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