public static void TrainUsingCnnNetwork()
{
using (var py = Py.GIL())
{
int imageX = 28;
int imageY = 28;
int batchSize = 128;
int numberOfClasses = 10;
int epochs = 2;
PythonEngine.Exec(RedirectOutput());
var mnistCnnDatasetGenerator = new MnistDataSetGenerator(numberOfClasses, imageX, imageY);
var mnistDatasetForCnn = mnistCnnDatasetGenerator.GetMnistDataSetForCnnNetwork();
var serializedModel = new MnistCnnNetworkConfiguration()
.AddConvolutional2DLayer(64, new PyTuple(new PyObject[] { new PyInt(3), new PyInt(3) }), KerasConstants.ActivationFunction.Relu, mnistDatasetForCnn.InputShape)
.AddConvolutional2DLayer(64, new PyTuple(new PyObject[] { new PyInt(3), new PyInt(3) }), KerasConstants.ActivationFunction.Relu)
.AddMaxPooling2DLayer(new PyTuple(new PyObject[] { new PyInt(2), new PyInt(2) }))
.AddDropoutLayer(0.25)
.AddFlattenLayer()
.AddDenseLayer(128, KerasConstants.ActivationFunction.Relu)
.AddDropoutLayer(0.5)
.AddDenseLayer(numberOfClasses, KerasConstants.ActivationFunction.Softmax).Build();
var executionConfiguration = new KerasExecutionConfiguration <MnistDatasetCnn>();
executionConfiguration.BatchSize = batchSize;
executionConfiguration.Epochs = epochs;
executionConfiguration.ModelDefinition = serializedModel;
executionConfiguration.LossFunction = KerasConstants.LossFunction.CategoricalCrossentropy;
executionConfiguration.OptimizationFunction = KerasConstants.ActivationFunction.Adadelta;
executionConfiguration.Metrics = new PyList(new PyObject[] { new PyString(KerasConstants.Metrics.Accuracy) });
executionConfiguration.MnistDataset = mnistDatasetForCnn;
var mnistExecution = new MnistCnnExecution(executionConfiguration);
mnistExecution.Fit();
mnistExecution.Evaluate();
}
}
public static void TrainUsingMlpNetwork()
{
using (var py = Py.GIL())
{
PythonEngine.Exec(RedirectOutput());
int numberOfClasses = 10;
int vectorLength = 784;
int trainVectorCount = 60000;
int testVectorCount = 10000;
int epoch = 20;
int batchSize = 128;
var data = new MnistDataSetGenerator(numberOfClasses, vectorLength, trainVectorCount, testVectorCount);
var mnistDatasetForMlp = data.GetMnistDataSet();
PyTuple inputShape = new PyTuple(new PyObject[] { new PyInt(vectorLength) });
var modelDefinition = new MnistMlpNetwokConfiguration()
.AddDense(512, KerasConstants.ActivationFunction.Relu, inputShape)
.AddDropout(0.2)
.AddDense(512, KerasConstants.ActivationFunction.Relu)
.AddDropout(0.2)
.AddDense(10, KerasConstants.ActivationFunction.Softmax)
.Build();
var executionConfiguration = new KerasExecutionConfiguration <MnistDatasetMlp>();
executionConfiguration.ModelDefinition = modelDefinition;
executionConfiguration.MnistDataset = mnistDatasetForMlp;
executionConfiguration.OptimizationFunction = KerasConstants.ActivationFunction.RmsProp;
executionConfiguration.LossFunction = KerasConstants.LossFunction.CategoricalCrossentropy;
executionConfiguration.Metrics = new PyList(new PyObject[] { new PyString(KerasConstants.Metrics.Accuracy) });
executionConfiguration.BatchSize = batchSize;
executionConfiguration.Epochs = epoch;
var mnistMlpExec = new MnistMlpExecution(executionConfiguration);
mnistMlpExec.Fit();
mnistMlpExec.Evaluate();
}
}
