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https://github.com/sekwiatkowski/Komputation

Komputation is a neural network framework for the Java Virtual Machine written in Kotlin and CUDA C.
https://github.com/sekwiatkowski/Komputation

artificial-intelligence convolutional-neural-networks cuda framework gpu jvm kotlin machine-learning neural-networks nlp nvidia recurrent-neural-networks seq2seq

Last synced: 3 months ago
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Komputation is a neural network framework for the Java Virtual Machine written in Kotlin and CUDA C.

Host: GitHub
URL: https://github.com/sekwiatkowski/Komputation
Owner: sekwiatkowski
License: other
Archived: true
Created: 2017-06-09T18:41:51.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2018-01-15T03:11:08.000Z (about 7 years ago)
Last Synced: 2024-04-07T01:12:50.124Z (10 months ago)
Topics: artificial-intelligence, convolutional-neural-networks, cuda, framework, gpu, jvm, kotlin, machine-learning, neural-networks, nlp, nvidia, recurrent-neural-networks, seq2seq
Language: Kotlin
Homepage: http://komputation.com
Size: 1.55 MB
Stars: 292
Watchers: 15
Forks: 13
Open Issues: 5
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

trackawesomelist - sekwiatkowski/Komputation (⭐292) - A neural network framework written in Kotlin. (Recently Updated / [Sep 14, 2024](/content/2024/09/14/README.md))

README

        

# Komputation

Komputation is a neural network framework for the Java Virtual Machine written in Kotlin and CUDA C.

## Maven

Komputation is available through Maven Central:

```xml

    com.komputation

    komputation

    0.12.5

```

## Layers

- Entry points:

  - [Input](./src/main/kotlin/com/komputation/instructions/entry/Input.kt)

  - [Lookup](./src/main/kotlin/com/komputation/instructions/entry/Lookup.kt)

- Standard feed-forward networks:

  - [Weighting](./src/main/kotlin/com/komputation/instructions/continuation/projection/Weighting.kt)

  - [Bias](./src/main/kotlin/com/komputation/instructions/continuation/projection/Bias.kt)

  - [Projection](./src/main/kotlin/com/komputation/instructions/continuation/projection/Projection.kt)

  - [Dense](./src/main/kotlin/com/komputation/instructions/continuation/dense/Dense.kt)

- Convolutional neural networks (CNNs):

  - [Convolution](./src/main/kotlin/com/komputation/instructions/continuation/convolution/Convolution.kt)

  - [Max-pooling](./src/main/kotlin/com/komputation/instructions/continuation/convolution/MaxPooling.kt)

- Recurrent neural networks:

  - [Recurrent layer](./src/main/kotlin/com/komputation/instructions/recurrent/Recurrent.kt)

  - [Bidirectional recurrent layer](./src/main/kotlin/com/komputation/instructions/recurrent/BidirectionalRecurrent.kt)

- [Dropout](./src/main/kotlin/com/komputation/instructions/continuation/dropout/Dropout.kt)

- Activation functions:

  - [Identity](./src/main/kotlin/com/komputation/instructions/continuation/activation/Identity.kt)

  - [Rectified Linear Units (ReLUs)](./src/main/kotlin/com/komputation/instructions/continuation/activation/Relu.kt)

  - [Sigmoid](./src/main/kotlin/com/komputation/instructions/continuation/activation/Sigmoid.kt)

  - [Softmax](./src/main/kotlin/com/komputation/instructions/continuation/activation/Softmax.kt)

  - [Tanh](./src/main/kotlin/com/komputation/instructions/continuation/activation/Tanh.kt)

- Other layers:

  - [Stack](./src/main/kotlin/com/komputation/instructions/continuation/stack/stack.kt)

  - [Exponentiation](./src/main/kotlin/com/komputation/instructions/continuation/activation/ExponentiationLayer.kt)

  - [Normalization](./src/main/kotlin/com/komputation/instructions/continuation/NormalizationLayer.kt)

## CPU demos

- Boolean functions:

  - [AND](./src/main/kotlin/com/komputation/cpu/demos/and/AndSigmoid.kt)

  - [NOT](./src/main/kotlin/com/komputation/cpu/demos/not/Not.kt)

  - [XOR](./src/main/kotlin/com/komputation/cpu/demos/xor/Xor.kt)

- Total:

  - [Fixed length](./src/main/kotlin/com/komputation/cpu/demos/total/FixedLengthTotal.kt)

  - [Variable length](./src/main/kotlin/com/komputation/cpu/demos/total/VariableLengthTotal.kt)

- Running total:

  - Left-to-right:

    - [Fixed length](./src/main/kotlin/com/komputation/cpu/demos/runningtotal/lefttoright/FixedLengthRunningTotal.kt)

    - [Variable length](./src/main/kotlin/com/komputation/cpu/demos/runningtotal/lefttoright/VariableLengthRunningTotal.kt)

  - Right-to-left:

    - [Fixed length](./src/main/kotlin/com/komputation/cpu/demos/runningtotal/righttoleft/RightToLeftFixedLengthRunningTotal.kt)

    - [Variable length](./src/main/kotlin/com/komputation/cpu/demos/runningtotal/righttoleft/RightToLeftVariableLengthRunningTotal.kt)

  - Bidirectional:

    - [Fixed length](./src/main/kotlin/com/komputation/cpu/demos/runningtotal/bidirectional/BidirectionalFixedLengthRunningTotal.kt)

    - [Variable length](./src/main/kotlin/com/komputation/cpu/demos/runningtotal/bidirectional/BidirectionalVariableLengthRunningTotal.kt)

- Increment:

  - [One layer](./src/main/kotlin/com/komputation/cpu/demos/increment/Increment.kt)

  - [Two layers](./src/main/kotlin/com/komputation/cpu/demos/increment/IncrementTwice.kt)

- Word embedding toy problem:

  - [Feed-forward network](./src/main/kotlin/com/komputation/cpu/demos/embeddings/Embeddings.kt)

  - [CNN with one filter width](./src/main/kotlin/com/komputation/cpu/demos/embeddings/EmbeddingsWithConvolution.kt)

  - [CNN with two filter widths](./src/main/kotlin/com/komputation/cpu/demos/embeddings/EmbeddingsWithTwoFilterWidths.kt)

- [Sequence labeling toy problem](./src/main/kotlin/com/komputation/cpu/demos/sequencelabeling/SequenceLabeling.kt)

- [Computer vision toy problem](./src/main/kotlin/com/komputation/cpu/demos/lines/Lines.kt)

- MNIST:

  - [Minimal](./src/main/kotlin/com/komputation/cpu/demos/mnist/MnistMinimal.kt)

  - [Dropout](./src/main/kotlin/com/komputation/cpu/demos/mnist/MnistBatchDropout.kt)

- TREC:

  - [One filter width](./src/main/kotlin/com/komputation/cpu/demos/trec/TREC.kt)

  - [Two filter widths](./src/main/kotlin/com/komputation/cpu/demos/trec/TRECWithTwoFilterWidths.kt)

## GPU/CUDA demos

- Boolean functions:

  - [AND](./src/main/kotlin/com/komputation/cuda/demos/and/AndSigmoid.kt)

  - [Negation](./src/main/kotlin/com/komputation/cuda/demos/negation/Negation.kt)

  - [XOR](./src/main/kotlin/com/komputation/cuda/demos/xor/Xor.kt)

- Word embedding toy problem:

  - [Feed-forward network](./src/main/kotlin/com/komputation/cuda/demos/embeddings/Embeddings.kt)

  - [CNN with one filter width](./src/main/kotlin/com/komputation/cuda/demos/embeddings/EmbeddingsWithConvolution.kt)

  - [CNN with two filter widths](./src/main/kotlin/com/komputation/cuda/demos/embeddings/EmbeddingsWithTwoFilterWidths.kt)

- Total:

  - [Fixed length](./src/main/kotlin/com/komputation/cuda/demos/total/FixedLengthTotal.kt)

- Increment:

  - [One layer](./src/main/kotlin/com/komputation/cuda/demos/increment/Increment.kt)

  - [Two layers](./src/main/kotlin/com/komputation/cuda/demos/increment/IncrementTwice.kt)

- MNIST:

  - [Minimal](./src/main/kotlin/com/komputation/cuda/demos/mnist/MnistMinimal.kt)

  - [Dropout](./src/main/kotlin/com/komputation/cuda/demos/mnist/MnistBatchDropout.kt)

- TREC:

  - [One filter width](./src/main/kotlin/com/komputation/cuda/demos/trec/TREC.kt)

  - [Two filter widths](./src/main/kotlin/com/komputation/cuda/demos/trec/TRECWithTwoFilterWidths.kt)

## Sample code

The following code instantiates a GPU-accelerated convolutional neural network for sentence classification:

```kotlin

    val sentenceClassifier = cudaNetwork(

        batchSize,

        lookup(embeddings, maximumDocumentLength, embeddingDimension, optimization),

        convolution(numberFilters, filterWidth, filterHeight, initialization, optimization),

        relu(),

        dropout(random, keepProbability),

        dense(numberCategories, Activation.Softmax, initialization, optimization)

    )

```

See the [TREC demo](./src/main/kotlin/com/komputation/cuda/demos/trec/TREC.kt) for more details.

## Initialization

- [Provided](./src/main/kotlin/com/komputation/initialization/ProvidedInitialization.kt)

- [Constant](./src/main/kotlin/com/komputation/initialization/ConstantInitialization.kt)

- [Gaussian](./src/main/kotlin/com/komputation/initialization/GaussianInitialization.kt)

- [He](./src/main/kotlin/com/komputation/initialization/HeInitialization.kt)

- [Identity](./src/main/kotlin/com/komputation/initialization/IdentityInitialization.kt)

- [Uniform](./src/main/kotlin/com/komputation/initialization/UniformInitialization.kt)

- [Zero](./src/main/kotlin/com/komputation/initialization/ZeroInitialization.kt)

## Loss functions

- [Cross-entropy loss](./src/main/kotlin/com/komputation/instructions/loss/CrossEntropyLoss.kt)

- [Logistic loss](./src/main/kotlin/com/komputation/instructions/loss/LogisticLoss.kt)

- [Squared loss](./src/main/kotlin/com/komputation/instructions/loss/SquaredLoss.kt)

## Optimization

- [Stochastic Gradient Descent](./src/main/kotlin/com/komputation/optimization/StochasticGradientDescent.kt)

- Historical:

  - [Momentum](./src/main/kotlin/com/komputation/optimization/historical/Momentum.kt)

  - [Nesterov's Accelerated Gradient](./src/main/kotlin/com/komputation/optimization/historical/Nesterov.kt)

- Adaptive:

  - [Adagrad](./src/main/kotlin/com/komputation/optimization/adaptive/Adagrad.kt)

  - [Adadelta](./src/main/kotlin/com/komputation/optimization/adaptive/Adadelta.kt)

  - [RMSProp](./src/main/kotlin/com/komputation/optimization/adaptive/RMSProp.kt)

  - [Adam](./src/main/kotlin/com/komputation/optimization/adaptive/Adam.kt)