Overview
Ever wanted a PyTorch-like deep learning library for Roblox? Now you can!
Thanks to Lua being capable of copying object-orientated programming feature, this library is able to do automatic differentiation, distributed training and more!
Documentation And Tutorials:
Welcome to Aqwam’s DataPredict Neural Library! | DataPredict Neural
Features
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Take advantage of calculations combining both automatic differentiation and manual differentiation, simplifying complex calculations while maintaining high performance.
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Craft complex models effortlessly using dynamic computational graphs, giving you the ability to create any models you want and modify them at runtime.
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Take advantage of model and data parallelism capabilities for extremely fast training, prediction and experimentation.
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Build singular models that are interconnected between servers and clients through distributed training.
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Build models that handles multi-dimensional inputs and outputs to solve any demands of your projects.
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Dive into user-friendly API designed for you to learn in a couple of minutes.
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Built for production-grade and research-grade applications.
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Cross compatible with DataPredict library.
Preview Code
SequentialNeuralNetwork:setMultipleFunctionBlocks(
WeightBlocks.Linear.new({dimensionSizeArray = {1, 1, 3}}),
WeightBlocks.Linear.new({dimensionSizeArray = {1, 3, 5}}),
ActivationBlocks.LeakyReLU.new(),
DropoutBlocks.Dropout.new({dropoutRate = 0.5}),
WeightBlocks.Linear.new({dimensionSizeArray = {1, 5, 1}}),
ShapeTransformationBlocks.Transpose.new({dimensionIndexArray = {2, 3}}),
ActivationBlocks.LeakyReLU.new()
)
for i = 1, 100000 do
local generatedLabelTensor = SequentialNeuralNetwork:forwardPropagate(inputTensor)
local lossTensor = CostFunction:calculateLossTensor(generatedLabelTensor, labelTensor)
local costValue = CostFunction:calculateCostValue(generatedLabelTensor, labelTensor)
SequentialNeuralNetwork:backPropagate(lossTensor)
print(costValue)
task.wait()
end
DataPredict Library
Development Priority Poll
What should be the next update will be?
- Internal parts such as convolutional layers and pooling layers.
- External models such as reinforcement learning, generative and recurrent models.
0 voters
