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

kawatan/Merkurius