Index (com.deepnetts:deepnetts-core 1.13.2 API)

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

A

absMax(float[], float[]) - Static method in class deepnetts.util.Tensors: Returns array with max values for each position in the given input vectors.
absMax(Tensor, Tensor) - Static method in class deepnetts.util.Tensors: Returns tensors with max value for each component of input tensors.
absMin(float[], float[]) - Static method in class deepnetts.util.Tensors
absMin(Tensor, Tensor) - Static method in class deepnetts.util.Tensors
AbstractLayer - Class in deepnetts.net.layers: Base class for different types of layers (except data/input layer) Provides common functionality for all type of layers
AbstractLayer() - Constructor for class deepnetts.net.layers.AbstractLayer
accuracy - Variable in class deepnetts.eval.ClassificationMetrics.Stats
activation - Variable in class deepnetts.net.layers.AbstractLayer
ActivationFunction - Interface in deepnetts.net.layers.activation: Common base interface for all activation functions used in layers.
activationType - Variable in class deepnetts.net.layers.AbstractLayer: Activation function type for this layer.
ActivationType - Enum in deepnetts.net.layers.activation: Supported types of activation functions.
add(float[], float[]) - Method in class deepnetts.eval.MeanSquaredError
add(float[], float[]) - Method in class deepnetts.eval.RootMeanSquaredError
add(float[], float[]) - Static method in class deepnetts.util.Tensors
add(int, float) - Method in class deepnetts.util.Tensor
add(int, int, float) - Method in class deepnetts.util.Tensor: Adds specified value to matrix value at position x, y
add(int, int, int, float) - Method in class deepnetts.util.Tensor
add(int, int, int, int, float) - Method in class deepnetts.util.Tensor
add(ExampleImage) - Method in class deepnetts.data.ImageSet: Adds image to this image set.
add(Tensor) - Method in class deepnetts.data.DataSetStats
add(Tensor) - Method in class deepnetts.util.Tensor: Adds specified tensor t to this tensor.
addConvolutionalLayer(int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder
addConvolutionalLayer(int, int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder
addConvolutionalLayer(int, int, int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder
addConvolutionalLayer(int, int, int, int, ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder
addConvolutionalLayer(int, int, int, ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder
addConvolutionalLayer(int, int, ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder
addFullyConnectedLayer(int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder
addFullyConnectedLayer(int) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Adds fully connected layer with specified width and Sigmoid activation function to the network.
addFullyConnectedLayer(int, ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Add dense layer with specified width and activation function.
addFullyConnectedLayer(int, ActivationType) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Adds fully connected addLayer with specified width and activation function to the network.
addFullyConnectedLayers(int...) - Method in class deepnetts.net.FeedForwardNetwork.Builder
addFullyConnectedLayers(ActivationType, int...) - Method in class deepnetts.net.FeedForwardNetwork.Builder
addInputLayer(int) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Adds input addLayer with specified width to the network.
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).
addLayer(AbstractLayer) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Adds custom layer to this network (which inherits from AbstractLayer)
addLayer(AbstractLayer) - Method in class deepnetts.net.NeuralNetwork
addListener(TrainingListener) - Method in class deepnetts.net.train.BackpropagationTrainer
addMaxPoolingLayer(int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder
addMaxPoolingLayer(int, int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder
addOutputLayer(int, ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder
addOutputLayer(int, ActivationType) - Method in class deepnetts.net.FeedForwardNetwork.Builder
addOutputLayer(int, Class<? extends OutputLayer>) - Method in class deepnetts.net.ConvolutionalNetwork.Builder
addPatternError(float[], float[]) - Method in class deepnetts.net.loss.BinaryCrossEntropyLoss: Calculates pattern error to total error, and returns output error vector for specified actual and target outputs.
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 regularization sum to loss function
addRegularizationSum(float) - Method in class deepnetts.net.loss.MeanSquaredErrorLoss
apply(Function<Float, Float>) - Method in class deepnetts.util.Tensor
apply(DataSet<MLDataItem>) - Method in class deepnetts.data.preprocessing.scale.DecimalScaler: Performs normalization on the given inputs.
apply(DataSet<MLDataItem>) - Method in class deepnetts.data.preprocessing.scale.MaxScaler: Performs max scaling on all columns of the given data set.
apply(DataSet<MLDataItem>) - Method in class deepnetts.data.preprocessing.scale.MinMaxScaler: Performs scaling on the given data set.
apply(DataSet<MLDataItem>) - Method in class deepnetts.data.preprocessing.scale.RangeScaler: Performs normalization on the given inputs.
apply(DataSet<MLDataItem>) - Method in class deepnetts.data.preprocessing.scale.Standardizer
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.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: Apply calculated weight changes to all layers.
average(ClassificationMetrics[]) - Static method in class deepnetts.eval.ClassificationMetrics
averagePerformance(List<EvaluationMetrics>) - Static method in class deepnetts.eval.ClassifierEvaluator: Calculates macro average for the given list of perfromance measures.
averagePerformance(List<EvaluationMetrics>) - Static method in class deepnetts.eval.RegresionEvaluator

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 and instance of Backpropagation Trainer for the specified neural network.
BackpropagationTrainer(Properties) - Constructor for class deepnetts.net.train.BackpropagationTrainer
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.FullyConnectedLayer
backward() - Method in class deepnetts.net.layers.InputLayer: This method does nothing in input layer
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
batchMode - Variable in class deepnetts.net.layers.AbstractLayer
batchSize - Variable in class deepnetts.net.layers.AbstractLayer
biases - Variable in class deepnetts.net.layers.AbstractLayer
BINARY - deepnetts.util.ColumnType
BinaryCrossEntropyLoss - Class in deepnetts.net.loss: Cross Entropy Loss for binary(single output, two classes) classification.
BinaryCrossEntropyLoss(NeuralNetwork) - Constructor for class deepnetts.net.loss.BinaryCrossEntropyLoss
BoundingBox - Class in deepnetts.util: A rectangle to mark object in image including position, size, score and label.
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, float) - 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
build() - Method in class deepnetts.net.FeedForwardNetwork.Builder
build() - Method in class deepnetts.net.train.KFoldCrossValidation.Builder
builder() - Static method in class deepnetts.net.ConvolutionalNetwork
builder() - Static method in class deepnetts.net.FeedForwardNetwork: Returns builder for Feed Forward Network
builder() - Static method in class deepnetts.net.train.KFoldCrossValidation
Builder() - Constructor for class deepnetts.net.ConvolutionalNetwork.Builder
Builder() - Constructor for class deepnetts.net.FeedForwardNetwork.Builder
Builder() - Constructor for class deepnetts.net.train.KFoldCrossValidation.Builder

C

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.SgdOptimizer
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.SgdOptimizer
CenterOnWhiteBackground - Class in deepnetts.util: Center images on backgounds and save at target path.
CenterOnWhiteBackground() - Constructor for class deepnetts.util.CenterOnWhiteBackground
ClassificationMetrics - Class in deepnetts.eval: Container class for all metrics which use confusion matrix for their computation.
ClassificationMetrics(int, int, int, int) - Constructor for class deepnetts.eval.ClassificationMetrics: Constructs a new measure using arguments
ClassificationMetrics(ConfusionMatrix) - Constructor for class deepnetts.eval.ClassificationMetrics
ClassificationMetrics.Stats - Class in deepnetts.eval
ClassifierEvaluator - Class in deepnetts.eval: Evaluation method for binary and multi-class classifiers.
ClassifierEvaluator() - Constructor for class deepnetts.eval.ClassifierEvaluator
COL_IDX - Static variable in class deepnetts.net.train.opt.MomentumOptimizer
columnNames - Variable in class deepnetts.data.TabularDataSet
ColumnType - Enum in deepnetts.util
ConfusionMatrix - Class in deepnetts.eval: Confusion matrix container, holds class labels and matrix values.
ConfusionMatrix(String[]) - Constructor for class deepnetts.eval.ConfusionMatrix: Creates a new confusion matrix for specified class labels
CONVOLUTIONAL - deepnetts.net.layers.LayerType
CONVOLUTIONAL - deepnetts.net.NetworkType
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 filter.
ConvolutionalLayer(int, int, int, int, ActivationType) - Constructor for class deepnetts.net.layers.ConvolutionalLayer
ConvolutionalLayer(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 specialized to learn to recognize features in images.
ConvolutionalNetwork.Builder - Class in deepnetts.net
copy() - Method in class deepnetts.util.Tensor
copy(float[], float[]) - Static method in class deepnetts.util.Tensor
copy(Tensor, Tensor) - Static method in class deepnetts.util.Tensor
copyFrom(float[]) - Method in class deepnetts.util.Tensor
copyOf(float[]) - Static method in class deepnetts.util.Tensors
correlationCoefficient - Variable in class deepnetts.eval.ClassificationMetrics.Stats
countByClasses() - Method in class deepnetts.data.ImageSet
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
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
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(File) - Static method in class deepnetts.util.FileIO
createFromFile(String, Class<T>) - Static method in class deepnetts.util.FileIO
createFromJson(File) - Static method in class deepnetts.util.FileIO
createFromJson(String) - Static method in class deepnetts.util.FileIO
createFromJson(JSONObject) - Static method in class deepnetts.util.FileIO
createImageIndex(String) - Static method in class deepnetts.util.ImageSetUtils: List all files in all subdirectories and write them into single index.txt file.index.txt is created in specifed path.
CreateImageIndex - Class in deepnetts.util
CreateImageIndex() - Constructor for class deepnetts.util.CreateImageIndex
createIndexFile(HashMap<File, BufferedImage>, String, boolean) - Static method in class deepnetts.util.ImageUtils
createLabelsIndex(String) - Static method in class deepnetts.util.ImageSetUtils: Creates a labels index file from subdirectories at the given path.
CreateLabelsIndex - Class in deepnetts.util
CreateLabelsIndex() - Constructor for class deepnetts.util.CreateLabelsIndex
createRandomlyCroppedImages(String, String, int, int, int) - Static method in class deepnetts.util.ImageSetUtils
createStats(TabularDataSet) - Static method in class deepnetts.data.DataSetStats
createsTrainingSnaphots() - Method in class deepnetts.net.train.BackpropagationTrainer
createSubSampledImageIndex(String, String, int, boolean) - Static method in class deepnetts.util.ImageSetUtils: Copies specified number of samples of each class from
CROSS_ENTROPY - deepnetts.net.loss.LossType: Cross Entropy Loss, used for classificaton tasks, implemented by CrossEntropyLoss
CrossEntropyLoss - Class in deepnetts.net.loss: Represents Average Cross Entropy Loss function.
CrossEntropyLoss(NeuralNetwork) - Constructor for class deepnetts.net.loss.CrossEntropyLoss
CsvFormat - Class in deepnetts.util
CsvFormat() - Constructor for class deepnetts.util.CsvFormat

D

dataSet(DataSet) - Method in class deepnetts.net.train.KFoldCrossValidation.Builder
DataSets - Class in deepnetts.data: Data set related utility methods.
DataSets() - Constructor for class deepnetts.data.DataSets
DataSetStats - Class in deepnetts.data
DataSetStats(Tensor) - Constructor for class deepnetts.data.DataSetStats
DECIMAL - deepnetts.util.ColumnType
DecimalScaler - Class in deepnetts.data.preprocessing.scale: Decimal scale normalization for the given data set.
DecimalScaler(DataSet<MLDataItem>) - Constructor for class deepnetts.data.preprocessing.scale.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.android - package deepnetts.android
deepnetts.core - package deepnetts.core: Core engine configuration and settings.
deepnetts.data - package deepnetts.data: Data collections used to build machine learning models.
deepnetts.data.preprocessing - package deepnetts.data.preprocessing: Data pre-processing techniques.
deepnetts.data.preprocessing.scale - package deepnetts.data.preprocessing.scale
deepnetts.eval - package deepnetts.eval: Evaluation for machine learning models, used to estimate how good they are performing for given data.
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: Deep learning training algorithms and 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.
deepnetts.util - package deepnetts.util: Various utility classes including Tensor, image operations, 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
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
deltaBiases - Variable in class deepnetts.net.layers.AbstractLayer
deltas - Variable in class deepnetts.net.layers.AbstractLayer: Deltas used for learning
deltaWeights - Variable in class deepnetts.net.layers.AbstractLayer: Weight changes for current and previous iteration
deNormalizeInputs(Tensor) - Method in class deepnetts.data.preprocessing.scale.MaxScaler
deNormalizeOutputs(Tensor) - Method in class deepnetts.data.preprocessing.scale.MaxScaler: De-normalize given output vector in-place.
depth - Variable in class deepnetts.net.layers.AbstractLayer
detectCsvFormat(String) - Static method in class deepnetts.data.DataSets
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(float[], float) - Static method in class deepnetts.util.Tensors
div(float[], float[]) - Static method in class deepnetts.util.Tensors
div(Tensor) - Method in class deepnetts.util.Tensor

E

EPOCH_FINISHED - deepnetts.net.train.TrainingEvent.Type
equals(Tensor, float) - Method in class deepnetts.util.Tensor
equals(Object) - 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<MLDataItem>) - Method in class deepnetts.eval.RegresionEvaluator
evaluateClassifier(NeuralNetwork<?>, DataSet<MLDataItem>) - Static method in class deepnetts.eval.Evaluators
evaluateRegressor(NeuralNetwork<?>, DataSet<MLDataItem>) - Static method in class deepnetts.eval.Evaluators
evaluator(Evaluator<NeuralNetwork, DataSet<? extends MLDataItem>>) - Method in class deepnetts.net.train.KFoldCrossValidation.Builder
Evaluators - Class in deepnetts.eval: This class provides various utility methods for evaluating machine learning models.
ExampleImage - Class in deepnetts.data: This class represents example image to train the network.
ExampleImage(BufferedImage) - Constructor for class deepnetts.data.ExampleImage
ExampleImage(BufferedImage, String) - Constructor for class deepnetts.data.ExampleImage
ExampleImage(BufferedImage, String, int, int) - 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

F

FALSE_NEGATIVE - Static variable in class deepnetts.eval.ConfusionMatrix
FALSE_POSITIVE - Static variable in class deepnetts.eval.ConfusionMatrix
FEEDFORWARD - deepnetts.net.NetworkType
FeedForwardNetwork - Class in deepnetts.net: Feed forward neural network architecture, also known as Multi Layer Perceptron.
FeedForwardNetwork.Builder - Class in deepnetts.net: Builder for FeedForwardNetwork
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
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.FullyConnectedLayer
forward() - Method in class deepnetts.net.layers.InputLayer: This method does nothing in input layer
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.
forward() - Method in class deepnetts.net.NeuralNetwork
fScore - Variable in class deepnetts.eval.ClassificationMetrics.Stats
FULLY_CONNECTED - deepnetts.net.layers.LayerType
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 sigmoid 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
GAUSSIAN - deepnetts.net.weights.RandomWeightsType
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.
GenerateRandomNegative - Class in deepnetts.util
GenerateRandomNegative() - Constructor for class deepnetts.util.GenerateRandomNegative
get(int) - Method in class deepnetts.util.Tensor: Gets value at specified index position.
get(int, int) - Method in class deepnetts.eval.ConfusionMatrix: Returns a value of confusion matrix at specified 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 row, col, z
get(int, int, int, int) - Method in class deepnetts.util.Tensor
getAccuracy() - Method in class deepnetts.eval.ClassificationMetrics: A percent of correct classiifcations/predictions for both positive and negative classes.
getActivation() - Method in class deepnetts.net.layers.AbstractLayer
getActivationType() - Method in class deepnetts.net.layers.AbstractLayer
getAllOutputs() - Method in class deepnetts.net.ConvolutionalNetwork
getBalancedClassificationRate() - Method in class deepnetts.eval.ClassificationMetrics
getBatchSize() - Method in class deepnetts.net.layers.AbstractLayer
getBatchSize() - Method in class deepnetts.net.train.BackpropagationTrainer
getBiases() - Method in class deepnetts.net.layers.AbstractLayer
getCheckpointEpochs() - Method in class deepnetts.net.train.BackpropagationTrainer
getClassCount() - Method in class deepnetts.eval.ConfusionMatrix
getClassLabel() - Method in class deepnetts.eval.ClassificationMetrics: Returns class label for
getClassLabels() - Method in class deepnetts.eval.ConfusionMatrix
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.ClassifierEvaluator
getContext(String, ClassLoader, Object, boolean) - Method in class deepnetts.android.LoggerContextFactory_Dummy: Creates a LoggerContext.
getContext(String, ClassLoader, Object, boolean, URI, String) - Method in class deepnetts.android.LoggerContextFactory_Dummy: Creates a LoggerContext.
getCurrentEpoch() - Method in class deepnetts.net.train.BackpropagationTrainer
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 (as a tensor).
getDeltaWeights() - Method in class deepnetts.net.ConvolutionalNetwork
getDeltaWeights() - Method in class deepnetts.net.layers.AbstractLayer
getDepth() - Method in class deepnetts.net.layers.AbstractLayer
getDepth() - Method in class deepnetts.util.Tensor
getDimensions() - Method in class deepnetts.util.Tensor
getEarlyStopping() - Method in class deepnetts.net.train.BackpropagationTrainer
getEarlyStoppingMinDelta() - Method in class deepnetts.net.train.BackpropagationTrainer
getEarlyStoppingPatience() - Method in class deepnetts.net.train.BackpropagationTrainer
getErrorRate() - Method in class deepnetts.eval.ClassificationMetrics: A percent of wrong classifications/predictions made.
getF1Score() - Method in class deepnetts.eval.ClassificationMetrics: Calculates and returns F1 score - harmonic mean of recall and precision f1 = 2 * ( (precision*recall) / (precision+recall)) https://en.wikipedia.org/wiki/F1_score
getFalseDiscoveryRate() - Method in class deepnetts.eval.ClassificationMetrics: When its actauly no, how often it is classified as yes
getFalseNegative() - Method in class deepnetts.eval.ConfusionMatrix: How many positive items has been (falsely) classified as negative.
getFalseNegative(int) - Method in class deepnetts.eval.ConfusionMatrix
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
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
getFloat(String) - Method in class deepnetts.util.TypedProperties
getFourthDim() - Method in class deepnetts.util.Tensor
getFScore(int) - Method in class deepnetts.eval.ClassificationMetrics: Returns the F-score.
getGradients() - Method in class deepnetts.net.layers.AbstractLayer
getHeight() - Method in class deepnetts.data.ExampleImage
getHeight() - Method in class deepnetts.net.layers.AbstractLayer
getHeight() - Method in class deepnetts.util.BoundingBox
getId() - Method in class deepnetts.util.BoundingBox
getImageType(File) - Static method in class deepnetts.util.ImageUtils
getInput() - Method in class deepnetts.data.ExampleImage
getInput() - Method in interface deepnetts.data.MLDataItem
getInput() - Method in class deepnetts.data.TabularDataSet.Item
getInputLayer() - Method in class deepnetts.net.NeuralNetwork
getInstance() - Static method in class deepnetts.core.DeepNetts
getInt(String) - Method in class deepnetts.util.TypedProperties
getInvertImages() - Method in class deepnetts.data.ImageSet
getL1() - Method in class deepnetts.net.layers.AbstractLayer
getL1Reg() - Method in class deepnetts.net.NeuralNetwork
getL2() - Method in class deepnetts.net.layers.AbstractLayer
getL2Reg() - Method in class deepnetts.net.NeuralNetwork
getLabel() - Method in class deepnetts.data.ExampleImage
getLabel() - Method in class deepnetts.net.NeuralNetwork
getLabel() - Method in class deepnetts.util.BoundingBox
getLabelsCount() - Method in class deepnetts.data.ImageSet
getLayers() - Method in class deepnetts.net.NeuralNetwork
getLearningRate() - Method in class deepnetts.net.layers.AbstractLayer
getLearningRate() - Method in class deepnetts.net.train.BackpropagationTrainer
getLossFunction() - Method in class deepnetts.net.NeuralNetwork
getLossType() - Method in class deepnetts.net.layers.OutputLayer
getMatthewsCorrelationCoefficient() - Method in class deepnetts.eval.ClassificationMetrics
getMax() - Method in class deepnetts.data.DataSetStats
getMaxEpochs() - Method in class deepnetts.net.train.BackpropagationTrainer
getMaxError() - Method in class deepnetts.net.train.BackpropagationTrainer
getMaxInputs() - Method in class deepnetts.data.preprocessing.scale.MaxScaler
getMaxOutputs() - Method in class deepnetts.data.preprocessing.scale.MaxScaler
getMean() - Method in class deepnetts.data.DataSetStats
getMeanSquaredSum() - Method in class deepnetts.eval.MeanSquaredError: Returns mean squared error
getMin() - Method in class deepnetts.data.DataSetStats
getMomentum() - Method in class deepnetts.net.layers.AbstractLayer
getMomentum() - Method in class deepnetts.net.train.BackpropagationTrainer
getNextLayer() - Method in class deepnetts.net.layers.AbstractLayer
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.train.BackpropagationTrainer
getOptimizerType() - Method in class deepnetts.net.layers.AbstractLayer
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
getOutputLabels() - Method in class deepnetts.net.NeuralNetwork
getOutputLayer() - Method in class deepnetts.net.NeuralNetwork
getOutputs() - Method in class deepnetts.net.layers.AbstractLayer
getOutputs() - Method in interface deepnetts.net.layers.Layer: Returns layer outputs (as a tensor).
getPerformanceByClass() - Method in class deepnetts.eval.ClassifierEvaluator
getPrecision() - Method in class deepnetts.eval.ClassificationMetrics: What percent of those predicted as positive are really positive.
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.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
getProperty(String) - Method in class deepnetts.core.DeepNetts
getRandom() - Method in class deepnetts.util.RandomGenerator
getRecall() - Method in class deepnetts.eval.ClassificationMetrics: Ratio between those classified as positive compared to those that are actually positive, When ACTUAL class is YES, how often classifier predicts YES? Recall, sensitivity, or true positive rate.
getRgbVector() - Method in class deepnetts.data.ExampleImage
getRootMeanSquaredSum() - Method in class deepnetts.eval.MeanSquaredError
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
getShuffle() - Method in class deepnetts.net.train.BackpropagationTrainer
getSnapshotEpochs() - Method in class deepnetts.net.train.BackpropagationTrainer
getSnapshotPath() - Method in class deepnetts.net.train.BackpropagationTrainer
getSource() - Method in class deepnetts.net.train.TrainingEvent
getSpecificity() - Method in class deepnetts.eval.ClassificationMetrics: Specificity or true negative rate.
getSquaredSum() - Method in class deepnetts.eval.MeanSquaredError: Returns squared error sum (RSS, or residual square sum)
getStd() - Method in class deepnetts.data.DataSetStats
getStride() - Method in class deepnetts.net.layers.ConvolutionalLayer
getStride() - Method in class deepnetts.net.layers.MaxPoolingLayer
getString(String) - Method in class deepnetts.util.TypedProperties
getTargetOutput() - Method in class deepnetts.data.ExampleImage
getTargetOutput() - Method in interface deepnetts.data.MLDataItem
getTargetOutput() - Method in class deepnetts.data.TabularDataSet.Item
getTestSet() - Method in class deepnetts.net.train.BackpropagationTrainer
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
getTotalAverage() - Method in class deepnetts.eval.ClassifierEvaluator
getTotalItems() - Method in class deepnetts.eval.ConfusionMatrix
getTrainedNetworks() - Method in class deepnetts.net.train.KFoldCrossValidation
getTrainer() - Method in class deepnetts.net.NeuralNetwork
getTrainer() - Method in interface deepnetts.net.train.TrainerProvider
getTrainingAccuracy() - Method in class deepnetts.net.train.BackpropagationTrainer
getTrainingLoss() - Method in class deepnetts.net.train.BackpropagationTrainer
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 for binary classification.
getTruePositive(int) - Method in class deepnetts.eval.ConfusionMatrix: Returns true positive for specified class idx for multiclass classification
getType() - Method in class deepnetts.net.train.TrainingEvent
getValidationAccuracy() - Method in class deepnetts.net.train.BackpropagationTrainer
getValidationLoss() - Method in class deepnetts.net.train.BackpropagationTrainer
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.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
getValues() - Method in class deepnetts.util.Tensor
getVar() - Method in class deepnetts.data.DataSetStats
getWeights() - Method in class deepnetts.net.ConvolutionalNetwork: Returns all weights from this network as list of strings.
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.util.BoundingBox
getWithStride(int[]) - Method in class deepnetts.util.Tensor
getX() - Method in class deepnetts.util.BoundingBox
getY() - Method in class deepnetts.util.BoundingBox
gradients - Variable in class deepnetts.net.layers.AbstractLayer

H

handleEvent(TrainingEvent) - Method in interface deepnetts.net.train.TrainingListener
hashCode() - Method in class deepnetts.util.Tensor
he(float[], int) - Static method in class deepnetts.net.weights.RandomWeights
HE - deepnetts.net.weights.RandomWeightsType
height - Variable in class deepnetts.net.layers.AbstractLayer
hiddenActivationFunction(ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder
hiddenActivationFunction(ActivationType) - Method in class deepnetts.net.FeedForwardNetwork.Builder

I

IMAGE_IDX_FILE - Static variable in class deepnetts.util.ImageSetUtils
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
ImageSet(int, int, String) - Constructor for class deepnetts.data.ImageSet
ImageSetUtils - Class in deepnetts.util
ImageSetUtils() - Constructor for class deepnetts.util.ImageSetUtils
ImageUtils - Class in deepnetts.util: Utility methods to work with images.
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 when layer is added to network (create weights, outputs, deltas, randomization etc.) The code is called in 2 scenarios: 1.
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.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
initSeed(long) - Method in class deepnetts.util.RandomGenerator
INPUT - deepnetts.net.layers.LayerType
InputLayer - Class in deepnetts.net.layers: Input layer in neural network, which accepts external input, and sends it to next layer in a network.
InputLayer(int) - Constructor for class deepnetts.net.layers.InputLayer: Creates input layer with specified width, and with height and depth equals to one.
InputLayer(int, int) - Constructor for class deepnetts.net.layers.InputLayer: Creates input layer with specified width and height, with depth=1 (single channel).
InputLayer(int, int, int) - Constructor for class deepnetts.net.layers.InputLayer: Creates input layer with specified width, height, and depth (number of channels).
inputs - Variable in class deepnetts.net.layers.AbstractLayer: Inputs to this layer (a reference to outputs matrix in prev layer, or external input in input layer)
INTEGER - deepnetts.util.ColumnType
invert() - Method in class deepnetts.data.ExampleImage
invert() - Method in class deepnetts.data.ImageSet
isBatchMode() - Method in class deepnetts.net.layers.AbstractLayer
isBatchMode() - Method in class deepnetts.net.train.BackpropagationTrainer
isHasHeader() - Method in class deepnetts.util.CsvFormat
isImageFile(File) - Method in class deepnetts.util.RandomlyTranslateImages
Item(float[], float[]) - Constructor for class deepnetts.data.TabularDataSet.Item
Item(Tensor, Tensor) - Constructor for class deepnetts.data.TabularDataSet.Item
ITERATION_FINISHED - deepnetts.net.train.TrainingEvent.Type

K

KFoldCrossValidation - Class in deepnetts.net.train: Split data set into k parts of equal sizes (folds) Train with data from k-1 folds(parts), and test with 1 fold, repeat k times each with different test fold.
KFoldCrossValidation() - Constructor for class deepnetts.net.train.KFoldCrossValidation
KFoldCrossValidation.Builder - Class in deepnetts.net.train

L

labels - Variable in class deepnetts.net.layers.OutputLayer
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.
LEAKY_RELU - deepnetts.net.layers.activation.ActivationType
learningRate - Variable in class deepnetts.net.layers.AbstractLayer: Learning rate for this layer
Linear - Class in deepnetts.net.layers.activation: Linear activation function and its derivative y = x y' = 1
Linear() - Constructor for class deepnetts.net.layers.activation.Linear
LINEAR - deepnetts.net.layers.activation.ActivationType
loadFileImageMapFromDirectory(File) - Static method in class deepnetts.util.ImageUtils: Loads images (jpg, jpeg, png) from specificed directory and returns them as a map with File object as a key and BufferedImage object as a value.
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.
loadImages(String) - Method in class deepnetts.data.ImageSet
loadImagesFromDirectory(File) - Static method in class deepnetts.util.ImageUtils: Loads all images from the specified directory, and returns them as a list.
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.
loadLabels(String) - Method in class deepnetts.data.ImageSet: Loads and returns image labels to train neural network from the specified file.
LoggerContextFactory_Dummy - Class in deepnetts.android
LoggerContextFactory_Dummy() - Constructor for class deepnetts.android.LoggerContextFactory_Dummy
lossFunction(LossType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder
lossFunction(LossType) - Method in class deepnetts.net.FeedForwardNetwork.Builder
lossFunction(Class<? extends LossFunction>) - Method in class deepnetts.net.ConvolutionalNetwork.Builder
LossFunction - Interface in deepnetts.net.loss: Base Interface for all loss functions.
lossType - Variable in class deepnetts.net.layers.OutputLayer
LossType - Enum in deepnetts.net.loss: Supported types of Loss Functions in Deep Netts engine.

M

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.GenerateRandomNegative
main(String[]) - Static method in class deepnetts.util.ImageSetUtils
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.ScaleImages
main(String[]) - Static method in class deepnetts.util.Serialization
MAXPOOLING - deepnetts.net.layers.LayerType
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.
MaxScaler - Class in deepnetts.data.preprocessing.scale: Performs max normalization, rescales data to corresponding max value in each column.
MaxScaler(DataSet<MLDataItem>) - Constructor for class deepnetts.data.preprocessing.scale.MaxScaler: Creates a new instance of max normalizer initialized to max values in given data set.
MEAN_SQUARED_ERROR - deepnetts.net.loss.LossType: Mean Squared Error loss, used for regression tasks, implemented by MeanSquaredErrorLoss
MeanSquaredError - Class in deepnetts.eval: This class calculates values used for evaluation metrics for regression problems.
MeanSquaredError() - Constructor for class deepnetts.eval.MeanSquaredError
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.
MINI_BATCH - deepnetts.net.train.TrainingEvent.Type
MinMaxScaler - Class in deepnetts.data.preprocessing.scale: Performs Min Max scaling on the given data set.
MinMaxScaler(DataSet<MLDataItem>) - Constructor for class deepnetts.data.preprocessing.scale.MinMaxScaler: Creates a new instance of max normalizer initialized to max values in given data set.
MLDataItem - Interface in deepnetts.data: Single data item for machine learning algorithms.
model(NeuralNetwork) - Method in class deepnetts.net.train.KFoldCrossValidation.Builder
momentum - Variable in class deepnetts.net.layers.AbstractLayer
MOMENTUM - deepnetts.net.train.opt.OptimizerType
MomentumOptimizer - Class in deepnetts.net.train.opt
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[], float[]) - Static method in class deepnetts.util.Tensors
multiplyElementWise(Tensor) - Method in class deepnetts.util.Tensor

N

negativeFreqency() - Method in class deepnetts.eval.ClassificationMetrics
NETWORK_FILE_EXT - Static variable in class deepnetts.util.FileIO
NetworkType - Enum in deepnetts.net: Neural network architecture types
NeuralNetwork<T extends Trainer> - Class in deepnetts.net: Base class for all neural networks in DeepNetts.
NeuralNetwork() - Constructor for class deepnetts.net.NeuralNetwork
nextFloat() - Method in class deepnetts.util.RandomGenerator
nextGaussian() - Method in class deepnetts.util.RandomGenerator
nextInt() - Method in class deepnetts.util.RandomGenerator
nextLayer - Variable in class deepnetts.net.layers.AbstractLayer: Next layer in network
normal(float[]) - Static method in class deepnetts.net.weights.RandomWeights

O

ObjectsOnBackgrounds - Class in deepnetts.util: Center images on backgounds and save at target path.
ObjectsOnBackgrounds() - Constructor for class deepnetts.util.ObjectsOnBackgrounds
of(Class) - Static method in enum deepnetts.net.loss.LossType
Of(Class) - Static method in enum deepnetts.net.NetworkType
oneHotEncode(String, String[]) - Static method in class deepnetts.data.DataSets
ones(int) - Static method in class deepnetts.util.Tensors
optim - Variable in class deepnetts.net.layers.AbstractLayer
Optimizer - Interface in deepnetts.net.train.opt
optimizerType - Variable in class deepnetts.net.layers.AbstractLayer
OptimizerType - Enum in deepnetts.net.train.opt: Optimization methods used by back-propagation training algorithm.
OUTPUT - deepnetts.net.layers.LayerType
outputErrors - Variable in class deepnetts.net.layers.OutputLayer
OutputLayer - Class in deepnetts.net.layers: Output layer of a neural network, which gives the final output of a 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 - Variable in class deepnetts.net.layers.AbstractLayer: Layer outputs

P

positiveFreqency() - Method in class deepnetts.eval.ClassificationMetrics
precision - Variable in class deepnetts.eval.ClassificationMetrics.Stats
predict(float[]) - Method in class deepnetts.net.FeedForwardNetwork: Predict output for the given input
prevBiasSqrSum - Variable in class deepnetts.net.layers.AbstractLayer: Previous delta sums used by AdaGrad and AdaDelta
prevDeltaBiases - Variable in class deepnetts.net.layers.AbstractLayer
prevDeltaBiasSqrSum - Variable in class deepnetts.net.layers.AbstractLayer: Previous delta sums used by AdaGrad and AdaDelta
prevDeltaWeights - Variable in class deepnetts.net.layers.AbstractLayer: Weight changes for current and previous iteration
prevDeltaWeightSqrSum - Variable in class deepnetts.net.layers.AbstractLayer: Previous delta sums used by AdaGrad and AdaDelta
prevGradSqrSum - Variable in class deepnetts.net.layers.AbstractLayer: Previous delta sums used by AdaGrad and AdaDelta
prevLayer - Variable in class deepnetts.net.layers.AbstractLayer: Previous layer in network
PROP_BATCH_MODE - Static variable in class deepnetts.net.train.BackpropagationTrainer
PROP_BATCH_SIZE - Static variable in class deepnetts.net.train.BackpropagationTrainer
PROP_LEARNING_RATE - Static variable in class deepnetts.net.train.BackpropagationTrainer
PROP_MAX_EPOCHS - Static variable in class deepnetts.net.train.BackpropagationTrainer
PROP_MAX_ERROR - Static variable in class deepnetts.net.train.BackpropagationTrainer
PROP_MOMENTUM - Static variable in class deepnetts.net.train.BackpropagationTrainer
PROP_OPTIMIZER_TYPE - Static variable in class deepnetts.net.train.BackpropagationTrainer

R

random(int, int) - Static method in class deepnetts.util.Tensors
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 subimages of specified dimensions.
RandomGenerator - Class in deepnetts.util: Random number generator singleton.
randomize() - Method in class deepnetts.util.Tensor
randomize(float[]) - Static method in class deepnetts.net.weights.RandomWeights: Fills the specified array with random numbers in range [-0.5, 0.5] from the current random seed
RandomlyTranslateImages - Class in deepnetts.util: just move 2(x) pix to left right up down
RandomlyTranslateImages() - Constructor for class deepnetts.util.RandomlyTranslateImages
randomSeed(long) - Method in class deepnetts.net.ConvolutionalNetwork.Builder
randomSeed(long) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Initializes random number generator with 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
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: This class provides various randomization methods.
RandomWeights() - Constructor for class deepnetts.net.weights.RandomWeights
randomWeightsType - Variable in class deepnetts.net.layers.AbstractLayer
RandomWeightsType - Enum in deepnetts.net.weights
RangeScaler - Class in deepnetts.data.preprocessing.scale: Scale data set to specified range.
RangeScaler(float, float) - Constructor for class deepnetts.data.preprocessing.scale.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) - 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.
readCsv(String, int, int, boolean) - Static method in class deepnetts.data.DataSets
readCsv(String, int, int, boolean, String) - Static method in class deepnetts.data.DataSets
readCsv(String, int, int, String) - Static method in class deepnetts.data.DataSets
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
regularization - Variable in class deepnetts.net.layers.AbstractLayer
Relu - Class in deepnetts.net.layers.activation: Rectified Linear Activation and its Derivative.
Relu() - Constructor for class deepnetts.net.layers.activation.Relu
RELU - deepnetts.net.layers.activation.ActivationType
removeContext(LoggerContext) - Method in class deepnetts.android.LoggerContextFactory_Dummy: Removes knowledge of a LoggerContext.
removeListener(TrainingListener) - Method in class deepnetts.net.train.BackpropagationTrainer
renameFilesAsClasses(String, String) - Static method in class deepnetts.util.ImageSetUtils: Renames files in specified directory.
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
RootMeanSquaredError - Class in deepnetts.eval: A measure of error for regression problems.
RootMeanSquaredError() - Constructor for class deepnetts.eval.RootMeanSquaredError
ROW_IDX - Static variable in class deepnetts.net.train.opt.MomentumOptimizer
run() - Method in class deepnetts.util.RandomlyTranslateImages
run() - Method in class deepnetts.util.ScaleImages
runCrossValidation() - Method in class deepnetts.net.train.KFoldCrossValidation

S

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.Scaling factor is calculated using larger dimension (width or height).
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.
scaleImage(BufferedImage, int, int) - Static method in class deepnetts.util.ImageUtils: Scales specified image and returns new image with specified dimensions.
ScaleImages - Class in deepnetts.util
ScaleImages() - Constructor for class deepnetts.util.ScaleImages
scaleMax(DataSet) - Static method in class deepnetts.data.DataSets: Normalize specified data set and return used normalizer.
Serialization - Class in deepnetts.util
Serialization() - Constructor for class deepnetts.util.Serialization
set(int, float) - Method in class deepnetts.util.Tensor: Sets value at specified index position.
set(int, int, float) - Method in class deepnetts.util.Tensor: Sets matrix value at specified [row, col] position
set(int, int, int, float) - Method in class deepnetts.util.Tensor
set(int, int, int, int, float) - Method in class deepnetts.util.Tensor
setActivation(ActivationFunction) - Method in class deepnetts.net.layers.AbstractLayer
setActivationType(ActivationType) - Method in class deepnetts.net.layers.AbstractLayer
setBatchMode(boolean) - Method in class deepnetts.net.layers.AbstractLayer
setBatchMode(boolean) - Method in class deepnetts.net.train.BackpropagationTrainer
setBatchSize(int) - Method in class deepnetts.net.layers.AbstractLayer
setBatchSize(int) - Method in class deepnetts.net.train.BackpropagationTrainer
setBiases(float[]) - Method in class deepnetts.net.layers.AbstractLayer
setCheckpointEpochs(int) - Method in class deepnetts.net.train.BackpropagationTrainer
setClassLabel(String) - Method in class deepnetts.eval.ClassificationMetrics
setColumnNames(String[]) - Method in class deepnetts.data.TabularDataSet
setColumnNames(String[]) - Method in class deepnetts.util.CsvFormat
setColumnTypes(ColumnType[]) - Method in class deepnetts.util.CsvFormat
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
setEarlyStopping(boolean) - Method in class deepnetts.net.train.BackpropagationTrainer
setEarlyStoppingMinDelta(float) - Method in class deepnetts.net.train.BackpropagationTrainer
setEarlyStoppingPatience(int) - Method in class deepnetts.net.train.BackpropagationTrainer
setFilters(Tensor[]) - Method in class deepnetts.net.layers.ConvolutionalLayer
setFilters(String) - Method in class deepnetts.net.layers.ConvolutionalLayer
setHasHeader(boolean) - Method in class deepnetts.util.CsvFormat
setId(int) - Method in class deepnetts.util.BoundingBox
setInput(float[]) - Method in class deepnetts.net.FeedForwardNetwork
setInput(Tensor) - Method in class deepnetts.net.layers.InputLayer: Sets network input
setInput(Tensor) - Method in class deepnetts.net.NeuralNetwork: Sets network input vector and triggers forward pass.
setInputLayer(InputLayer) - Method in class deepnetts.net.NeuralNetwork
setInvertImages(boolean) - Method in class deepnetts.data.ImageSet
setL1Regularization(float) - Method in class deepnetts.net.train.BackpropagationTrainer
setL2Regularization(float) - Method in class deepnetts.net.train.BackpropagationTrainer
setLabel(String) - Method in class deepnetts.net.NeuralNetwork
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
setLossFunction(LossFunction) - Method in class deepnetts.net.NeuralNetwork
setLossType(LossType) - Method in class deepnetts.net.layers.OutputLayer
setMaxEpochs(long) - Method in class deepnetts.net.train.BackpropagationTrainer
setMaxError(float) - Method in class deepnetts.net.train.BackpropagationTrainer
setMaxInputs(Tensor) - Method in class deepnetts.data.preprocessing.scale.MaxScaler
setMaxOutputs(Tensor) - Method in class deepnetts.data.preprocessing.scale.MaxScaler
setMomentum(float) - Method in class deepnetts.net.layers.AbstractLayer
setMomentum(float) - Method in class deepnetts.net.train.BackpropagationTrainer
setNextlayer(AbstractLayer) - Method in class deepnetts.net.layers.AbstractLayer
setOptimizer(OptimizerType) - Method in class deepnetts.net.train.BackpropagationTrainer
setOptimizerType(OptimizerType) - Method in class deepnetts.net.layers.AbstractLayer
setOutputError(float[]) - Method in class deepnetts.net.NeuralNetwork
setOutputErrors(float[]) - Method in class deepnetts.net.layers.OutputLayer
setOutputLabels(String...) - Method in class deepnetts.net.NeuralNetwork
setOutputLayer(OutputLayer) - Method in class deepnetts.net.NeuralNetwork
setOutputs(Tensor) - Method in class deepnetts.net.layers.AbstractLayer
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.
setRegularization(float) - Method in class deepnetts.net.layers.AbstractLayer
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
setSnapshotEpochs(int) - Method in class deepnetts.net.train.BackpropagationTrainer
setSnapshotPath(String) - Method in class deepnetts.net.train.BackpropagationTrainer
setTargetOutput(Tensor) - Method in class deepnetts.data.ExampleImage
setTestSet(DataSet<MLDataItem>) - Method in class deepnetts.net.train.BackpropagationTrainer
setThreshold(float) - Method in class deepnetts.eval.ClassifierEvaluator
setTrainer(T) - Method in class deepnetts.net.NeuralNetwork
setTrainer(T) - Method in interface deepnetts.net.train.TrainerProvider
setTrainingSnapshots(boolean) - Method in class deepnetts.net.train.BackpropagationTrainer
setValues(float...) - Method in class deepnetts.util.Tensor
setValuesFromString(String) - Method in class deepnetts.util.Tensor: Sets tensor values from csv string.
setWeights(Tensor) - Method in class deepnetts.net.layers.AbstractLayer
setWeights(String) - Method in class deepnetts.net.layers.AbstractLayer
setWeights(List<String>) - Method in class deepnetts.net.ConvolutionalNetwork
SGD - deepnetts.net.train.opt.OptimizerType: Stochastic Gradient Descent, a basic type of neural network optimization algorithm.
SgdOptimizer - Class in deepnetts.net.train.opt
SgdOptimizer(AbstractLayer) - Constructor for class deepnetts.net.train.opt.SgdOptimizer
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
Sigmoid - Class in deepnetts.net.layers.activation: Sigmoid activation function TODO: slope, amplitude?, avoid NaN
Sigmoid() - Constructor for class deepnetts.net.layers.activation.Sigmoid
SIGMOID - deepnetts.net.layers.activation.ActivationType
size() - Method in class deepnetts.data.TabularDataSet.Item
size() - Method in class deepnetts.util.Tensor
SOFTMAX - deepnetts.net.layers.activation.ActivationType
SoftmaxOutputLayer - Class in deepnetts.net.layers: Output layer with softmax activation function.
SoftmaxOutputLayer(int) - Constructor for class deepnetts.net.layers.SoftmaxOutputLayer
SoftmaxOutputLayer(String[]) - 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(double...) - Method in class deepnetts.data.TabularDataSet: 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.
splitsNum(int) - Method in class deepnetts.net.train.KFoldCrossValidation.Builder
sqrt() - Method in class deepnetts.util.Tensor
standardize(DataSet) - Static method in class deepnetts.data.DataSets
Standardizer - Class in deepnetts.data.preprocessing.scale: Performs standardization in order to get desired statistical properties of the data set.
Standardizer(DataSet<MLDataItem>) - Constructor for class deepnetts.data.preprocessing.scale.Standardizer
STARTED - deepnetts.net.train.TrainingEvent.Type
Stats() - Constructor for class deepnetts.eval.ClassificationMetrics.Stats
stop() - Method in class deepnetts.net.train.BackpropagationTrainer
STOPPED - deepnetts.net.train.TrainingEvent.Type
STRING - deepnetts.util.ColumnType
sub(float) - Method in class deepnetts.util.Tensor
sub(float[], float) - Static method in class deepnetts.util.Tensors
sub(float[], float[]) - Static method in class deepnetts.util.Tensors
sub(int, int, float) - Method in class deepnetts.util.Tensor
sub(int, int, int, float) - Method in class deepnetts.util.Tensor
sub(int, int, int, int, float) - Method in class deepnetts.util.Tensor
sub(Tensor) - Method in class deepnetts.util.Tensor: Subtracts specified tensor t from this tensor.
sub(Tensor, Tensor) - Static method in class deepnetts.util.Tensor: Subtracts tensor t2 from t1.
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 sqr values of this tensor - L2 norm

T

TabularDataSet<E extends MLDataItem> - Class in deepnetts.data: Basic data set used for training neural networks in deep netts.
TabularDataSet() - Constructor for class deepnetts.data.TabularDataSet
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
TANH - deepnetts.net.layers.activation.ActivationType
Tensor - Class in deepnetts.util: This class represents multidimensional array/matrix (can be 1D, 2D, 3D or 4D).
Tensor(float...) - Constructor for class deepnetts.util.Tensor: Creates a single row tensor with specified values.
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, float[]) - Constructor for class deepnetts.util.Tensor
Tensor(int, int, int, int) - Constructor for class deepnetts.util.Tensor
Tensor(int, int, int, int, float[]) - Constructor for class deepnetts.util.Tensor
Tensors - Class in deepnetts.util: Static utility methods for tensors.
test(DataSet<MLDataItem>) - Method in class deepnetts.net.NeuralNetwork
TEST_FILE - Static variable in class deepnetts.util.ImageSetUtils
toJson(NeuralNetwork<?>) - Static method in class deepnetts.util.FileIO: Returns JSON representation of specified neural network object.
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.net.layers.ConvolutionalLayer
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
toString() - Method in class deepnetts.util.BoundingBox
toString() - Method in class deepnetts.util.CsvFormat
toString() - Method in class deepnetts.util.Tensor
train(DataSet<?>, double) - Method in class deepnetts.net.train.BackpropagationTrainer
train(DataSet<? extends MLDataItem>) - Method in class deepnetts.net.NeuralNetwork
train(DataSet<? extends MLDataItem>) - Method in class deepnetts.net.train.BackpropagationTrainer: Run training using specified training set.
train(DataSet<? extends MLDataItem>) - Method in interface deepnetts.net.train.Trainer
train(DataSet<MLDataItem>, DataSet<MLDataItem>) - Method in class deepnetts.net.train.BackpropagationTrainer: Run training using specified training and validation sets.
TRAIN_FILE - Static variable in class deepnetts.util.ImageSetUtils
trainer(BackpropagationTrainer) - Method in class deepnetts.net.train.KFoldCrossValidation.Builder
Trainer - Interface in deepnetts.net.train
TrainerProvider<T> - Interface in deepnetts.net.train
TrainingEvent - Class in deepnetts.net.train: This class holds source and type of training event.
TrainingEvent(BackpropagationTrainer, TrainingEvent.Type) - Constructor for class deepnetts.net.train.TrainingEvent
TrainingEvent.Type - Enum in deepnetts.net.train
TrainingListener - Interface in deepnetts.net.train
trainTestSplit(DataSet<?>, double) - Static method in class deepnetts.data.DataSets
TRUE_NEGATIVE - Static variable in class deepnetts.eval.ConfusionMatrix
TRUE_POSITIVE - Static variable in class deepnetts.eval.ConfusionMatrix
TypedProperties - Class in deepnetts.util
TypedProperties() - Constructor for class deepnetts.util.TypedProperties

U

uniform(float[], float, float) - Static method in class deepnetts.net.weights.RandomWeights
uniform(float[], int) - Static method in class deepnetts.net.weights.RandomWeights: Uniform U[-a,a] where a=1/sqrt(in).
UNIFORM - deepnetts.net.weights.RandomWeightsType

V

valueFor(NeuralNetwork, DataSet<? extends MLDataItem>) - Method in interface deepnetts.net.loss.LossFunction: Calculates and returns loss function value for the given neural network and test set.
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.
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.
valuesAsString(Tensor[]) - Static method in class deepnetts.util.Tensor
version() - Method in class deepnetts.core.DeepNetts

W

weights - Variable in class deepnetts.net.layers.AbstractLayer: Input weight matrix / connectivity matrix for previous layer
WIDDROW_HOFF - deepnetts.net.weights.RandomWeightsType
widrowHoff(float[], float, float) - Static method in class deepnetts.net.weights.RandomWeights
width - Variable in class deepnetts.net.layers.AbstractLayer
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 specified 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)).
XAVIER - deepnetts.net.weights.RandomWeightsType

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
All Classes All Packages