Seq2SeqSharp

---

Seq2SeqSharp

Developer(s)	Zhongkai Fu
Repository	https://github.com/zhongkaifu/Seq2SeqSharp
Engine
Type	Library for deep learning
License	BSD 3
Website	github.com/zhongkaifu/Seq2SeqSharp

Search Seq2SeqSharp on Amazon.

Machine learning and data mining
Problems Classification Clustering Regression Anomaly detection AutoML Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction
Supervised learning (classification • regression) Decision trees Ensembles Bagging Boosting Random forest k-NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM)
Clustering BIRCH CURE Hierarchical k-means Expectation–maximization (EM) DBSCAN OPTICS Mean-shift
Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA t-SNE
Structured prediction Graphical models Bayes net Conditional random field Hidden Markov
Anomaly detection k-NN Local outlier factor
Artificial neural networks Autoencoder Deep learning DeepDream Multilayer perceptron RNN LSTM GRU) Restricted Boltzmann machine SOM Convolutional neural network U-Net
Reinforcement learning Q-learning SARSA Temporal difference (TD)
Theory Bias-variance dilemma Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical learning VC theory
Machine-learning venues NIPS ICML ML JMLR ArXiv:cs.LG
Glossary of artificial intelligence Glossary of artificial intelligence
Related articles List of datasets for machine-learning research Outline of machine learning

Seq2SeqSharp is a tensor based fast & flexible encoder-decoder deep neural network framework written by .NET (C#). It has many highlighted features, such as automatic differentiation, many different types of encoders/decoders(Transformer, LSTM, BiLSTM and so on), multi-GPUs supported and so on.

History

It is hosted at GitHub.

Features

• Pure C# framework
• Deep bi-directional LSTM encoder
• Deep attention based LSTM decoder
• Transformer encoder
• Graph based neural network
• Automatic differentiation
• Tensor based operations
• Running on both CPU and GPU (CUDA)
• Support multi-GPUs
• Mini-batch
• Dropout
• RMSProp optmization
• Embedding & Pre-trained model
• Auto data shuffling
• Auto vocabulary building
• Beam search decoder
• Visualize neural network

How it works

Benefit from automatic differentiation, tensor based compute graph and built-in operations, neural network can get built by a few code, and the framework will automatically build the corresponding backward part for you, and make the network could run on multi-GPUs or CPUs. Here is an example about attentioned based LSTM cells in C# code.

       /// <summary>
       /// Update LSTM-Attention cells according to given weights
       /// </summary>
       /// <param name="context">The context weights for attention</param>
       /// <param name="input">The input weights</param>
       /// <param name="computeGraph">The compute graph to build workflow</param>
       /// <returns>Update hidden weights</returns>
       public IWeightTensor Step(IWeightTensor context, IWeightTensor input, IComputeGraph g)
       {
           var computeGraph = g.CreateSubGraph(m_name);

           var cell_prev = Cell;
           var hidden_prev = Hidden;

           var hxhc = computeGraph.ConcatColumns(input, hidden_prev, context);
           var hhSum = computeGraph.Affine(hxhc, m_Wxhc, m_b);
           var hhSum2 = layerNorm1.Process(hhSum, computeGraph);

           (var gates_raw, var cell_write_raw) = computeGraph.SplitColumns(hhSum2, m_hdim * 3, m_hdim);
           var gates = computeGraph.Sigmoid(gates_raw);
           var cell_write = computeGraph.Tanh(cell_write_raw);

           (var input_gate, var forget_gate, var output_gate) = computeGraph.SplitColumns(gates, m_hdim, m_hdim, m_hdim);

           // compute new cell activation: ct = forget_gate * cell_prev + input_gate * cell_write
           Cell = computeGraph.EltMulMulAdd(forget_gate, cell_prev, input_gate, cell_write);
           var ct2 = layerNorm2.Process(Cell, computeGraph);

           Hidden = computeGraph.EltMul(output_gate, computeGraph.Tanh(ct2));

           return Hidden;
       }

Another example about scaled multi-heads attention component which is the core part in Transformer model written by C#.

       /// <summary>
       /// Scaled multi-heads attention component with skip connectioned feed forward layers
       /// </summary>
       /// <param name="input">The input tensor</param>
       /// <param name="g">The instance of computing graph</param>
       /// <returns></returns>
       public IWeightTensor Perform(IWeightTensor input, IComputeGraph graph)
       {
           IComputeGraph g = graph.CreateSubGraph(m_name);

           var seqLen = input.Rows / m_batchSize;

           //Input projections
           var allQ = g.View(Q.Process(input, g), m_batchSize, seqLen, m_multiHeadNum, m_d);
           var allK = g.View(K.Process(input, g), m_batchSize, seqLen, m_multiHeadNum, m_d);
           var allV = g.View(V.Process(input, g), m_batchSize, seqLen, m_multiHeadNum, m_d);

           //Multi-head attentions
           var Qs = g.View(g.Permute(allQ, 2, 0, 1, 3), m_multiHeadNum * m_batchSize, seqLen, m_d);
           var Ks = g.View(g.Permute(allK, 2, 0, 3, 1), m_multiHeadNum * m_batchSize, m_d, seqLen);
           var Vs = g.View(g.Permute(allV, 2, 0, 1, 3), m_multiHeadNum * m_batchSize, seqLen, m_d);

           // Scaled softmax
           float scale = 1.0f / (float)Math.Sqrt(m_d);
           var attn = g.MulBatch(Qs, Ks, m_multiHeadNum * m_batchSize, scale);
           var attn2 = g.View(attn, m_multiHeadNum * m_batchSize * seqLen, seqLen);

           var softmax = g.Softmax(attn2);
           var softmax2 = g.View(softmax, m_multiHeadNum * m_batchSize, seqLen, seqLen);
           var o = g.View(g.MulBatch(softmax2, Vs, m_multiHeadNum * m_batchSize), m_multiHeadNum, m_batchSize, seqLen, m_d);
           var W = g.View(g.Permute(o, 1, 2, 0, 3), m_batchSize * seqLen, m_multiHeadNum * m_d);

           // Output projection
           var finalAttResults = g.Affine(W, W0, b0);

           //Skip connection and layer normaliztion
           var addedAttResult = g.Add(finalAttResults, input);
           var normAddedAttResult = layerNorm1.Process(addedAttResult, g);

           //Feed forward
           var ffnResult = feedForwardLayer1.Process(normAddedAttResult, g);
           var reluFFNResult = g.Relu(ffnResult);
           var ffn2Result = feedForwardLayer2.Process(reluFFNResult, g);

           //Skip connection and layer normaliztion
           var addFFNResult = g.Add(ffn2Result, normAddedAttResult);
           var normAddFFNResult = layerNorm2.Process(addFFNResult, g);

           return normAddFFNResult;
       }

```

See also

• Comparison of deep learning software

Modify the page according to the feedback from reviewer

This article "Seq2SeqSharp" is from Wikipedia. The list of its authors can be seen in its historical and/or the page Edithistory:Seq2SeqSharp. Articles copied from Draft Namespace on Wikipedia could be seen on the Draft Namespace of Wikipedia and not main one.

📰 Article(s) of the same category(ies)

• Pixelorama
• Famingo Corporation
• Ge Wang (Scientist)
• Codewind
• HelloSign
• OneAPI Deep Neural Network Library
• Apex Launcher

---