and fit.
var model = CreateModel(inputVariable, targetVariable, targetCount, dataType, device);
model.Fit(trainSource, batchSize: 128, epochs: 10);
// Predict.
var predictionsRaw = model.Predict(validationSource);
var currentPredictions = predictionsRaw.Select(v => (double)v.Single()).ToArray();
// set cross-validation predictions
var validationIndices = set.validationIndices;
for (int i = 0; i < validationIndices.Length; i++)
{
predictions[validationIndices[i]] = currentPredictions[i];
}
}
Trace.WriteLine(FormatErrorString(targets, predictions));
}
