Index

A B C D E F G H I K L M N O P R S T U V W Z
All Classes|All Packages|Constant Field Values

G

genRandBoolean(double) - Static method in class com.jml.core.Stats

Generates a random boolean with a specified probability of being true.

getBlock() - Method in class com.jml.core.Block

Gets the markup format of the block.

getDetails() - Method in interface com.jml.neural_network.layers.BaseLayer

Constructs a string containing this all details of the model pertinent for saving the model to a file.

getDetails() - Method in class com.jml.neural_network.layers.Dense

Constructs a string containing this all details of the model pertinent for saving the model to a file.

getDetails() - Method in class com.jml.neural_network.layers.Dropout

Constructs a string containing this all details of the model pertinent for saving the model to a file.

getDetails() - Method in class com.jml.neural_network.layers.Linear

Constructs a string containing this all details of the model pertinent for saving the model to a file.

getDetails() - Method in class com.jml.optimizers.Adam

Gets the details of this optimizer.

getDetails() - Method in class com.jml.optimizers.GradientDescent

Gets the details of this optimizer.

getDetails() - Method in class com.jml.optimizers.Momentum

Gets the details of this optimizer.

getDetails() - Method in class com.jml.optimizers.Optimizer

Gets the details of this optimizer.

getInDim() - Method in interface com.jml.neural_network.layers.BaseLayer

Gets the input dimension of this layer.

getInDim() - Method in class com.jml.neural_network.layers.Dense

Gets the input dimension of this layer.

getInDim() - Method in class com.jml.neural_network.layers.Dropout

Gets the input dimension of this layer.

getInDim() - Method in class com.jml.neural_network.layers.Linear

Gets the input dimension of this layer.

getLearningRate() - Method in class com.jml.optimizers.Optimizer

Gets the learning rate for this optimizer.

getLossHist() - Method in class com.jml.classifiers.LogisticRegression

Gets the loss history from the optimizer.

getLossHist() - Method in class com.jml.linear_models.LinearRegressionSGD

Gets the loss history from training.

getLossHist() - Method in class com.jml.linear_models.MultipleLinearRegressionSGD

Gets the loss history from the optimizer.

getLossHist() - Method in class com.jml.linear_models.PolynomialRegressionSGD

Gets the loss history from training.

getLossHist() - Method in class com.jml.neural_network.NeuralNetwork

getName() - Method in interface com.jml.neural_network.activations.ActivationFunction

Gets the name of the activation function.

getName() - Method in class com.jml.neural_network.activations.Linear

Gets the name of the activation function.

getName() - Method in class com.jml.neural_network.activations.Relu

Gets the name of the activation function.

getName() - Method in class com.jml.neural_network.activations.Sigmoid

Gets the name of the activation function.

getName() - Method in class com.jml.neural_network.activations.Softmax

Gets the name of the activation function.

getName() - Method in class com.jml.neural_network.activations.Tanh

Gets the name of the activation function.

getOutDim() - Method in interface com.jml.neural_network.layers.BaseLayer

Gets the output dimension of this layer.

getOutDim() - Method in class com.jml.neural_network.layers.Dense

Gets the output dimension of this layer.

getOutDim() - Method in class com.jml.neural_network.layers.Dropout

Gets the output dimension of this layer.

getOutDim() - Method in class com.jml.neural_network.layers.Linear

Gets the output dimension of this layer.

getParams() - Method in class com.jml.classifiers.KNearestNeighbors

Gets the parameters of the trained model.

getParams() - Method in class com.jml.classifiers.LogisticRegression

Gets the parameters of the trained model.

getParams() - Method in class com.jml.core.Model

Gets the parameters of the trained model.

getParams() - Method in class com.jml.linear_models.MultipleLinearRegression

Gets the parameters of the trained model.

getParams() - Method in class com.jml.linear_models.Perceptron

Gets the parameters of the trained model.

getParams() - Method in class com.jml.linear_models.PolynomialRegression

Gets the parameters of the trained model.

getParams() - Method in interface com.jml.neural_network.layers.BaseLayer

Gets the trainable parameters of this layer in an array.

getParams() - Method in class com.jml.neural_network.layers.Dense

Gets the trainable parameters for this layer as an array of matrices.

getParams() - Method in class com.jml.neural_network.layers.Linear

Gets the trainable parameters for this layer as an array of matrices.

getParams() - Method in interface com.jml.neural_network.layers.TrainableLayer

Gets the trainable parameters for this layer as an array of matrices.

getParams() - Method in class com.jml.neural_network.NeuralNetwork

Gets the parameters of the trained model.

getUpdates() - Method in interface com.jml.neural_network.layers.BaseLayer

Gets the update matrices for the trainable parameters of this layer.

getUpdates() - Method in class com.jml.neural_network.layers.Dense

Gets the update matrices for parameters of this layer.

getUpdates() - Method in class com.jml.neural_network.layers.Linear

Gets the update matrices for parameters of this layer.

getUpdates() - Method in interface com.jml.neural_network.layers.TrainableLayer

Gets the update matrices for parameters of this layer.

GlorotNormal - Class in com.jml.neural_network.layers.initilizers

{layer parameter initializer to produce random values from a truncated normal distribution with standard deviation std = sqrt(2 / (fanIn + fanOut)) and mean of 0 where fanIn is the input dimension for the layer and fanOut is the output dimension for the layer.

GlorotNormal() - Constructor for class com.jml.neural_network.layers.initilizers.GlorotNormal

Creates a GlorotNormal initializer with specified seed.

GlorotNormal(long) - Constructor for class com.jml.neural_network.layers.initilizers.GlorotNormal

Creates a GlorotNormal initializer with specified seed.

GlorotUniform - Class in com.jml.neural_network.layers.initilizers

layer parameter initializer to produce random values from a clipped uniform distribution in [-lim, lim] where lim = sqrt(6 / (fanIn + fanOut)), fanIn is the input dimension for the layer, and fanOut is the output dimension for the layer.

GlorotUniform() - Constructor for class com.jml.neural_network.layers.initilizers.GlorotUniform

Creates a GlorotNormal initializer with specified seed.

GlorotUniform(long) - Constructor for class com.jml.neural_network.layers.initilizers.GlorotUniform

Creates a GlorotNormal initializer with specified seed.

GradientDescent - Class in com.jml.optimizers

Vanilla gradient descent optimizer.

GradientDescent(double) - Constructor for class com.jml.optimizers.GradientDescent

Creates a vanilla gradient descent optimizer with the specified learning rate.

A B C D E F G H I K L M N O P R S T U V W Z
All Classes|All Packages|Constant Field Values