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AI Library to create efficient Artificial Neural Networks. Computation uses SIMD (Single Instruction Multiple Data) to improve performance.

1.1.1 #

  • ActivationFunction:
    • Added base class ActivationFunctionFloat32x4.
    • SIMD Optimization:
      • Improved performance in 2x.
      • ActivationFunctionLinear, ActivationFunctionSigmoid, ActivationFunctionSigmoidFast, ActivationFunctionSigmoidBoundedFast.
  • eneural_net_fast_math.dart:
    • exp: Improved performance and input range bounded to -87..87.
    • expFloat32x4: new SIMD Optimized Exponential function.
  • Chronometer:
    • Improved toString numbers.
    • Comparable.
    • operator +.
  • eneural_net_extensions:
    • Improved extensions.
    • Improved documentation.
  • Training:
    • Added logProgressEnabled.
  • intl: ^0.17.0

1.1.0 #

  • ActivationFunction:
    • Added field flatSpot for derivativeEntryWithFlatSpot().
    • Added ActivationFunctionLinear.
    • ActivationFunctionSigmoid: activation with bounds (-700 .. 700).
  • Improved collections and numeric extensions.
  • Improved DataStatistics and add CSV generator.
  • Signal:
    • Added SIMD related operations.
    • Added: computeSumSquaresMean, computeSumSquares, valuesAsDouble.
    • Set extra values (out of length range): setExtraValuesToZero, setExtraValuesToOne, setExtraValues.
    • Improved documentation.
  • Sample:
    • Input/Output statistics and proximity.
  • Added SamplesSet:
    • With per set computed defaultTargetGlobalError.
    • Automatic removeConflicts.
  • Training:
    • Split into Propagation and ParameterStrategy, allowing other algorithms.
    • Added Backpropagation with SIMD, smart learning rate and smart momentum.
    • Added iRprop+.
    • Added TrainingLogger.
    • Added selectInitialANN.
  • ANN:
    • Optional bias neuron.
    • Allow different ActivationFunction for each layer.

1.0.2 #

  • Expose fast math as an additional library.

1.0.1 #

  • README.md:
    • Improve text.
    • Improve activation function text.
    • Fix example.

1.0.0 #

  • Initial version.
  • Training algorithms: Backpropagation.
  • Activation functions: Sigmoid and approximation versions.
  • Fast math functions.
  • SIMD: Float32x4
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AI Library to create efficient Artificial Neural Networks. Computation uses SIMD (Single Instruction Multiple Data) to improve performance.

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Dependencies

collection, intl, swiss_knife

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