kawatan / merkurius

Portable Deep Learning Library for .NET

Merkurius

This repository contains the portable deep learning (deep neural networks) library implementation for .NET platform. This library supports inference and training. Furthermore, all codes are written in C#.

Installation

You can install the Merkurius NuGet package from the .NET Core CLI command.

> dotnet add package Merkurius

or from the NuGet package manager.

PM> Install-Package Merkurius

Build

To build Merkurius, run .NET Core CLI command.

> dotnet build Merkurius.csproj

Examples

Convolutional neural network (CNN).

var model = new Model(
  new Convolution(ch, iw, ih, f, fw, fh, (fanIn, fanOut) => Initializers.HeNormal(fanIn),
  new Activation(new ReLU(),
  new MaxPooling(f, mw, mh, pw, ph,
  new FullyConnected(f * ow * oh, (fanIn, fanOut) => Initializers.HeNormal(fanIn),
  new Activation(new ReLU(),
  new FullyConnected(100, 10, (fanIn, fanOut) => Initializers.GlorotNormal(fanIn, fanOut))))))));

model.Fit(trainingList, 50, 100, new Adam(), new SoftmaxCrossEntropy());

Recurrent neural network (RNN).

var model = new Model(
  new Recurrent(1, 128, 10, true, false, (fanIn, fanOut) => Initializers.LeCunNormal(fanIn),
  new FullyConnected(128, 10, (fanIn, fanOut) => Initializers.LeCunNormal(fanIn),
  new Activation(10, new Identity()))));

model.Fit(trainingList, 50, 10, new SGD(), new MeanSquaredError());

Features

• Inference
• Training
• Code first modeling
• .NET Standard 2.1 library
• Dependency-free

Activation Functions

• ELU (Exponential linear unit)
• Hyperbolic tangent
• Identity
• ReLU (Rectified linear unit)
• SELU (Scaled exponential linear unit)
• Sigmoid
• Softmax
• SoftPlus
• Softsign

Layers

• Batch normalization
• Convolution
• Dropout
• Embedding
• GRU (Gated recurrent unit)
• Fully connected
• LSTM (Long short-term memory)
• Max pooling
• Recurrent

Loss Functions

• Cross-entropy
• Mean squared error (MSE)

Optimizers

• AdaDelta
• AdaGrad
• Adam
• Momentum
• Nesterov
• RMSprop
• SGD