eneural_net library
eNeural.net library.
Classes
-
ActivationFunction<
N extends num, E> - Base class for Activation Functions.
- ActivationFunctionFloat32x4
- Base class for SIMD optimized functions using Float32x4.
- ActivationFunctionLinear
- Linear Activation Function (SIMD optimized).
- ActivationFunctionSigmoid
- Sigmoid Activation Function (SIMD optimized).
- ActivationFunctionSigmoidBoundedFast
- Fast Pseudo-Sigmoid Activation Function Bounded (SIMD optimized).
- ActivationFunctionSigmoidFast
- Fast Pseudo-Sigmoid Activation Function (SIMD optimized).
- ActivationFunctionSigmoidFastInt
- Experimental Integer Sigmoid Function.
- ActivationFunctionSigmoidFastInt100
- Experimental Integer Sigmoid Function (scale 100).
-
ANN<
N extends num, E, T extends Signal< N, E, T> , S extends Scale<N> > - Artificial Neural Network
-
Backpropagation<
N extends num, E, T extends Signal< N, E, T> , S extends Scale<N> , P extends Sample<N, E, T, S> > - Implementation of Backpropagation training algorithm.
- Chronometer
- A Chronometer useful for benchmarks.
- DataEntry
-
DataStatistics<
N extends num> -
HiddenLayerConfig<
N extends num, E> - The configuration for the hidden layers.
-
Layer<
N extends num, E, T extends Signal< N, E, T> , S extends Scale<N> > - Base class for ANN layers.
- LayerFloat32x4
- ANN Layer for Float32x4 types.
-
LayerHidden<
N extends num, E, T extends Signal< N, E, T> , S extends Scale<N> > - Layer specialized for hidden neurons.
-
LayerInput<
N extends num, E, T extends Signal< N, E, T> , S extends Scale<N> > - Layer specialized for input neurons.
- LayerInt32x4
- ANN Layer for Int32x4 types.
-
LayerOutput<
N extends num, E, T extends Signal< N, E, T> , S extends Scale<N> > - Layer specialized for output neurons.
-
RProp<
N extends num, E, T extends Signal< N, E, T> , S extends Scale<N> , P extends Sample<N, E, T, S> > - Implementation of Resilient Backpropagation (version iRProp+).
-
Sample<
N extends num, E, T extends Signal< N, E, T> , S extends Scale<N> > - Base class for ANN samples.
- SampleFloat32x4
- ANN sample based in Float32x4 data.
- SampleInt32x4
- SamplesGenerator
- Samples Generator.
-
SamplesSet<
P extends Sample< num, dynamic, dynamic, Scale< >num> > - Samples Set.
-
Scale<
N extends num> - Base class for scales used for ANN, Signal and Sample.
- ScaleDouble
-
A
Scale<double>. - ScaleInt
-
A
Scale<int>. -
ScaleZoomable<
N extends num> - ScaleZoomableDouble
- ScaleZoomableInt
-
Signal<
N extends num, E, T extends Signal< N, E, T> > - SignalFloat32x4
- SignalFloat32x4Mod4
- SignalInt32x4
-
Training<
N extends num, E, T extends Signal< N, E, T> , S extends Scale<N> , P extends Sample<N, E, T, S> > - Base class for training algorithms.
Enums
- ActivationFunctionScope
- Scope of the activation function.
Extensions
-
DataEntryExtension
on List<
E> -
SeriesMapExtension
on Map<
String, List< N> ?>
Properties
- DefaultTrainingLogger → TrainingLogger
-
The default TrainingLogger.
final
Typedefs
-
TrainingBuilder<
N extends num, E, T extends Signal< N, E, T> , S extends Scale<N> , P extends Sample<N, E, T, S> > = Training<N, E, T, S, P> Function(ANN<N, E, T, S> ann, SamplesSet<P> samplesSet) - A builder for Training instances.
-
TrainingLogger
= void Function(Training<
num, dynamic, Signal< training, String type, String message, [dynamic error, StackTrace? stackTrace])num, dynamic, dynamic> , Scale<num> , Sample<num, dynamic, Signal< >num, dynamic, dynamic> , Scale<num> > - Training logger.