MLStudy.NET

Description

//create a fully connected neural network
var nn = new NeuralNetwork()
 .AddFullLayer(100) //fully connected layer with 100 units
 .AddReLU() //ReLU activation
 .AddFullLayer(10)
 .AddSoftmax() //softmax output
 .UseAdam() //use Adam optimizer
 .UseCrossEntropyLoss(); //use cross entropy loss function
//create a Trainer to train the model
var trainer = new Trainer(nn, batchSize = 64, epoch = 10, randomBatch = true)
 {
 LabelCodec = codec, //set label codec
 Normalizer = norm, //set normalizer
 };
trainer.StartTrain(trainX, trainY, testX, testY);
//get the machine after train
trainer.GetClassificationMachine();
//you can also save the training result to a file
Storage.Save(trainer, "filename");
//and load it from file
var trainer = Storage.Load<Trainer>("filename");
//you can save and load models also
Storage.Save(nn, "filename");
var model = Storage.Load<NeuralNetwork>("filename");

<?xml version="1.0" encoding="utf-8"?>
<Trainer>
 <Mission>MNIST</Mission>
 <BatchSize>64</BatchSize>
 <Epoch>10</Epoch>
 <RandomBatch>False</RandomBatch>
 <PrintSteps>10</PrintSteps>
 <LastTrainLoss>0</LastTrainLoss>
 <LastTrainAccuracy>0</LastTrainAccuracy>
 <LastTestLoss>0</LastTestLoss>
 <LastTestAccuracy>0</LastTestAccuracy>
 <PreProcessor />
 <LabelCodec>
 <OneHotCodec>a,b,c</OneHotCodec>
 </LabelCodec>
 <Normalizer />
 <Model>
 <NeuralNetwork>
 <LossFunction>
 <CrossEntropy />
 </LossFunction>
 <Optimizer>
 <Adam>
 <Alpha>0.001</Alpha>
 <Beta1>0.9</Beta1>
 <Beta2>0.999</Beta2>
 </Adam>
 </Optimizer>
 <Regularizer />
 <Layers>
 <FullLayer>
 <UnitCount>10</UnitCount>
 <Weights />
 <Bias />
 </FullLayer>
 <Sigmoid />
 <FullLayer>
 <UnitCount>6</UnitCount>
 <Weights />
 <Bias />
 </FullLayer>
 <Sigmoid />
 <FullLayer>
 <UnitCount>3</UnitCount>
 <Weights />
 <Bias />
 </FullLayer>
 <Softmax />
 </Layers>
 </NeuralNetwork>
 </Model>
</Trainer>

Demo