All Classes

Class	Description
Accuracy	Accuracy metric.
Activation	Abstract activation function.
Adam	Adam optimizer.
CrossEntropy	Cross entropy loss.
Dataset	Basic input output dataset.
DatasetTT	Dataset with a train and test.
Dense	Basic fully-connected layer.
ElementwiseActivation	Activation that is applied across all numbers the same.
GaussianInit	Gaussian distribution initialization.
Genetic	Run a genetic algorithm.
He	He initialization.
Individual	Individual neural network.
Initializer	Abstract initializer class.
InputLayer	Layer for input.
Iris	Train a network on the iris flowers dataset.
JLSettings	Global settings for JLearn.
Layer	Abstract layer class.
LeakyReLU	Leaky Rectified Linear Unit activation.
Linear	Most basic linear activation.
Loss	Class for loss function.
Matrix	Matrix class and operations.
Matrix.Init	Initialization methods for Matrix.
Matrix.Init.Empty	Init all with 0s.
Matrix.Init.Fill	Init all with a number.
Matrix.Init.Gaussian	Generate random numbers from the gaussian distribution to fill the matrix.
Matrix.Init.Uniform	Generate a uniform range of random numbers to fill the matrix.
MatrixMathException	Exception when executing matrix math.
MeanSquaredError	Mean squared error loss.
Metric	Class for a metric.
MNIST	Train a network on the MNIST digits dataset.
MNISTDigits	Class for the MNIST digits dataset.
MNISTSave	Train a model on the MNIST dataset, then save it to a file and load it back to evaluate.
Model	Neural network model.
Model.FitBuilder	Builder class for fit operation.
Model.FitPrint	Thread to print progress when fitting.
Momentum	Gradient descent with momentum.
NeuralNetworkException	Exception in neural network.
Optimizer	Abstract class for optimizers.
Population	Population of individuals.
ReLU	Rectified Linear Unit activation.
SGD	Gradient descent optimizer.
Sigmoid	Sigmoid activation.
Softmax	Softmax activation.
Xavier	Xavier/Glorot initialization.
XOR	Train a model to perform the XOR bitwise operation.