Index (Deep Netts Core Pro 3.0.0 API)

A B C D E F G H I K L M N O P R S T U V W X Z

A

absMax(Tensor, Tensor) - Static method in class deepnetts.util.Tensors: Returns tensors with max value for each component of input tensors.
absMax(float[], float[]) - Static method in class deepnetts.util.Tensors: Returns array with max values for each position in the given input vectors.
absMin(Tensor, Tensor) - Static method in class deepnetts.util.Tensors
absMin(float[], float[]) - Static method in class deepnetts.util.Tensors
absPrime(float) - Static method in class deepnetts.net.layers.activation.MathFunctions: First derivative of the abs function.
AbstractLayer - Class in deepnetts.net.layers: Base class for different types of layers.
AbstractLayer(ActivationType) - Constructor for class deepnetts.net.layers.AbstractLayer
AbstractOptimizer - Class in deepnetts.net.train.opt: Skeletal implementation of the Optimizer interface to minimize effort to implement specific optimizers.
AbstractOptimizer(AbstractLayer) - Constructor for class deepnetts.net.train.opt.AbstractOptimizer
AbstractScaler - Class in deepnetts.data.norm: Base class to simplify implementation of custom normalization procedure.
AbstractScaler() - Constructor for class deepnetts.data.norm.AbstractScaler
accept(int, int) - Method in interface deepnetts.util.RangeConsumer
accuracy - Variable in class deepnetts.eval.ClassificationMetrics.Stats
ActivationFunction - Interface in deepnetts.net.layers.activation: Common base interface for all activation functions used in layers.
ActivationType - Enum in deepnetts.net.layers.activation: Supported types of activation functions.
AdaDeltaOptimizer - Class in deepnetts.net.train.opt: Implementation of ADADELTA Optimizer which is a modification od AdaGrad that uses only a limited window or previous gradients.
AdaDeltaOptimizer(AbstractLayer) - Constructor for class deepnetts.net.train.opt.AdaDeltaOptimizer
AdaGradOptimizer - Class in deepnetts.net.train.opt: Implementation of ADAGRAD Optimizer , which uses sum of squared previous gradients to adjust a global learning rate for each weight.
AdaGradOptimizer(AbstractLayer) - Constructor for class deepnetts.net.train.opt.AdaGradOptimizer
AdamOptimizer - Class in deepnetts.net.train.opt: Implementation of Adam optimizer which is a variation of RmsProp which includes momentum-like factor.
AdamOptimizer(AbstractLayer) - Constructor for class deepnetts.net.train.opt.AdamOptimizer
add(String, T...) - Method in class deepnetts.automl.Parameters
add(String, List<T>) - Method in class deepnetts.automl.Parameters: Adds a list of possible values for specified parameter name
add(Parameter) - Method in class deepnetts.automl.Parameters
add(ExampleImage) - Method in class deepnetts.data.ImageSet: Adds an example image that will be used to train deep learning model to this set.
add(float[], float[]) - Method in class deepnetts.eval.RootMeanSquaredError
add(float, int) - Method in class deepnetts.util.Tensor
add(float, int, int) - Method in class deepnetts.util.Tensor: Adds specified value to matrix value at position x, y
add(float, int, int, int) - Method in class deepnetts.util.Tensor
add(float, int, int, int, int) - Method in class deepnetts.util.Tensor
add(Tensor) - Method in class deepnetts.util.Tensor: Adds specified tensor t to this tensor.
add(float[], float[]) - Static method in class deepnetts.util.Tensors: Adds given vector and store result in first.
addConvolutionalLayer(int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Adds a convolutional layer with the given number of channels(filters), with default 3x3 filter size and default activation function.
addConvolutionalLayer(int, ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Adds a convolutional layer with the given number of channels(filters), with given activation function type and default 3x3 filter size.
addConvolutionalLayer(int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Adds a convolutional layer with the given number of channels(filters), with given filter size (same width and height) and default activation function.
addConvolutionalLayer(int, int, ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Adds a convolutional layer with the given number of channels(filters), with given filter size (same width and height) and given type of activation function.
addConvolutionalLayer(int, int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Adds a convolutional layer with the given number of channels(filters), with given width and height of convolutional filter and default type of activation function.
addConvolutionalLayer(int, Filter) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Adds a convolutional layer with the given number of channels(filters), with given settings of a convolutional filter and default type of activation function.
addConvolutionalLayer(int, int, int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Adds a convolutional layer with the given width and height of convolutional filters, given number of channels(filters) and stride(filter step), and default type of activation function.
addConvolutionalLayer(int, Filter, ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Adds a convolutional layer with the given number of channels(filters), with given settings of a convolutional filter and given type of activation function.
addConvolutionalLayer(int, int, int, int, ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Adds a convolutional layer with the given width and height of convolutional filters, given number of channels(filters) and stride(filter step), and given type of activation function.
addFullyConnectedLayer(int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Adds fully connected layer with specified width and default activation function.
addFullyConnectedLayer(int, ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Adds fully connected layer with specified width and activation function.
addFullyConnectedLayer(int) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Adds to the network a fully connected layer with specified width and Relu activation function by default.
addFullyConnectedLayer(int, ActivationType) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Adds fully connected addLayer with specified width and activation function to the network.
addHiddenFullyConnectedLayers(int...) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Adds to the network several hidden fully connected layers with specified widths and default hidden activation function by default.
addHiddenFullyConnectedLayers(ActivationType, int...) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Adds fully connected hidden layers with widths given in layerWidths param and given activation function type.
addInputLayer(int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Input layer with specified width and height, and 3 channels by default.
addInputLayer(int, int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Input layer with specified width, height and number of channels (depth).
addInputLayer(int) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Adds input layer with the specified layerWidth (number of inputs) to the network.
addLayer(AbstractLayer) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Adds a given layer to the network.
addLayer(AbstractLayer) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Adds custom layer to this network (which inherits from AbstractLayer)
addListener(TrainingListener) - Method in class deepnetts.net.train.BackpropagationTrainer: Adds training listener to this trainer.
addMaxPoolingLayer(int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Adds a max pooling layer with given filter size and stride(filter step).
addMaxPoolingLayer(int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder
addMaxPoolingLayer(int, int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Adds a max pooling layer with given filter size and stride(filter step).Max pooling layer comes after convolutional layer and reduces the dimensions of the input received from the previous layer.Typically filter sizes of 2 are used, which effectively halves the dimensions of the input from the previous layer.
addMaxPoolingLayer(Filter) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Adds a max pooling layer with the given filter settings.Max pooling layer comes after convolutional layer and reduces the dimensions of the input received from the previous layer.
addOutputLayer(int, Class<? extends OutputLayer>) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Adds output layer to the neural network with specified width (number of outputs) and layer class.
addOutputLayer(int, ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Adds output layer to the neural network with specified width (number of outputs) and activation function type.
addOutputLayer(int, ActivationType) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Adds output layer to the neural network with specified width (number of outputs) and activation function type.
addPatternError(float[], float[]) - Method in class deepnetts.net.loss.BinaryCrossEntropyLoss: Calculates error for given actual and target patterns and adds that error to total error.
addPatternError(float[], float[]) - Method in class deepnetts.net.loss.CrossEntropyLoss: Calculates and returns error vector for specified actual and target outputs.
addPatternError(float[], float[]) - Method in interface deepnetts.net.loss.LossFunction: Calculates pattern error for singe pattern for the specified predicted and target outputs, adds the error to total error, and returns the pattern error.
addPatternError(float[], float[]) - Method in class deepnetts.net.loss.MeanSquaredErrorLoss: Adds output error vector for the given predicted and target output vectors to total error sum and returns and error vector.
addRegularizationSum(float) - Method in class deepnetts.net.loss.BinaryCrossEntropyLoss
addRegularizationSum(float) - Method in class deepnetts.net.loss.CrossEntropyLoss
addRegularizationSum(float) - Method in interface deepnetts.net.loss.LossFunction: Adds specified regularization sum to total loss.
addRegularizationSum(float) - Method in class deepnetts.net.loss.MeanSquaredErrorLoss: Add regularization sum to total loss
apply(DataSet<MLDataItem>) - Method in class deepnetts.data.norm.DecimalScaler: Performs normalization on the given inputs.
apply(DataSet<MLDataItem>) - Method in class deepnetts.data.norm.MaxScaler: Performs normalization on the given inputs.
apply(DataSet<MLDataItem>) - Method in class deepnetts.data.norm.MinMaxScaler: Performs normalization on the given inputs.
apply(DataSet<MLDataItem>) - Method in class deepnetts.data.norm.RangeScaler: Performs normalization on the given inputs.
apply(DataSet<MLDataItem>) - Method in class deepnetts.data.norm.Standardizer
apply(T) - Method in interface deepnetts.data.Preprocessing
apply(Tensor, int) - Method in interface deepnetts.net.layers.activation.ActivationFunction
apply(Tensor, int, int) - Method in interface deepnetts.net.layers.activation.ActivationFunction
apply(Tensor, int) - Method in class deepnetts.net.layers.activation.LeakyRelu
apply(Tensor, int, int) - Method in class deepnetts.net.layers.activation.LeakyRelu
apply(Tensor, int) - Method in class deepnetts.net.layers.activation.Linear
apply(Tensor, int, int) - Method in class deepnetts.net.layers.activation.Linear
apply(Tensor, int) - Method in class deepnetts.net.layers.activation.Relu
apply(Tensor, int, int) - Method in class deepnetts.net.layers.activation.Relu
apply(Tensor, int) - Method in class deepnetts.net.layers.activation.Sigmoid
apply(Tensor, int, int) - Method in class deepnetts.net.layers.activation.Sigmoid
apply(Tensor, int) - Method in class deepnetts.net.layers.activation.Tanh
apply(Tensor, int, int) - Method in class deepnetts.net.layers.activation.Tanh
apply(Tensor) - Method in class deepnetts.util.ImagePreprocessing
apply(Function<Float, Float>) - Method in class deepnetts.util.Tensor
applyWeightChanges() - Method in class deepnetts.net.layers.AbstractLayer: Applies weight changes to current weights Must be diferent for convolutional does nothing for MaxPooling Same for FullyConnected and OutputLayer
applyWeightChanges() - Method in class deepnetts.net.layers.ConvolutionalLayer: Apply weight changes calculated in backward pass
applyWeightChanges() - Method in class deepnetts.net.layers.FlattenLayer
applyWeightChanges() - Method in class deepnetts.net.layers.FullyConnectedLayer
applyWeightChanges() - Method in class deepnetts.net.layers.InputLayer: This method does nothing in input layer.
applyWeightChanges() - Method in class deepnetts.net.layers.MaxPoolingLayer: Does nothing for pooling layer since it does not have weights It just propagates deltas from next layer to previous through connections that had max activation in forward pass
applyWeightChanges() - Method in class deepnetts.net.layers.OutputLayer: Applies weight changes after one learning iteration or batch
applyWeightChanges() - Method in class deepnetts.net.NeuralNetwork: Applies calculated weight changes to all layers.
asJson() - Method in class deepnetts.net.train.TrainingResult
AugmentImagesTest - Class in deepnetts.util: just move 2(x) pix to left right up down
AugmentImagesTest() - Constructor for class deepnetts.util.AugmentImagesTest
average(ClassificationMetrics[]) - Static method in class deepnetts.eval.ClassificationMetrics

B

BackpropagationTrainer - Class in deepnetts.net.train: Backpropagation training algorithm for feed forward and convolutional neural networks.
BackpropagationTrainer(NeuralNetwork) - Constructor for class deepnetts.net.train.BackpropagationTrainer: Creates an instance of BackpropagationTrainer for the given neural network to train.
BackpropagationTrainer(Properties) - Constructor for class deepnetts.net.train.BackpropagationTrainer: Creates an instance of BackpropagationTrainer with the given properties.
backward() - Method in class deepnetts.net.layers.AbstractLayer: This method should implement backward pass in subclasses
backward() - Method in class deepnetts.net.layers.ConvolutionalLayer: Backward pass for convolutional layer tweaks the weights in filters.
backward() - Method in class deepnetts.net.layers.FlattenLayer
backward() - Method in class deepnetts.net.layers.FullyConnectedLayer
backward() - Method in class deepnetts.net.layers.InputLayer: This method does nothing in the input layer, and should never be called.
backward() - Method in interface deepnetts.net.layers.Layer: Performs weight parameters adjustment in backward pass during training of a neural network.
backward() - Method in class deepnetts.net.layers.MaxPoolingLayer: backward pass for a max(x, y) operation has a simple interpretation as only routing the gradient to the input that had the highest value in the forward pass.
backward() - Method in class deepnetts.net.layers.OutputLayer: This method implements backward pass for the output layer.
backward() - Method in class deepnetts.net.layers.SoftmaxOutputLayer
backward() - Method in class deepnetts.net.NeuralNetwork: Performs a backward bass across all layers in neural network, which is the calculation of corrections for the network internal parameters (weights).
balanceUpsample(ImageSet, Map<String, Integer>) - Static method in class deepnetts.util.ImageSetUtils: Balance specified data set by adding random images of each class, to get equal number of examples for each class.
BinaryCrossEntropyLoss - Class in deepnetts.net.loss: Cross Entropy Loss is a loss function used for binary classification tasks (two classes, single output which represents probability ).
BinaryCrossEntropyLoss(NeuralNetwork) - Constructor for class deepnetts.net.loss.BinaryCrossEntropyLoss
bins() - Method in class deepnetts.util.StatUtils.Histogram
binWidth() - Method in class deepnetts.util.StatUtils.Histogram
BoundingBox - Class in deepnetts.util
BoundingBox(int, int, int, int, int, float) - Constructor for class deepnetts.util.BoundingBox
BoundingBox(int, int, int, int) - Constructor for class deepnetts.util.BoundingBox
BoundingBox(int, int, int, int, int) - Constructor for class deepnetts.util.BoundingBox
BoundingBox(int, int, int, int, int, String, float) - Constructor for class deepnetts.util.BoundingBox
build() - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Builds an instance of ConvolutionalNetwork with settings specified in this builder.
build() - Method in class deepnetts.net.FeedForwardNetwork.Builder: Builds an instance of FeedForwardNetwork with settings specified in this builder.
build() - Method in class deepnetts.net.train.KFoldCrossValidation.Builder
builder() - Static method in class deepnetts.net.ConvolutionalNetwork: Returns a builder for the ConvolutionalNetwork
Builder() - Constructor for class deepnetts.net.ConvolutionalNetwork.Builder
builder() - Static method in class deepnetts.net.FeedForwardNetwork: Returns a builder for the FeedForwardNetwork
Builder() - Constructor for class deepnetts.net.FeedForwardNetwork.Builder
builder() - Static method in class deepnetts.net.train.KFoldCrossValidation
Builder() - Constructor for class deepnetts.net.train.KFoldCrossValidation.Builder

C

calculate(float) - Method in class deepnetts.net.train.opt.AbstractOptimizer
calculateCellsPerThread(int, int) - Static method in class deepnetts.util.DeepNettsThreadPool
calculateDeltaBias(float, int) - Method in class deepnetts.net.train.opt.AdaDeltaOptimizer
calculateDeltaBias(float, int) - Method in class deepnetts.net.train.opt.AdaGradOptimizer
calculateDeltaBias(float, int) - Method in class deepnetts.net.train.opt.AdamOptimizer
calculateDeltaBias(float, int) - Method in class deepnetts.net.train.opt.MomentumOptimizer
calculateDeltaBias(float, int) - Method in interface deepnetts.net.train.opt.Optimizer
calculateDeltaBias(float, int) - Method in class deepnetts.net.train.opt.RmsPropOptimizer
calculateDeltaBias(float, int) - Method in class deepnetts.net.train.opt.SgdOptimizer
calculateDeltaWeight(float, int...) - Method in class deepnetts.net.train.opt.AdaDeltaOptimizer
calculateDeltaWeight(float, int...) - Method in class deepnetts.net.train.opt.AdaGradOptimizer
calculateDeltaWeight(float, int...) - Method in class deepnetts.net.train.opt.AdamOptimizer
calculateDeltaWeight(float, int...) - Method in class deepnetts.net.train.opt.MomentumOptimizer
calculateDeltaWeight(float, int...) - Method in interface deepnetts.net.train.opt.Optimizer
calculateDeltaWeight(float, int...) - Method in class deepnetts.net.train.opt.RmsPropOptimizer
calculateDeltaWeight(float, int...) - Method in class deepnetts.net.train.opt.SgdOptimizer
call() - Method in class deepnetts.util.CallableIntConsumer
call() - Method in class deepnetts.util.CallableRangeConsumer
CallableIntConsumer - Class in deepnetts.util: Callable int consumer.
CallableIntConsumer(int, int, IntConsumer) - Constructor for class deepnetts.util.CallableIntConsumer
CallableRangeConsumer - Class in deepnetts.util: Callable range consumer.
CallableRangeConsumer(int, int, RangeConsumer) - Constructor for class deepnetts.util.CallableRangeConsumer
CenterOnWhiteBackground - Class in deepnetts.util: Center images on backgounds and save at target path.
CenterOnWhiteBackground() - Constructor for class deepnetts.util.CenterOnWhiteBackground
checkLicense() - Static method in class deepnetts.core.DeepNetts: Validates the Deep Netts license.
checkLicense() - Method in class deepnetts.util.LicenseChecker
ClassificationMetrics - Class in deepnetts.eval: Various metrics that tell us how good is a classifier.
ClassificationMetrics(ConfusionMatrix) - Constructor for class deepnetts.eval.ClassificationMetrics: Constructs a new classification metrics from specified confusion matrix.
ClassificationMetrics(ConfusionMatrix, String, int) - Constructor for class deepnetts.eval.ClassificationMetrics: Constructs a new classification metrics of a single class for multi class classification.
ClassificationMetrics(int, int, int, int) - Constructor for class deepnetts.eval.ClassificationMetrics: Constructs a new classification metrics using specified arguments.
ClassificationMetrics.Stats - Class in deepnetts.eval: Average values of commonly used classification metrics.
ClassifierEvaluator - Class in deepnetts.eval: Evaluation method for binary and multi-class classifiers.
ClassifierEvaluator() - Constructor for class deepnetts.eval.ClassifierEvaluator
clone() - Method in class deepnetts.util.Tensor
COL_IDX - Static variable in interface deepnetts.net.train.opt.Optimizer
COL_IDX - Static variable in class deepnetts.util.Shape
ColorUtils - Class in deepnetts.util
ColumnType - Enum in deepnetts.util
COMPANY - Static variable in class deepnetts.util.LicenseChecker
ConfusionMatrix - Class in deepnetts.eval: Confusion matrix contains raw classifier evaluation results.
ConfusionMatrix(String[]) - Constructor for class deepnetts.eval.ConfusionMatrix: Creates a new confusion matrix for specified class labels
ConvolutionalLayer - Class in deepnetts.net.layers: Convolutional layer performs image convolution operation on outputs of a previous layer using filters.
ConvolutionalLayer(int, int, int) - Constructor for class deepnetts.net.layers.ConvolutionalLayer: Create a new instance of convolutional layer with specified number of channels filter size, default padding (filter-1)/2, and default stride stride value 1, and specified number of channels.
ConvolutionalLayer(int, Filter) - Constructor for class deepnetts.net.layers.ConvolutionalLayer
ConvolutionalLayer(int, Filter, ActivationType) - Constructor for class deepnetts.net.layers.ConvolutionalLayer
ConvolutionalLayer(int, int, int, int, ActivationType) - Constructor for class deepnetts.net.layers.ConvolutionalLayer
ConvolutionalNetwork - Class in deepnetts.net: Convolutional neural network is an extension of feed forward network, which can include 2D and 3D adaptive preprocessing layers (Convolutional and MaxPooling layer), which is specialized to learn to recognize features in images.
ConvolutionalNetwork.Builder - Class in deepnetts.net: Builder for a convolutional neural network.
copy() - Method in class deepnetts.util.Tensor
copy(Tensor, Tensor) - Static method in class deepnetts.util.Tensors
copy(float[], float[]) - Static method in class deepnetts.util.Tensors
copyFrom(float[]) - Method in class deepnetts.util.Tensor
copyOf(float[]) - Static method in class deepnetts.util.Tensors
correlation(float[], float[]) - Static method in class deepnetts.util.StatUtils: Calculates and returns Pearson correlation coefficient for the given arrays.
correlationCoefficient - Variable in class deepnetts.eval.ClassificationMetrics.Stats
correlationMatrix(float[][]) - Static method in class deepnetts.util.StatUtils
count() - Method in class deepnetts.util.DescriptiveStatistics
countByClasses() - Method in class deepnetts.data.ImageSet
countMissingValues(int) - Method in class deepnetts.data.TabularDataSet
countMissingValues() - Method in class deepnetts.data.TabularDataSet
create(ActivationType) - Static method in interface deepnetts.net.layers.activation.ActivationFunction: Creates and returns specified type of activation function.
create(OptimizerType, AbstractLayer) - Static method in interface deepnetts.net.train.opt.Optimizer: Factory method to create different types of optimizers
create(int, int, float[]) - Static method in class deepnetts.util.Tensor: Factory method for creating tensor instance,
create(int, int, int, float[]) - Static method in class deepnetts.util.Tensor
create(int, int, int, int, float[]) - Static method in class deepnetts.util.Tensor
createConvolutionalNetworkFromJson(JSONObject) - Static method in class deepnetts.util.FileIO
createDataSetFromRawImages(String, String, int, int, boolean) - Static method in class deepnetts.util.ImageSetUtils: Creates image data set from raw images by resizing and randomly croping to target dimensions.
createFeedForwardNetworkFromJson(JSONObject) - Static method in class deepnetts.util.FileIO
createFrom(ConfusionMatrix) - Static method in class deepnetts.eval.ClassificationMetrics: Creates classification metrics from the given confusion matrix.
createFromFile(String, Class<T>) - Static method in class deepnetts.util.FileIO
createFromFile(File, Class<T>) - Static method in class deepnetts.util.FileIO
createFromFile(File) - Static method in class deepnetts.util.FileIO
createFromJson(String) - Static method in class deepnetts.util.FileIO
createFromJson(File) - Static method in class deepnetts.util.FileIO
createFromJson(JSONObject) - Static method in class deepnetts.util.FileIO
CreateImageIndex - Class in deepnetts.util
CreateImageIndex() - Constructor for class deepnetts.util.CreateImageIndex
createImageIndex(String) - Static method in class deepnetts.util.ImageSetUtils: Creates an index file with list of all files in all subdirectories and write them into single index.txt file.
createIndexFile(HashMap<File, BufferedImage>, String, boolean) - Static method in class deepnetts.util.ImageUtils
CreateLabelsIndex - Class in deepnetts.util
CreateLabelsIndex() - Constructor for class deepnetts.util.CreateLabelsIndex
createLabelsIndex(String) - Static method in class deepnetts.util.ImageSetUtils: Creates a file with list of labels/categories from subdirectories at the specified given path.
createNeuralNetwork(Properties) - Method in class deepnetts.automl.FeedForwardNetworkFactory
createNeuralNetwork(Properties) - Method in interface deepnetts.automl.NetworkFactory: Creates and returns a neural network of specified type
createRandomlyCroppedImages(String, String, int, int, int) - Static method in class deepnetts.util.ImageSetUtils
createSearchSpace() - Method in class deepnetts.automl.Parameters: Returns parameters search space - all possible combination of all given parameter values.
createsTrainingSnaphots() - Method in class deepnetts.net.train.BackpropagationTrainer: Returns true if network creates training snapshots, false otherwise.
createSubSampledImageIndex(String, String, int, boolean) - Static method in class deepnetts.util.ImageSetUtils: Copies specified number of samples of each class from
cropAtCornersAndCenter(BufferedImage, int, int) - Static method in class deepnetts.util.ImageUtils: Returns five crops of a given image: four from each corner and one at center
CrossEntropyLoss - Class in deepnetts.net.loss: Average Cross Entropy Loss function commonly used for multi class classification problems.
CrossEntropyLoss(NeuralNetwork) - Constructor for class deepnetts.net.loss.CrossEntropyLoss
CrossEntropyLoss(int) - Constructor for class deepnetts.net.loss.CrossEntropyLoss
CsvFormat - Class in deepnetts.util: Various options for CSV file format including delimiter, column names and column types.
CsvFormat() - Constructor for class deepnetts.util.CsvFormat

D

DataSets - Class in deepnetts.data: Data set utility methods ans constants.
DataSets() - Constructor for class deepnetts.data.DataSets
DecimalScaler - Class in deepnetts.data.norm: Decimal scale normalization for the given data set.
DecimalScaler(DataSet<MLDataItem>) - Constructor for class deepnetts.data.norm.DecimalScaler: Creates a new instance of max normalizer initialized to max values in given data set.
DeepNetts - Class in deepnetts.core: Global configuration and settings for Deep Netts Engine.
deepnetts.automl - package deepnetts.automl: Support for automatically building deep learning models using hyper-parameter search.
deepnetts.core - package deepnetts.core: Core engine configuration and settings and runtime properties.
deepnetts.data - package deepnetts.data: Data structures to store example data used for building machine learning models.
deepnetts.data.norm - package deepnetts.data.norm: Data normalization methods, used to scale data to specific range, in order to make them suitable for use by a neural network.
deepnetts.eval - package deepnetts.eval: Evaluation procedures for machine learning models, used to estimate how good models are performing when given new data that (that was not used for training).
deepnetts.net - package deepnetts.net: Neural network architectures with their corresponding builders.
deepnetts.net.layers - package deepnetts.net.layers: Neural network layers, which are main building blocks of a neural network.
deepnetts.net.layers.activation - package deepnetts.net.layers.activation: Activation functions for neural network layers.
deepnetts.net.loss - package deepnetts.net.loss: Commonly used loss functions, which are used to calculate error during the training as a difference between predicted and target output.
deepnetts.net.train - package deepnetts.net.train: Training algorithms and related utilities.
deepnetts.net.train.opt - package deepnetts.net.train.opt: Optimization methods used by training algorithm.
deepnetts.net.weights - package deepnetts.net.weights: Weights randomization techniques, used for initializing layer's internal parameters.
deepnetts.util - package deepnetts.util: Various utility classes including Tensor, image operations, multithreading, exceptions etc.
DeepNettsException - Exception in deepnetts.util
DeepNettsException() - Constructor for exception deepnetts.util.DeepNettsException: Creates a new instance of JDeepNettsException without detail message.
DeepNettsException(String) - Constructor for exception deepnetts.util.DeepNettsException: Constructs an instance of JDeepNettsException with the specified detail message.
DeepNettsException(String, Throwable) - Constructor for exception deepnetts.util.DeepNettsException
DeepNettsException(Throwable) - Constructor for exception deepnetts.util.DeepNettsException
DeepNettsLogManager - Class in deepnetts.util
DeepNettsLogManager() - Constructor for class deepnetts.util.DeepNettsLogManager
DeepNettsRunner - Class in deepnetts.util
DeepNettsRunner() - Constructor for class deepnetts.util.DeepNettsRunner
DeepNettsThreadPool - Class in deepnetts.util: Dedicated thread pool for Deep Netts Engine.
DeleteAugmentImages - Class in deepnetts.util: just move 2(x) pix to left right up down
DeleteAugmentImages() - Constructor for class deepnetts.util.DeleteAugmentImages
DELIMITER_COMMA - Static variable in class deepnetts.data.DataSets
DELIMITER_SEMICOLON - Static variable in class deepnetts.data.DataSets
DELIMITER_SPACE - Static variable in class deepnetts.data.DataSets
DELIMITER_TAB - Static variable in class deepnetts.data.DataSets
deNormalizeInputs(Tensor) - Method in class deepnetts.data.norm.MaxScaler
deNormalizeOutputs(Tensor) - Method in class deepnetts.data.norm.MaxScaler: De-normalize given output vector in-place.
DEPTH_IDX - Static variable in interface deepnetts.net.train.opt.Optimizer
DEPTH_IDX - Static variable in class deepnetts.util.Shape
DescriptiveStatistics - Class in deepnetts.util
DescriptiveStatistics() - Constructor for class deepnetts.util.DescriptiveStatistics
descriptiveStatistics(float[]) - Static method in class deepnetts.util.StatUtils
detectCsvFormat(String) - Static method in class deepnetts.data.DataSets
DEVICE_ID - Static variable in class deepnetts.util.LicenseChecker
div(float) - Method in class deepnetts.util.Tensor: Divide all values in this tensor with specified value.
div(float[]) - Method in class deepnetts.util.Tensor
div(Tensor) - Method in class deepnetts.util.Tensor
div(float[], float) - Static method in class deepnetts.util.Tensors
div(float[], float[]) - Static method in class deepnetts.util.Tensors
DIVIDE_AND_CONQUER - Static variable in class deepnetts.automl.HyperParameterSearch

E

EMAIL - Static variable in class deepnetts.util.LicenseChecker
EPOCH_FINISHED - Static variable in class deepnetts.net.train.TrainingEvent: Specifies that training epoch has finished.
EPOCH_STARTED - Static variable in class deepnetts.net.train.TrainingEvent: Specifies that training epoch has started.
EPS - Static variable in interface deepnetts.net.train.opt.Optimizer: Smoothing term to prevent division by zero if sqr grad sum becomes zero 1e-8 should be also tried https://d2l.ai/chapter_optimization/adagrad.html 1e-6 The value to use is 1e-6, 1e-8, Keras uses 1e-7 for adam
equals(Object) - Method in class deepnetts.automl.Parameter
equals(Object) - Method in class deepnetts.util.Tensor
equals(Tensor, float) - Method in class deepnetts.util.Tensor
equalsName(String) - Method in enum deepnetts.net.layers.LayerType
equalsName(String) - Method in enum deepnetts.net.loss.LossType
equalsName(String) - Method in enum deepnetts.net.NetworkType
evaluate(NeuralNetwork, DataSet<? extends MLDataItem>) - Method in class deepnetts.eval.ClassifierEvaluator: Performs classifier evaluation and returns classification performance metrics.
evaluate(NeuralNetwork, DataSet<? extends MLDataItem>) - Method in class deepnetts.eval.RegresionEvaluator
evaluateClassifier(NeuralNetwork<?>, DataSet<? extends MLDataItem>) - Static method in class deepnetts.eval.Evaluators
evaluateRegressor(NeuralNetwork<?>, DataSet<? extends MLDataItem>) - Static method in class deepnetts.eval.Evaluators: Evaluates specified neural network with test set, as a regression model and returns basic regression evauation metrics.
evaluator(Evaluator<NeuralNetwork, DataSet<? extends MLDataItem>>) - Method in class deepnetts.automl.HyperParameterSearch
evaluator(Evaluator<NeuralNetwork, DataSet<? extends MLDataItem>>) - Method in class deepnetts.net.train.KFoldCrossValidation.Builder
Evaluators - Class in deepnetts.eval: Utility methods for evaluating machine learning models.
ExampleImage - Class in deepnetts.data: Example image to train a deep learning model.
ExampleImage(String, String) - Constructor for class deepnetts.data.ExampleImage
ExampleImage(File, String) - Constructor for class deepnetts.data.ExampleImage: Creates an instance of new example image with specified image and label Loads image from specified file and creates matrix structures with color information
ExampleImage(BufferedImage, String) - Constructor for class deepnetts.data.ExampleImage
ExampleImage(BufferedImage, String, int) - Constructor for class deepnetts.data.ExampleImage
ExampleImage(File, BufferedImage, String) - Constructor for class deepnetts.data.ExampleImage
ExampleImage(BufferedImage) - Constructor for class deepnetts.data.ExampleImage
ExampleImage(BufferedImage, String, int, int) - Constructor for class deepnetts.data.ExampleImage

F

FALSE_NEGATIVE - Static variable in class deepnetts.eval.ConfusionMatrix: A label for items falsely classified as negative, which are actually positive.
FALSE_POSITIVE - Static variable in class deepnetts.eval.ConfusionMatrix: A label for items falsely classified as positive, which are actually negative.
FeedForwardNetwork - Class in deepnetts.net: Feed forward neural network architecture, also known as Multi Layer Perceptron.
FeedForwardNetwork.Builder - Class in deepnetts.net: Builder of a FeedForwardNetwork instance.
FeedForwardNetworkFactory - Class in deepnetts.automl: Factory for FeedForwardNetwork.
FeedForwardNetworkFactory() - Constructor for class deepnetts.automl.FeedForwardNetworkFactory
FILE_TRAINING_PROP - Static variable in class deepnetts.util.DeepNettsRunner
FileIO - Class in deepnetts.util: File utilities for saving and loading neural networks.
fill(float) - Method in class deepnetts.util.Tensor: Fills the entire tensor with specified value.
fill(float[], float) - Static method in class deepnetts.util.Tensor
fillFourthDim(Tensor, int, float) - Static method in class deepnetts.util.Tensors
Filter - Class in deepnetts.net.layers: Settings of a convolutional filter which is used to learn to detect pixel patterns.
Filter(int) - Constructor for class deepnetts.net.layers.Filter
Filter(int, int) - Constructor for class deepnetts.net.layers.Filter
FlattenLayer - Class in deepnetts.net.layers: Just transform outputs from prev 3d layer into flat 1d tensor in formward pass backward pass will be same as backward for fc, and for 3d layers backwardFromFc Automaticly add after 2d or 3d layer to transition to fc layers.
FlattenLayer() - Constructor for class deepnetts.net.layers.FlattenLayer
flipHorizontal(BufferedImage) - Static method in class deepnetts.util.ImageUtils
flipVertical(BufferedImage) - Static method in class deepnetts.util.ImageUtils
forward() - Method in class deepnetts.net.layers.AbstractLayer: This method should implement forward pass in subclasses
forward() - Method in class deepnetts.net.layers.ConvolutionalLayer: Forward pass for convolutional layer.
forward() - Method in class deepnetts.net.layers.FlattenLayer
forward() - Method in class deepnetts.net.layers.FullyConnectedLayer
forward() - Method in class deepnetts.net.layers.InputLayer: This method does nothing in the input layer, and should never be called.
forward() - Method in interface deepnetts.net.layers.Layer: Performs layer calculation in forward pass of a neural network.
forward() - Method in class deepnetts.net.layers.MaxPoolingLayer: Max pooling forward pass outputs the max value for each filter position.
forward() - Method in class deepnetts.net.layers.OutputLayer: This method implements forward pass for the output layer.
forward() - Method in class deepnetts.net.layers.SoftmaxOutputLayer: This method implements forward pass for the output layer.
FOURTH_DIM_IDX - Static variable in interface deepnetts.net.train.opt.Optimizer
fScore - Variable in class deepnetts.eval.ClassificationMetrics.Stats
FullyConnectedLayer - Class in deepnetts.net.layers: Fully connected layer is used as hidden layer in the neural network, and it has a single row of units/nodes/neurons connected to all neurons in previous and next layer.
FullyConnectedLayer(int) - Constructor for class deepnetts.net.layers.FullyConnectedLayer: Creates an instance of fully connected layer with specified width (number of neurons) and ReLU activation function.
FullyConnectedLayer(int, ActivationType) - Constructor for class deepnetts.net.layers.FullyConnectedLayer: Creates an instance of fully connected layer with specified width (number of neurons) and activation function type.

G

gaussian(float[], float, float) - Static method in class deepnetts.net.weights.RandomWeights
generateNoisyImage(int, int, int, String) - Static method in class deepnetts.util.ImageUtils
generateRandomColoredImages(int, int, int, String) - Static method in class deepnetts.util.ImageUtils: Generates a specified number of randomly full colored images of a specified size.
generateRandomFilledRectImages(int, int, int, String) - Static method in class deepnetts.util.ImageUtils
GenerateRandomNegative - Class in deepnetts.util
GenerateRandomNegative() - Constructor for class deepnetts.util.GenerateRandomNegative
get(String) - Method in class deepnetts.automl.Parameters
get(int, int) - Method in class deepnetts.eval.ConfusionMatrix: Returns a value of confusion matrix at specified position.
get(String) - Method in class deepnetts.util.Parameters
get(int) - Method in class deepnetts.util.Tensor: Gets value at specified index position.
get(int, int) - Method in class deepnetts.util.Tensor: Returns matrix value at row, col
get(int, int, int) - Method in class deepnetts.util.Tensor: Returns value at specified index [row, col, z].
get(int, int, int, int) - Method in class deepnetts.util.Tensor
getAccuracy() - Method in class deepnetts.eval.ClassificationMetrics: Percent of correct classifications (for both positive and negative classes).
getActivation() - Method in class deepnetts.net.layers.AbstractLayer
getActivationType() - Method in class deepnetts.net.layers.AbstractLayer
getAsProperties() - Method in class deepnetts.automl.Parameters.ParameterCombination
getBalancedAccuracy() - Method in class deepnetts.eval.ClassificationMetrics: Balanced accuracy is a good metric to use when data set is not balanced.
getBatchSize() - Method in class deepnetts.net.layers.AbstractLayer
getBatchSize() - Method in class deepnetts.net.train.BackpropagationTrainer: Batch size is number of training examples after which network's weights are adjusted.
getBestNetwork() - Method in class deepnetts.net.train.KFoldCrossValidation
getBestResult() - Method in class deepnetts.net.train.KFoldCrossValidation
getBiases() - Method in class deepnetts.net.layers.AbstractLayer
getBlue(int) - Static method in class deepnetts.util.ColorUtils
getBoolean(String) - Method in class deepnetts.util.Parameters
getBoolean(String) - Method in class deepnetts.util.TypedProperties
getBrightness() - Method in class deepnetts.data.ImageSet
getCheckpointEpochs() - Method in class deepnetts.net.train.BackpropagationTrainer: On how many epochs the snapshots of the trained network should be created.
getClassCount() - Method in class deepnetts.eval.ConfusionMatrix
getClassIdx() - Method in class deepnetts.eval.ClassificationMetrics
getClassLabel() - Method in class deepnetts.eval.ClassificationMetrics: Returns class label that these metric correspond to (used for multi class classification).
getClassLabels() - Method in class deepnetts.eval.ConfusionMatrix
getColorFor(float, float, float) - Static method in class deepnetts.util.ColorUtils
getCols() - Method in class deepnetts.util.Tensor
getColumnNames() - Method in class deepnetts.data.TabularDataSet
getColumnNames() - Method in class deepnetts.util.CsvFormat
getColumnTypes() - Method in class deepnetts.util.CsvFormat
getConfusionMatrix() - Method in class deepnetts.eval.ClassificationMetrics: Returns a confusion matrix that is used to generate these metrics.
getConfusionMatrix() - Method in class deepnetts.eval.ClassifierEvaluator
getCropCornersAndCenter() - Method in class deepnetts.data.ImageSet
getCurrentEpoch() - Method in class deepnetts.net.train.BackpropagationTrainer: Returns the current training epoch(iteration) of this trainer.
getDefault() - Static method in class deepnetts.util.RandomGenerator
getDelimiter() - Method in class deepnetts.data.ImageSet
getDelimiter() - Method in class deepnetts.util.CsvFormat
getDeltaBiases() - Method in class deepnetts.net.layers.AbstractLayer
getDeltas() - Method in class deepnetts.net.layers.AbstractLayer
getDeltas() - Method in interface deepnetts.net.layers.Layer: Returns layer deltas/errors (as a tensor).
getDeltaWeights() - Method in class deepnetts.net.ConvolutionalNetwork: Returns delta weights for all layers.
getDeltaWeights() - Method in class deepnetts.net.layers.AbstractLayer
getDepth() - Method in class deepnetts.net.layers.AbstractLayer
getDepth() - Method in class deepnetts.util.Tensor
getDim(int) - Method in class deepnetts.util.Shape
getDimensions() - Method in class deepnetts.util.Shape
getDouble(String) - Method in class deepnetts.util.TypedProperties
getDropout() - Method in class deepnetts.net.layers.FullyConnectedLayer
getDropout() - Method in class deepnetts.net.train.BackpropagationTrainer: Dropout is a technique to prevent overfitting, which skips adjusting weights for some neurons with given probability.
getEarlyStopping() - Method in class deepnetts.net.train.BackpropagationTrainer: Early stopping stops training if it starts converging slow, and prevents overfitting.
getEarlyStoppingMinLossChange() - Method in class deepnetts.net.train.BackpropagationTrainer: Early stopping stops training if the error/loss start converging to slow.
getEarlyStoppingPatience() - Method in class deepnetts.net.train.BackpropagationTrainer: How many epochs to wait to see if the loss is lowering to slow.
getEpochs() - Method in class deepnetts.net.train.TrainingResult
getErrorRate() - Method in class deepnetts.eval.ClassificationMetrics: A percent of wrong classifications/predictions made.
getEvaluationMetrics() - Method in class deepnetts.net.train.TrainingResult
getEvaluator() - Method in class deepnetts.automl.HyperParameterSearch
getExtendedLogging() - Method in class deepnetts.net.train.BackpropagationTrainer: Extended logging includes additional info for debugging the training.
getF1Score() - Method in class deepnetts.eval.ClassificationMetrics: Calculates and returns F1 score - a balance between recall and precision.
getFalseDiscoveryRate() - Method in class deepnetts.eval.ClassificationMetrics: When its actually no, how often it is classified as yes
getFalseNegative(int) - Method in class deepnetts.eval.ConfusionMatrix
getFalseNegative() - Method in class deepnetts.eval.ConfusionMatrix: How many positive items has been (falsely) classified as negative.
getFalseNegativeRate() - Method in class deepnetts.eval.ClassificationMetrics: When its actually yes, how often does it predicts no
getFalsePositive() - Method in class deepnetts.eval.ConfusionMatrix: Returns number of false positive classifications.
getFalsePositive(int) - Method in class deepnetts.eval.ConfusionMatrix
getFalsePositiveRate() - Method in class deepnetts.eval.ClassificationMetrics: When it's actually no, how often does it predict yes? FP/actual no
getFile() - Method in class deepnetts.data.ExampleImage
getFileHandler() - Method in class deepnetts.util.DeepNettsLogManager
getFilterDeltaWeights() - Method in class deepnetts.net.layers.ConvolutionalLayer
getFilterDepth() - Method in class deepnetts.net.layers.ConvolutionalLayer
getFilterHeight() - Method in class deepnetts.net.layers.ConvolutionalLayer
getFilterHeight() - Method in class deepnetts.net.layers.MaxPoolingLayer
getFilters() - Method in class deepnetts.net.layers.ConvolutionalLayer
getFilterWidth() - Method in class deepnetts.net.layers.ConvolutionalLayer
getFilterWidth() - Method in class deepnetts.net.layers.MaxPoolingLayer
getFlipHorizontal() - Method in class deepnetts.data.ImageSet
getFloat(String) - Method in class deepnetts.util.Parameters
getFloat(String) - Method in class deepnetts.util.TypedProperties
getFourthDim() - Method in class deepnetts.util.Tensor
getFScore(int) - Method in class deepnetts.eval.ClassificationMetrics: Balance between precision and recall.
getFStat() - Method in class deepnetts.eval.RegressionMetrics
getGradients() - Method in class deepnetts.net.layers.AbstractLayer
getGrayscale() - Method in class deepnetts.data.ImageSet
getGreen(int) - Static method in class deepnetts.util.ColorUtils
getGroups() - Method in class deepnetts.net.layers.Filter
getHeight() - Method in class deepnetts.data.ExampleImage
getHeight() - Method in class deepnetts.net.layers.AbstractLayer
getHeight() - Method in class deepnetts.net.layers.Filter
getHeight() - Method in class deepnetts.util.BoundingBox
getId() - Method in class deepnetts.util.BoundingBox
getImageType(File) - Static method in class deepnetts.util.ImageUtils: Returns the extension of the given image file.
getInput() - Method in class deepnetts.data.ExampleImage
getInput() - Method in interface deepnetts.data.MLDataItem: Returns an input for machine learning model of this item.
getInput() - Method in class deepnetts.data.TabularDataSet.Item
getInputLayer() - Method in class deepnetts.net.NeuralNetwork: Returns the input layer of this neural network.
getInstance() - Static method in class deepnetts.core.DeepNetts: Returns a singleton instance of a Deep Netts engine.The instance provides global configuration settings for the Deep Netts runtime.
getInstance() - Static method in class deepnetts.util.DeepNettsThreadPool
getInt(String) - Method in class deepnetts.automl.Parameters
getInt(String) - Method in class deepnetts.util.TypedProperties
getInteger(String) - Method in class deepnetts.util.Parameters
getInvertImages() - Method in class deepnetts.data.ImageSet
getIqr() - Method in class deepnetts.util.DescriptiveStatistics
getL1RegSum() - Method in class deepnetts.net.NeuralNetwork: Calculates and returns L1 regularization sum of the entire network (all layers included).
getL1Regularization() - Method in class deepnetts.net.layers.AbstractLayer
getL1WeightSum() - Method in class deepnetts.net.layers.AbstractLayer
getL1WeightSum() - Method in class deepnetts.net.layers.ConvolutionalLayer
getL1WeightSum() - Method in class deepnetts.net.layers.MaxPoolingLayer
getL2RegSum() - Method in class deepnetts.net.NeuralNetwork: Calculates and returns L2 regularization sum of the entire network (all layers included).
getL2Regularization() - Method in class deepnetts.net.layers.AbstractLayer
getL2WeightSum() - Method in class deepnetts.net.layers.AbstractLayer
getL2WeightSum() - Method in class deepnetts.net.layers.ConvolutionalLayer
getL2WeightSum() - Method in class deepnetts.net.layers.MaxPoolingLayer
getLabel() - Method in class deepnetts.data.ExampleImage
getLabel() - Method in class deepnetts.net.NeuralNetwork: Returns the label(name) of this neural network
getLabel() - Method in class deepnetts.util.BoundingBox
getLabelsCount() - Method in class deepnetts.data.ImageSet
getLayers() - Method in class deepnetts.net.NeuralNetwork: Gets layers of this neural network.
getLayersOutputs() - Method in class deepnetts.net.ConvolutionalNetwork: Returns outputs of all layers.
getLearningRate() - Method in class deepnetts.net.layers.AbstractLayer
getLearningRate() - Method in class deepnetts.net.train.BackpropagationTrainer: Learning rate controls the step size as a percent of the error to use for adjusting internal parameters(weights) of the neural network.
getLicenseProperties() - Method in class deepnetts.util.LicenseChecker
getLoss() - Method in class deepnetts.net.train.TrainingResult
getLossFunction() - Method in class deepnetts.net.NeuralNetwork: Returns a loss function of this network, which is used to calculate total network error during the training.
getLossType() - Method in class deepnetts.net.layers.OutputLayer
getMacroAverage() - Method in class deepnetts.eval.ClassifierEvaluator
getMacroAverage() - Method in class deepnetts.net.train.KFoldCrossValidation
getMatthewsCorrelationCoefficient() - Method in class deepnetts.eval.ClassificationMetrics: Calculates and returns the matthews corellation coefficient.
getMax() - Method in class deepnetts.automl.Range
getMax() - Method in class deepnetts.util.DescriptiveStatistics
getMaxEpochs() - Method in class deepnetts.net.train.BackpropagationTrainer: Returns the setting for maximum number of training epochs(iterations).
getMaxError() - Method in class deepnetts.eval.RegressionMetrics
getMaxError() - Method in class deepnetts.net.train.BackpropagationTrainer: Returns the setting for the stopping error threshold.
getMaxInputs() - Method in class deepnetts.data.norm.MaxScaler
getMaxOutputs() - Method in class deepnetts.data.norm.MaxScaler
getMean() - Method in class deepnetts.data.ImageSet
getMean() - Method in class deepnetts.util.DescriptiveStatistics
getMean() - Method in class deepnetts.util.ImagePreprocessing
getMeanAbsoluteError() - Method in class deepnetts.eval.RegressionMetrics
getMeanAbsolutePercentageError() - Method in class deepnetts.eval.RegressionMetrics
getMeanSquaredError() - Method in class deepnetts.eval.RegressionMetrics: Mean squared error is the average value of the sum of squared errors.
getMedian() - Method in class deepnetts.util.DescriptiveStatistics
getMetricsByClass() - Method in class deepnetts.eval.ClassifierEvaluator
getMin() - Method in class deepnetts.automl.Range
getMin() - Method in class deepnetts.util.DescriptiveStatistics
getMomentum() - Method in class deepnetts.net.layers.AbstractLayer
getMomentum() - Method in class deepnetts.net.train.BackpropagationTrainer: Momentum settings helps to avoid oscillations in weight changes and get more stable and faster training.
getName() - Method in class deepnetts.automl.Parameter
getName() - Method in class deepnetts.util.Parameter
getNetwork() - Method in class deepnetts.automl.HyperParameterSearch
getNeuralNetwork() - Method in class deepnetts.net.train.BackpropagationTrainer: Returns a neural network trained by this trainer.
getNextLayer() - Method in class deepnetts.net.layers.AbstractLayer
getNormalizer() - Method in class deepnetts.net.NeuralNetwork: Returns data normalization method that is applied to network's inputs.
getNumColumns() - Method in class deepnetts.util.CsvFormat
getNumInputs() - Method in class deepnetts.data.TabularDataSet
getNumOutputs() - Method in class deepnetts.data.TabularDataSet
getOptimizer() - Method in class deepnetts.net.layers.AbstractLayer
getOptimizer() - Method in class deepnetts.net.train.BackpropagationTrainer
getOptimizerType() - Method in class deepnetts.net.layers.AbstractLayer
getOriginalImageFromFile() - Method in class deepnetts.data.ExampleImage
getOutput(float[]) - Method in class deepnetts.net.FeedForwardNetwork: Deprecated.
getOutput() - Method in class deepnetts.net.NeuralNetwork: Returns network's output.
getOutputErrors() - Method in class deepnetts.net.layers.OutputLayer
getOutputLabel(int) - Method in class deepnetts.net.NeuralNetwork: Gets a label of the i-th output this network.
getOutputLabels() - Method in class deepnetts.net.NeuralNetwork: Returns all labels for outputs of this network.
getOutputLayer() - Method in class deepnetts.net.NeuralNetwork: Returns the output layer of this network.
getOutputs() - Method in class deepnetts.net.layers.AbstractLayer
getOutputs() - Method in interface deepnetts.net.layers.Layer: Returns layer outputs (as a tensor).
getPadding() - Method in class deepnetts.net.layers.Filter
getParameters() - Method in class deepnetts.automl.HyperParameterSearch
getParameters() - Method in class deepnetts.automl.Parameters.ParameterCombination
getPrecision() - Method in class deepnetts.eval.ClassificationMetrics: What percent of those predicted as positive are really positive.
getPreprocessing() - Method in class deepnetts.net.NeuralNetwork: Gets preprocessing that needs to be performed before input is fed to this network.
getPrevDeltaBiases() - Method in class deepnetts.net.layers.AbstractLayer
getPrevDeltaWeights() - Method in class deepnetts.net.layers.AbstractLayer
getPrevlayer() - Method in class deepnetts.net.layers.AbstractLayer
getPrime(float) - Method in interface deepnetts.net.layers.activation.ActivationFunction: Returns the first derivative of activation function for specified output y
getPrime(float) - Method in class deepnetts.net.layers.activation.LeakyRelu
getPrime(float) - Method in class deepnetts.net.layers.activation.Linear
getPrime(float) - Method in class deepnetts.net.layers.activation.Relu
getPrime(float) - Method in class deepnetts.net.layers.activation.Sigmoid
getPrime(float) - Method in class deepnetts.net.layers.activation.Tanh
getProperties() - Method in class deepnetts.core.DeepNetts: Returns all the configuration properties for Deep Netts.
getProperties() - Method in class deepnetts.net.train.TrainingResult
getProperty(String) - Method in class deepnetts.core.DeepNetts: Returns a specified property of the Deep Netts runtime environment.
getQ1() - Method in class deepnetts.util.DescriptiveStatistics
getQ3() - Method in class deepnetts.util.DescriptiveStatistics
getR2() - Method in class deepnetts.eval.RegressionMetrics: Proportion of variance explained by the model.A value between 0 and 1, where 1 is the best and 0 worst.
getRandom() - Method in class deepnetts.util.RandomGenerator
getRandomNum() - Method in class deepnetts.automl.Range
getRange() - Method in class deepnetts.util.DescriptiveStatistics
getRank() - Method in class deepnetts.util.Tensor: Rank corresponds to number of dimensions in tensor.
getRecall() - Method in class deepnetts.eval.ClassificationMetrics: Ratio between those classified as positive compared to those that are actually positive.
getRed(int) - Static method in class deepnetts.util.ColorUtils
getResidualStandardError() - Method in class deepnetts.eval.RegressionMetrics
getResizeStrategy() - Method in class deepnetts.data.ImageSet
getResults() - Method in class deepnetts.automl.HyperParameterSearch
getRgbVector() - Method in class deepnetts.data.ExampleImage
getRootMeanSquaredError() - Method in class deepnetts.eval.RegressionMetrics
getRows() - Method in class deepnetts.util.Tensor
getScaleImages() - Method in class deepnetts.data.ImageSet: Returns flag that indicates wheather images should be scaled to specified dimensions while creating image set.
getScore() - Method in class deepnetts.util.BoundingBox
getSearchSpace() - Method in class deepnetts.automl.HyperParameterSearch
getShuffle() - Method in class deepnetts.net.train.BackpropagationTrainer: Returns shuffle flag which determines if training set should be shuffled before each epoch.
getSingleOutInput() - Method in class deepnetts.net.layers.OutputLayer
getSnapshotEpochs() - Method in class deepnetts.net.train.BackpropagationTrainer: On how many epochs to make training snapshots.
getSnapshotPath() - Method in class deepnetts.net.train.BackpropagationTrainer: Path to use for making snapshots - saving the current state of trained network during the training in order to be able to restore it from a training point if needed.
getSource() - Method in class deepnetts.net.train.TrainingEvent: Gets the source of the event.
getSpecificity() - Method in class deepnetts.eval.ClassificationMetrics: Specificity or true negative rate.
getSquaredErrorSum() - Method in class deepnetts.eval.RegressionMetrics
getStd() - Method in class deepnetts.util.DescriptiveStatistics
getStep() - Method in class deepnetts.automl.Range
getStopAccuracy() - Method in class deepnetts.net.train.BackpropagationTrainer
getStopError() - Method in class deepnetts.net.train.BackpropagationTrainer: Alias for getMaxError().
getStrategy() - Method in class deepnetts.automl.Range
getStride() - Method in class deepnetts.net.layers.ConvolutionalLayer
getStride() - Method in class deepnetts.net.layers.Filter
getStride() - Method in class deepnetts.net.layers.MaxPoolingLayer
getString(String) - Method in class deepnetts.util.Parameters
getString(String) - Method in class deepnetts.util.TypedProperties
getTargetColumnsNames() - Method in class deepnetts.data.ImageSet: Returns output/image labels.
getTargetColumnsNames() - Method in class deepnetts.data.TabularDataSet
getTargetOutput() - Method in class deepnetts.data.ExampleImage
getTargetOutput() - Method in interface deepnetts.data.MLDataItem: Returns target output for machine learning model of this item.
getTargetOutput() - Method in class deepnetts.data.TabularDataSet.Item
getTestSet() - Method in class deepnetts.data.TrainTestPair
getTestSet() - Method in class deepnetts.net.train.BackpropagationTrainer: Test set is used after the training to estimate performance of the trained model and generalization ability with new data.
getThreadCount() - Method in class deepnetts.util.DeepNettsThreadPool
getThreshold() - Method in class deepnetts.eval.ClassifierEvaluator
getTotal() - Method in class deepnetts.eval.ClassificationMetrics: Returns total number of classifications.
getTotal() - Method in class deepnetts.eval.RootMeanSquaredError
getTotal() - Method in class deepnetts.net.loss.BinaryCrossEntropyLoss
getTotal() - Method in class deepnetts.net.loss.CrossEntropyLoss
getTotal() - Method in interface deepnetts.net.loss.LossFunction: Returns the total error calculated by this loss function.
getTotal() - Method in class deepnetts.net.loss.MeanSquaredErrorLoss
getTotalItems() - Method in class deepnetts.eval.ConfusionMatrix
getTrainer() - Method in class deepnetts.net.NeuralNetwork: Returns a training algorithm of this neural network.
getTrainer() - Method in interface deepnetts.net.train.TrainerProvider
getTrainingAccuracy() - Method in class deepnetts.net.train.BackpropagationTrainer: Accuracy metric which tells us a percent of correct predictions for training set.
getTrainingeSet() - Method in class deepnetts.data.TrainTestPair
getTrainingListener() - Method in class deepnetts.net.train.KFoldCrossValidation
getTrainingLoss() - Method in class deepnetts.net.train.BackpropagationTrainer: Total training error/loss at the current epoch.
getTrainingResults() - Method in class deepnetts.net.train.KFoldCrossValidation
getTrainingSet() - Method in class deepnetts.automl.HyperParameterSearch
getTranslate() - Method in class deepnetts.data.ImageSet
getTrueNegative() - Method in class deepnetts.eval.ConfusionMatrix
getTrueNegative(int) - Method in class deepnetts.eval.ConfusionMatrix
getTruePositive() - Method in class deepnetts.eval.ConfusionMatrix: Return true positive metric for binary classification.
getTruePositive(int) - Method in class deepnetts.eval.ConfusionMatrix: Returns true positive metric for specified class idx for multiclass classification.
getType() - Method in class deepnetts.net.train.TrainingEvent: Gets the type of the event.
getValidationAccuracy() - Method in class deepnetts.net.train.BackpropagationTrainer: Accuracy metric which tells us a percent of correct predictions for validation set.
getValidationLoss() - Method in class deepnetts.net.train.BackpropagationTrainer: Validation loss is an error calculated using validation set, used to prevent overfitting, and validate architecture and training settings.
getValue() - Method in class deepnetts.automl.Parameter: If it does not hold list but a single value
getValue(float) - Method in interface deepnetts.net.layers.activation.ActivationFunction: Returns the value of activation function for specified input x
getValue(float) - Method in class deepnetts.net.layers.activation.LeakyRelu
getValue(float) - Method in class deepnetts.net.layers.activation.Linear
getValue(float) - Method in class deepnetts.net.layers.activation.Relu
getValue(float) - Method in class deepnetts.net.layers.activation.Sigmoid
getValue(float) - Method in class deepnetts.net.layers.activation.Tanh
getValue() - Method in class deepnetts.util.Parameter
getValues() - Method in class deepnetts.automl.Parameter: Returns the list of possible values for this parameter
getValues() - Method in class deepnetts.util.Tensor
getVar() - Method in class deepnetts.util.DescriptiveStatistics
getWeights() - Method in class deepnetts.net.ConvolutionalNetwork: Returns weights from all layers in this network as a list of tensors.
getWeights() - Method in class deepnetts.net.layers.AbstractLayer
getWidth() - Method in class deepnetts.data.ExampleImage
getWidth() - Method in class deepnetts.net.layers.AbstractLayer
getWidth() - Method in class deepnetts.net.layers.Filter
getWidth() - Method in class deepnetts.util.BoundingBox
getWithStride(int[]) - Method in class deepnetts.util.Tensor
getX() - Method in class deepnetts.util.BoundingBox
getY() - Method in class deepnetts.util.BoundingBox
getZeroMeanPixels() - Method in class deepnetts.data.ImageSet
grayscale(BufferedImage) - Static method in class deepnetts.util.ImageUtils: Returns grayscale version of the given image.
GRID - Static variable in class deepnetts.automl.HyperParameterSearch
groups(int) - Method in class deepnetts.net.layers.Filter

H

handleEvent(TrainingEvent) - Method in class deepnetts.net.train.opt.AdaDeltaOptimizer
handleEvent(TrainingEvent) - Method in class deepnetts.net.train.opt.AdamOptimizer
handleEvent(TrainingEvent) - Method in class deepnetts.net.train.opt.LearningRateDecay
handleEvent(TrainingEvent) - Method in interface deepnetts.net.train.TrainingListener: Invoked when a training event occurs.
hashCode() - Method in class deepnetts.automl.Parameter
hashCode() - Method in class deepnetts.util.Tensor
hasMissingValues(int) - Method in class deepnetts.data.TabularDataSet
hasMissingValues() - Method in class deepnetts.data.TabularDataSet
he(float[], int) - Static method in class deepnetts.net.weights.RandomWeights: He initialization, used for relu activations.zero-mean.
HIDDEN_ACTIVATION - Static variable in class deepnetts.automl.Parameters
HIDDEN_LAYERS - Static variable in class deepnetts.automl.Parameters
HIDDEN_NEURONS - Static variable in class deepnetts.automl.Parameters
hiddenActivationFunction(ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Sets default type of the activation function to use for all hidden layers in the network.
hiddenActivationFunction(ActivationType) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Sets default type of the activation function to use for all hidden layers in the network.
histogram(float[], int) - Static method in class deepnetts.util.StatUtils
histogram(float[], int, float, float) - Static method in class deepnetts.util.StatUtils
Histogram() - Constructor for class deepnetts.util.StatUtils.Histogram
HyperParameterSearch - Class in deepnetts.automl: The core automl class that performs automated model building and evaluation with specified parameters.
HyperParameterSearch() - Constructor for class deepnetts.automl.HyperParameterSearch

I

IMAGE_IDX_FILE - Static variable in class deepnetts.util.ImageSetUtils
ImagePreprocessing - Class in deepnetts.util: Used at inference time
ImagePreprocessing() - Constructor for class deepnetts.util.ImagePreprocessing
ImageResize - Enum in deepnetts.util
ImageSet - Class in deepnetts.data: Data set with images that will be used to train convolutional neural network.
ImageSet(int, int) - Constructor for class deepnetts.data.ImageSet: Creates empty image set for images of specified size.
ImageSet(int, int, String) - Constructor for class deepnetts.data.ImageSet: Creates image set with images from specified directory path.
ImageSetUtils - Class in deepnetts.util
ImageSetUtils() - Constructor for class deepnetts.util.ImageSetUtils
ImageUtils - Class in deepnetts.util: Utility methods to work with images.
importWeights(NeuralNetwork, String) - Static method in class deepnetts.util.TensorflowUtils
inc(int, int) - Method in class deepnetts.eval.ConfusionMatrix: Increments matrix value at specified position.
init() - Method in class deepnetts.net.layers.AbstractLayer: This method should implement layer initialization in subclasses, when a layer is added to the network (create weights, outputs, deltas, randomization etc.).
init() - Method in class deepnetts.net.layers.ConvolutionalLayer: Init dimensions, create matrices, filters, weights, biases and all internal structures etc.
init() - Method in class deepnetts.net.layers.FlattenLayer
init() - Method in class deepnetts.net.layers.FullyConnectedLayer: Creates all internal data structures: inputs, weights, biases, outputs, deltas, deltaWeights, deltaBiases prevDeltaWeights, prevDeltaBiases.
init() - Method in class deepnetts.net.layers.InputLayer: Initialize this layer in network.
init() - Method in class deepnetts.net.layers.MaxPoolingLayer
init() - Method in class deepnetts.net.layers.OutputLayer
init() - Method in class deepnetts.net.layers.SoftmaxOutputLayer
initSeed(long) - Static method in class deepnetts.net.weights.RandomWeights: Initializes random number generator with specified seed.
initSeed(long) - Method in class deepnetts.util.RandomGenerator
initTransientFields() - Method in class deepnetts.net.layers.AbstractLayer
initTransientFields() - Method in class deepnetts.net.layers.ConvolutionalLayer
initTransientFields() - Method in class deepnetts.net.layers.FullyConnectedLayer
initTransientFields() - Method in class deepnetts.net.layers.MaxPoolingLayer
initTransientFields() - Method in class deepnetts.net.layers.SoftmaxOutputLayer
InputLayer - Class in deepnetts.net.layers: Input layer in a neural network.
InputLayer(int, int, int) - Constructor for class deepnetts.net.layers.InputLayer: Creates input layer with specified width, height, and depth (number of channels).
InputLayer(int, int) - Constructor for class deepnetts.net.layers.InputLayer: Creates input layer with specified width and height, and depth=1 (single depth/channel).
InputLayer(int) - Constructor for class deepnetts.net.layers.InputLayer: Creates input layer with specified width, and with height and depth equals to one.
INPUTS - Static variable in class deepnetts.automl.Parameters
invert() - Method in class deepnetts.data.ExampleImage
isBatchMode() - Method in class deepnetts.net.layers.AbstractLayer
isBatchMode() - Method in class deepnetts.net.train.BackpropagationTrainer: In batch mode weights are adjusted after the pass of all examples from the training set, while in online mode weights are adjusted after each training example.
isEnabled() - Method in class deepnetts.util.ImagePreprocessing
isHasHeader() - Method in class deepnetts.util.CsvFormat
isImageFile(File) - Method in class deepnetts.util.AugmentImagesTest
isImageFile(File) - Method in class deepnetts.util.DeleteAugmentImages
isImageFile(File) - Method in class deepnetts.util.RandomlyTranslateImages
isInvertPixels() - Method in class deepnetts.util.ImagePreprocessing
isSubMean() - Method in class deepnetts.util.ImagePreprocessing
ISSUED_DATE - Static variable in class deepnetts.util.LicenseChecker
isTrainable() - Method in class deepnetts.net.layers.AbstractLayer
Item(float[], float[]) - Constructor for class deepnetts.data.TabularDataSet.Item
Item(Tensor, Tensor) - Constructor for class deepnetts.data.TabularDataSet.Item
ITERATION_FINISHED - Static variable in class deepnetts.net.train.TrainingEvent: Specifies that training iteration has finished.

K

KFoldCrossValidation - Class in deepnetts.net.train: Split data set into k parts of equal sizes (folds), then train model with k-1 folds, and validate with remaining 1 fold.
KFoldCrossValidation.Builder - Class in deepnetts.net.train: Builder object for KFoldCrossValidation.

L

LABELS_FILE - Static variable in class deepnetts.util.ImageSetUtils
labelsFromSubDirectories(String) - Static method in class deepnetts.util.ImageSetUtils: Returns a list of category/class labels from the names of subdirectories for the given path.
Layer - Interface in deepnetts.net.layers: Common base interface for all types of neural network layers.
LayerType - Enum in deepnetts.net.layers: Supported types of layers.
LeakyRelu - Class in deepnetts.net.layers.activation: Leaky Rectified Linear Activation and its Derivative.
LeakyRelu() - Constructor for class deepnetts.net.layers.activation.LeakyRelu
LeakyRelu(float) - Constructor for class deepnetts.net.layers.activation.LeakyRelu
LEARNING_RATE - Static variable in class deepnetts.automl.Parameters
LearningRateDecay - Class in deepnetts.net.train.opt: https://www.coursera.org/learn/deep-neural-network/lecture/hjgIA/learning-rate-decay
LearningRateDecay(float, float) - Constructor for class deepnetts.net.train.opt.LearningRateDecay
LicenceException(String) - Constructor for exception deepnetts.util.LicenseChecker.LicenceException
LICENSE_NUMBER - Static variable in class deepnetts.util.LicenseChecker
LICENSE_TYPE - Static variable in class deepnetts.util.LicenseChecker
LicenseChecker - Class in deepnetts.util: Provides methods that perform license file checks.
LicenseChecker() - Constructor for class deepnetts.util.LicenseChecker
LicenseChecker.LicenceException - Exception in deepnetts.util: This exception is thrown if there is some issue with the license file (invalid, expired or not found).
Linear - Class in deepnetts.net.layers.activation: Linear activation function and its derivative.
Linear() - Constructor for class deepnetts.net.layers.activation.Linear
listOf(Parameter...) - Static method in class deepnetts.util.Parameter
load(String, Class<T>) - Static method in class deepnetts.net.NeuralNetwork: Loads and returns neural network previously saved to a file.
loadFileImageMapFromDirectory(File) - Static method in class deepnetts.util.ImageUtils: Loads JPG, JPEG or PNG images from specified directory and returns them as a map with File object as a key and BufferedImage object as a value.
loadImages(String) - Method in class deepnetts.data.ImageSet: Loads images from the specified image index file.
loadImages(File) - Method in class deepnetts.data.ImageSet: Loads example images with corresponding labels from the specified file.
loadImages(File, int) - Method in class deepnetts.data.ImageSet: Loads specified number of example images with corresponding labels from the specified file.
loadImagesFromDirectory(File) - Static method in class deepnetts.util.ImageUtils: Loads all images from the specified directory, and returns them as a list.
loadLabels(String) - Method in class deepnetts.data.ImageSet: Loads and returns image labels to train neural network from the specified file.
loadLabels(File) - Method in class deepnetts.data.ImageSet: Loads and returns image labels to train neural network from the specified file.These labels will be used to label network's outputs.
LOGGER - Static variable in class deepnetts.core.DeepNetts
LOGGER - Static variable in class deepnetts.util.DeepNettsLogManager
lossFunction(Class<? extends LossFunction>) - Method in class deepnetts.net.ConvolutionalNetwork.Builder
lossFunction(LossType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Sets loss function to be used by created neural network.
lossFunction(LossType) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Sets loss function to be used by created neural network.
LossFunction - Interface in deepnetts.net.loss: Base Interface for all loss functions.
LossType - Enum in deepnetts.net.loss: Supported types of Loss Functions in Deep Netts engine.

M

macroAverage(Collection<EvaluationMetrics>) - Static method in class deepnetts.eval.ClassifierEvaluator: Calculates macro average for the given list of ClassificationMetrics.
macroAverage(Collection<EvaluationMetrics>) - Static method in class deepnetts.eval.RegresionEvaluator
main(String[]) - Static method in class deepnetts.util.AugmentImagesTest
main(String[]) - Static method in class deepnetts.util.CenterOnWhiteBackground
main(String[]) - Static method in class deepnetts.util.CreateImageIndex
main(String[]) - Static method in class deepnetts.util.CreateLabelsIndex
main(String[]) - Static method in class deepnetts.util.DeepNettsRunner
main(String[]) - Static method in class deepnetts.util.DeleteAugmentImages
main(String[]) - Static method in class deepnetts.util.GenerateRandomNegative
main(String[]) - Static method in class deepnetts.util.ObjectsOnBackgrounds
main(String[]) - Static method in class deepnetts.util.RandomlyTranslateImages
main(String[]) - Static method in class deepnetts.util.RunScaleImages
MathFunctions - Class in deepnetts.net.layers.activation: Misc math utility functions.
max() - Method in class deepnetts.util.Range
max(float[]) - Static method in class deepnetts.util.StatUtils
max(Tensor, Tensor, Tensor) - Static method in class deepnetts.util.Tensors
MAX_EPOCHS - Static variable in class deepnetts.automl.Parameters
MAX_ERROR - Static variable in class deepnetts.automl.Parameters
MaxPoolingLayer - Class in deepnetts.net.layers: This layer performs max pooling operation in convolutional neural network, which scales down output from previous layer by taking max outputs from small predefined filter areas.
MaxPoolingLayer(int, int, int) - Constructor for class deepnetts.net.layers.MaxPoolingLayer: Creates a new max pooling layer with specified filter dimensions and stride.
MaxPoolingLayer(Filter) - Constructor for class deepnetts.net.layers.MaxPoolingLayer
MaxScaler - Class in deepnetts.data.norm: Performs max normalization, rescales data to corresponding max value in each column.
MaxScaler(DataSet<MLDataItem>) - Constructor for class deepnetts.data.norm.MaxScaler: Creates a new instance of max normalizer initialized to max values in given data set.
mean(float[]) - Static method in class deepnetts.util.StatUtils
mean() - Method in class deepnetts.util.Tensor
MeanSquaredErrorLoss - Class in deepnetts.net.loss: Mean Squared Error Loss function.
MeanSquaredErrorLoss(NeuralNetwork) - Constructor for class deepnetts.net.loss.MeanSquaredErrorLoss: Creates a new mean squared error loss for the given neural network.
MeanSquaredErrorLoss(int) - Constructor for class deepnetts.net.loss.MeanSquaredErrorLoss
min() - Method in class deepnetts.util.Range
min(float[]) - Static method in class deepnetts.util.StatUtils
min(Tensor, Tensor, Tensor) - Static method in class deepnetts.util.Tensors
MINI_BATCH - Static variable in class deepnetts.net.train.TrainingEvent: Specifies mini batch event.
MinMaxScaler - Class in deepnetts.data.norm: Performs Min Max normalization on the given data set.
MinMaxScaler(DataSet<MLDataItem>) - Constructor for class deepnetts.data.norm.MinMaxScaler: Creates a new instance of max normalizer initialized to max values in given data set.
missingValuesCount(float[]) - Static method in class deepnetts.util.StatUtils
missingValuesCount(float[][]) - Static method in class deepnetts.util.StatUtils
missingValuesCount(TabularDataSet<TabularDataSet.Item>) - Static method in class deepnetts.util.StatUtils
MLDataItem - Interface in deepnetts.data: Single data item that will be used to train machine learning model.
model(NeuralNetwork) - Method in class deepnetts.net.train.KFoldCrossValidation.Builder
MOMENTUM - Static variable in class deepnetts.automl.Parameters
MomentumOptimizer - Class in deepnetts.net.train.opt: Momentum optimization adds momentum parameter to basic Stochastic Gradient Descent, which can accelerate the process.
MomentumOptimizer(AbstractLayer) - Constructor for class deepnetts.net.train.opt.MomentumOptimizer
mserror - Variable in class deepnetts.eval.ClassificationMetrics.Stats
multiply(float) - Method in class deepnetts.util.Tensor
multiply(float, int) - Method in class deepnetts.util.Tensor
multiply(float[], float[]) - Static method in class deepnetts.util.Tensors
multiplyElementWise(Tensor) - Method in class deepnetts.util.Tensor

N

NAME - Static variable in class deepnetts.util.LicenseChecker
names() - Method in class deepnetts.automl.Parameters
negativeFreqency() - Method in class deepnetts.eval.ClassificationMetrics: How often does negative class actually occur in the sample
NETWORK_FILE_EXT - Static variable in class deepnetts.util.FileIO
networkFactory(NetworkFactory<?>) - Method in class deepnetts.automl.HyperParameterSearch
NetworkFactory<T extends NeuralNetwork> - Interface in deepnetts.automl: Base interface for all network factories.
NetworkType - Enum in deepnetts.net: Neural network architecture types.
NeuralNetwork<T extends Trainer> - Class in deepnetts.net: Base class for all neural networks in Deep Netts.
nextFloat() - Method in class deepnetts.util.RandomGenerator
nextGaussian() - Method in class deepnetts.util.RandomGenerator
nextInt() - Method in class deepnetts.util.RandomGenerator
normal(float[]) - Static method in class deepnetts.net.weights.RandomWeights
normalizeInput(Tensor) - Method in class deepnetts.data.norm.MaxScaler
numBins() - Method in class deepnetts.util.StatUtils.Histogram
numDimensions() - Method in class deepnetts.util.Shape
numSplits(int) - Method in class deepnetts.net.train.KFoldCrossValidation.Builder

O

ObjectsOnBackgrounds - Class in deepnetts.util: Center images on backgounds and save at target path.
ObjectsOnBackgrounds() - Constructor for class deepnetts.util.ObjectsOnBackgrounds
of(T, T) - Static method in class deepnetts.automl.Range
of(Class) - Static method in enum deepnetts.net.loss.LossType
Of(Class) - Static method in enum deepnetts.net.NetworkType
of(float, float) - Static method in class deepnetts.util.Range
of(int...) - Static method in class deepnetts.util.Shape
of(float[]) - Static method in class deepnetts.util.Tensor
ofSize(int) - Static method in class deepnetts.net.layers.Filter: Factory method that creates filter settings with specified size (using same size for filter width and height).
ofSize(int, int) - Static method in class deepnetts.net.layers.Filter: Factory method that creates a filter settings with specified width and height
oneHotEncode(String, String[]) - Static method in class deepnetts.data.DataSets: Returns one hot encoded vector for the given label.
ones(int) - Static method in class deepnetts.util.Tensors
OPTIMIZER - Static variable in class deepnetts.automl.Parameters
Optimizer - Interface in deepnetts.net.train.opt: Optimization technique to tune network's weights parameters used by training algorithm.
OptimizerType - Enum in deepnetts.net.train.opt: Supported types of optimization methods used by back-propagation training algorithm.
OUTPUT_ACTIVATION - Static variable in class deepnetts.automl.Parameters
OutputLayer - Class in deepnetts.net.layers: Output layer of a neural network.
OutputLayer(int) - Constructor for class deepnetts.net.layers.OutputLayer: Creates an instance of output layer with specified width (number of outputs) and sigmoid activation function by default.
OutputLayer(int, ActivationType) - Constructor for class deepnetts.net.layers.OutputLayer: Creates an instance of output layer with specified width (number of outputs) and specified activation function.
OutputLayer(String[]) - Constructor for class deepnetts.net.layers.OutputLayer: Creates an instance of output layer with specified width (number of outputs) which corresponds to number of labels and sigmoid activation function by default.
OutputLayer(String[], ActivationType) - Constructor for class deepnetts.net.layers.OutputLayer
OUTPUTS - Static variable in class deepnetts.automl.Parameters

P

padding(int) - Method in class deepnetts.net.layers.Filter
param(String, T) - Method in class deepnetts.automl.HyperParameterSearch
param(String, List<T>) - Method in class deepnetts.automl.HyperParameterSearch
param(String, Range<T>, String) - Method in class deepnetts.automl.HyperParameterSearch
Parameter<T> - Class in deepnetts.automl: A single parameter of the deep learning model to tune.
Parameter(String, List<T>) - Constructor for class deepnetts.automl.Parameter
Parameter<T> - Class in deepnetts.util
Parameter(String, T) - Constructor for class deepnetts.util.Parameter
ParameterCombination() - Constructor for class deepnetts.automl.Parameters.ParameterCombination
ParameterCombination(List<Parameter>) - Constructor for class deepnetts.automl.Parameters.ParameterCombination
Parameters - Class in deepnetts.automl: Parameter search space: a collection of parameters and methods for generating all possible combinations.
Parameters() - Constructor for class deepnetts.automl.Parameters
Parameters - Class in deepnetts.util: TODO: setMethod, throw meannigfull exceptions
Parameters() - Constructor for class deepnetts.util.Parameters
Parameters.ParameterCombination - Class in deepnetts.automl: Combination of parameters to search through.
physicalCoreCount() - Static method in class deepnetts.util.PhysicalCores: Returns the number of "physical" hardware threads available.
PhysicalCores - Class in deepnetts.util: Static utility class for finding the number of physical CPU cores.
positiveFreqency() - Method in class deepnetts.eval.ClassificationMetrics: How often does positive class actually occur in the sample
precision - Variable in class deepnetts.eval.ClassificationMetrics.Stats
predict(float...) - Method in class deepnetts.net.FeedForwardNetwork: Returns the network's prediction (outputs) for the given input.
predict(Tensor) - Method in class deepnetts.net.NeuralNetwork: Returns the prediction of this neural network for the given input.
Preprocessing<T> - Interface in deepnetts.data: Data pre-processing abstraction.
printLicense() - Method in class deepnetts.util.LicenseChecker
PROP_BATCH_MODE - Static variable in class deepnetts.net.train.BackpropagationTrainer: Name of the batchMode property
PROP_BATCH_SIZE - Static variable in class deepnetts.net.train.BackpropagationTrainer: Name of the batchSize property
PROP_IMAGE_HEIGHT - Static variable in class deepnetts.util.DeepNettsRunner
PROP_IMAGE_WIDTH - Static variable in class deepnetts.util.DeepNettsRunner
PROP_LABELS_FILE - Static variable in class deepnetts.util.DeepNettsRunner
PROP_LEARNING_RATE - Static variable in class deepnetts.net.train.BackpropagationTrainer: Name of the learningRate property
PROP_LEARNING_RATE - Static variable in class deepnetts.util.DeepNettsRunner
PROP_MAX_EPOCHS - Static variable in class deepnetts.net.train.BackpropagationTrainer: Name of the maxEpochs property
PROP_MAX_ERROR - Static variable in class deepnetts.net.train.BackpropagationTrainer: Name of the maxError property
PROP_MAX_ERROR - Static variable in class deepnetts.util.DeepNettsRunner
PROP_MOMENTUM - Static variable in class deepnetts.net.train.BackpropagationTrainer: Name of the momentum property
PROP_OPTIMIZER_TYPE - Static variable in class deepnetts.net.train.BackpropagationTrainer: Name of the optimizer property
PROP_TEST_FILE - Static variable in class deepnetts.util.DeepNettsRunner
PROP_TRAINING_FILE - Static variable in class deepnetts.util.DeepNettsRunner
put(Parameter<?>) - Method in class deepnetts.util.Parameters

R

RANDOM - Static variable in class deepnetts.automl.HyperParameterSearch
random(int) - Static method in class deepnetts.util.Tensors
random(int, int) - Static method in class deepnetts.util.Tensors: Generate and return random tensor with specified number of rows and cols.
random(int, int, int) - Static method in class deepnetts.util.Tensors
random(int, int, int, int) - Static method in class deepnetts.util.Tensors
randomCrop(BufferedImage, int, int, int, Random) - Static method in class deepnetts.util.ImageUtils: Crops specified number of random sub-images with specified dimensions.
randomCrop(BufferedImage, int, int) - Static method in class deepnetts.util.ImageUtils: Randomly crop image with specified width and height.
RandomGenerator - Class in deepnetts.util: Random number generator singleton.
randomize(float[]) - Static method in class deepnetts.net.weights.RandomWeights: Initialize the elements of specified array with random numbers with uniform distribution in range [-0.5, 0.5].
randomize() - Method in class deepnetts.util.Tensor: Randomize all values in tensor
RandomlyTranslateImages - Class in deepnetts.util: just move 2(x) pix to left right up down
RandomlyTranslateImages() - Constructor for class deepnetts.util.RandomlyTranslateImages
randomNum(int) - Method in class deepnetts.automl.Range
randomSeed(long) - Method in class deepnetts.automl.HyperParameterSearch
randomSeed(long) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Initializes random number generator with the specified seed in order to get same random number sequences used for weights initialization.
randomSeed(long) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Initializes random number generator with the specified seed in order to get same random number sequences used for weights initialization.
randomTintAndBrightness(BufferedImage, float, int, int, Random) - Static method in class deepnetts.util.ImageUtils: Still not working as it should
randomTintAndBrightness(BufferedImage) - Static method in class deepnetts.util.ImageUtils
randomTranslateImage(BufferedImage, int, int) - Static method in class deepnetts.util.ImageUtils: Returns an array of images created by translating specified input image by specified number of count with specified step size.
RandomWeights - Class in deepnetts.net.weights: Weights randomization utility methods.
RandomWeights() - Constructor for class deepnetts.net.weights.RandomWeights
RandomWeightsType - Enum in deepnetts.net.weights: Types of supported weights randomization techniques.
Range<T> - Class in deepnetts.automl: A value range of type T for the parameter.
Range(T, T) - Constructor for class deepnetts.automl.Range
Range(T, T, T) - Constructor for class deepnetts.automl.Range
Range - Class in deepnetts.util
Range(float, float) - Constructor for class deepnetts.util.Range
range(float[]) - Static method in class deepnetts.util.StatUtils
RangeConsumer - Interface in deepnetts.util
RangeScaler - Class in deepnetts.data.norm: Normalize data set to specified range.
RangeScaler(float, float) - Constructor for class deepnetts.data.norm.RangeScaler: Creates a new instance of range normalizer initialized to given min and max values.
readCsv(File, int, int, boolean, String) - Static method in class deepnetts.data.DataSets: Creates and returns data set from specified CSV file.
readCsv(String, int, int, boolean, String) - Static method in class deepnetts.data.DataSets
readCsv(String, int, int, boolean) - Static method in class deepnetts.data.DataSets
readCsv(String, int, int, String) - Static method in class deepnetts.data.DataSets
readCsv(String, int, int) - Static method in class deepnetts.data.DataSets: Create data set from CSV file, using coma (,) as default delimiter and no header (column names) in first row.
recall - Variable in class deepnetts.eval.ClassificationMetrics.Stats
RegresionEvaluator - Class in deepnetts.eval: Evaluates regressor neural network for specified data set.
RegresionEvaluator() - Constructor for class deepnetts.eval.RegresionEvaluator
RegressionMetrics - Class in deepnetts.eval: Common metrics for regression models.
RegressionMetrics() - Constructor for class deepnetts.eval.RegressionMetrics
Relu - Class in deepnetts.net.layers.activation: Rectified Linear Activation and its Derivative.
Relu() - Constructor for class deepnetts.net.layers.activation.Relu
removeHandler(Handler) - Method in class deepnetts.util.DeepNettsLogManager
removeListener(TrainingListener) - Method in class deepnetts.net.train.BackpropagationTrainer: Removes training listener from this trainer.
renameFilesAsClasses(String, String) - Static method in class deepnetts.util.ImageSetUtils: Renames files in specified directory.
replace(float, float) - Method in class deepnetts.util.Tensor
reset() - Method in class deepnetts.net.loss.BinaryCrossEntropyLoss
reset() - Method in class deepnetts.net.loss.CrossEntropyLoss
reset() - Method in interface deepnetts.net.loss.LossFunction: Resets the total error and pattern counter.
reset() - Method in class deepnetts.net.loss.MeanSquaredErrorLoss
RmsPropOptimizer - Class in deepnetts.net.train.opt: A variation of AdaDelta optimizer.
RmsPropOptimizer(AbstractLayer) - Constructor for class deepnetts.net.train.opt.RmsPropOptimizer
RootMeanSquaredError - Class in deepnetts.eval: A measure of error for regression tasks.
RootMeanSquaredError() - Constructor for class deepnetts.eval.RootMeanSquaredError
ROW_IDX - Static variable in interface deepnetts.net.train.opt.Optimizer
ROW_IDX - Static variable in class deepnetts.util.Shape
run() - Method in class deepnetts.automl.HyperParameterSearch
run() - Method in class deepnetts.net.train.KFoldCrossValidation
run() - Method in class deepnetts.util.AugmentImagesTest
run(String) - Method in class deepnetts.util.DeepNettsRunner
run(Collection<Callable<Void>>) - Method in class deepnetts.util.DeepNettsThreadPool
run(Runnable) - Method in class deepnetts.util.DeepNettsThreadPool
run() - Method in class deepnetts.util.DeleteAugmentImages
run() - Method in class deepnetts.util.RandomlyTranslateImages
run() - Method in class deepnetts.util.RunScaleImages
RunScaleImages - Class in deepnetts.util
RunScaleImages() - Constructor for class deepnetts.util.RunScaleImages

S

save(String) - Method in class deepnetts.net.NeuralNetwork: Saves this network using serialization to file with specified fileName.
scaleAndCenter(BufferedImage, int, int, int, Color) - Static method in class deepnetts.util.ImageUtils: Scales input image to specified target width or height, centers and returns resulting image.
scaleBySmallerAndCrop(BufferedImage, int, int) - Static method in class deepnetts.util.ImageUtils: Scales input image to specified target width or height, crops and returns resulting image.
scaleBySmallerTarget(BufferedImage, int, int) - Static method in class deepnetts.util.ImageUtils
scaleImage(BufferedImage, int, int) - Static method in class deepnetts.util.ImageUtils: Scales specified image to given size and returns new image with specified width and height.
scaleInput(Tensor) - Method in class deepnetts.data.norm.AbstractScaler: Normalize input of deployed model
scaleInput(Tensor) - Method in class deepnetts.data.norm.DecimalScaler
scaleInput(Tensor) - Method in class deepnetts.data.norm.MaxScaler
scaleInput(Tensor) - Method in class deepnetts.data.norm.MinMaxScaler
scaleInput(Tensor) - Method in class deepnetts.data.norm.RangeScaler
scaleInput(Tensor) - Method in class deepnetts.data.norm.Standardizer
scaleToMax(DataSet) - Static method in class deepnetts.data.DataSets
scaleToMinMax(DataSet) - Static method in class deepnetts.data.DataSets
SearchStrategy - Enum in deepnetts.automl: Strategy for hyper-parameter search.
set(float, int) - Method in class deepnetts.util.Tensor: Sets value at specified index position.
set(float, int, int) - Method in class deepnetts.util.Tensor: Set value at specified [row, col] position.
set(float, int, int, int) - Method in class deepnetts.util.Tensor
set(float, int, int, int, int) - Method in class deepnetts.util.Tensor
setBatchMode(boolean) - Method in class deepnetts.net.layers.AbstractLayer
setBatchMode(boolean) - Method in class deepnetts.net.train.BackpropagationTrainer: Sets flag whether to use batch mode during the training.
setBatchSize(int) - Method in class deepnetts.net.layers.AbstractLayer
setBatchSize(int) - Method in class deepnetts.net.train.BackpropagationTrainer: Batch size is number of training examples after which network's weights are adjusted.
setBiases(float[]) - Method in class deepnetts.net.layers.AbstractLayer
setBins(int[]) - Method in class deepnetts.util.StatUtils.Histogram
setBinWidth(float) - Method in class deepnetts.util.StatUtils.Histogram
setBrightness(boolean) - Method in class deepnetts.data.ImageSet
setCheckpointEpochs(int) - Method in class deepnetts.net.train.BackpropagationTrainer: On how many epochs the snapshots of the trained network should be created.
setClassLabel(String) - Method in class deepnetts.eval.ClassificationMetrics: Sets class label to which this metrics corresponds too
setColumnNames(String[]) - Method in class deepnetts.data.TabularDataSet
setColumnNames(String[]) - Method in class deepnetts.util.CsvFormat
setColumnTypes(ColumnType[]) - Method in class deepnetts.util.CsvFormat
setCount(int) - Method in class deepnetts.util.DescriptiveStatistics
setCropCornersAndCenter(boolean) - Method in class deepnetts.data.ImageSet
setDelimiter(String) - Method in class deepnetts.data.ImageSet
setDelimiter(String) - Method in class deepnetts.util.CsvFormat
setDeltas(Tensor) - Method in class deepnetts.net.layers.AbstractLayer
setDropout(float) - Method in class deepnetts.net.layers.FullyConnectedLayer
setDropout(float) - Method in class deepnetts.net.train.BackpropagationTrainer: Dropout is a technique to prevent overfitting, which skips adjusting weights for some neurons with given probability.
setEarlyStopping(boolean) - Method in class deepnetts.net.train.BackpropagationTrainer: Early stopping stops training if it starts converging slow, and prevents overfitting.
setEarlyStoppingMinLossChange(float) - Method in class deepnetts.net.train.BackpropagationTrainer: Early stopping stops training if the error/loss start converging to slow.
setEarlyStoppingPatience(int) - Method in class deepnetts.net.train.BackpropagationTrainer: How many epochs to wait to see if the loss is lowering to slow.
setEnabled(boolean) - Method in class deepnetts.util.ImagePreprocessing
setExtendedLogging(boolean) - Method in class deepnetts.net.train.BackpropagationTrainer: Extended logging includes additional info for debugging the training.
setFileHandler(String) - Method in class deepnetts.util.DeepNettsLogManager
setFilters(Tensor) - Method in class deepnetts.net.layers.ConvolutionalLayer
setFilters(String) - Method in class deepnetts.net.layers.ConvolutionalLayer
setFlipHorizontal(boolean) - Method in class deepnetts.data.ImageSet
setFStat(float) - Method in class deepnetts.eval.RegressionMetrics
setGrayscale(boolean) - Method in class deepnetts.data.ImageSet
setHasHeader(boolean) - Method in class deepnetts.util.CsvFormat
setHiddenActivation(ActivationType) - Method in class deepnetts.automl.FeedForwardNetworkFactory
setId(int) - Method in class deepnetts.util.BoundingBox
setInput(Tensor) - Method in class deepnetts.net.ConvolutionalNetwork
setInput(float...) - Method in class deepnetts.net.FeedForwardNetwork: Sets network's input using given inputs and invokes the calculation of the network for the given input (forward pass).
setInput(Tensor) - Method in class deepnetts.net.layers.InputLayer: Sets network input
setInput(Tensor) - Method in class deepnetts.net.NeuralNetwork: Sets network input and calculates entire network (triggers forward pass).
setInvertImages(boolean) - Method in class deepnetts.data.ImageSet
setInvertPixels(boolean) - Method in class deepnetts.util.ImagePreprocessing
setL1Regularization(float) - Method in class deepnetts.net.layers.AbstractLayer
setL1Regularization(float) - Method in class deepnetts.net.train.BackpropagationTrainer: L1 regularization (sum of abs values) is used to prevent overfitting and too large weights.
setL2Regularization(float) - Method in class deepnetts.net.layers.AbstractLayer
setL2Regularization(float) - Method in class deepnetts.net.train.BackpropagationTrainer: L2 regularization (sum of squares) is used to prevent overfitting and too large weights.
setLabel(String) - Method in class deepnetts.net.NeuralNetwork: Sets label(name) for this neural network.
setLabel(String) - Method in class deepnetts.util.BoundingBox
setLearningRate(float) - Method in class deepnetts.net.layers.AbstractLayer
setLearningRate(float) - Method in class deepnetts.net.train.BackpropagationTrainer: Learning rate controls the step size as a percent of the error to use for adjusting internal parameters(weights) of the neural network.
setLearningRate(float) - Method in class deepnetts.net.train.opt.AdaDeltaOptimizer
setLearningRate(float) - Method in class deepnetts.net.train.opt.AdaGradOptimizer
setLearningRate(float) - Method in class deepnetts.net.train.opt.AdamOptimizer
setLearningRate(float) - Method in class deepnetts.net.train.opt.MomentumOptimizer
setLearningRate(float) - Method in interface deepnetts.net.train.opt.Optimizer
setLearningRate(float) - Method in class deepnetts.net.train.opt.RmsPropOptimizer
setLearningRate(float) - Method in class deepnetts.net.train.opt.SgdOptimizer
setLearningRateDecay(float) - Method in class deepnetts.net.train.BackpropagationTrainer: Learning rate decay lowers the learning rate with each epoch by devayRate factor, which may improve error lowering the error.
setLossFunction(LossFunction) - Method in class deepnetts.net.NeuralNetwork: Sets a loss function of this network, which is used to calculate total network error during the training.
setLossType(LossType) - Method in class deepnetts.automl.FeedForwardNetworkFactory
setLossType(LossType) - Method in class deepnetts.net.layers.OutputLayer
setMax(float) - Method in class deepnetts.util.DescriptiveStatistics
setMaxEpochs(long) - Method in class deepnetts.net.train.BackpropagationTrainer: Deprecated.
Use setStopEpochs instead
setMaxError(float) - Method in class deepnetts.eval.RegressionMetrics
setMaxError(float) - Method in class deepnetts.net.train.BackpropagationTrainer: Sets stopping error threshold for this training.
setMaxInputs(Tensor) - Method in class deepnetts.data.norm.MaxScaler
setMaxOutputs(Tensor) - Method in class deepnetts.data.norm.MaxScaler
setMean(float) - Method in class deepnetts.util.DescriptiveStatistics
setMean(Tensor) - Method in class deepnetts.util.ImagePreprocessing
setMeanAbsoluteError(float) - Method in class deepnetts.eval.RegressionMetrics
setMeanAbsolutePercentageError(float) - Method in class deepnetts.eval.RegressionMetrics
setMeanSquaredError(float) - Method in class deepnetts.eval.RegressionMetrics
setMedian(float) - Method in class deepnetts.util.DescriptiveStatistics
setMin(float) - Method in class deepnetts.util.DescriptiveStatistics
setMomentum(float) - Method in class deepnetts.net.layers.AbstractLayer
setMomentum(float) - Method in class deepnetts.net.train.BackpropagationTrainer: Momentum settings helps to avoid oscillations in weight changes and get more stable and faster training.
setNextlayer(AbstractLayer) - Method in class deepnetts.net.layers.AbstractLayer
setNormalizer(AbstractScaler) - Method in class deepnetts.net.NeuralNetwork: Sets normalization data normalization method that is applied to network's inputs.
setNumBins(int) - Method in class deepnetts.util.StatUtils.Histogram
setNumInputs(int) - Method in class deepnetts.automl.FeedForwardNetworkFactory
setNumOutputs(int) - Method in class deepnetts.automl.FeedForwardNetworkFactory
setOptimizer(OptimizerType) - Method in class deepnetts.net.train.BackpropagationTrainer
setOptimizerType(OptimizerType) - Method in class deepnetts.net.layers.AbstractLayer
setOptimizerType(OptimizerType) - Method in class deepnetts.net.layers.FlattenLayer
setOutputError(float[]) - Method in class deepnetts.net.NeuralNetwork: Sets the network's output errors, which are a difference between actual(predicted) and target output.
setOutputErrors(float[]) - Method in class deepnetts.net.layers.OutputLayer
setOutputLabels(String...) - Method in class deepnetts.net.NeuralNetwork: Sets output labels of this network.
setOutputs(Tensor) - Method in class deepnetts.net.layers.AbstractLayer
setPreprocessing(Preprocessing<Tensor>) - Method in class deepnetts.net.NeuralNetwork: Sets preprocessing that needs to be performed before input is fed to this network.
setPrevDeltaWeights(Tensor) - Method in class deepnetts.net.layers.AbstractLayer
setPrevLayer(AbstractLayer) - Method in class deepnetts.net.layers.AbstractLayer
setProperties(Properties) - Method in class deepnetts.net.train.BackpropagationTrainer: Sets properties from available keys in specified prop object.
setQ1(float) - Method in class deepnetts.util.DescriptiveStatistics
setQ3(float) - Method in class deepnetts.util.DescriptiveStatistics
setR2(float) - Method in class deepnetts.eval.RegressionMetrics
setResidualStandardError(float) - Method in class deepnetts.eval.RegressionMetrics
setResizeStrategy(ImageResize) - Method in class deepnetts.data.ImageSet
setRootMeanSquaredError(float) - Method in class deepnetts.eval.RegressionMetrics
setScaleImages(boolean) - Method in class deepnetts.data.ImageSet
setScore(float) - Method in class deepnetts.util.BoundingBox
setShuffle(boolean) - Method in class deepnetts.net.train.BackpropagationTrainer: Sets shuffle flag which determines if training set should be shuffled before each epoch.
setSnapshotEpochs(int) - Method in class deepnetts.net.train.BackpropagationTrainer: On how many epochs to make training snapshots.
setSnapshotPath(String) - Method in class deepnetts.net.train.BackpropagationTrainer: Path to use for making snapshots - saving the current state of trained network during the training in order to be able to restore it from a training point.
setSquaredErrorSum(float) - Method in class deepnetts.eval.RegressionMetrics
setStd(float) - Method in class deepnetts.util.DescriptiveStatistics
setStopAccuracy(float) - Method in class deepnetts.net.train.BackpropagationTrainer
setStopEpochs(long) - Method in class deepnetts.net.train.BackpropagationTrainer: Sets number of epochs/iterations to run the training.
setStopError(float) - Method in class deepnetts.net.train.BackpropagationTrainer: The training stops when/if training error has reached this value.
setSubMean(boolean) - Method in class deepnetts.util.ImagePreprocessing
setTargetOutput(Tensor) - Method in class deepnetts.data.ExampleImage
setTestSet(DataSet<MLDataItem>) - Method in class deepnetts.net.train.BackpropagationTrainer: Test set is used after the training to estimate performance of the trained model and generalization ability with new data.
setThreshold(float) - Method in class deepnetts.eval.ClassifierEvaluator
setTrainable(boolean) - Method in class deepnetts.net.layers.AbstractLayer: Set trainable to false to freeze learned weights.
setTrainer(T) - Method in class deepnetts.net.NeuralNetwork: Sets the training algorithm of this neural network.
setTrainer(T) - Method in interface deepnetts.net.train.TrainerProvider
setTrainingSnapshots(boolean) - Method in class deepnetts.net.train.BackpropagationTrainer: Training snapshots save the current state of the trained neural network during the training in order to be able to restore it from a training point if needed.
setTranslate(boolean) - Method in class deepnetts.data.ImageSet
setValues(float...) - Method in class deepnetts.util.Tensor
setValuesFromString(String) - Method in class deepnetts.util.Tensor: Sets tensor values from CSV string.
setVar(float) - Method in class deepnetts.util.DescriptiveStatistics
setWeights(List<String>) - Method in class deepnetts.net.ConvolutionalNetwork: Sets network's weights for all layers.
setWeights(Tensor) - Method in class deepnetts.net.layers.AbstractLayer
setWeights(String) - Method in class deepnetts.net.layers.AbstractLayer
SgdOptimizer - Class in deepnetts.net.train.opt: Basic Stochastic Gradient Descent optimization algorithm, which iteratively change weights towards value which gives minimum error.
SgdOptimizer(AbstractLayer) - Constructor for class deepnetts.net.train.opt.SgdOptimizer
Shape - Class in deepnetts.util: Immutable class that represents Tensor shape.
Shape(int[]) - Constructor for class deepnetts.util.Shape
shuffle() - Method in class deepnetts.data.TabularDataSet: Shuffles the data set items using the default random generator.
shuffle(int) - Method in class deepnetts.data.TabularDataSet: Shuffles data set items using java random generator initializes with specified seed
shutdown() - Static method in class deepnetts.core.DeepNetts: Shuts down all the threads in DeepNets thread pool.
shutdown() - Method in class deepnetts.util.DeepNettsThreadPool
shutdownNow() - Method in class deepnetts.util.DeepNettsThreadPool
Sigmoid - Class in deepnetts.net.layers.activation: Sigmoid activation function
Sigmoid() - Constructor for class deepnetts.net.layers.activation.Sigmoid
size() - Method in class deepnetts.util.Tensor: Total number of values in tensor.
SoftmaxOutputLayer - Class in deepnetts.net.layers: Output layer with softmax activation function.
SoftmaxOutputLayer(int) - Constructor for class deepnetts.net.layers.SoftmaxOutputLayer
split(double...) - Method in class deepnetts.data.ImageSet: Splits data set into several parts specified by the input parameter partSizes.
split(int) - Method in class deepnetts.data.TabularDataSet: Split data set into specified number of part of equal sizes.
split(double...) - Method in class deepnetts.data.TabularDataSet: Splits data set into several parts specified by the input parameter partSizes.
sqrt() - Method in class deepnetts.util.Tensor
standardDeviation(float[], float) - Static method in class deepnetts.util.StatUtils
Standardizer - Class in deepnetts.data.norm: Performs standardization on inputs in order to get desired statistical properties of the data set (zero mean and one standard deviation).
Standardizer(DataSet<MLDataItem>) - Constructor for class deepnetts.data.norm.Standardizer
STARTED - Static variable in class deepnetts.net.train.TrainingEvent: Specifies that training has started.
Stats() - Constructor for class deepnetts.eval.ClassificationMetrics.Stats
StatUtils - Class in deepnetts.util
StatUtils() - Constructor for class deepnetts.util.StatUtils
StatUtils.Histogram - Class in deepnetts.util
std(float) - Method in class deepnetts.util.Tensor
step(T) - Method in class deepnetts.automl.Range
stop() - Method in class deepnetts.net.train.BackpropagationTrainer: Stops the training.
STOPPED - Static variable in class deepnetts.net.train.TrainingEvent: Specifies that training has stopped.
strategy(String) - Method in class deepnetts.automl.Range
stride(int) - Method in class deepnetts.net.layers.Filter
sub(float, int, int) - Method in class deepnetts.util.Tensor
sub(float, int, int, int) - Method in class deepnetts.util.Tensor
sub(float, int, int, int, int) - Method in class deepnetts.util.Tensor
sub(Tensor) - Method in class deepnetts.util.Tensor: Subtracts specified tensor t from this tensor.
sub(float) - Method in class deepnetts.util.Tensor
sub(Tensor, Tensor) - Static method in class deepnetts.util.Tensor: Subtracts tensor t2 from t1.
sub(float[], float) - Static method in class deepnetts.util.Tensors
sub(float[], float[]) - Static method in class deepnetts.util.Tensors
submit(Callable<?>) - Method in class deepnetts.util.DeepNettsThreadPool: Submit a single task to thread pool.
sumAbs() - Method in class deepnetts.util.Tensor: Returns sum of abs values of this tensor - L1 norm
sumSqr() - Method in class deepnetts.util.Tensor: Returns sum of squared values of this tensor - L2 norm

T

TabularDataSet<T extends MLDataItem> - Class in deepnetts.data: Basic data set with tabular data.
TabularDataSet(int, int) - Constructor for class deepnetts.data.TabularDataSet: Create a new instance of BasicDataSet with specified size of input and output.
TabularDataSet.Item - Class in deepnetts.data: Represents a basic data set item (single row) with input tensor and target vector in a data set.
Tanh - Class in deepnetts.net.layers.activation: Hyperbolic tangens activation function
Tanh() - Constructor for class deepnetts.net.layers.activation.Tanh
Tensor - Class in deepnetts.util: This class represents a wrapper for multidimensional array.
Tensor(float...) - Constructor for class deepnetts.util.Tensor: Creates a single row tensor with specified values.
Tensor(Shape, float...) - Constructor for class deepnetts.util.Tensor
Tensor(float[][]) - Constructor for class deepnetts.util.Tensor: Creates a 2D tensor / matrix with specified values.
Tensor(float[][][]) - Constructor for class deepnetts.util.Tensor: Creates a 3D tensor from specified 3D array
Tensor(float[][][][]) - Constructor for class deepnetts.util.Tensor
Tensor(int) - Constructor for class deepnetts.util.Tensor: Creates an empty single row tensor with specified number of columns.
Tensor(int, float) - Constructor for class deepnetts.util.Tensor
Tensor(int, int) - Constructor for class deepnetts.util.Tensor: Creates a tensor with specified number of rows and columns.
Tensor(int, int, float[]) - Constructor for class deepnetts.util.Tensor
Tensor(int, int, int) - Constructor for class deepnetts.util.Tensor: Creates a 3D tensor with specified number of rows, cols and depth.
Tensor(int, int, int, int) - Constructor for class deepnetts.util.Tensor
Tensor(int, int, int, int, float[]) - Constructor for class deepnetts.util.Tensor
Tensor(int, int, int, float[]) - Constructor for class deepnetts.util.Tensor
Tensor(Tensor) - Constructor for class deepnetts.util.Tensor: Public deep copy / clone constructor.
TensorflowUtils - Class in deepnetts.util
TensorflowUtils() - Constructor for class deepnetts.util.TensorflowUtils
Tensors - Class in deepnetts.util: Static utility methods for tensors.
test(DataSet<? extends MLDataItem>) - Method in class deepnetts.net.NeuralNetwork: Tests how good are predictions of this network using specified test set.
TEST_FILE - Static variable in class deepnetts.util.ImageSetUtils
testSet(DataSet) - Method in class deepnetts.automl.HyperParameterSearch
toJson(NeuralNetwork<?>) - Static method in class deepnetts.util.FileIO: Returns JSON representation of specified neural network object.
toString() - Method in class deepnetts.automl.Parameter
toString() - Method in class deepnetts.automl.Parameters.ParameterCombination
toString() - Method in class deepnetts.automl.Parameters
toString() - Method in class deepnetts.data.TabularDataSet.Item
toString() - Method in class deepnetts.eval.ClassificationMetrics.Stats
toString() - Method in class deepnetts.eval.ClassificationMetrics
toString() - Method in class deepnetts.eval.ClassifierEvaluator
toString() - Method in class deepnetts.eval.ConfusionMatrix
toString() - Method in class deepnetts.eval.RegressionMetrics
toString() - Method in class deepnetts.net.layers.ConvolutionalLayer
toString() - Method in class deepnetts.net.layers.FlattenLayer
toString() - Method in class deepnetts.net.layers.FullyConnectedLayer
toString() - Method in class deepnetts.net.layers.InputLayer
toString() - Method in enum deepnetts.net.layers.LayerType
toString() - Method in class deepnetts.net.layers.MaxPoolingLayer
toString() - Method in class deepnetts.net.layers.OutputLayer
toString() - Method in enum deepnetts.net.loss.LossType
toString() - Method in enum deepnetts.net.NetworkType
toString() - Method in class deepnetts.net.NeuralNetwork: Returns string representation of this network including all layers and settings.
toString() - Method in class deepnetts.net.train.TrainingResult
toString() - Method in class deepnetts.util.BoundingBox
toString() - Method in class deepnetts.util.CsvFormat
toString() - Method in class deepnetts.util.DescriptiveStatistics
toString() - Method in class deepnetts.util.Parameter
toString() - Method in class deepnetts.util.Tensor
train(DataSet<? extends MLDataItem>) - Method in class deepnetts.net.NeuralNetwork: Trains the neural network using specified training set.
train(DataSet<MLDataItem>, DataSet<MLDataItem>) - Method in class deepnetts.net.train.BackpropagationTrainer: Runs training using given training and validation sets.
train(DataSet<?>, double) - Method in class deepnetts.net.train.BackpropagationTrainer: Run training using given training set, and split part of it to use as a validation set.
train(DataSet<? extends MLDataItem>) - Method in class deepnetts.net.train.BackpropagationTrainer: Runs training using specified training set.
train(DataSet<? extends MLDataItem>) - Method in interface deepnetts.net.train.Trainer: Trains this model using specified training set.
TRAIN_FILE - Static variable in class deepnetts.util.ImageSetUtils
Trainer - Interface in deepnetts.net.train: Generic interface for deep learning training algorithm.
TrainerProvider<T extends Trainer> - Interface in deepnetts.net.train: This interface is implemented by trainable deep learning models, in order to provide access to training algorithm.
TrainingEvent - Class in deepnetts.net.train: TrainingEvent is used to notify interested parties that training event has happened.
TrainingEvent(BackpropagationTrainer, TrainingEvent.Type) - Constructor for class deepnetts.net.train.TrainingEvent: Constructs a new TrainingEvent with specified source and type.
TrainingEvent.Type - Enum in deepnetts.net.train: Type of a training event.
trainingListener(TrainingListener) - Method in class deepnetts.automl.HyperParameterSearch
trainingListener(TrainingListener) - Method in class deepnetts.net.train.KFoldCrossValidation.Builder
TrainingListener - Interface in deepnetts.net.train: The listener interface for receiving notifications about training events.
TrainingResult - Class in deepnetts.net.train: All information about the completed training including training settings, epochs, loss and evaluation metrics.
TrainingResult(Properties, int, float, EvaluationMetrics) - Constructor for class deepnetts.net.train.TrainingResult
trainingSet(DataSet) - Method in class deepnetts.automl.HyperParameterSearch
trainingSet(DataSet) - Method in class deepnetts.net.train.KFoldCrossValidation.Builder
TrainTestPair - Class in deepnetts.data: This class holds training and test data set pair.
TrainTestPair(DataSet, DataSet) - Constructor for class deepnetts.data.TrainTestPair
trainTestSplit(DataSet<?>, double) - Static method in class deepnetts.data.DataSets
translateImage(BufferedImage) - Static method in class deepnetts.util.ImageUtils: Generates 4 translated variations of the given image: translates it for one third of an image width and height in each direction.
TRUE_NEGATIVE - Static variable in class deepnetts.eval.ConfusionMatrix: A label for items classified as negative which are really negative.
TRUE_POSITIVE - Static variable in class deepnetts.eval.ConfusionMatrix: A label for items classified as positive which are really positive.
TypedProperties - Class in deepnetts.util: Provides methods for getting typed properties for the given key.
TypedProperties(Properties) - Constructor for class deepnetts.util.TypedProperties

U

uniform(float[], int) - Static method in class deepnetts.net.weights.RandomWeights: Uniform U[-a,a] where a=1/sqrt(in).
uniform(float[], float, float) - Static method in class deepnetts.net.weights.RandomWeights
updateLearningRate(float) - Method in class deepnetts.net.train.BackpropagationTrainer: Updates learning rate for all layers during the learning rate decay.

V

VALID_UNTIL_DATE - Static variable in class deepnetts.util.LicenseChecker
valueFor(NeuralNetwork, DataSet<? extends MLDataItem>) - Method in interface deepnetts.net.loss.LossFunction: Calculates and returns loss function value for the given neural network and data set.
valueOf(String) - Static method in enum deepnetts.automl.SearchStrategy: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum deepnetts.net.layers.activation.ActivationType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum deepnetts.net.layers.LayerType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum deepnetts.net.loss.LossType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum deepnetts.net.NetworkType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum deepnetts.net.train.opt.OptimizerType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum deepnetts.net.train.TrainingEvent.Type: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum deepnetts.net.weights.RandomWeightsType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum deepnetts.util.ColumnType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum deepnetts.util.ImageResize: Returns the enum constant of this type with the specified name.
values() - Method in class deepnetts.automl.Parameters
values() - Static method in enum deepnetts.automl.SearchStrategy: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum deepnetts.net.layers.activation.ActivationType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum deepnetts.net.layers.LayerType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum deepnetts.net.loss.LossType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum deepnetts.net.NetworkType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum deepnetts.net.train.opt.OptimizerType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum deepnetts.net.train.TrainingEvent.Type: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum deepnetts.net.weights.RandomWeightsType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum deepnetts.util.ColumnType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum deepnetts.util.ImageResize: Returns an array containing the constants of this enum type, in the order they are declared.
valuesAsString(Tensor[]) - Static method in class deepnetts.util.Tensor
variance(float[], float) - Static method in class deepnetts.util.StatUtils
version() - Method in class deepnetts.core.DeepNetts: Returns the Deep Netts version.

W

widrowHoff(float[], float, float) - Static method in class deepnetts.net.weights.RandomWeights
writeImages(List<BufferedImage>, String, String, String) - Static method in class deepnetts.util.ImageUtils: Writes list of images to specified file path.
writeToFile(NeuralNetwork, String) - Static method in class deepnetts.util.FileIO: Serializes specified neural network to file with specified file.
writeToFile(List<String>, String) - Static method in class deepnetts.util.ImageSetUtils: Writes a given list of strings to file.
writeToFileAsJson(NeuralNetwork, String) - Static method in class deepnetts.util.FileIO

X

xavier(float[], int, int) - Static method in class deepnetts.net.weights.RandomWeights: Normalized uniform initialization U[-a,a] with a = sqrt(6/(in + out)).

Z

zeroMean() - Method in class deepnetts.data.ImageSet: Applies zero mean normalization to entire dataset, and returns mean tensor.
zeros(int) - Static method in class deepnetts.util.Tensors

A B C D E F G H I K L M N O P R S T U V W X Z