DataPredict Neural [Release 1.8] - PyTorch-like Deep Learning Library Meets Roblox!

Release Version 1.0 / Beta Version 0.6.0

Added

  • Shape Transformation Blocks:

    • Permute
  • Attention Blocks:

    • BaseAttentionBlock

    • ScaledDotProductAttention

  • Deep Reinforcement Models:

    • DeepQLearning

    • DeepDoubleQLearningV1

    • DeepDoubleQLearningV2

    • DeepClippedDoubleQLearning

    • DeepStateActionRewardStateAction

    • DeepDoubleStateActionRewardStateActionV1

    • DeepDoubleStateActionRewardStateActionV2

    • DeepExpectedStateActionRewardStateAction

    • DeepDoubleExpectedStateActionRewardStateActionV1

    • DeepDoubleExpectedStateActionRewardStateActionV2

    • ActorCritic

    • AdvantageActorCritic

    • ProximalPolicyOptimization

    • ProximalPolicyOptimizationClip

    • REINFORCE

    • VanillaPolicyGradient

  • Value Schedulers:

    • BaseValueScheduler

    • TimeDecay

    • StepDecay

Changes

  • setClassesList() function and getClassesList() function are added to Sequential Container.

  • Removed “numberOfData” parameter from the BaseCostFunction’s calculateCostValue() function and calculateLossTensor() function.

  • setLearningRateScheduler() function and getLearningRateScheduler() function are added to the BaseOptimizer

Side Notes

Please update your TensorL library for bug fixes.

3 Likes

Release Version 1.1 / Beta Version 0.7.0

Added

  • Expansion Blocks:

    • BaseExpansionBlock

    • ExpandNumberOfDimensions

    • ExpandDimensionSizes

1 Like

(post deleted by author)

Release Version 1.2 / Beta Version 0.8.0

Changes

  • Moved InputHolder to Holder Blocks

  • Changed all the Shape Transformation Blocks settings so that it saves the transformed tensor.

Added

  • Holder Blocks:

    • BaseHolderBlock

    • VariableHolder

    • NullaryFunctionHolder

2 Likes

Release Version 1.3 / Beta Version 0.9.0

Changes

  • If any of the input tensors have its number of dimensions or dimension sizes expanded, it will collapse the partial first derivative tensor to the input tensor’s number of dimensions and dimension sizes.

Added

  • Operator Blocks:

    • Clamp

    • Maximum

    • Minimum

Release Version 1.4 / Beta Version 1.0.0

Changes

  • Everything related to “FirstDerivative” is renamed to “ChainRuleFirstDerivative”.

  • Everything related to “SecondStepFirstDerivative” is renamed to “FirstDerivative”.

  • Calculations for first derivative tensors inside the FunctionBlocks are now more efficient.

Fixes

  • Replaced forgotten TensorL’s getSize() functions to getDimensionSizeArray().

  • Fixed a bug where MaxUnpooling FunctionBlocks requires incorrect input tensors dimension sizes.

1 Like

Release Version 1.5 / Beta Version 1.1.0

Added

  • Added RandomNetworkDistillation under “Models”.
1 Like

Which would be the best module for a self-learning AI out of your two modules and if they are equally as good, what is the best one to start on as someone who has never stepped into anything related to this?

Start with the other library, DataPredict. It has a lot more tutorials and simpler to set up.

Come back to this library if you want more specialized neural networks to build your self-learning AIs with it.

1 Like

Hello guys! I would like to inform you on the change of what we determine commercial use as to support smaller developers who only started off their game development business.

Here are the list of changes:

  • 1000 USD in a life time to 1000 USD per year.

  • 100 active players in a single time frame to no limit.

  • When a company has subsidiaries or related entities, the total revenue is combined.

Hopefully, by making the threshold more lenient, it would help such businesses to grow further and better in shorter amount of time.

Release Version 1.6 / Beta Version 1.2.0

Added

  • Added CircularPadding, ConstantPadding, ReflectionPadding and ReplicationPadding under “PaddingBlocks”.

Release Version 1.7 / Beta Version 1.3.0

Added

  • Added Dropout1D, Dropout2D and Dropout3D under “DropoutBlocks”.

  • Added DataPredictLinearAndBias under “WeightBlocks”.

Announcement: Changes to the Definition of What Qualifies as Commercial Use

Dear Users,

I would like to inform you of an important update regarding the definition of commercial use and the requirements for obtaining a separate commercial agreement.

Previously, a separate commercial agreement was required for companies (and their related entities) whose combined revenue exceeded $1,000 per year. However, after careful consideration and in an effort to streamline the process and reduce the administrative tasks involved, I have decided to increase the threshold to $3,000 within 365 days (not per 365 days).

This change will allow smaller businesses and individual developers more flexibility to use the library without the need for a separate commercial agreement, while ensuring that larger companies or those with substantial revenue engage in the necessary commercial use agreement.

Key Points:

  • A separate commercial agreement is required for companies (or individuals, if applicable) whose combined revenue (including subsidiaries or related entities) exceeds $3,000 within 365 days (not per 365 days). If you exceed this threshold and do not wish to enter into a separate agreement, you must follow the commercial use conditions outlined in the Terms and Conditions.

  • Business-to-business (B2B) activities remain unchanged and still require a separate commercial agreement, regardless of revenue. If you fulfil this condition and do not wish to enter into a separate agreement, you must follow the commercial use conditions outlined in the Terms and Conditions.

This change is aimed at reducing the administrative workload while continuing to foster a community of developers who can benefit from the library.

Thank you for your understanding and continued support.

Best regards,

Aqwam Harish Aiman (a.k.a. MyOriginsWorkshop)

Release Version 1.8 / Beta Version 1.4.0

Added

  • Added “MonteCarloControl” and “OffPolicyMonteCarloControl” under “Models”.

  • Added “Power”, “Exponent” and “Logarithm” under “OperatorBlocks”.