This is a project for machine learning study, especially for deep learning.
I love C# so I write it by pure C#, no 3rd part dependency. It is easy to rewrite to Python or other language.
//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");There're a lot of objects can be stored.
The storage file is xml format:
<?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>
of cause you can write a xml file directly and use Storage.Load("filename") to load it, as long as you like， but personally i don't like this way ;)
I trained a handwriting network use MNIST dataset, and save the machine to a xml file, then write a WPF desktop application and load the network to recognize the new handwriting digit from user.
The project is: https://github.com/durow/MLStudy.NET/tree/master/MNISTDemo