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A

A_MINUS_B - org.tensorflow.framework.op.sets.Sets.Operation

AbstractActivation - Class in org.tensorflow.framework.activations

Abstract base class for Activations

AbstractActivation() - Constructor for class org.tensorflow.framework.activations.AbstractActivation

Creates the abstract class for an AbstractActivation

AbstractConstraint - Class in org.tensorflow.framework.constraints

Base class for Constraints.

AbstractConstraint() - Constructor for class org.tensorflow.framework.constraints.AbstractConstraint

Creates a AbstractConstraint

AbstractRegularizer - Class in org.tensorflow.framework.regularizers

Base class for Regularizers

AbstractRegularizer() - Constructor for class org.tensorflow.framework.regularizers.AbstractRegularizer

Creates a AbstractRegularizer, using Class.getSimpleName() for the name

AbstractRegularizer(String) - Constructor for class org.tensorflow.framework.regularizers.AbstractRegularizer

Creates a AbstractRegularizer

ACCUMULATOR - Static variable in class org.tensorflow.framework.optimizers.AdaDelta

ACCUMULATOR - Static variable in class org.tensorflow.framework.optimizers.AdaGrad

ACCUMULATOR - Static variable in class org.tensorflow.framework.optimizers.AdaGradDA

ACCUMULATOR - Static variable in class org.tensorflow.framework.optimizers.Ftrl

ACCUMULATOR_UPDATE - Static variable in class org.tensorflow.framework.optimizers.AdaDelta

Accuracy<T extends TNumber> - Class in org.tensorflow.framework.metrics

Metric that calculates how often predictions equals labels.

Accuracy(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Accuracy

Creates an Accuracy Metric using Class.getSimpleName() for the metric name

Accuracy(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Accuracy

Creates an Accuracy Metric

Activation - Interface in org.tensorflow.framework.activations

Interface for Activations

Activations - Enum in org.tensorflow.framework.activations

The Enumerations for creating Activations based an activation name, with either an empty constructor or a constructor that takes a Map object that contains the Activation's state.

AdaDelta - Class in org.tensorflow.framework.optimizers

Optimizer that implements the Adadelta algorithm.

AdaDelta(Graph) - Constructor for class org.tensorflow.framework.optimizers.AdaDelta

AdaDelta(Graph, float) - Constructor for class org.tensorflow.framework.optimizers.AdaDelta

Creates an AdaDelta Optimizer

AdaDelta(Graph, float, float, float) - Constructor for class org.tensorflow.framework.optimizers.AdaDelta

Creates an AdaDelta Optimizer

AdaDelta(Graph, String, float) - Constructor for class org.tensorflow.framework.optimizers.AdaDelta

Creates an AdaDelta Optimizer

AdaDelta(Graph, String, float, float, float) - Constructor for class org.tensorflow.framework.optimizers.AdaDelta

Creates an AdaDelta Optimizer

ADADELTA - org.tensorflow.framework.optimizers.Optimizers

AdaGrad - Class in org.tensorflow.framework.optimizers

Optimizer that implements the Adagrad algorithm.

AdaGrad(Graph) - Constructor for class org.tensorflow.framework.optimizers.AdaGrad

Creates an AdaGrad Optimizer

AdaGrad(Graph, float) - Constructor for class org.tensorflow.framework.optimizers.AdaGrad

Creates an AdaGrad Optimizer

AdaGrad(Graph, float, float) - Constructor for class org.tensorflow.framework.optimizers.AdaGrad

Creates an AdaGrad Optimizer

AdaGrad(Graph, String, float) - Constructor for class org.tensorflow.framework.optimizers.AdaGrad

Creates an AdaGrad Optimizer

AdaGrad(Graph, String, float, float) - Constructor for class org.tensorflow.framework.optimizers.AdaGrad

Creates an AdaGrad Optimizer

ADAGRAD - org.tensorflow.framework.optimizers.Optimizers

ADAGRAD_DA - org.tensorflow.framework.optimizers.Optimizers

AdaGradDA - Class in org.tensorflow.framework.optimizers

Optimizer that implements the Adagrad Dual-Averaging algorithm.

AdaGradDA(Graph) - Constructor for class org.tensorflow.framework.optimizers.AdaGradDA

Creates an AdaGradDA Optimizer

AdaGradDA(Graph, float) - Constructor for class org.tensorflow.framework.optimizers.AdaGradDA

Creates an AdaGradDA Optimizer

AdaGradDA(Graph, float, float, float, float) - Constructor for class org.tensorflow.framework.optimizers.AdaGradDA

Creates an AdaGradDA Optimizer

AdaGradDA(Graph, String, float) - Constructor for class org.tensorflow.framework.optimizers.AdaGradDA

Creates an AdaGradDA Optimizer

AdaGradDA(Graph, String, float, float, float, float) - Constructor for class org.tensorflow.framework.optimizers.AdaGradDA

Creates an AdaGradDA Optimizer

Adam - Class in org.tensorflow.framework.optimizers

Optimizer that implements the Adam algorithm.

Adam(Graph) - Constructor for class org.tensorflow.framework.optimizers.Adam

Creates an Adam optimizer

Adam(Graph, float) - Constructor for class org.tensorflow.framework.optimizers.Adam

Creates an Adam optimizer

Adam(Graph, float, float, float, float) - Constructor for class org.tensorflow.framework.optimizers.Adam

Creates an Adam optimizer

Adam(Graph, String, float) - Constructor for class org.tensorflow.framework.optimizers.Adam

Creates an Adam optimizer

Adam(Graph, String, float, float, float, float) - Constructor for class org.tensorflow.framework.optimizers.Adam

Creates an Adam optimizer

ADAM - org.tensorflow.framework.optimizers.Optimizers

Adamax - Class in org.tensorflow.framework.optimizers

Optimizer that implements the Adamax algorithm.

Adamax(Graph) - Constructor for class org.tensorflow.framework.optimizers.Adamax

Creates an Optimizer that implements the Adamax algorithm.

Adamax(Graph, float) - Constructor for class org.tensorflow.framework.optimizers.Adamax

Creates an Optimizer that implements the Adamax algorithm.

Adamax(Graph, float, float, float, float) - Constructor for class org.tensorflow.framework.optimizers.Adamax

Creates an Optimizer that implements the Adamax algorithm.

Adamax(Graph, String) - Constructor for class org.tensorflow.framework.optimizers.Adamax

Creates an Optimizer that implements the Adamax algorithm.

Adamax(Graph, String, float) - Constructor for class org.tensorflow.framework.optimizers.Adamax

Creates an Optimizer that implements the Adamax algorithm.

Adamax(Graph, String, float, float, float, float) - Constructor for class org.tensorflow.framework.optimizers.Adamax

Creates an Optimizer that implements the Adamax algorithm.

ADAMAX - org.tensorflow.framework.optimizers.Optimizers

allAxes(Scope, Operand<? extends TType>) - Static method in class org.tensorflow.framework.op.math.Axes

Creates an Operand that has all axes contained in the Operand's shape.

allAxes(Operand<? extends TType>) - Method in class org.tensorflow.framework.op.MathOps

Creates an Operand that has all axes contained in the Operand's shape.

ALPHA_DEFAULT - Static variable in class org.tensorflow.framework.activations.ReLU

applyDense(Ops, Output<T>, Output<T>) - Method in class org.tensorflow.framework.optimizers.AdaDelta

Generates the gradient update operations for the specific variable and gradient.

applyDense(Ops, Output<T>, Output<T>) - Method in class org.tensorflow.framework.optimizers.AdaGrad

Generates the gradient update operations for the specific variable and gradient.

applyDense(Ops, Output<T>, Output<T>) - Method in class org.tensorflow.framework.optimizers.AdaGradDA

Generates the gradient update operations for the specific variable and gradient.

applyDense(Ops, Output<T>, Output<T>) - Method in class org.tensorflow.framework.optimizers.Adam

Generates the gradient update operations for the specific variable and gradient.

applyDense(Ops, Output<T>, Output<T>) - Method in class org.tensorflow.framework.optimizers.Adamax

Generates the gradient update operations for the specific variable and gradient.

applyDense(Ops, Output<T>, Output<T>) - Method in class org.tensorflow.framework.optimizers.Ftrl

Generates the gradient update operations for the specific variable and gradient.

applyDense(Ops, Output<T>, Output<T>) - Method in class org.tensorflow.framework.optimizers.GradientDescent

Generates the gradient update operations for the specific variable and gradient.

applyDense(Ops, Output<T>, Output<T>) - Method in class org.tensorflow.framework.optimizers.Momentum

Generates the gradient update operations for the specific variable and gradient.

applyDense(Ops, Output<T>, Output<T>) - Method in class org.tensorflow.framework.optimizers.Nadam

Generates the gradient update operations for the specific variable and gradient.

applyDense(Ops, Output<T>, Output<T>) - Method in class org.tensorflow.framework.optimizers.Optimizer

Generates the gradient update operations for the specific variable and gradient.

applyDense(Ops, Output<T>, Output<T>) - Method in class org.tensorflow.framework.optimizers.RMSProp

Generates the gradient update operations for the specific variable and gradient.

applyGradients(List<Optimizer.GradAndVar<? extends TType>>, String) - Method in class org.tensorflow.framework.optimizers.Optimizer

Applies gradients to variables

asLoss() - Method in class org.tensorflow.framework.regularizers.AbstractRegularizer

Returns this AbstractRegularizer as a AbstractLoss This is a convenience to use regularize a loss.

AUC<T extends TNumber> - Class in org.tensorflow.framework.metrics

Metric that computes the approximate AUC (Area under the curve) via a Riemann sum.

AUC(float[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.AUC

Creates an AUC (Area under the curve) metric using AUC.DEFAULT_NAME for the metric name, AUCCurve.ROC for the curve type, AUCSummationMethod.INTERPOLATION for the summation method, null for numThresholds, false for multiLabel, and null for labelWeights.

AUC(float[], AUCCurve, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.AUC

Creates an AUC (Area under the curve) metric using null for numThresholds, AUCSummationMethod.INTERPOLATION for the summation method, false for multiLabel, and null for labelWeights.

AUC(float[], AUCCurve, AUCSummationMethod, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.AUC

Creates an AUC (Area under the curve) metric using AUC.DEFAULT_NAME for the metric name, null for numThresholds, false for multiLabel, and null for labelWeights.

AUC(int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.AUC

Creates an AUC (Area under the curve) metric using AUC.DEFAULT_NAME for the metric name, AUCCurve.ROC for the curve type, AUCSummationMethod.INTERPOLATION for the summation method, null for thresholds, false for multiLabel, and null for labelWeights.

AUC(int, AUCCurve, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.AUC

Creates an AUC (Area under the curve) metric using AUC.DEFAULT_NAME for the metric name, AUCSummationMethod.INTERPOLATION for the summation method, null for thresholds, false for multiLabel, and null for labelWeights.

AUC(int, AUCCurve, AUCSummationMethod, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.AUC

Creates an AUC (Area under the curve) metric.

AUC(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.AUC

Creates an AUC (Area under the curve) metric using AUC.DEFAULT_NAME for the metric name, AUC.DEFAULT_NUM_THRESHOLDS for the numThresholds, AUCCurve.ROC for the curve type, AUCSummationMethod.INTERPOLATION for the summation method, null for thresholds, false for multiLabel, and null for labelWeights.

AUC(String, float[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.AUC

Creates an AUC (Area under the curve) metric using null for numThresholds, AUCCurve.ROC for the curve type, AUCSummationMethod.INTERPOLATION for the summation method, AUC.DEFAULT_NUM_THRESHOLDS num thresholds, false for multiLabel, and null for labelWeights.

AUC(String, float[], AUCCurve, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.AUC

Creates an AUC (Area under the curve) metric using null for numThresholds, AUCSummationMethod.INTERPOLATION for the summation method, AUC.DEFAULT_NUM_THRESHOLDS num thresholds, false for multiLabel, and null for labelWeights.

AUC(String, float[], AUCCurve, AUCSummationMethod, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.AUC

Creates an AUC (Area under the curve) metric.

AUC(String, int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.AUC

Creates an AUC (Area under the curve) metric.

AUC(String, int, AUCCurve, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.AUC

Creates an AUC (Area under the curve) metric using AUCSummationMethod.INTERPOLATION for the summation method, null for thresholds, false for multiLabel, and null for labelWeights.

AUC(String, int, AUCCurve, AUCSummationMethod, float[], boolean, Operand<T>, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.AUC

Creates an AUC (Area under the curve) metric.

AUC(String, int, AUCCurve, AUCSummationMethod, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.AUC

Creates an AUC (Area under the curve) metric.

AUC(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.AUC

Creates an AUC (Area under the curve) metric using AUC.DEFAULT_NUM_THRESHOLDS for the numThresholds, AUCCurve.ROC for the curve type, AUCSummationMethod.INTERPOLATION for the summation method, null for thresholds, false for multiLabel, and null for labelWeights.

AUCCurve - Enum in org.tensorflow.framework.metrics

Specifies the type of the curve to be computed, AUCCurve.ROC for a Receiver Operator Characteristic curve [default] or AUCCurve.PR for a Precision-Recall-curve.

AUCSummationMethod - Enum in org.tensorflow.framework.metrics

Specifies the Riemann summation method used.

AUTO - org.tensorflow.framework.losses.Reduction

Axes - Class in org.tensorflow.framework.op.math

Axes Operations

Axes() - Constructor for class org.tensorflow.framework.op.math.Axes

AXIS_DEFAULT - Static variable in class org.tensorflow.framework.constraints.MaxNorm

AXIS_DEFAULT - Static variable in class org.tensorflow.framework.constraints.MinMaxNorm

AXIS_DEFAULT - Static variable in class org.tensorflow.framework.constraints.UnitNorm

AXIS_DEFAULT - Static variable in class org.tensorflow.framework.losses.SparseCategoricalCrossentropy

B

B_MINUS_A - org.tensorflow.framework.op.sets.Sets.Operation

BaseInitializer<T extends TType> - Class in org.tensorflow.framework.initializers

Abstract base class for all Initializers

BaseInitializer() - Constructor for class org.tensorflow.framework.initializers.BaseInitializer

Creates an Initializer

BaseMetric - Class in org.tensorflow.framework.metrics

Base class for Metrics

BaseMetric(long) - Constructor for class org.tensorflow.framework.metrics.BaseMetric

Creates a Metric with a name of Class.getSimpleName()

BaseMetric(String, long) - Constructor for class org.tensorflow.framework.metrics.BaseMetric

Creates a Metric

batch(long) - Method in class org.tensorflow.framework.data.Dataset

Groups elements of this dataset into batches.

batch(long, boolean) - Method in class org.tensorflow.framework.data.Dataset

Groups elements of this dataset into batches.

BETA_ONE_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Adam

BETA_ONE_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Adamax

BETA_ONE_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Nadam

BETA_TWO_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Adam

BETA_TWO_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Adamax

BETA_TWO_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Nadam

BinaryAccuracy<T extends TNumber> - Class in org.tensorflow.framework.metrics

Metric that calculates how often predictions matches binary labels.

BinaryAccuracy(float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.BinaryAccuracy

Creates a BinaryAccuracy Metric using Class.getSimpleName() for the metric name

BinaryAccuracy(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.BinaryAccuracy

Creates a BinaryAccuracy Metric using Class.getSimpleName() for the metric name and BinaryAccuracy.DEFAULT_THRESHOLD for the threshold value.

BinaryAccuracy(String, float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.BinaryAccuracy

Creates a BinaryAccuracy Metric

binaryCrossentropy(Ops, Operand<? extends TNumber>, Operand<T>, boolean, float) - Static method in class org.tensorflow.framework.losses.Losses

Computes the binary crossentropy loss between labels and predictions.

BinaryCrossentropy - Class in org.tensorflow.framework.losses

Computes the cross-entropy loss between true labels and predicted labels.

BinaryCrossentropy<T extends TNumber> - Class in org.tensorflow.framework.metrics

A Metric that computes the binary cross-entropy loss between true labels and predicted labels.

BinaryCrossentropy() - Constructor for class org.tensorflow.framework.losses.BinaryCrossentropy

Creates a Binary Crossentropy AbstractLoss using Class.getSimpleName() as the loss name, BinaryCrossentropy.FROM_LOGITS_DEFAULT for fromLogits, BinaryCrossentropy.LABEL_SMOOTHING_DEFAULT for labelSmoothing and a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT

BinaryCrossentropy(boolean) - Constructor for class org.tensorflow.framework.losses.BinaryCrossentropy

Creates a Binary Crossentropy loss using using Class.getSimpleName() as the loss name, labelSmoothing of BinaryCrossentropy.LABEL_SMOOTHING_DEFAULT, a reduction of AbstractLoss.REDUCTION_DEFAULT,

BinaryCrossentropy(boolean, float) - Constructor for class org.tensorflow.framework.losses.BinaryCrossentropy

Creates a Binary Crossentropy loss using using Class.getSimpleName() as the loss name, and a reduction of AbstractLoss.REDUCTION_DEFAULT.

BinaryCrossentropy(boolean, float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.BinaryCrossentropy

Creates a BinaryCrossentropy metric where name is Class.getSimpleName().

BinaryCrossentropy(boolean, float, Reduction) - Constructor for class org.tensorflow.framework.losses.BinaryCrossentropy

Creates a Binary Crossentropy loss

BinaryCrossentropy(String, boolean) - Constructor for class org.tensorflow.framework.losses.BinaryCrossentropy

Creates a Binary Crossentropy loss using labelSmoothing of BinaryCrossentropy.LABEL_SMOOTHING_DEFAULT a reduction of AbstractLoss.REDUCTION_DEFAULT.

BinaryCrossentropy(String, boolean, float) - Constructor for class org.tensorflow.framework.losses.BinaryCrossentropy

Creates a Binary Crossentropy loss using a reduction of AbstractLoss.REDUCTION_DEFAULT.

BinaryCrossentropy(String, boolean, float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.BinaryCrossentropy

Creates a BinaryCrossentropy metric

BinaryCrossentropy(String, boolean, float, Reduction) - Constructor for class org.tensorflow.framework.losses.BinaryCrossentropy

Creates a Binary Crossentropy loss

BinaryCrossentropy(Reduction) - Constructor for class org.tensorflow.framework.losses.BinaryCrossentropy

Creates a Binary Crossentropy loss using Class.getSimpleName() as the loss name, BinaryCrossentropy.FROM_LOGITS_DEFAULT for fromLogits, and BinaryCrossentropy.LABEL_SMOOTHING_DEFAULT for labelSmoothing

C

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.Accuracy

Calculates how often predictions equals labels.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.BinaryAccuracy

Calculates how often predictions match binary labels.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.BinaryCrossentropy

Computes the binary crossentropy loss between labels and predictions.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.CategoricalAccuracy

Computes the categorical crossentropy loss.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.CategoricalCrossentropy

Computes the crossentropy loss between the labels and predictions.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.CategoricalHinge

Computes the categorical hinge metric between labels and @{code predictions}.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.CosineSimilarity

Computes the cosine similarity loss between labels and predictions.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.Hinge

Computes the hinge loss between labels and predictions.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.KLDivergence

Computes Kullback-Leibler divergence metric between labels and predictions.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.LogCoshError

Calculates the Logarithm of the hyperbolic cosine of the prediction error.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.MeanAbsoluteError

Computes the mean absolute error loss between labels and predictions.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.MeanAbsolutePercentageError

Computes the mean absolute percentage error loss between labels and predictions.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.MeanSquaredError

Computes the mean squared error between the labels and predictions.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.MeanSquaredLogarithmicError

Computes the mean squared logarithmic error between labels and predictions.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.Poisson

Computes the Poisson loss between labels and predictions.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.SparseCategoricalAccuracy

Calculates how often predictions matches integer labels.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.SparseCategoricalCrossentropy

Calculates how often predictions matches integer labels.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.SparseTopKCategoricalAccuracy

Computes how often integer targets are in the top K predictions.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.SquaredHinge

Computes the squared hinge loss between labels and predictions.

call(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<U>) - Method in class org.tensorflow.framework.metrics.TopKCategoricalAccuracy

Computes how often targets are in the top K predictions.

call(Ops, Operand<? extends TNumber>, Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.losses.BinaryCrossentropy

Generates an Operand that calculates the loss.

call(Ops, Operand<? extends TNumber>, Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.losses.CategoricalCrossentropy

Generates an Operand that calculates the loss.

call(Ops, Operand<? extends TNumber>, Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.losses.CategoricalHinge

Generates an Operand that calculates the loss.

call(Ops, Operand<? extends TNumber>, Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.losses.CosineSimilarity

Generates an Operand that calculates the loss.

call(Ops, Operand<? extends TNumber>, Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.losses.Hinge

Generates an Operand that calculates the loss.

call(Ops, Operand<? extends TNumber>, Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.losses.Huber

Generates an Operand that calculates the loss.

call(Ops, Operand<? extends TNumber>, Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.losses.KLDivergence

Generates an Operand that calculates the loss.

call(Ops, Operand<? extends TNumber>, Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.losses.LogCosh

Generates an Operand that calculates the loss.

call(Ops, Operand<? extends TNumber>, Operand<T>, Operand<T>) - Method in interface org.tensorflow.framework.losses.Loss

Generates an Operand that calculates the loss.

call(Ops, Operand<? extends TNumber>, Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.losses.MeanAbsoluteError

Generates an Operand that calculates the loss.

call(Ops, Operand<? extends TNumber>, Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.losses.MeanAbsolutePercentageError

Generates an Operand that calculates the loss.

call(Ops, Operand<? extends TNumber>, Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.losses.MeanSquaredError

Generates an Operand that calculates the loss.

call(Ops, Operand<? extends TNumber>, Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.losses.MeanSquaredLogarithmicError

Generates an Operand that calculates the loss.

call(Ops, Operand<? extends TNumber>, Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.losses.Poisson

Generates an Operand that calculates the loss.

call(Ops, Operand<? extends TNumber>, Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.losses.SparseCategoricalCrossentropy

Generates an Operand the calculates the loss.

call(Ops, Operand<? extends TNumber>, Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.losses.SquaredHinge

Generates an Operand that calculates the loss.

call(Ops, Operand<TInt64>, Class<T>) - Method in class org.tensorflow.framework.initializers.Constant

Generates the operation used to perform the initialization.

call(Ops, Operand<TInt64>, Class<T>) - Method in class org.tensorflow.framework.initializers.Identity

Generates the operation used to perform the initialization.

call(Ops, Operand<TInt64>, Class<T>) - Method in interface org.tensorflow.framework.initializers.Initializer

Generates the operation used to perform the initialization.

call(Ops, Operand<TInt64>, Class<T>) - Method in class org.tensorflow.framework.initializers.Ones

Generates the operation used to perform the initialization.

call(Ops, Operand<TInt64>, Class<T>) - Method in class org.tensorflow.framework.initializers.Orthogonal

Generates the operation used to perform the initialization.

call(Ops, Operand<TInt64>, Class<T>) - Method in class org.tensorflow.framework.initializers.RandomNormal

Generates the operation used to perform the initialization.

call(Ops, Operand<TInt64>, Class<T>) - Method in class org.tensorflow.framework.initializers.RandomUniform

Generates the operation used to perform the initialization.

call(Ops, Operand<TInt64>, Class<T>) - Method in class org.tensorflow.framework.initializers.TruncatedNormal

Generates the operation used to perform the initialization.

call(Ops, Operand<TInt64>, Class<T>) - Method in class org.tensorflow.framework.initializers.VarianceScaling

Generates the operation used to perform the initialization.

call(Ops, Operand<TInt64>, Class<T>) - Method in class org.tensorflow.framework.initializers.Zeros

call(Ops, Operand<R>) - Method in class org.tensorflow.framework.regularizers.L1L2

Computes a regularization penalty from an input.

call(Ops, Operand<R>) - Method in interface org.tensorflow.framework.regularizers.Regularizer

Computes a regularization penalty from an input.

call(Ops, Operand<T>) - Method in interface org.tensorflow.framework.activations.Activation

Gets the calculation operation for the activation.

call(Ops, Operand<T>) - Method in class org.tensorflow.framework.activations.ELU

Gets the calculation operation for the activation.

call(Ops, Operand<T>) - Method in class org.tensorflow.framework.activations.Exponential

Gets the calculation operation for the activation.

call(Ops, Operand<T>) - Method in class org.tensorflow.framework.activations.GELU

Gets the calculation operation for the activation.

call(Ops, Operand<T>) - Method in class org.tensorflow.framework.activations.HardSigmoid

Gets the calculation operation for the activation.

call(Ops, Operand<T>) - Method in class org.tensorflow.framework.activations.ReLU

Gets the calculation operation for the activation.

call(Ops, Operand<T>) - Method in class org.tensorflow.framework.activations.SELU

Gets the calculation operation for the activation.

call(Ops, Operand<T>) - Method in class org.tensorflow.framework.activations.Sigmoid

Gets the calculation operation for the activation.

call(Ops, Operand<T>) - Method in class org.tensorflow.framework.activations.Softmax

Gets the calculation operation for the activation.

call(Ops, Operand<T>) - Method in class org.tensorflow.framework.activations.Softplus

Gets the calculation operation for the activation.

call(Ops, Operand<T>) - Method in class org.tensorflow.framework.activations.Softsign

Gets the calculation operation for the activation.

call(Ops, Operand<T>) - Method in class org.tensorflow.framework.activations.Swish

Gets the calculation operation for the activation.

call(Ops, Operand<T>) - Method in class org.tensorflow.framework.activations.Tanh

Gets the calculation operation for the activation.

call(Ops, Operand<T>) - Method in interface org.tensorflow.framework.constraints.Constraint

Applies the constraint against the provided weights

call(Ops, Operand<T>) - Method in class org.tensorflow.framework.constraints.MaxNorm

Applies the constraint against the provided weights

call(Ops, Operand<T>) - Method in class org.tensorflow.framework.constraints.MinMaxNorm

Applies the constraint against the provided weights

call(Ops, Operand<T>) - Method in class org.tensorflow.framework.constraints.NonNeg

Applies the constraint against the provided weights

call(Ops, Operand<T>) - Method in class org.tensorflow.framework.constraints.UnitNorm

Applies the constraint against the provided weights

call(Ops, Operand<U>) - Method in class org.tensorflow.framework.activations.Linear

Gets the calculation operation for the activation.

callOnce(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<T>) - Method in class org.tensorflow.framework.metrics.BaseMetric

Calls update state once, followed by a call to get the result

callOnce(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Class<T>) - Method in interface org.tensorflow.framework.metrics.Metric

Calls update state once, followed by a call to get the result

cast(Ops, Operand<U>, Class<T>) - Static method in class org.tensorflow.framework.utils.CastHelper

Casts an operand to the desired type.

CastHelper - Class in org.tensorflow.framework.utils

A helper class for casting an Operand

CastHelper() - Constructor for class org.tensorflow.framework.utils.CastHelper

CategoricalAccuracy<T extends TNumber> - Class in org.tensorflow.framework.metrics

Metric that calculates how often predictions matches one-hot labels.

CategoricalAccuracy(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.CategoricalAccuracy

Creates a CategoricalAccuracy metric, using Class.getSimpleName() for the metric name

CategoricalAccuracy(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.CategoricalAccuracy

Creates a CategoricalAccuracy metric

categoricalCrossentropy(Ops, Operand<? extends TNumber>, Operand<T>, boolean, float, int) - Static method in class org.tensorflow.framework.losses.Losses

Computes the categorical crossentropy loss between labels and predictions.

CategoricalCrossentropy - Class in org.tensorflow.framework.losses

Computes the crossentropy loss between the labels and predictions.

CategoricalCrossentropy<T extends TNumber> - Class in org.tensorflow.framework.metrics

A Metric that computes the categorical cross-entropy loss between true labels and predicted labels.

CategoricalCrossentropy() - Constructor for class org.tensorflow.framework.losses.CategoricalCrossentropy

Creates a categorical cross entropy AbstractLoss using Class.getSimpleName() as the loss name, CategoricalCrossentropy.FROM_LOGITS_DEFAULT for fromLogits, CategoricalCrossentropy.LABEL_SMOOTHING_DEFAULT for labelSmoothing, a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT, and an axis of CategoricalCrossentropy.DEFAULT_AXIS

CategoricalCrossentropy(boolean) - Constructor for class org.tensorflow.framework.losses.CategoricalCrossentropy

Creates a categorical cross entropy AbstractLoss using Class.getSimpleName() as the loss name, CategoricalCrossentropy.LABEL_SMOOTHING_DEFAULT for labelSmoothing, a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT, and an axis of CategoricalCrossentropy.DEFAULT_AXIS

CategoricalCrossentropy(boolean, float) - Constructor for class org.tensorflow.framework.losses.CategoricalCrossentropy

Creates a categorical cross entropy AbstractLoss using Class.getSimpleName() as the loss name, a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT, and a channel axis of CategoricalCrossentropy.DEFAULT_AXIS

CategoricalCrossentropy(boolean, float, int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.CategoricalCrossentropy

Creates a CategoricalCrossentropy metric that computes the crossentropy metric between the labels and predictions using Class.getSimpleName() for the metric name.

CategoricalCrossentropy(boolean, float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.CategoricalCrossentropy

Creates a CategoricalCrossentropy metric that computes the crossentropy metric between the labels and predictions using Class.getSimpleName() for the metric name

CategoricalCrossentropy(boolean, float, Reduction) - Constructor for class org.tensorflow.framework.losses.CategoricalCrossentropy

Creates a categorical cross entropy AbstractLoss using Class.getSimpleName() as the loss name and a channel axis of CategoricalCrossentropy.DEFAULT_AXIS

CategoricalCrossentropy(String) - Constructor for class org.tensorflow.framework.losses.CategoricalCrossentropy

Creates a categorical cross entropy AbstractLoss using CategoricalCrossentropy.FROM_LOGITS_DEFAULT for fromLogits, CategoricalCrossentropy.LABEL_SMOOTHING_DEFAULT for labelSmoothing, a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT, and an axis of CategoricalCrossentropy.DEFAULT_AXIS

CategoricalCrossentropy(String, boolean) - Constructor for class org.tensorflow.framework.losses.CategoricalCrossentropy

Creates a categorical cross entropy AbstractLoss using CategoricalCrossentropy.LABEL_SMOOTHING_DEFAULT for labelSmoothing, a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT, and a channel axis of CategoricalCrossentropy.DEFAULT_AXIS

CategoricalCrossentropy(String, boolean, float) - Constructor for class org.tensorflow.framework.losses.CategoricalCrossentropy

Creates a categorical cross entropy AbstractLoss using a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT, and a channel axis of CategoricalCrossentropy.DEFAULT_AXIS

CategoricalCrossentropy(String, boolean, float, int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.CategoricalCrossentropy

Creates a CategoricalCrossentropy metric that computes the crossentropy metric between the labels and predictions.

CategoricalCrossentropy(String, boolean, float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.CategoricalCrossentropy

Creates a CategoricalCrossentropy metric that computes the crossentropy metric between the labels and predictions.

CategoricalCrossentropy(String, boolean, float, Reduction, int) - Constructor for class org.tensorflow.framework.losses.CategoricalCrossentropy

Creates a categorical cross entropy AbstractLoss

CategoricalCrossentropy(String, Reduction) - Constructor for class org.tensorflow.framework.losses.CategoricalCrossentropy

Creates a categorical cross entropy AbstractLoss CategoricalCrossentropy.FROM_LOGITS_DEFAULT for fromLogits, CategoricalCrossentropy.LABEL_SMOOTHING_DEFAULT for labelSmoothing, and an axis of CategoricalCrossentropy.DEFAULT_AXIS

CategoricalCrossentropy(Reduction) - Constructor for class org.tensorflow.framework.losses.CategoricalCrossentropy

Creates a categorical cross entropy AbstractLoss using Class.getSimpleName() as the loss name, CategoricalCrossentropy.FROM_LOGITS_DEFAULT for fromLogits, CategoricalCrossentropy.LABEL_SMOOTHING_DEFAULT for labelSmoothing and an axis of CategoricalCrossentropy.DEFAULT_AXIS

categoricalHinge(Ops, Operand<? extends TNumber>, Operand<T>) - Static method in class org.tensorflow.framework.losses.Losses

Computes the categorical hinge loss between labels and predictions.

CategoricalHinge - Class in org.tensorflow.framework.losses

Computes the categorical hinge loss between labels and predictions.

CategoricalHinge<T extends TNumber> - Class in org.tensorflow.framework.metrics

A Metric that computes the categorical hinge loss metric between labels and predictions.

CategoricalHinge() - Constructor for class org.tensorflow.framework.losses.CategoricalHinge

Creates a Categorical Hinge AbstractLoss using Class.getSimpleName() as the loss name and a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT

CategoricalHinge(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.CategoricalHinge

Creates a CategoricalHinge metric using Class.getSimpleName() for the metric name.

CategoricalHinge(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.CategoricalHinge

Creates a CategoricalHinge metric

CategoricalHinge(String, Reduction) - Constructor for class org.tensorflow.framework.losses.CategoricalHinge

Creates a Categorical Hinge

CategoricalHinge(Reduction) - Constructor for class org.tensorflow.framework.losses.CategoricalHinge

Creates a Categorical Hinge AbstractLoss using Class.getSimpleName() as the loss name

CENTERED_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.RMSProp

CHANNELS_FIRST - Static variable in class org.tensorflow.framework.losses.Losses

CHANNELS_LAST - Static variable in class org.tensorflow.framework.losses.Losses

checkClassName(Map<String, Object>) - Method in class org.tensorflow.framework.activations.AbstractActivation

Verifies that the configuration is for the same Activation class.

checkConfigKeys(Set<String>, Set<String>) - Method in class org.tensorflow.framework.activations.AbstractActivation

Verifies that any key in keysToCheck is also in the allowedKeys set.

checkIsGraph(Ops) - Method in class org.tensorflow.framework.metrics.BaseMetric

Checks if the TensorFlow Ops encapsulates a Graph environment.

clip(Ops, Operand<T>, double, double) - Method in class org.tensorflow.framework.constraints.AbstractConstraint

Gets the element-wise value clipping.

computeGradients(Operand<?>) - Method in class org.tensorflow.framework.optimizers.Optimizer

Computes the gradients based on a loss operand.

confusionMatrix(Scope, Operand<T>, Operand<T>) - Static method in class org.tensorflow.framework.op.math.ConfusionMatrix

Computes the confusion matrix from predictions and labels.

confusionMatrix(Scope, Operand<T>, Operand<T>, Operand<T>) - Static method in class org.tensorflow.framework.op.math.ConfusionMatrix

Computes the confusion matrix from predictions and labels.

confusionMatrix(Scope, Operand<T>, Operand<T>, Operand<T>, Operand<TInt64>) - Static method in class org.tensorflow.framework.op.math.ConfusionMatrix

Computes the confusion matrix from predictions and labels.

confusionMatrix(Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.op.MathOps

Computes the confusion matrix from predictions and labels.

confusionMatrix(Operand<T>, Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.op.MathOps

Computes the confusion matrix from predictions and labels.

confusionMatrix(Operand<T>, Operand<T>, Operand<T>, Operand<TInt64>) - Method in class org.tensorflow.framework.op.MathOps

Computes the confusion matrix from predictions and labels.

ConfusionMatrix - Class in org.tensorflow.framework.op.math

Confusion Matrix Operations

ConfusionMatrix() - Constructor for class org.tensorflow.framework.op.math.ConfusionMatrix

Constant<T extends TType> - Class in org.tensorflow.framework.initializers

Initializer that generates tensors with a constant value.

Constant(boolean) - Constructor for class org.tensorflow.framework.initializers.Constant

Creates an Initializer that generates tensors with a constant value.

Constant(double) - Constructor for class org.tensorflow.framework.initializers.Constant

Creates an Initializer that generates tensors with a constant value.

Constant(long) - Constructor for class org.tensorflow.framework.initializers.Constant

Creates an Initializer that generates tensors with a constant value.

Constraint - Interface in org.tensorflow.framework.constraints

core - Variable in class org.tensorflow.framework.op.FrameworkOps

cosineSimilarity(Ops, Operand<? extends TNumber>, Operand<T>, int[]) - Static method in class org.tensorflow.framework.losses.Losses

Computes the cosine similarity loss between labels and predictions.

CosineSimilarity - Class in org.tensorflow.framework.losses

Computes the cosine similarity between labels and predictions.

CosineSimilarity<T extends TNumber> - Class in org.tensorflow.framework.metrics

A metric that computes the cosine similarity metric between labels and predictions.

CosineSimilarity() - Constructor for class org.tensorflow.framework.losses.CosineSimilarity

Creates a Cosine Similarity AbstractLoss using Class.getSimpleName() as the loss name, an axis of CosineSimilarity.DEFAULT_AXIS, and a AbstractLoss Reduction of CosineSimilarity.DEFAULT_REDUCTION

CosineSimilarity(int) - Constructor for class org.tensorflow.framework.losses.CosineSimilarity

Creates a Cosine Similarity AbstractLoss using Class.getSimpleName() as the loss name, and a AbstractLoss Reduction of CosineSimilarity.DEFAULT_REDUCTION

CosineSimilarity(int[]) - Constructor for class org.tensorflow.framework.losses.CosineSimilarity

Creates a Cosine Similarity AbstractLoss using Class.getSimpleName() as the loss name, and a AbstractLoss Reduction of CosineSimilarity.DEFAULT_REDUCTION

CosineSimilarity(int[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.CosineSimilarity

Creates a CosineSimilarity metric using Class.getSimpleName() for the metric name.

CosineSimilarity(int[], Reduction) - Constructor for class org.tensorflow.framework.losses.CosineSimilarity

Creates a Cosine Similarity AbstractLoss using Class.getSimpleName() as the loss name

CosineSimilarity(int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.CosineSimilarity

Creates a metric that computes the cosine similarity metric between labels and predictions using Class.getSimpleName() for the metric name.

CosineSimilarity(int, Reduction) - Constructor for class org.tensorflow.framework.losses.CosineSimilarity

Creates a Cosine Similarity AbstractLoss using Class.getSimpleName() as the loss name

CosineSimilarity(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.CosineSimilarity

Creates a metric that computes the cosine similarity metric between labels and predictions with a default axis, CosineSimilarity.DEFAULT_AXIS and using Class.getSimpleName() for the metric name.

CosineSimilarity(String) - Constructor for class org.tensorflow.framework.losses.CosineSimilarity

Creates a Cosine Similarity AbstractLoss using an axis of CosineSimilarity.DEFAULT_AXIS, and a AbstractLoss Reduction of CosineSimilarity.DEFAULT_REDUCTION

CosineSimilarity(String, int) - Constructor for class org.tensorflow.framework.losses.CosineSimilarity

Creates a Cosine Similarity AbstractLoss using a AbstractLoss Reduction of CosineSimilarity.DEFAULT_REDUCTION

CosineSimilarity(String, int[]) - Constructor for class org.tensorflow.framework.losses.CosineSimilarity

Creates a Cosine Similarity AbstractLoss using a AbstractLoss Reduction of CosineSimilarity.DEFAULT_REDUCTION

CosineSimilarity(String, int[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.CosineSimilarity

Creates a CosineSimilarity metric

CosineSimilarity(String, int[], Reduction) - Constructor for class org.tensorflow.framework.losses.CosineSimilarity

Creates a Cosine Similarity AbstractLoss

CosineSimilarity(String, int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.CosineSimilarity

Creates a metric that computes the cosine similarity metric between labels and predictions.

CosineSimilarity(String, int, Reduction) - Constructor for class org.tensorflow.framework.losses.CosineSimilarity

Creates a Cosine Similarity AbstractLoss

CosineSimilarity(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.CosineSimilarity

Creates a metric that computes the cosine similarity metric between labels and predictions with a default axis, CosineSimilarity.DEFAULT_AXIS

CosineSimilarity(String, Reduction) - Constructor for class org.tensorflow.framework.losses.CosineSimilarity

Creates a Cosine Similarity AbstractLoss using an axis of CosineSimilarity.DEFAULT_AXIS

CosineSimilarity(Reduction) - Constructor for class org.tensorflow.framework.losses.CosineSimilarity

Creates a Cosine Similarity AbstractLoss using Class.getSimpleName() as the loss name and an axis of CosineSimilarity.DEFAULT_AXIS

COUNT - Static variable in class org.tensorflow.framework.metrics.MeanTensor

create() - Static method in class org.tensorflow.framework.op.FrameworkOps

Creates an API for building operations in the default eager execution environment

create(String) - Static method in interface org.tensorflow.framework.activations.Activation

Creates an Activation instance based on the name as known to the TensorFlow engine.

create(Map<String, Object>) - Static method in interface org.tensorflow.framework.activations.Activation

Creates an Activation getInstance based on a configuration as produced by TensorFlow.

create(ExecutionEnvironment) - Static method in class org.tensorflow.framework.op.FrameworkOps

Creates an API for building operations in the provided execution environment

create(Ops) - Static method in class org.tensorflow.framework.op.FrameworkOps

Creates an API for building operations in the default eager execution environment

createAdamMinimize(Scope, Operand<T>, float, float, float, float, Optimizer.Options...) - Static method in class org.tensorflow.framework.optimizers.Adam

Creates the Operation that minimizes the loss

createName(Output<? extends TType>, String) - Static method in class org.tensorflow.framework.optimizers.Optimizer

Creates a name by combining a variable name and a slot name

createOptimizer(Graph) - Method in enum org.tensorflow.framework.optimizers.Optimizers

Creates an Optimizer with default settings.

createSlot(Output<T>, String, Operand<T>) - Method in class org.tensorflow.framework.optimizers.Optimizer

Creates a slot in the graph for the specified variable with the specified name.

createSlots(List<Output<? extends TType>>) - Method in class org.tensorflow.framework.optimizers.AdaDelta

Performs a No-op slot creation method.

createSlots(List<Output<? extends TType>>) - Method in class org.tensorflow.framework.optimizers.AdaGrad

Performs a No-op slot creation method.

createSlots(List<Output<? extends TType>>) - Method in class org.tensorflow.framework.optimizers.AdaGradDA

Performs a No-op slot creation method.

createSlots(List<Output<? extends TType>>) - Method in class org.tensorflow.framework.optimizers.Adam

Performs a No-op slot creation method.

createSlots(List<Output<? extends TType>>) - Method in class org.tensorflow.framework.optimizers.Adamax

Performs a No-op slot creation method.

createSlots(List<Output<? extends TType>>) - Method in class org.tensorflow.framework.optimizers.Ftrl

Performs a No-op slot creation method.

createSlots(List<Output<? extends TType>>) - Method in class org.tensorflow.framework.optimizers.Momentum

Performs a No-op slot creation method.

createSlots(List<Output<? extends TType>>) - Method in class org.tensorflow.framework.optimizers.Nadam

Performs a No-op slot creation method.

createSlots(List<Output<? extends TType>>) - Method in class org.tensorflow.framework.optimizers.Optimizer

Performs a No-op slot creation method.

createSlots(List<Output<? extends TType>>) - Method in class org.tensorflow.framework.optimizers.RMSProp

Performs a No-op slot creation method.

D

Dataset - Class in org.tensorflow.framework.data

Represents a potentially large list of independent elements (samples), and allows iteration and transformations to be performed across these elements.

Dataset(Dataset) - Constructor for class org.tensorflow.framework.data.Dataset

Creates a Dataset that is a copy of another Dataset

Dataset(Ops, Operand<?>, List<Class<? extends TType>>, List<Shape>) - Constructor for class org.tensorflow.framework.data.Dataset

Creates a Dataset

DatasetIterator - Class in org.tensorflow.framework.data

Represents the state of an iteration through a tf.data Datset.

DatasetIterator(DatasetIterator) - Constructor for class org.tensorflow.framework.data.DatasetIterator

DatasetIterator(Ops, Operand<?>, List<Class<? extends TType>>, List<Shape>) - Constructor for class org.tensorflow.framework.data.DatasetIterator

DatasetIterator(Ops, Operand<?>, Op, List<Class<? extends TType>>, List<Shape>) - Constructor for class org.tensorflow.framework.data.DatasetIterator

DatasetOptional - Class in org.tensorflow.framework.data

An optional represents the result of a dataset getNext operation that may fail, when the end of the dataset has been reached.

DatasetOptional(DatasetOptional) - Constructor for class org.tensorflow.framework.data.DatasetOptional

Creates a Dataset that is a copy of another Dataset

DatasetOptional(Ops, Operand<?>, List<Class<? extends TType>>, List<Shape>) - Constructor for class org.tensorflow.framework.data.DatasetOptional

Creates a DatasetOptional dataset

DECAY_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.RMSProp

DEFAULT_AXIS - Static variable in class org.tensorflow.framework.losses.CategoricalCrossentropy

DEFAULT_AXIS - Static variable in class org.tensorflow.framework.losses.CosineSimilarity

DEFAULT_AXIS - Static variable in class org.tensorflow.framework.metrics.CosineSimilarity

DEFAULT_K - Static variable in class org.tensorflow.framework.metrics.SparseTopKCategoricalAccuracy

DEFAULT_K - Static variable in class org.tensorflow.framework.metrics.TopKCategoricalAccuracy

DEFAULT_NAME - Static variable in class org.tensorflow.framework.metrics.AUC

DEFAULT_NUM_THRESHOLDS - Static variable in class org.tensorflow.framework.metrics.AUC

DEFAULT_REDUCTION - Static variable in class org.tensorflow.framework.losses.CosineSimilarity

DEFAULT_REGULARIZATION_PENALTY - Static variable in class org.tensorflow.framework.regularizers.AbstractRegularizer

DEFAULT_THRESHOLD - Static variable in class org.tensorflow.framework.metrics.BinaryAccuracy

the default threshold value for deciding whether prediction values are 1 or 0

DEFAULT_THRESHOLD - Static variable in class org.tensorflow.framework.metrics.Precision

DEFAULT_THRESHOLD - Static variable in class org.tensorflow.framework.metrics.Recall

DELTA_DEFAULT - Static variable in class org.tensorflow.framework.losses.Huber

difference(Scope, Operand<T>, Operand<T>) - Static method in class org.tensorflow.framework.op.sets.Sets

Computes set difference of elements in last dimension of a and b with aMinusB set to true.

difference(Scope, Operand<T>, Operand<T>, boolean) - Static method in class org.tensorflow.framework.op.sets.Sets

Computes set difference of elements in last dimension of a and b.

difference(Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.op.SetOps

Computes set difference of elements in last dimension of a and b with aMinusB set to true.

difference(Operand<T>, Operand<T>, boolean) - Method in class org.tensorflow.framework.op.SetOps

Computes set difference of elements in last dimension of a and b.

DISTRIBUTION_DEFAULT - Static variable in class org.tensorflow.framework.initializers.VarianceScaling

E

elu(Ops, Operand<T>, double) - Static method in class org.tensorflow.framework.activations.ELU

Computes the Exponential linear unit.

ELU - Class in org.tensorflow.framework.activations

Exponential linear unit.

ELU - org.tensorflow.framework.activations.Activations

ELU() - Constructor for class org.tensorflow.framework.activations.ELU

Creates a new ELU with alpha=ELU.ALPHA_DEFAULT.

ELU(double) - Constructor for class org.tensorflow.framework.activations.ELU

Creates a new ELU

ELU(Map<String, Object>) - Constructor for class org.tensorflow.framework.activations.ELU

Creates a new ELU from a configuration Map

EMPTY_SHARED_NAME - Static variable in class org.tensorflow.framework.data.DatasetIterator

EPSILON - Static variable in class org.tensorflow.framework.constraints.AbstractConstraint

EPSILON - Static variable in class org.tensorflow.framework.losses.Losses

Default Fuzz factor.

EPSILON - Static variable in class org.tensorflow.framework.metrics.AUC

Default Fuzz factor.

EPSILON_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.AdaDelta

EPSILON_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Adam

EPSILON_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Adamax

EPSILON_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Nadam

EPSILON_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.RMSProp

exponential(Ops, Operand<T>) - Static method in class org.tensorflow.framework.activations.Exponential

Computes the Exponential activation function.

Exponential - Class in org.tensorflow.framework.activations

Exponential activation function.

Exponential() - Constructor for class org.tensorflow.framework.activations.Exponential

Creates an Exponential activation.

Exponential(Map<String, Object>) - Constructor for class org.tensorflow.framework.activations.Exponential

Creates a new Exponential from a configuration Map

EXPONENTIAL - org.tensorflow.framework.activations.Activations

F

FALSE_NEGATIVES - Static variable in class org.tensorflow.framework.metrics.AUC

FALSE_NEGATIVES - Static variable in class org.tensorflow.framework.metrics.Recall

FALSE_POSITIVES - Static variable in class org.tensorflow.framework.metrics.AUC

FALSE_POSITIVES - Static variable in class org.tensorflow.framework.metrics.Precision

FalseNegatives<T extends TNumber> - Class in org.tensorflow.framework.metrics

Metric that calculates the number of false negatives.

FalseNegatives(float[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.FalseNegatives

Creates a FalseNegatives metric, using Class.getSimpleName() for the metric name

FalseNegatives(float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.FalseNegatives

Creates a FalseNegatives metric, using Class.getSimpleName() for the metric name

FalseNegatives(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.FalseNegatives

Creates a FalseNegatives metric, using Class.getSimpleName() for the metric name and a default threshold of ConfusionMatrixConditionCount.DEFAULT_THRESHOLD.

FalseNegatives(String, float[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.FalseNegatives

Creates a FalseNegatives metric

FalseNegatives(String, float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.FalseNegatives

Creates a FalseNegatives metric

FalseNegatives(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.FalseNegatives

Creates a FalseNegatives metric, using a default threshold of ConfusionMatrixConditionCount.DEFAULT_THRESHOLD.

FalsePositives<T extends TNumber> - Class in org.tensorflow.framework.metrics

Metric that calculates the number of false positives.

FalsePositives(float[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.FalsePositives

Creates a FalsePositives metric, using Class.getSimpleName() for the metric name

FalsePositives(float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.FalsePositives

Creates a FalsePositives metric, using Class.getSimpleName() for the metric name

FalsePositives(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.FalsePositives

Creates a FalsePositives metric, using Class.getSimpleName() for the metric name and a default threshold of ConfusionMatrixConditionCount.DEFAULT_THRESHOLD.

FalsePositives(String, float[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.FalsePositives

Creates a FalsePositives metric

FalsePositives(String, float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.FalsePositives

Creates a FalsePositives metric

FalsePositives(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.FalsePositives

Creates a FalsePositives metric, using a default threshold of ConfusionMatrixConditionCount.DEFAULT_THRESHOLD.

FAN_AVG - org.tensorflow.framework.initializers.VarianceScaling.Mode

FAN_IN - org.tensorflow.framework.initializers.VarianceScaling.Mode

FAN_OUT - org.tensorflow.framework.initializers.VarianceScaling.Mode

finish(List<Op>, String) - Method in class org.tensorflow.framework.optimizers.AdaGradDA

Gathers up the update operations into a single op that can be used as a run target.

finish(List<Op>, String) - Method in class org.tensorflow.framework.optimizers.Adam

Gathers up the update operations into a single op that can be used as a run target.

finish(List<Op>, String) - Method in class org.tensorflow.framework.optimizers.Adamax

Gathers up the update operations into a single op that can be used as a run target.

finish(List<Op>, String) - Method in class org.tensorflow.framework.optimizers.Nadam

Gathers up the update operations into a single op that can be used as a run target.

finish(List<Op>, String) - Method in class org.tensorflow.framework.optimizers.Optimizer

Gathers up the update operations into a single op that can be used as a run target.

FIRST_MOMENT - Static variable in class org.tensorflow.framework.optimizers.Adam

FIRST_MOMENT - Static variable in class org.tensorflow.framework.optimizers.Adamax

FIRST_MOMENT - Static variable in class org.tensorflow.framework.optimizers.Nadam

FrameworkOps - Class in org.tensorflow.framework.op

An API for building framework operations as Ops

FROM_LOGITS_DEFAULT - Static variable in class org.tensorflow.framework.losses.BinaryCrossentropy

FROM_LOGITS_DEFAULT - Static variable in class org.tensorflow.framework.losses.CategoricalCrossentropy

FROM_LOGITS_DEFAULT - Static variable in class org.tensorflow.framework.losses.SparseCategoricalCrossentropy

fromComponents(Ops, List<Operand<?>>, List<Class<? extends TType>>, List<Shape>) - Static method in class org.tensorflow.framework.data.DatasetOptional

Creates a DatasetOptional from components.

fromStructure(Ops, List<Class<? extends TType>>, List<Shape>) - Static method in class org.tensorflow.framework.data.DatasetIterator

Creates a new iterator from a "structure" defined by `outputShapes` and `outputTypes`.

fromTensorSlices(Ops, List<Operand<?>>, List<Class<? extends TType>>) - Static method in class org.tensorflow.framework.data.Dataset

Creates an in-memory `Dataset` whose elements are slices of the given tensors.

Ftrl - Class in org.tensorflow.framework.optimizers

Optimizer that implements the FTRL algorithm.

Ftrl(Graph) - Constructor for class org.tensorflow.framework.optimizers.Ftrl

Creates a Ftrl Optimizer

Ftrl(Graph, float) - Constructor for class org.tensorflow.framework.optimizers.Ftrl

Creates a Ftrl Optimizer

Ftrl(Graph, float, float, float, float, float, float) - Constructor for class org.tensorflow.framework.optimizers.Ftrl

Creates a Ftrl Optimizer

Ftrl(Graph, String) - Constructor for class org.tensorflow.framework.optimizers.Ftrl

Creates a Ftrl Optimizer

Ftrl(Graph, String, float) - Constructor for class org.tensorflow.framework.optimizers.Ftrl

Creates a Ftrl Optimizer

Ftrl(Graph, String, float, float, float, float, float, float) - Constructor for class org.tensorflow.framework.optimizers.Ftrl

Creates a Ftrl Optimizer

FTRL - org.tensorflow.framework.optimizers.Optimizers

G

GAIN_DEFAULT - Static variable in class org.tensorflow.framework.initializers.Identity

GAIN_DEFAULT - Static variable in class org.tensorflow.framework.initializers.Orthogonal

gelu(Ops, Operand<T>) - Static method in class org.tensorflow.framework.activations.GELU

Applies the Gaussian error linear unit (GELU) activation function with approximate set to false.

gelu(Ops, Operand<T>, boolean) - Static method in class org.tensorflow.framework.activations.GELU

Applies the Gaussian error linear unit (GELU) activation function.

gelu(Scope, Operand<T>) - Static method in class org.tensorflow.framework.op.nn.GELU

Compute the Gaussian Error Linear Unit (GELU) activation function without approximation.

gelu(Scope, Operand<T>, boolean) - Static method in class org.tensorflow.framework.op.nn.GELU

Compute the Gaussian Error Linear Unit (GELU) activation function.

gelu(Operand<T>) - Method in class org.tensorflow.framework.op.NnOps

Compute the Gaussian Error Linear Unit (GELU) activation function without approximation.

gelu(Operand<T>, boolean) - Method in class org.tensorflow.framework.op.NnOps

Compute the Gaussian Error Linear Unit (GELU) activation function.

GELU - Class in org.tensorflow.framework.activations

Applies the Gaussian error linear unit (GELU) activation function.

GELU - Class in org.tensorflow.framework.op.nn

The Gaussian Error Linear Unit (GELU) activation function.

GELU - org.tensorflow.framework.activations.Activations

GELU() - Constructor for class org.tensorflow.framework.activations.GELU

Creates a Gaussian error linear unit (GELU) activation.

GELU() - Constructor for class org.tensorflow.framework.op.nn.GELU

GELU(boolean) - Constructor for class org.tensorflow.framework.activations.GELU

Creates a Gaussian error linear unit (GELU) activation.

GELU(Map<String, Object>) - Constructor for class org.tensorflow.framework.activations.GELU

Creates a GELU activation from a config map.

get(String) - Method in enum org.tensorflow.framework.metrics.AUCCurve

Gets the AUCCurve enum value by name, regardless of case

get(String) - Method in enum org.tensorflow.framework.metrics.AUCSummationMethod

Gets the AUCSummationMethod enum value by name, regardless of case

getAlpha() - Method in class org.tensorflow.framework.activations.ELU

Gets the slope of negative section.

getAlpha() - Method in class org.tensorflow.framework.activations.ReLU

Gets the value that governs the slope for values lower than the threshold.

getAxes() - Method in class org.tensorflow.framework.constraints.MaxNorm

Gets the axes

getAxes() - Method in class org.tensorflow.framework.constraints.MinMaxNorm

Gets the axes

getAxes() - Method in class org.tensorflow.framework.constraints.UnitNorm

Gets the axes

getAxis() - Method in class org.tensorflow.framework.activations.Softmax

Gets the axis along which the softmax normalization is applied.

getClassId() - Method in class org.tensorflow.framework.metrics.Precision

Gets the classId, may be null

getClassId() - Method in class org.tensorflow.framework.metrics.Recall

Gets the class id

getConfig() - Method in class org.tensorflow.framework.activations.AbstractActivation

Gets a configuration map

getConfig() - Method in class org.tensorflow.framework.activations.ELU

Gets a configuration map

getConfig() - Method in class org.tensorflow.framework.activations.Exponential

Gets a configuration map

getConfig() - Method in class org.tensorflow.framework.activations.GELU

Gets a configuration map with entries approximate and value set with GELU.approximate.

getConfig() - Method in class org.tensorflow.framework.activations.HardSigmoid

Gets a configuration map

getConfig() - Method in class org.tensorflow.framework.activations.Linear

Gets a configuration map

getConfig() - Method in class org.tensorflow.framework.activations.ReLU

Gets a configuration map with entries alpha and value set with ReLU.alpha.

getConfig() - Method in class org.tensorflow.framework.activations.SELU

Gets a configuration map

getConfig() - Method in class org.tensorflow.framework.activations.Sigmoid

Gets a configuration map

getConfig() - Method in class org.tensorflow.framework.activations.Softmax

Gets a configuration map with entries axis and value set with Softmax.axis.

getConfig() - Method in class org.tensorflow.framework.activations.Softplus

Gets a configuration map

getConfig() - Method in class org.tensorflow.framework.activations.Softsign

Gets a configuration map

getConfig() - Method in class org.tensorflow.framework.activations.Swish

Gets a configuration map

getConfig() - Method in class org.tensorflow.framework.activations.Tanh

Gets a configuration map

getCount() - Method in class org.tensorflow.framework.metrics.MeanTensor

getCurve() - Method in class org.tensorflow.framework.metrics.AUC

getDefaultConfig(String) - Method in class org.tensorflow.framework.activations.AbstractActivation

Gets a configuration map, this default implementation returns a singleton Map, with AbstractActivation.NAME_KEY as the key, and the name parameter as its value;

getDenseShape() - Method in class org.tensorflow.framework.utils.SparseTensor

Gets the dense shape for the Sparse Tensor

getFalseNegatives() - Method in class org.tensorflow.framework.metrics.AUC

getFalseNegatives() - Method in class org.tensorflow.framework.metrics.Recall

Gets the falseNegatives variable

getFalseNegativesName() - Method in class org.tensorflow.framework.metrics.AUC

getFalseNegativesName() - Method in class org.tensorflow.framework.metrics.Recall

Gets the falseNegatives variable name

getFalsePositives() - Method in class org.tensorflow.framework.metrics.AUC

getFalsePositives() - Method in class org.tensorflow.framework.metrics.Precision

Gets the falsePositives variable

getFalsePositivesName() - Method in class org.tensorflow.framework.metrics.AUC

getFalsePositivesName() - Method in class org.tensorflow.framework.metrics.Precision

Gets the name of the falsePositives variable

getGradient() - Method in class org.tensorflow.framework.optimizers.Optimizer.GradAndVar

Gets the gradient

getIndices() - Method in class org.tensorflow.framework.utils.SparseTensor

Gets the indices for the Sparse Tensor

getInitializer() - Method in class org.tensorflow.framework.data.DatasetIterator

getInstance() - Method in enum org.tensorflow.framework.activations.Activations

Gets an Activation Instance

getInstance(Map<String, Object>) - Method in enum org.tensorflow.framework.activations.Activations

Gets an Activation Instance

getIntArray(Scope, Operand<TInt32>) - Static method in class org.tensorflow.framework.utils.ShapeUtils

Converts a TInt32 type Operand to a Java int array

getIteratorResource() - Method in class org.tensorflow.framework.data.DatasetIterator

getL1() - Method in class org.tensorflow.framework.regularizers.L1L2

Gets the L1 regularization factor

getL2() - Method in class org.tensorflow.framework.regularizers.L1L2

Gets the L2 regularization factor

getLabelWeights() - Method in class org.tensorflow.framework.metrics.AUC

getLongArray(Scope, Operand<T>) - Static method in class org.tensorflow.framework.utils.ShapeUtils

Converts a TInt32 or TInt64 Operand to a java long array

getLongArray(T) - Static method in class org.tensorflow.framework.utils.ShapeUtils

Converts a TInt32 or TInt64 to a java long array

getMaxValue() - Method in class org.tensorflow.framework.activations.ReLU

Gets the saturation threshold (the largest value the function will return).

getMaxValue() - Method in class org.tensorflow.framework.constraints.MaxNorm

Gets the max value

getMaxValue() - Method in class org.tensorflow.framework.constraints.MinMaxNorm

Gets the maxValue

getMinValue() - Method in class org.tensorflow.framework.constraints.MinMaxNorm

Gets the minValue

getName() - Method in class org.tensorflow.framework.activations.AbstractActivation

Get the name of the activation as known by the TensorFlow Engine

getName() - Method in class org.tensorflow.framework.activations.ELU

Get the name of the activation as known by the TensorFlow Engine

getName() - Method in class org.tensorflow.framework.activations.Exponential

Get the name of the activation as known by the TensorFlow Engine

getName() - Method in class org.tensorflow.framework.activations.GELU

Get the name of the activation as known by the TensorFlow Engine

getName() - Method in class org.tensorflow.framework.activations.HardSigmoid

Get the name of the activation as known by the TensorFlow Engine

getName() - Method in class org.tensorflow.framework.activations.Linear

Get the name of the activation as known by the TensorFlow Engine

getName() - Method in class org.tensorflow.framework.activations.ReLU

Get the name of the activation as known by the TensorFlow Engine

getName() - Method in class org.tensorflow.framework.activations.SELU

Get the name of the activation as known by the TensorFlow Engine

getName() - Method in class org.tensorflow.framework.activations.Sigmoid

Get the name of the activation as known by the TensorFlow Engine

getName() - Method in class org.tensorflow.framework.activations.Softmax

Get the name of the activation as known by the TensorFlow Engine

getName() - Method in class org.tensorflow.framework.activations.Softplus

Get the name of the activation as known by the TensorFlow Engine

getName() - Method in class org.tensorflow.framework.activations.Softsign

Get the name of the activation as known by the TensorFlow Engine

getName() - Method in class org.tensorflow.framework.activations.Swish

Get the name of the activation as known by the TensorFlow Engine

getName() - Method in class org.tensorflow.framework.activations.Tanh

Get the name of the activation as known by the TensorFlow Engine

getName() - Method in class org.tensorflow.framework.initializers.BaseInitializer

Gets the name for this initializer

getName() - Method in class org.tensorflow.framework.metrics.BaseMetric

The name for this metric.

getName() - Method in class org.tensorflow.framework.regularizers.AbstractRegularizer

Gets the name for this regularizer

getNext() - Method in class org.tensorflow.framework.data.DatasetIterator

Returns a list of Operand<?> representing the components of the next dataset element.

getNextAsOptional() - Method in class org.tensorflow.framework.data.DatasetIterator

Returns a `DatasetOptional` representing the components of the next dataset element.

getNormalizer() - Method in class org.tensorflow.framework.metrics.MeanRelativeError

Gets the normalizer Operand

getNumLabels() - Method in class org.tensorflow.framework.metrics.AUC

getNumThresholds() - Method in class org.tensorflow.framework.metrics.AUC

getOpsInstance() - Method in class org.tensorflow.framework.data.Dataset

Gets the TensorFlow Ops instance for this dataset

getOpsInstance() - Method in class org.tensorflow.framework.data.DatasetIterator

getOpsInstance() - Method in class org.tensorflow.framework.data.DatasetOptional

Gets the TensorFlow Ops instance for this dataset

getOptimizerName() - Method in class org.tensorflow.framework.optimizers.AdaDelta

Get the Name of the optimizer.

getOptimizerName() - Method in class org.tensorflow.framework.optimizers.AdaGrad

Get the Name of the optimizer.

getOptimizerName() - Method in class org.tensorflow.framework.optimizers.AdaGradDA

Get the Name of the optimizer.

getOptimizerName() - Method in class org.tensorflow.framework.optimizers.Adam

Get the Name of the optimizer.

getOptimizerName() - Method in class org.tensorflow.framework.optimizers.Adamax

Get the Name of the optimizer.

getOptimizerName() - Method in class org.tensorflow.framework.optimizers.Ftrl

Get the Name of the optimizer.

getOptimizerName() - Method in class org.tensorflow.framework.optimizers.GradientDescent

Get the Name of the optimizer.

getOptimizerName() - Method in class org.tensorflow.framework.optimizers.Momentum

Get the Name of the optimizer.

getOptimizerName() - Method in class org.tensorflow.framework.optimizers.Nadam

Get the Name of the optimizer.

getOptimizerName() - Method in class org.tensorflow.framework.optimizers.Optimizer

Get the Name of the optimizer.

getOptimizerName() - Method in class org.tensorflow.framework.optimizers.RMSProp

Get the Name of the optimizer.

getOptionalVariant() - Method in class org.tensorflow.framework.data.DatasetOptional

Gets the optional variant for this Dataset

getOutputShapes() - Method in class org.tensorflow.framework.data.Dataset

Gets a list of shapes for each component of this dataset.

getOutputTypes() - Method in class org.tensorflow.framework.data.Dataset

Gets a list of output types for each component of this dataset.

getPrecision() - Method in class org.tensorflow.framework.metrics.RecallAtPrecision

Gets the precision

getRate() - Method in class org.tensorflow.framework.constraints.MinMaxNorm

Gets the rate

getRecall() - Method in class org.tensorflow.framework.metrics.PrecisionAtRecall

Gets the recall value

getSeed() - Method in class org.tensorflow.framework.metrics.BaseMetric

Gets the random number generator seed value

getSensitivity() - Method in class org.tensorflow.framework.metrics.SpecificityAtSensitivity

Gets the sensitivity

getSetOperation() - Method in enum org.tensorflow.framework.op.sets.Sets.Operation

Gets the set operation String value used to pass as the stringOperation value to SparseOps.denseToDenseSetOperation(org.tensorflow.Operand<T>, org.tensorflow.Operand<T>, java.lang.String, org.tensorflow.op.sparse.DenseToDenseSetOperation.Options...)

getSlot(Output<T>, String) - Method in class org.tensorflow.framework.optimizers.Optimizer

Gets the slot associated with the specified variable and slot name.

getSpecificity() - Method in class org.tensorflow.framework.metrics.SensitivityAtSpecificity

Gets the specificity

getSummationMethod() - Method in class org.tensorflow.framework.metrics.AUC

getTensorFlowName() - Method in enum org.tensorflow.framework.activations.Activations

Gets the activation name as known to the TensorFlow engine.

getTF() - Method in class org.tensorflow.framework.activations.AbstractActivation

Gets the TensorFlow Ops

getTF() - Method in class org.tensorflow.framework.metrics.BaseMetric

Gets the TensorFlow Ops for this metric

getTF() - Method in class org.tensorflow.framework.optimizers.Optimizer

Gets the Optimizer's Ops instance

getThreshold() - Method in class org.tensorflow.framework.activations.ReLU

Gets the saturation threshold (the largest value the function will return).

getThresholds() - Method in class org.tensorflow.framework.metrics.AUC

getThresholds() - Method in class org.tensorflow.framework.metrics.Precision

Gets the thresholds

getThresholds() - Method in class org.tensorflow.framework.metrics.Recall

Gets the thresholds

getTopK() - Method in class org.tensorflow.framework.metrics.Precision

Gets the topK value, may be null

getTopK() - Method in class org.tensorflow.framework.metrics.Recall

Gets the topK value

getTotal() - Method in class org.tensorflow.framework.metrics.MeanTensor

getTrueNegatives() - Method in class org.tensorflow.framework.metrics.AUC

getTrueNegativesName() - Method in class org.tensorflow.framework.metrics.AUC

getTruePositives() - Method in class org.tensorflow.framework.metrics.AUC

getTruePositives() - Method in class org.tensorflow.framework.metrics.Precision

Gets the truePositives variable

getTruePositives() - Method in class org.tensorflow.framework.metrics.Recall

Gets the truePositives variable

getTruePositivesName() - Method in class org.tensorflow.framework.metrics.AUC

getTruePositivesName() - Method in class org.tensorflow.framework.metrics.Precision

Gets the name of the truePositives variable

getTruePositivesName() - Method in class org.tensorflow.framework.metrics.Recall

Gets the truePositives variable name

getValue() - Method in class org.tensorflow.framework.data.DatasetOptional

Returns the value of the dataset element represented by this optional, if it exists.

getValues() - Method in class org.tensorflow.framework.utils.SparseTensor

Get the values for the Sparse Tensor

getVariable() - Method in class org.tensorflow.framework.optimizers.Optimizer.GradAndVar

Gets the variable

getVariableName(String) - Method in class org.tensorflow.framework.metrics.BaseMetric

Gets a formatted name for a variable, in the form BaseMetric.name + "_" + varName.

getVariant() - Method in class org.tensorflow.framework.data.Dataset

Gets the variant tensor representing this dataset.

globals - Variable in class org.tensorflow.framework.optimizers.Optimizer

Global state variables

Glorot<T extends TFloating> - Class in org.tensorflow.framework.initializers

The Glorot initializer, also called Xavier initializer.

Glorot(VarianceScaling.Distribution, long) - Constructor for class org.tensorflow.framework.initializers.Glorot

Creates a Glorot initializer

GradAndVar(Output<T>, Output<T>) - Constructor for class org.tensorflow.framework.optimizers.Optimizer.GradAndVar

Creates a Gradient and Variable pair

GRADIENT_DESCENT - org.tensorflow.framework.optimizers.Optimizers

GradientDescent - Class in org.tensorflow.framework.optimizers

Basic Stochastic gradient descent optimizer.

GradientDescent(Graph) - Constructor for class org.tensorflow.framework.optimizers.GradientDescent

Creates a GradientDescent Optimizer

GradientDescent(Graph, float) - Constructor for class org.tensorflow.framework.optimizers.GradientDescent

Creates a GradientDescent Optimizer

GradientDescent(Graph, String, float) - Constructor for class org.tensorflow.framework.optimizers.GradientDescent

Creates a GradientDescent Optimizer

graph - Variable in class org.tensorflow.framework.optimizers.Optimizer

The Graph this optimizer is operating on.

H

HARD_SIGMOID - org.tensorflow.framework.activations.Activations

hardSigmoid(Ops, Operand<T>) - Static method in class org.tensorflow.framework.activations.HardSigmoid

Computes the hard sigmoid activation function.

HardSigmoid - Class in org.tensorflow.framework.activations

Hard sigmoid activation.

HardSigmoid() - Constructor for class org.tensorflow.framework.activations.HardSigmoid

Creates Hard sigmoid activation.

HardSigmoid(Map<String, Object>) - Constructor for class org.tensorflow.framework.activations.HardSigmoid

Creates a new Exponential from a configuration Map

hasValue() - Method in class org.tensorflow.framework.data.DatasetOptional

Gets the indicator of whether this optional has a value.

He<T extends TFloating> - Class in org.tensorflow.framework.initializers

He initializer.

He(VarianceScaling.Distribution, long) - Constructor for class org.tensorflow.framework.initializers.He

Creates an He Initializer

hinge(Ops, Operand<? extends TNumber>, Operand<T>) - Static method in class org.tensorflow.framework.losses.Losses

Computes the hinge loss between labels and predictions

Hinge - Class in org.tensorflow.framework.losses

Computes the hinge loss between labels and predictions.

Hinge<T extends TNumber> - Class in org.tensorflow.framework.metrics

A metric that computes the hinge loss metric between labels and predictions.

Hinge() - Constructor for class org.tensorflow.framework.losses.Hinge

Creates a Hinge AbstractLoss using Class.getSimpleName() as the loss name and a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT

Hinge(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Hinge

Creates a Hinge metric using Class.getSimpleName() for the metric name.

Hinge(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Hinge

Creates a Hinge metric

Hinge(String, Reduction) - Constructor for class org.tensorflow.framework.losses.Hinge

Creates a Hinge

Hinge(Reduction) - Constructor for class org.tensorflow.framework.losses.Hinge

Creates a Hinge AbstractLoss using Class.getSimpleName() as the loss name

huber(Ops, Operand<? extends TNumber>, Operand<T>, float) - Static method in class org.tensorflow.framework.losses.Losses

Computes the Huber loss between labels and predictions.

Huber - Class in org.tensorflow.framework.losses

Computes the Huber loss between labels and predictions.

Huber() - Constructor for class org.tensorflow.framework.losses.Huber

Creates a Huber AbstractLoss using Class.getSimpleName() as the loss name, Huber.DELTA_DEFAULT as the delta and a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT

Huber(String) - Constructor for class org.tensorflow.framework.losses.Huber

Creates a Huber AbstractLoss using Huber.DELTA_DEFAULT as the delta and a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT

Huber(String, float, Reduction) - Constructor for class org.tensorflow.framework.losses.Huber

Creates a Huber AbstractLoss

Huber(String, Reduction) - Constructor for class org.tensorflow.framework.losses.Huber

Creates a Huber AbstractLoss using Huber.DELTA_DEFAULT as the delta

Huber(Reduction) - Constructor for class org.tensorflow.framework.losses.Huber

Creates a Huber AbstractLoss using Class.getSimpleName() as the loss name and and Huber.DELTA_DEFAULT as the delta

I

Identity<T extends TFloating> - Class in org.tensorflow.framework.initializers

Initializer that generates the identity matrix.

Identity() - Constructor for class org.tensorflow.framework.initializers.Identity

Creates an Initializer that generates the identity matrix.

Identity(double) - Constructor for class org.tensorflow.framework.initializers.Identity

Creates an Initializer that generates the identity matrix.

init(Ops) - Method in class org.tensorflow.framework.metrics.AUC

Initialize the TensorFlow Ops

init(Ops) - Method in class org.tensorflow.framework.metrics.BaseMetric

Initialize the TensorFlow Ops

init(Ops) - Method in class org.tensorflow.framework.metrics.MeanIoU

Initialize the TensorFlow Ops

init(Ops) - Method in class org.tensorflow.framework.metrics.MeanRelativeError

Initialize the TensorFlow Ops

init(Ops) - Method in class org.tensorflow.framework.metrics.MeanTensor

Initialize the TensorFlow Ops

init(Ops) - Method in class org.tensorflow.framework.metrics.Precision

Initialize the TensorFlow Ops

init(Ops) - Method in class org.tensorflow.framework.metrics.Recall

Initialize the TensorFlow Ops

INITIAL_ACCUMULATOR_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.AdaGrad

INITIAL_ACCUMULATOR_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.AdaGradDA

INITIAL_ACCUMULATOR_VALUE_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Ftrl

Initializer<T extends TType> - Interface in org.tensorflow.framework.initializers

An interface for Initializers

INTERPOLATION - org.tensorflow.framework.metrics.AUCSummationMethod

Apply mid-point summation scheme for AUCCurve.ROC.

intersection(Scope, Operand<T>, Operand<T>) - Static method in class org.tensorflow.framework.op.sets.Sets

Computes set intersection of elements in last dimension of a and b.

intersection(Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.op.SetOps

Computes set intersection of elements in last dimension of a and b.

INTERSECTION - org.tensorflow.framework.op.sets.Sets.Operation

isApproximate() - Method in class org.tensorflow.framework.activations.GELU

Gets the flag whether to enable approximation.

isInitialized() - Method in class org.tensorflow.framework.metrics.BaseMetric

Checks whether the Metric is initialized or not.

isMultiLabel() - Method in class org.tensorflow.framework.metrics.AUC

iterator() - Method in class org.tensorflow.framework.data.Dataset

Creates an iterator which iterates through all batches of this Dataset in an eager fashion.

iterator() - Method in class org.tensorflow.framework.data.DatasetIterator

K

KLDivergence - Class in org.tensorflow.framework.losses

Computes Kullback-Leibler divergence loss between labels and predictions.

KLDivergence<T extends TNumber> - Class in org.tensorflow.framework.metrics

A metric that computes the Kullback-Leibler divergence loss metric between labels and predictions.

KLDivergence() - Constructor for class org.tensorflow.framework.losses.KLDivergence

Creates a Kullback Leibler Divergence AbstractLoss using Class.getSimpleName() as the loss name and a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT

KLDivergence(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.KLDivergence

Creates a KLDivergence metric using Class.getSimpleName() for the metric name.

KLDivergence(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.KLDivergence

Creates a KLDivergence metric

KLDivergence(String, Reduction) - Constructor for class org.tensorflow.framework.losses.KLDivergence

Creates a Kullback Leibler Divergence AbstractLoss

KLDivergence(Reduction) - Constructor for class org.tensorflow.framework.losses.KLDivergence

Creates a Kullback Leibler Divergence AbstractLoss AbstractLoss using Class.getSimpleName() as the loss name

kullbackLeiblerDivergence(Ops, Operand<? extends TNumber>, Operand<T>) - Static method in class org.tensorflow.framework.losses.Losses

Computes the Kullback-Leibler divergence loss between labels and predictions.

L

L1 - Class in org.tensorflow.framework.regularizers

A regularizer that applies an L1 or Lasso(least absolute shrinkage and selection operator) Regression, regularization penalty.

L1() - Constructor for class org.tensorflow.framework.regularizers.L1

Create a regularizer that applies an L1 regularization penalty of AbstractRegularizer.DEFAULT_REGULARIZATION_PENALTY and a name based on the class name.

L1(float) - Constructor for class org.tensorflow.framework.regularizers.L1

Create a regularizer that applies an L1 regularization penalty and a name based on the class name.

L1(String) - Constructor for class org.tensorflow.framework.regularizers.L1

Create a regularizer that applies an L1 regularization penalty of AbstractRegularizer.DEFAULT_REGULARIZATION_PENALTY

L1(String, float) - Constructor for class org.tensorflow.framework.regularizers.L1

Create a regularizer that applies an L1 regularization penalty

L1_STRENGTH_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.AdaGradDA

L1L2 - Class in org.tensorflow.framework.regularizers

A regularizer that applies both L1 and L2 regularization penalties.

L1L2() - Constructor for class org.tensorflow.framework.regularizers.L1L2

Creates an L1L2 regularizer with no l1 or l2 penalty with zero penalty

L1L2(float, float) - Constructor for class org.tensorflow.framework.regularizers.L1L2

Creates an L1L2 regularizer

L1L2(String, float, float) - Constructor for class org.tensorflow.framework.regularizers.L1L2

Creates an L1L2 regularizer

L1STRENGTH_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Ftrl

L2 - Class in org.tensorflow.framework.regularizers

A regularizer that applies a L2 (Ridge Regression) regularization penalty.

L2() - Constructor for class org.tensorflow.framework.regularizers.L2

Create a regularizer that applies an L2 regularization penalty of AbstractRegularizer.DEFAULT_REGULARIZATION_PENALTY and a name based on the class name.

L2(float) - Constructor for class org.tensorflow.framework.regularizers.L2

Create a regularizer that applies an L1 regularization penalty and a name based on the class name.

L2(String) - Constructor for class org.tensorflow.framework.regularizers.L2

Create a regularizer that applies an L2 regularization penalty of AbstractRegularizer.DEFAULT_REGULARIZATION_PENALTY

L2(String, float) - Constructor for class org.tensorflow.framework.regularizers.L2

Create a regularizer that applies an L1 regularization penalty

L2_SHRINKAGE_REGULARIZATION_STRENGTH_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Ftrl

L2_STRENGTH_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.AdaGradDA

l2Normalize(Ops, Operand<T>, int[]) - Static method in class org.tensorflow.framework.losses.Losses

Normalizes along dimension axis using an L2 norm.

l2Normalize(Scope, Operand<T>, int[]) - Static method in class org.tensorflow.framework.op.math.L2Normalize

Normalizes along dimension axis using an L2 norm.

l2Normalize(Operand<T>, int[]) - Method in class org.tensorflow.framework.op.MathOps

Normalizes along dimension axis using an L2 norm.

L2Normalize - Class in org.tensorflow.framework.op.math

L2 Normalization Operations

L2Normalize() - Constructor for class org.tensorflow.framework.op.math.L2Normalize

L2STRENGTH_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Ftrl

LABEL_SMOOTHING_DEFAULT - Static variable in class org.tensorflow.framework.losses.BinaryCrossentropy

LABEL_SMOOTHING_DEFAULT - Static variable in class org.tensorflow.framework.losses.CategoricalCrossentropy

LEARNING_RATE_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.AdaDelta

LEARNING_RATE_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.AdaGrad

LEARNING_RATE_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.AdaGradDA

LEARNING_RATE_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Adam

LEARNING_RATE_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Adamax

LEARNING_RATE_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Ftrl

LEARNING_RATE_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.GradientDescent

LEARNING_RATE_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Momentum

LEARNING_RATE_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Nadam

LEARNING_RATE_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.RMSProp

LEARNING_RATE_POWER_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Ftrl

LeCun<T extends TFloating> - Class in org.tensorflow.framework.initializers

LeCun normal initializer.

LeCun(VarianceScaling.Distribution, long) - Constructor for class org.tensorflow.framework.initializers.LeCun

Creates a LeCunNormal Initializer

linalg - Variable in class org.tensorflow.framework.op.FrameworkOps

LinalgOps - Class in org.tensorflow.framework.op

linear(Ops, Operand<T>) - Static method in class org.tensorflow.framework.activations.Linear

Computes the linear activation function (pass-through).

Linear - Class in org.tensorflow.framework.activations

Linear activation function (pass-through).

Linear() - Constructor for class org.tensorflow.framework.activations.Linear

Creates a linear activation.

Linear(Map<String, Object>) - Constructor for class org.tensorflow.framework.activations.Linear

Creates a new Exponential from a configuration Map

LINEAR - org.tensorflow.framework.activations.Activations

LINEAR_ACCUMULATOR - Static variable in class org.tensorflow.framework.optimizers.Ftrl

logCosh(Ops, Operand<? extends TNumber>, Operand<T>) - Static method in class org.tensorflow.framework.losses.Losses

Computes the hyperbolic cosine loss between labels and predictions.

LogCosh - Class in org.tensorflow.framework.losses

Computes Computes the logarithm of the hyperbolic cosine of the prediction error.

LogCosh() - Constructor for class org.tensorflow.framework.losses.LogCosh

Creates a LogCosh AbstractLoss using Class.getSimpleName() as the loss name and a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT

LogCosh(String) - Constructor for class org.tensorflow.framework.losses.LogCosh

Creates a LogCosh AbstractLoss using a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT

LogCosh(String, Reduction) - Constructor for class org.tensorflow.framework.losses.LogCosh

Creates a LogCosh AbstractLoss

LogCosh(Reduction) - Constructor for class org.tensorflow.framework.losses.LogCosh

Creates a LogCosh AbstractLoss using Class.getSimpleName() as the loss name

LogCoshError<T extends TNumber> - Class in org.tensorflow.framework.metrics

A metric that computes the logarithm of the hyperbolic cosine of the prediction error metric between labels and predictions.

LogCoshError(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.LogCoshError

Creates a LogCoshError metric using Class.getSimpleName() for the metric name.

LogCoshError(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.LogCoshError

Creates a LogCoshError metric

Loss - Interface in org.tensorflow.framework.losses

Interface for loss calc ulation

Losses - Class in org.tensorflow.framework.losses

Built-in loss functions.

Losses() - Constructor for class org.tensorflow.framework.losses.Losses

M

MAJORING - org.tensorflow.framework.metrics.AUCSummationMethod

Apply right summation for increasing intervals and left summation for decreasing intervals

makeInitializeableIterator() - Method in class org.tensorflow.framework.data.Dataset

Creates a `DatasetIterator` that can be used to iterate over elements of this dataset.

makeInitializer(Dataset) - Method in class org.tensorflow.framework.data.DatasetIterator

Creates and returns a TF `Op` that can be run to initialize this iterator on a dataset.

makeOneShotIterator() - Method in class org.tensorflow.framework.data.Dataset

Creates a `DatasetIterator` that can be used to iterate over elements of this dataset.

map(Function<List<Operand<?>>, List<Operand<?>>>) - Method in class org.tensorflow.framework.data.Dataset

Returns a new Dataset which maps a function over all elements returned by this dataset.

mapAllComponents(Function<Operand<?>, Operand<?>>) - Method in class org.tensorflow.framework.data.Dataset

Returns a new Dataset which maps a function across all elements from this dataset, on all components of each element.

mapOneComponent(int, Function<Operand<?>, Operand<?>>) - Method in class org.tensorflow.framework.data.Dataset

Returns a new Dataset which maps a function across all elements from this dataset, on a single component of each element.

math - Variable in class org.tensorflow.framework.op.FrameworkOps

MathOps - Class in org.tensorflow.framework.op

matmul(Scope, Operand<T>, Operand<T>) - Static method in class org.tensorflow.framework.op.linalg.MatMul

Multiplies matrix a by matrix b, producing a * b .

matmul(Scope, Operand<T>, Operand<T>, boolean, boolean) - Static method in class org.tensorflow.framework.op.linalg.MatMul

Multiplies matrix a by matrix b, producing a * b .

matmul(Scope, Operand<T>, Operand<T>, boolean, boolean, boolean, boolean, boolean, boolean) - Static method in class org.tensorflow.framework.op.linalg.MatMul

Multiplies matrix a by matrix b, producing a * b .

matmul(Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.op.LinalgOps

Multiplies matrix a by matrix b, producing a * b .

matmul(Operand<T>, Operand<T>, boolean, boolean) - Method in class org.tensorflow.framework.op.LinalgOps

Multiplies matrix a by matrix b, producing a * b .

matmul(Operand<T>, Operand<T>, boolean, boolean, boolean, boolean, boolean, boolean) - Method in class org.tensorflow.framework.op.LinalgOps

Multiplies matrix a by matrix b, producing a * b .

MatMul - Class in org.tensorflow.framework.op.linalg

Multiplication matrix operations

MatMul() - Constructor for class org.tensorflow.framework.op.linalg.MatMul

MAX_VALUE_DEFAULT - Static variable in class org.tensorflow.framework.activations.ReLU

MAX_VALUE_DEFAULT - Static variable in class org.tensorflow.framework.constraints.MaxNorm

MAX_VALUE_DEFAULT - Static variable in class org.tensorflow.framework.constraints.MinMaxNorm

MaxNorm - Class in org.tensorflow.framework.constraints

Constrains the weights incident to each hidden unit to have a norm less than or equal to a desired value.

MaxNorm() - Constructor for class org.tensorflow.framework.constraints.MaxNorm

Create a MaxNorm constraint using MaxNorm.MAX_VALUE_DEFAULT for the max value and MaxNorm.AXIS_DEFAULT for the axis.

MaxNorm(double) - Constructor for class org.tensorflow.framework.constraints.MaxNorm

Create a MaxNorm constraint using MaxNorm.AXIS_DEFAULT for the axis.

MaxNorm(double, int) - Constructor for class org.tensorflow.framework.constraints.MaxNorm

Create a MaxNorm constraint

MaxNorm(double, int[]) - Constructor for class org.tensorflow.framework.constraints.MaxNorm

Create a MaxNorm constraint

MAXVAL_DEFAULT - Static variable in class org.tensorflow.framework.initializers.RandomUniform

Mean<T extends TNumber> - Class in org.tensorflow.framework.metrics

A metric that that implements a weighted mean MetricReduction.WEIGHTED_MEAN

Mean(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Mean

Creates a Reducible Metric with a metric reductions of MetricReduction.SUM and using Class.getSimpleName() for the metric name.

Mean(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Mean

Creates a Reducible Metric with a metric reductions of MetricReduction.SUM

MEAN_DEFAULT - Static variable in class org.tensorflow.framework.initializers.RandomNormal

MEAN_DEFAULT - Static variable in class org.tensorflow.framework.initializers.TruncatedNormal

meanAbsoluteError(Ops, Operand<? extends TNumber>, Operand<T>) - Static method in class org.tensorflow.framework.losses.Losses

Calculates the mean absolute error between labels and predictions.

MeanAbsoluteError - Class in org.tensorflow.framework.losses

Computes the mean of absolute difference between labels and predictions.

MeanAbsoluteError<T extends TNumber> - Class in org.tensorflow.framework.metrics

A metric that computes the mean of absolute difference between labels and predictions.

MeanAbsoluteError() - Constructor for class org.tensorflow.framework.losses.MeanAbsoluteError

Creates a MeanAbsoluteError AbstractLoss using Class.getSimpleName() as the loss name and a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT

MeanAbsoluteError(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanAbsoluteError

Creates a Mean Absolute Error metric using Class.getSimpleName() for the metric name.

MeanAbsoluteError(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanAbsoluteError

Creates a Mean Absolute Error metric

MeanAbsoluteError(String, Reduction) - Constructor for class org.tensorflow.framework.losses.MeanAbsoluteError

Creates a MeanAbsoluteError

MeanAbsoluteError(Reduction) - Constructor for class org.tensorflow.framework.losses.MeanAbsoluteError

Creates a MeanAbsoluteError AbstractLoss using Class.getSimpleName() as the loss name

meanAbsolutePercentageError(Ops, Operand<? extends TNumber>, Operand<T>) - Static method in class org.tensorflow.framework.losses.Losses

Calculates the mean absolute percentage error between labels and predictions.

MeanAbsolutePercentageError - Class in org.tensorflow.framework.losses

Computes the mean absolute percentage error between labels and predictions.

MeanAbsolutePercentageError<T extends TNumber> - Class in org.tensorflow.framework.metrics

A metric that computes the mean of absolute difference between labels and predictions.

MeanAbsolutePercentageError() - Constructor for class org.tensorflow.framework.losses.MeanAbsolutePercentageError

Creates a MeanAbsolutePercentageError AbstractLoss using Class.getSimpleName() as the loss name and a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT

MeanAbsolutePercentageError(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanAbsolutePercentageError

Creates a Mean Absolute Error metric using Class.getSimpleName() for the metric name.

MeanAbsolutePercentageError(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanAbsolutePercentageError

Creates a Mean Absolute Error metric

MeanAbsolutePercentageError(String, Reduction) - Constructor for class org.tensorflow.framework.losses.MeanAbsolutePercentageError

Creates a MeanAbsolutePercentageError

MeanAbsolutePercentageError(Reduction) - Constructor for class org.tensorflow.framework.losses.MeanAbsolutePercentageError

Creates a MeanAbsolutePercentageError AbstractLoss using Class.getSimpleName() as the loss name

MeanIoU<T extends TNumber> - Class in org.tensorflow.framework.metrics

Computes the mean Intersection-Over-Union metric.

MeanIoU(long, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanIoU

Creates a metric MeanIoU, using name as Class.getSimpleName()

MeanIoU(String, long, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanIoU

Creates a MeanIoU metric

MeanRelativeError<T extends TNumber> - Class in org.tensorflow.framework.metrics

Computes the mean relative error by normalizing with the given values.

MeanRelativeError(double[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanRelativeError

Creates a MeanRelativeError metric using Class.getSimpleName() as the name

MeanRelativeError(float[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanRelativeError

Creates a MeanRelativeError metric using Class.getSimpleName() as the name

MeanRelativeError(String, double[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanRelativeError

Creates a MeanRelativeError metric

MeanRelativeError(String, float[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanRelativeError

Creates a MeanRelativeError metric

MeanRelativeError(String, Operand<T>, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanRelativeError

Creates a MeanRelativeError metric

MeanRelativeError(Operand<T>, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanRelativeError

Creates a MeanRelativeError metric using Class.getSimpleName() as the name

meanSquaredError(Ops, Operand<? extends TNumber>, Operand<T>) - Static method in class org.tensorflow.framework.losses.Losses

Computes the mean squared error between labels and predictions.

MeanSquaredError - Class in org.tensorflow.framework.losses

Computes the mean of squares of errors between labels and predictions.

MeanSquaredError<T extends TNumber> - Class in org.tensorflow.framework.metrics

A metric that computes the mean of absolute difference between labels and predictions.

MeanSquaredError() - Constructor for class org.tensorflow.framework.losses.MeanSquaredError

Creates a MeanSquaredError AbstractLoss using Class.getSimpleName() as the loss name and a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT

MeanSquaredError(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanSquaredError

Creates a Mean Absolute Error metric using Class.getSimpleName() for the metric name.

MeanSquaredError(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanSquaredError

Creates a Mean Absolute Error metric

MeanSquaredError(String, Reduction) - Constructor for class org.tensorflow.framework.losses.MeanSquaredError

Creates a MeanSquaredError

MeanSquaredError(Reduction) - Constructor for class org.tensorflow.framework.losses.MeanSquaredError

Creates a MeanSquaredError AbstractLoss using Class.getSimpleName() as the loss name

meanSquaredLogarithmicError(Ops, Operand<? extends TNumber>, Operand<T>) - Static method in class org.tensorflow.framework.losses.Losses

Calculates the mean squared logarithmic error between labels and predictions.

MeanSquaredLogarithmicError - Class in org.tensorflow.framework.losses

Computes the mean squared logarithmic errors between labels and predictions.

MeanSquaredLogarithmicError<T extends TNumber> - Class in org.tensorflow.framework.metrics

A metric that computes the mean of absolute difference between labels and predictions.

MeanSquaredLogarithmicError() - Constructor for class org.tensorflow.framework.losses.MeanSquaredLogarithmicError

Creates a MeanSquaredError AbstractLoss using Class.getSimpleName() as the loss name and a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT

MeanSquaredLogarithmicError(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanSquaredLogarithmicError

Creates a Mean Absolute Error metric using Class.getSimpleName() for the metric name.

MeanSquaredLogarithmicError(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanSquaredLogarithmicError

Creates a Mean Absolute Error metric

MeanSquaredLogarithmicError(String, Reduction) - Constructor for class org.tensorflow.framework.losses.MeanSquaredLogarithmicError

Creates a MeanSquaredError

MeanSquaredLogarithmicError(Reduction) - Constructor for class org.tensorflow.framework.losses.MeanSquaredLogarithmicError

Creates a MeanSquaredError AbstractLoss using Class.getSimpleName() as the loss name

MeanTensor<T extends TNumber> - Class in org.tensorflow.framework.metrics

Metric that computes the element-wise (weighted) mean of the given tensors.

MeanTensor(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanTensor

Creates a MeanTensor metric, using Class.getSimpleName() as the name

MeanTensor(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.MeanTensor

Creates a MeanTensor metric

Metric - Interface in org.tensorflow.framework.metrics

Interface for metrics

MetricReduction - Enum in org.tensorflow.framework.metrics

Defines the different types of metric reductions

Metrics - Class in org.tensorflow.framework.metrics

Static methods for computing metrics.

Metrics() - Constructor for class org.tensorflow.framework.metrics.Metrics

MG - Static variable in class org.tensorflow.framework.optimizers.RMSProp

MIN_VALUE_DEFAULT - Static variable in class org.tensorflow.framework.constraints.MinMaxNorm

minimize(Operand<?>) - Method in class org.tensorflow.framework.optimizers.Optimizer

Minimizes the loss by updating the variables

minimize(Operand<?>, String) - Method in class org.tensorflow.framework.optimizers.Optimizer

Minimizes the loss by updating the variables

MinMaxNorm - Class in org.tensorflow.framework.constraints

Constrains the weights to have the norm between a lower bound and an upper bound.

MinMaxNorm() - Constructor for class org.tensorflow.framework.constraints.MinMaxNorm

Create a MinMaxNorm constraint using MinMaxNorm.MIN_VALUE_DEFAULT for the min value, MinMaxNorm.MAX_VALUE_DEFAULT for the max value, MinMaxNorm.RATE_DEFAULT for the rate and MinMaxNorm.AXIS_DEFAULT for the axis

MinMaxNorm(double, double) - Constructor for class org.tensorflow.framework.constraints.MinMaxNorm

Create a MinMaxNorm constraint using MinMaxNorm.RATE_DEFAULT for the rate and MinMaxNorm.AXIS_DEFAULT for the axis

MinMaxNorm(double, double, double, int) - Constructor for class org.tensorflow.framework.constraints.MinMaxNorm

Create a MinMaxNorm constraint

MinMaxNorm(double, double, double, int[]) - Constructor for class org.tensorflow.framework.constraints.MinMaxNorm

Create a MinMaxNorm constraint

MINORING - org.tensorflow.framework.metrics.AUCSummationMethod

Apply left summation for increasing intervals and right summation for decreasing intervals

MINVAL_DEFAULT - Static variable in class org.tensorflow.framework.initializers.RandomUniform

MODE_DEFAULT - Static variable in class org.tensorflow.framework.initializers.VarianceScaling

Momentum - Class in org.tensorflow.framework.optimizers

Stochastic gradient descent plus momentum, either nesterov or traditional.

Momentum(Graph) - Constructor for class org.tensorflow.framework.optimizers.Momentum

Creates a Momentum Optimizer

Momentum(Graph, float) - Constructor for class org.tensorflow.framework.optimizers.Momentum

Creates a Momentum Optimizer

Momentum(Graph, float, float) - Constructor for class org.tensorflow.framework.optimizers.Momentum

Creates a Momentum Optimizer

Momentum(Graph, float, float, boolean) - Constructor for class org.tensorflow.framework.optimizers.Momentum

Creates a Momentum Optimizer

Momentum(Graph, String, float, float, boolean) - Constructor for class org.tensorflow.framework.optimizers.Momentum

Creates a Momentum Optimizer

MOMENTUM - org.tensorflow.framework.optimizers.Optimizers

MOMENTUM - Static variable in class org.tensorflow.framework.optimizers.Momentum

MOMENTUM - Static variable in class org.tensorflow.framework.optimizers.Nadam

MOMENTUM - Static variable in class org.tensorflow.framework.optimizers.RMSProp

MOMENTUM_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Momentum

MOMENTUM_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.RMSProp

N

Nadam - Class in org.tensorflow.framework.optimizers

Nadam Optimizer that implements the NAdam algorithm.

Nadam(Graph) - Constructor for class org.tensorflow.framework.optimizers.Nadam

Creates a Nadam Optimizer

Nadam(Graph, float) - Constructor for class org.tensorflow.framework.optimizers.Nadam

Creates a Nadam Optimizer

Nadam(Graph, float, float, float, float) - Constructor for class org.tensorflow.framework.optimizers.Nadam

Creates a Nadam Optimizer

Nadam(Graph, String, float) - Constructor for class org.tensorflow.framework.optimizers.Nadam

Creates a Nadam Optimizer

Nadam(Graph, String, float, float, float, float) - Constructor for class org.tensorflow.framework.optimizers.Nadam

Creates a Nadam Optimizer

NADAM - org.tensorflow.framework.optimizers.Optimizers

NAME - Static variable in class org.tensorflow.framework.activations.ELU

The activation name as known by TensorFlow

NAME - Static variable in class org.tensorflow.framework.activations.Exponential

The activation name as known by TensorFlow

NAME - Static variable in class org.tensorflow.framework.activations.GELU

The activation name as known by TensorFlow

NAME - Static variable in class org.tensorflow.framework.activations.HardSigmoid

The activation name as known by TensorFlow

NAME - Static variable in class org.tensorflow.framework.activations.Linear

The activation name as known by TensorFlow

NAME - Static variable in class org.tensorflow.framework.activations.ReLU

The activation name as known by TensorFlow

NAME - Static variable in class org.tensorflow.framework.activations.SELU

The activation name as known by TensorFlow

NAME - Static variable in class org.tensorflow.framework.activations.Sigmoid

The activation name as known by TensorFlow

NAME - Static variable in class org.tensorflow.framework.activations.Softmax

The activation name as known by TensorFlow

NAME - Static variable in class org.tensorflow.framework.activations.Softplus

The activation name as known by TensorFlow

NAME - Static variable in class org.tensorflow.framework.activations.Softsign

The activation name as known by TensorFlow

NAME - Static variable in class org.tensorflow.framework.activations.Swish

The activation name as known by TensorFlow

NAME - Static variable in class org.tensorflow.framework.activations.Tanh

The activation name as known by TensorFlow

NAME_KEY - Static variable in class org.tensorflow.framework.activations.AbstractActivation

NESTEROV_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.Momentum

nn - Variable in class org.tensorflow.framework.op.FrameworkOps

NnOps - Class in org.tensorflow.framework.op

Creates Framework nerual network Operations

NONE - org.tensorflow.framework.losses.Reduction

NonNeg - Class in org.tensorflow.framework.constraints

Constrains the weights to be non-negative.

NonNeg() - Constructor for class org.tensorflow.framework.constraints.NonNeg

Create a NonNeg constraint

norm(Ops, Operand<T>, int[]) - Method in class org.tensorflow.framework.constraints.AbstractConstraint

Calculates the norm of the weights along the axes

NORMAL - org.tensorflow.framework.initializers.VarianceScaling.Distribution

NotBroadcastableException - Exception in org.tensorflow.framework.metrics.exceptions

Exception that indicates that static shapes are not able to broadcast among each other during arithmetic operations.

NotBroadcastableException(String) - Constructor for exception org.tensorflow.framework.metrics.exceptions.NotBroadcastableException

Creates a new NotBroadcastableException exception with the specified detail message

NotBroadcastableException(String, Throwable) - Constructor for exception org.tensorflow.framework.metrics.exceptions.NotBroadcastableException

Creates a new NotBroadcastableException exception with the specified detail message

O

of(String) - Static method in enum org.tensorflow.framework.activations.Activations

Gets the ActivationType based on the TensorFlow name for the activation

ofName(String) - Static method in enum org.tensorflow.framework.losses.Reduction

Get the Reduction based on name

Ones<T extends TType> - Class in org.tensorflow.framework.initializers

Initializer that generates tensors initialized to 1.

Ones() - Constructor for class org.tensorflow.framework.initializers.Ones

Creates an Initializer that sets all values to one.

Optimizer - Class in org.tensorflow.framework.optimizers

Base class for gradient optimizers.

Optimizer(Graph) - Constructor for class org.tensorflow.framework.optimizers.Optimizer

Builds an optimizer for the supplied graph.

Optimizer(Graph, String) - Constructor for class org.tensorflow.framework.optimizers.Optimizer

Builds an optimizer for the supplied graph.

Optimizer.GradAndVar<T extends TType> - Class in org.tensorflow.framework.optimizers

A class that holds a paired gradient and variable.

Optimizer.Options - Class in org.tensorflow.framework.optimizers

Optional attributes for Optimizer

Optimizers - Enum in org.tensorflow.framework.optimizers

Enumerator used to create a new Optimizer with default parameters.

org.tensorflow.framework.activations - package org.tensorflow.framework.activations

org.tensorflow.framework.constraints - package org.tensorflow.framework.constraints

org.tensorflow.framework.data - package org.tensorflow.framework.data

org.tensorflow.framework.initializers - package org.tensorflow.framework.initializers

org.tensorflow.framework.losses - package org.tensorflow.framework.losses

org.tensorflow.framework.metrics - package org.tensorflow.framework.metrics

org.tensorflow.framework.metrics.exceptions - package org.tensorflow.framework.metrics.exceptions

org.tensorflow.framework.op - package org.tensorflow.framework.op

org.tensorflow.framework.op.linalg - package org.tensorflow.framework.op.linalg

org.tensorflow.framework.op.math - package org.tensorflow.framework.op.math

org.tensorflow.framework.op.nn - package org.tensorflow.framework.op.nn

org.tensorflow.framework.op.sets - package org.tensorflow.framework.op.sets

org.tensorflow.framework.optimizers - package org.tensorflow.framework.optimizers

org.tensorflow.framework.regularizers - package org.tensorflow.framework.regularizers

org.tensorflow.framework.utils - package org.tensorflow.framework.utils

Orthogonal<T extends TFloating> - Class in org.tensorflow.framework.initializers

Initializer that generates an orthogonal matrix.

Orthogonal(double, long) - Constructor for class org.tensorflow.framework.initializers.Orthogonal

Creates an Orthogonal Initializer

Orthogonal(long) - Constructor for class org.tensorflow.framework.initializers.Orthogonal

Creates an Orthogonal Initializer using Orthogonal.GAIN_DEFAULT for the gain.

outputShapes - Variable in class org.tensorflow.framework.data.DatasetIterator

outputTypes - Variable in class org.tensorflow.framework.data.DatasetIterator

P

poisson(Ops, Operand<? extends TNumber>, Operand<T>) - Static method in class org.tensorflow.framework.losses.Losses

Computes the Poisson loss between labels and predictions.

Poisson - Class in org.tensorflow.framework.losses

Computes the Poisson loss between labels and predictions.

Poisson<T extends TNumber> - Class in org.tensorflow.framework.metrics

A metric that computes the poisson loss metric between labels and predictions.

Poisson() - Constructor for class org.tensorflow.framework.losses.Poisson

Creates a Poisson AbstractLoss using Class.getSimpleName() as the loss name and a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT

Poisson(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Poisson

Creates a Poisson metric using Class.getSimpleName() for the metric name.

Poisson(String) - Constructor for class org.tensorflow.framework.losses.Poisson

Creates a Poisson AbstractLoss using a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT

Poisson(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Poisson

Creates a Poisson metric

Poisson(String, Reduction) - Constructor for class org.tensorflow.framework.losses.Poisson

Creates a Poisson AbstractLoss

Poisson(Reduction) - Constructor for class org.tensorflow.framework.losses.Poisson

Creates a Poisson AbstractLoss using Class.getSimpleName() as the loss name

PR - org.tensorflow.framework.metrics.AUCCurve

Precision-Recall-curve

Precision<T extends TNumber> - Class in org.tensorflow.framework.metrics

Computes the precision of the predictions with respect to the labels.

Precision(float[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Precision

Creates a Precision Metric with a name of Class.getSimpleName() and no topK or classId values.

Precision(float[], Integer, Integer, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Precision

Creates a Precision Metric with a name of Class.getSimpleName()

Precision(float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Precision

Creates a Precision Metric with a name of Class.getSimpleName() and no topK or classId values.

Precision(float, Integer, Integer, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Precision

Creates a Precision Metric with a name of Class.getSimpleName()

Precision(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Precision

Creates a Precision Metric with a name of Class.getSimpleName() and no topK or classId values and with a threshold of Precision.DEFAULT_THRESHOLD.

Precision(String, float[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Precision

Creates a Precision Metric with no topK or classId values.

Precision(String, float[], Integer, Integer, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Precision

Creates a Precision Metric.

Precision(String, float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Precision

Creates a Precision Metric with no topK or classId values.

Precision(String, float, Integer, Integer, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Precision

Creates a Precision Metric.

Precision(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Precision

Creates a Precision Metric with no topK or classId values with a threshold of Precision.DEFAULT_THRESHOLD.

PrecisionAtRecall<T extends TNumber> - Class in org.tensorflow.framework.metrics

Computes best precision where recall is >= specified value.

PrecisionAtRecall(float, int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.PrecisionAtRecall

Creates a PrecisionRecall metric with a name of Class.getSimpleName().

PrecisionAtRecall(float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.PrecisionAtRecall

Creates a PrecisionRecall metric with a name of Class.getSimpleName() and SensitivitySpecificityBase.DEFAULT_NUM_THRESHOLDS for the number of thresholds

PrecisionAtRecall(String, float, int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.PrecisionAtRecall

Creates a PrecisionRecall metric.

PrecisionAtRecall(String, float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.PrecisionAtRecall

Creates a PrecisionRecall metric with SensitivitySpecificityBase.DEFAULT_NUM_THRESHOLDS for the number of thresholds

prepare(String) - Method in class org.tensorflow.framework.optimizers.AdaGradDA

Returns a No-op prepare.

prepare(String) - Method in class org.tensorflow.framework.optimizers.Adam

Returns a No-op prepare.

prepare(String) - Method in class org.tensorflow.framework.optimizers.Adamax

Returns a No-op prepare.

prepare(String) - Method in class org.tensorflow.framework.optimizers.Nadam

Returns a No-op prepare.

prepare(String) - Method in class org.tensorflow.framework.optimizers.Optimizer

Returns a No-op prepare.

R

RandomNormal<T extends TFloating> - Class in org.tensorflow.framework.initializers

Initializer that generates tensors with a normal distribution.

RandomNormal(double, double, long) - Constructor for class org.tensorflow.framework.initializers.RandomNormal

creates the RandomUniform initializer

RandomNormal(double, long) - Constructor for class org.tensorflow.framework.initializers.RandomNormal

Creates the RandomUniform initializer using RandomNormal.STDDEV_DEFAULT for the standard deviation.

RandomNormal(long) - Constructor for class org.tensorflow.framework.initializers.RandomNormal

Creates the RandomUniform initializer using RandomNormal.MEAN_DEFAULT for the mean and RandomNormal.STDDEV_DEFAULT for the standard deviation.

RandomUniform<T extends TNumber> - Class in org.tensorflow.framework.initializers

Initializer that generates tensors with a uniform distribution.

RandomUniform(double, double, long) - Constructor for class org.tensorflow.framework.initializers.RandomUniform

Creates a RandomUniform initializer

RandomUniform(long) - Constructor for class org.tensorflow.framework.initializers.RandomUniform

Creates a RandomUniform initializer using RandomUniform.MINVAL_DEFAULT for the minval and RandomUniform.MAXVAL_DEFAULT for the maxval

RATE_DEFAULT - Static variable in class org.tensorflow.framework.constraints.MinMaxNorm

Recall<T extends TNumber> - Class in org.tensorflow.framework.metrics

Computes the recall of the predictions with respect to the labels.

Recall(float[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Recall

Creates a Recall metric with a name of Class.getSimpleName(), and topK and classId set to null.

Recall(float[], Integer, Integer, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Recall

Creates a Recall metric with a name of Class.getSimpleName()

Recall(float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Recall

Creates a Recall metric with a name of Class.getSimpleName(), and topK and classId set to null.

Recall(float, Integer, Integer, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Recall

Creates a Recall metric with a name of Class.getSimpleName()

Recall(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Recall

Creates a Recall metric with a name of Class.getSimpleName(), and topK and classId set to null, and thresholds set to Recall.DEFAULT_THRESHOLD

Recall(Integer, Integer, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Recall

Creates a Recall metric with a name of Class.getSimpleName() and using a threshold value of Recall.DEFAULT_THRESHOLD.

Recall(String, float[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Recall

Creates a Recall metric with topK and classId set to null.

Recall(String, float[], Integer, Integer, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Recall

Creates a Recall metric.

Recall(String, float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Recall

Creates a Recall metric with topK and classId set to null.

Recall(String, float, Integer, Integer, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Recall

Creates a Recall metric.

Recall(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Recall

Creates a Recall metric with topK and classId set to null and thresholds set to Recall.DEFAULT_THRESHOLD.

Recall(String, Integer, Integer, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Recall

Creates a Recall metric using a threshold value of Recall.DEFAULT_THRESHOLD.

RecallAtPrecision<T extends TNumber> - Class in org.tensorflow.framework.metrics

Computes best recall where precision is >= specified value.

RecallAtPrecision(float, int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.RecallAtPrecision

Creates a PrecisionRecall metric with a name of Class.getSimpleName().

RecallAtPrecision(float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.RecallAtPrecision

Creates a PrecisionRecall metric with a name of Class.getSimpleName() and SensitivitySpecificityBase.DEFAULT_NUM_THRESHOLDS for the number of thresholds

RecallAtPrecision(String, float, int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.RecallAtPrecision

Creates a PrecisionRecall metric.

RecallAtPrecision(String, float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.RecallAtPrecision

Creates a PrecisionRecall metric with SensitivitySpecificityBase.DEFAULT_NUM_THRESHOLDS for the number of thresholds

reduce(Shape, int) - Static method in class org.tensorflow.framework.utils.ShapeUtils

Reduces the shape by eliminating trailing Dimensions.

reduceLogSumExp(Scope, Operand<T>, int[], boolean) - Static method in class org.tensorflow.framework.op.math.ReduceLogSumExp

Computes log(sum(exp(elements across dimensions of a tensor))).

reduceLogSumExp(Operand<T>, int[], boolean) - Method in class org.tensorflow.framework.op.MathOps

Computes log(sum(exp(elements across dimensions of a tensor))).

ReduceLogSumExp - Class in org.tensorflow.framework.op.math

Reduce Log Sum Exp Operations

ReduceLogSumExp() - Constructor for class org.tensorflow.framework.op.math.ReduceLogSumExp

Reduction - Enum in org.tensorflow.framework.losses

Type of AbstractLoss Reduction

Regularizer - Interface in org.tensorflow.framework.regularizers

relu(Ops, Operand<T>) - Static method in class org.tensorflow.framework.activations.ReLU

Applies the rectified linear unit activation function with default values.

relu(Ops, Operand<T>, float, float, float) - Static method in class org.tensorflow.framework.activations.ReLU

Applies the rectified linear unit activation function.

ReLU - Class in org.tensorflow.framework.activations

Rectified Linear Unit(ReLU) activation.

ReLU() - Constructor for class org.tensorflow.framework.activations.ReLU

Creates a new ReLU with alpha=ReLU.ALPHA_DEFAULT, maxValue=ReLU.MAX_VALUE_DEFAULT, threshold=ReLU.THRESHOLD_DEFAULT,

ReLU(float, float, float) - Constructor for class org.tensorflow.framework.activations.ReLU

Creates a new ReLU

ReLU(Map<String, Object>) - Constructor for class org.tensorflow.framework.activations.ReLU

Creates a ReLU activation from a config map.

RELU - org.tensorflow.framework.activations.Activations

resetStates(Ops) - Method in class org.tensorflow.framework.metrics.AUC

Resets any state variables to their initial values

resetStates(Ops) - Method in class org.tensorflow.framework.metrics.MeanIoU

Resets any state variables to their initial values

resetStates(Ops) - Method in class org.tensorflow.framework.metrics.MeanTensor

Resets any state variables to their initial values

resetStates(Ops) - Method in interface org.tensorflow.framework.metrics.Metric

Resets any state variables to their initial values

resetStates(Ops) - Method in class org.tensorflow.framework.metrics.Precision

Resets any state variables to their initial values

resetStates(Ops) - Method in class org.tensorflow.framework.metrics.Recall

Resets any state variables to their initial values

result(Ops, Class<T>) - Method in interface org.tensorflow.framework.metrics.Metric

Gets the current result of the metric

result(Ops, Class<U>) - Method in class org.tensorflow.framework.metrics.AUC

Gets the current result of the metric

result(Ops, Class<U>) - Method in class org.tensorflow.framework.metrics.MeanIoU

Gets the current result of the metric

result(Ops, Class<U>) - Method in class org.tensorflow.framework.metrics.MeanTensor

Gets the current result of the metric

result(Ops, Class<U>) - Method in class org.tensorflow.framework.metrics.Precision

Gets the current result of the metric

result(Ops, Class<U>) - Method in class org.tensorflow.framework.metrics.PrecisionAtRecall

Gets the current result of the metric

result(Ops, Class<U>) - Method in class org.tensorflow.framework.metrics.Recall

result(Ops, Class<U>) - Method in class org.tensorflow.framework.metrics.RecallAtPrecision

Gets the current result of the metric

result(Ops, Class<U>) - Method in class org.tensorflow.framework.metrics.RootMeanSquaredError

Gets the current result of the metric

result(Ops, Class<U>) - Method in class org.tensorflow.framework.metrics.SensitivityAtSpecificity

Gets the current result of the metric

result(Ops, Class<U>) - Method in class org.tensorflow.framework.metrics.SpecificityAtSensitivity

Gets the current result of the metric

RHO_DEFAULT - Static variable in class org.tensorflow.framework.optimizers.AdaDelta

RMS - Static variable in class org.tensorflow.framework.optimizers.RMSProp

RMSProp - Class in org.tensorflow.framework.optimizers

Optimizer that implements the RMSProp algorithm.

RMSProp(Graph) - Constructor for class org.tensorflow.framework.optimizers.RMSProp

Creates an RMSPRrop Optimizer

RMSProp(Graph, float) - Constructor for class org.tensorflow.framework.optimizers.RMSProp

Creates an RMSPRrop Optimizer

RMSProp(Graph, float, float, float, float, boolean) - Constructor for class org.tensorflow.framework.optimizers.RMSProp

Creates an RMSPRrop Optimizer

RMSProp(Graph, String, float) - Constructor for class org.tensorflow.framework.optimizers.RMSProp

Creates an RMSPRrop Optimizer

RMSProp(Graph, String, float, float, float, float, boolean) - Constructor for class org.tensorflow.framework.optimizers.RMSProp

Creates an RMSPRrop Optimizer

RMSPROP - org.tensorflow.framework.optimizers.Optimizers

ROC - org.tensorflow.framework.metrics.AUCCurve

Receiver Operator Characteristic curve

RootMeanSquaredError<T extends TNumber> - Class in org.tensorflow.framework.metrics

Computes root mean squared error metric between labels and predictions .

RootMeanSquaredError(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.RootMeanSquaredError

Creates a RootMeanSquaredError metric with a name of Class.getSimpleName()

RootMeanSquaredError(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.RootMeanSquaredError

Creates a RootMeanSquaredError metric

S

SCALE - Static variable in class org.tensorflow.framework.initializers.Glorot

SCALE - Static variable in class org.tensorflow.framework.initializers.He

SCALE_DEFAULT - Static variable in class org.tensorflow.framework.initializers.VarianceScaling

scope() - Method in class org.tensorflow.framework.op.FrameworkOps

Returns the current scope of this API

SECOND_MOMENT - Static variable in class org.tensorflow.framework.optimizers.Adam

SECOND_MOMENT - Static variable in class org.tensorflow.framework.optimizers.Adamax

SECOND_MOMENT - Static variable in class org.tensorflow.framework.optimizers.Nadam

selu(Ops, Operand<T>) - Static method in class org.tensorflow.framework.activations.SELU

Applies Scaled Exponential Linear Unit (SELU) activation function

SELU - Class in org.tensorflow.framework.activations

Scaled Exponential Linear Unit (SELU).

SELU - org.tensorflow.framework.activations.Activations

SELU() - Constructor for class org.tensorflow.framework.activations.SELU

Creates a Scaled Exponential Linear Unit (SELU) activation.

SELU(Map<String, Object>) - Constructor for class org.tensorflow.framework.activations.SELU

Creates a new Exponential from a configuration Map

SensitivityAtSpecificity<T extends TNumber> - Class in org.tensorflow.framework.metrics

Computes best sensitivity where sensitivity is >= specified value.

SensitivityAtSpecificity(float, int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SensitivityAtSpecificity

Creates a PrecisionRecall metric with a name of Class.getSimpleName().

SensitivityAtSpecificity(float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SensitivityAtSpecificity

Creates a SpecificityAtSensitivity metric with a name of Class.getSimpleName() and SensitivitySpecificityBase.DEFAULT_NUM_THRESHOLDS for the number of thresholds

SensitivityAtSpecificity(String, float, int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SensitivityAtSpecificity

Creates a PrecisionRecall metric.

SensitivityAtSpecificity(String, float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SensitivityAtSpecificity

Creates a SpecificityAtSensitivity metric with SensitivitySpecificityBase.DEFAULT_NUM_THRESHOLDS for the number of thresholds

setInitialized(boolean) - Method in class org.tensorflow.framework.metrics.BaseMetric

Sets the initialized indicator

setName(String) - Method in class org.tensorflow.framework.metrics.BaseMetric

Sets the metric name

setNumLabels(Integer) - Method in class org.tensorflow.framework.metrics.AUC

setOperation(Scope, Operand<T>, Operand<T>, Sets.Operation) - Static method in class org.tensorflow.framework.op.sets.Sets

Compute set operation of elements in last dimension of a and b.

SetOps - Class in org.tensorflow.framework.op

Creates Framework set Operations

sets - Variable in class org.tensorflow.framework.op.FrameworkOps

Sets - Class in org.tensorflow.framework.op.sets

Sets() - Constructor for class org.tensorflow.framework.op.sets.Sets

Sets.Operation - Enum in org.tensorflow.framework.op.sets

Enumeration containing the string operation values to be passed to the TensorFlow Sparse Ops function SparseOps.denseToDenseSetOperation(org.tensorflow.Operand<T>, org.tensorflow.Operand<T>, java.lang.String, org.tensorflow.op.sparse.DenseToDenseSetOperation.Options...)

setTF(Ops) - Method in class org.tensorflow.framework.activations.AbstractActivation

Sets the TensorFlow Ops

setTF(Ops) - Method in class org.tensorflow.framework.metrics.BaseMetric

Sets the TensorFlow Ops for this metric.

ShapeUtils - Class in org.tensorflow.framework.utils

Various methods for processing with Shapes and Operands

ShapeUtils() - Constructor for class org.tensorflow.framework.utils.ShapeUtils

sharedName - Variable in class org.tensorflow.framework.optimizers.Optimizer.Options

sharedName(String) - Method in class org.tensorflow.framework.optimizers.Optimizer.Options

Sets the shared name

sigmoid(Ops, Operand<T>) - Static method in class org.tensorflow.framework.activations.Sigmoid

Applies the Sigmoid activation function, sigmoid(x) = 1 / (1 + exp(-x)).

Sigmoid - Class in org.tensorflow.framework.activations

Sigmoid activation.

Sigmoid() - Constructor for class org.tensorflow.framework.activations.Sigmoid

Creates a Sigmoid activation.

Sigmoid(Map<String, Object>) - Constructor for class org.tensorflow.framework.activations.Sigmoid

Creates a new Exponential from a configuration Map

SIGMOID - org.tensorflow.framework.activations.Activations

sigmoidCrossEntropyWithLogits(Scope, Operand<T>, Operand<T>) - Static method in class org.tensorflow.framework.op.nn.SigmoidCrossEntropyWithLogits

Computes sigmoid cross entropy given logits.

sigmoidCrossEntropyWithLogits(Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.op.NnOps

Computes sigmoid cross entropy given logits.

SigmoidCrossEntropyWithLogits - Class in org.tensorflow.framework.op.nn

SigmoidCrossEntropyWithLogits() - Constructor for class org.tensorflow.framework.op.nn.SigmoidCrossEntropyWithLogits

skip(long) - Method in class org.tensorflow.framework.data.Dataset

Returns a new `Dataset` which skips `count` initial elements from this dataset

softmax(Ops, Operand<T>) - Static method in class org.tensorflow.framework.activations.Softmax

Converts a vector of values to a probability distribution along the last axis.

softmax(Ops, Operand<T>, Operand<TInt32>) - Static method in class org.tensorflow.framework.activations.Softmax

Converts a vector of values to a probability distribution.

Softmax - Class in org.tensorflow.framework.activations

Softmax converts a real vector to a vector of categorical probabilities.

Softmax() - Constructor for class org.tensorflow.framework.activations.Softmax

Creates a softmax activation where the default axis is Softmax.AXIS_DEFAULT which indicates the last dimension.

Softmax(int) - Constructor for class org.tensorflow.framework.activations.Softmax

Creates a Softmax activation

Softmax(Map<String, Object>) - Constructor for class org.tensorflow.framework.activations.Softmax

Creates a Softmax activation from a config map.

SOFTMAX - org.tensorflow.framework.activations.Activations

softmaxCrossEntropyWithLogits(Scope, Operand<U>, Operand<T>, int) - Static method in class org.tensorflow.framework.op.nn.SoftmaxCrossEntropyWithLogits

Computes softmax cross entropy between logits and labels.

softmaxCrossEntropyWithLogits(Operand<U>, Operand<T>, int) - Method in class org.tensorflow.framework.op.NnOps

Computes softmax cross entropy between logits and labels.

SoftmaxCrossEntropyWithLogits - Class in org.tensorflow.framework.op.nn

SoftmaxCrossEntropyWithLogits() - Constructor for class org.tensorflow.framework.op.nn.SoftmaxCrossEntropyWithLogits

softplus(Ops, Operand<T>) - Static method in class org.tensorflow.framework.activations.Softplus

Applies the Softplus activation function, softplus(x) = log(exp(x) + 1).

Softplus - Class in org.tensorflow.framework.activations

Softplus activation function, softplus(x) = log(exp(x) + 1).

Softplus() - Constructor for class org.tensorflow.framework.activations.Softplus

Creates a Softplus activation function.

Softplus(Map<String, Object>) - Constructor for class org.tensorflow.framework.activations.Softplus

Creates a new Softplus from a configuration Map

SOFTPLUS - org.tensorflow.framework.activations.Activations

softsign(Ops, Operand<T>) - Static method in class org.tensorflow.framework.activations.Softsign

Applies the Softsign activation function, softsign(x) = x / (abs(x) + 1).

Softsign - Class in org.tensorflow.framework.activations

Softsign activation function, softsign(x) = x / (abs(x) + 1).

Softsign() - Constructor for class org.tensorflow.framework.activations.Softsign

Creates a Softsign activation.

Softsign(Map<String, Object>) - Constructor for class org.tensorflow.framework.activations.Softsign

Creates a new Softsign from a configuration Map

SOFTSIGN - org.tensorflow.framework.activations.Activations

SparseCategoricalAccuracy<T extends TNumber> - Class in org.tensorflow.framework.metrics

Calculates how often predictions matches integer labels.

SparseCategoricalAccuracy(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SparseCategoricalAccuracy

Creates a SparseCategoricalAccuracy metric, using name of Class.getSimpleName().

SparseCategoricalAccuracy(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SparseCategoricalAccuracy

Creates a SparseCategoricalAccuracy metric.

sparseCategoricalCrossentropy(Ops, Operand<? extends TNumber>, Operand<T>, boolean, int) - Static method in class org.tensorflow.framework.losses.Losses

Computes the sparse categorical crossentropy loss between labels and predictions.

SparseCategoricalCrossentropy - Class in org.tensorflow.framework.losses

Computes the crossentropy loss between labels and predictions.

SparseCategoricalCrossentropy<T extends TNumber> - Class in org.tensorflow.framework.metrics

A metric that computes the sparse categorical cross-entropy loss between true labels and predicted labels.

SparseCategoricalCrossentropy() - Constructor for class org.tensorflow.framework.losses.SparseCategoricalCrossentropy

Creates a SparseCategoricalCrossentropy loss using Class.getSimpleName() as the loss name, a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT, and fromLogits=SparseCategoricalCrossentropy.FROM_LOGITS_DEFAULT.

SparseCategoricalCrossentropy(boolean) - Constructor for class org.tensorflow.framework.losses.SparseCategoricalCrossentropy

Creates a SparseCategoricalCrossentropy loss using Class.getSimpleName() as the loss name, a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT and fromLogits=SparseCategoricalCrossentropy.FROM_LOGITS_DEFAULT.

SparseCategoricalCrossentropy(boolean, int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SparseCategoricalCrossentropy

Creates a SparseCategoricalCrossentropy metric using Class.getSimpleName() for the metric name.

SparseCategoricalCrossentropy(boolean, Reduction) - Constructor for class org.tensorflow.framework.losses.SparseCategoricalCrossentropy

Creates a SparseCategoricalCrossentropy loss using Class.getSimpleName() as the loss name,

SparseCategoricalCrossentropy(String) - Constructor for class org.tensorflow.framework.losses.SparseCategoricalCrossentropy

Creates a SparseCategoricalCrossentropy loss using a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT, and fromLogits=SparseCategoricalCrossentropy.FROM_LOGITS_DEFAULT.

SparseCategoricalCrossentropy(String, boolean) - Constructor for class org.tensorflow.framework.losses.SparseCategoricalCrossentropy

Creates a SparseCategoricalCrossentropy using a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT, and fromLogits=SparseCategoricalCrossentropy.FROM_LOGITS_DEFAULT.

SparseCategoricalCrossentropy(String, boolean, int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SparseCategoricalCrossentropy

Creates a SparseCategoricalCrossentropy metric

SparseCategoricalCrossentropy(String, boolean, Reduction, int) - Constructor for class org.tensorflow.framework.losses.SparseCategoricalCrossentropy

Creates a SparseCategoricalCrossentropy

SparseCategoricalCrossentropy(String, Reduction) - Constructor for class org.tensorflow.framework.losses.SparseCategoricalCrossentropy

Creates a SparseCategoricalCrossentropy loss with Reduction.AUTO and fromLogits=SparseCategoricalCrossentropy.FROM_LOGITS_DEFAULT.

SparseCategoricalCrossentropy(Reduction) - Constructor for class org.tensorflow.framework.losses.SparseCategoricalCrossentropy

Creates a SparseCategoricalCrossentropy loss using Class.getSimpleName() as the loss name, with Reduction.AUTO and fromLogits=SparseCategoricalCrossentropy.FROM_LOGITS_DEFAULT.

sparseSoftmaxCrossEntropyWithLogits(Scope, Operand<U>, Operand<T>) - Static method in class org.tensorflow.framework.op.nn.SparseSoftmaxCrossEntropyWithLogits

Computes sparse softmax cross entropy between logits and labels.

sparseSoftmaxCrossEntropyWithLogits(Operand<U>, Operand<T>) - Method in class org.tensorflow.framework.op.NnOps

Computes sparse softmax cross entropy between logits and labels.

SparseSoftmaxCrossEntropyWithLogits - Class in org.tensorflow.framework.op.nn

SparseSoftmaxCrossEntropyWithLogits() - Constructor for class org.tensorflow.framework.op.nn.SparseSoftmaxCrossEntropyWithLogits

SparseTensor<T extends TType> - Class in org.tensorflow.framework.utils

This is a helper class that represents a sparse tensor who's attributes may be passed to SparseOps methods.

SparseTensor(Operand<TInt64>, Operand<T>, Operand<TInt64>) - Constructor for class org.tensorflow.framework.utils.SparseTensor

Creates a SparseTensor

sparseTopKCategoricalAccuracy(Ops, Operand<U>, Operand<T>, int) - Static method in class org.tensorflow.framework.metrics.Metrics

Computes how often integer targets are in the top K predictions.

SparseTopKCategoricalAccuracy<T extends TNumber> - Class in org.tensorflow.framework.metrics

Computes how often integer targets are in the top `K` predictions.

SparseTopKCategoricalAccuracy(int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SparseTopKCategoricalAccuracy

Creates a SparseTopKCategoricalAccuracy metric using Class.getSimpleName() for the metric name.

SparseTopKCategoricalAccuracy(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SparseTopKCategoricalAccuracy

Creates a SparseTopKCategoricalAccuracy metric using SparseTopKCategoricalAccuracy.DEFAULT_K for the number of top elements using Class.getSimpleName() for the metric name.

SparseTopKCategoricalAccuracy(String, int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SparseTopKCategoricalAccuracy

Creates a SparseTopKCategoricalAccuracy metric.

SparseTopKCategoricalAccuracy(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SparseTopKCategoricalAccuracy

Creates a SparseTopKCategoricalAccuracy metric using SparseTopKCategoricalAccuracy.DEFAULT_K for the number of top elements.

SpecificityAtSensitivity<T extends TNumber> - Class in org.tensorflow.framework.metrics

Computes best specificity where sensitivity is >= specified value.

SpecificityAtSensitivity(float, int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SpecificityAtSensitivity

Creates a PrecisionRecall metric with a name of Class.getSimpleName().

SpecificityAtSensitivity(float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SpecificityAtSensitivity

Creates a SpecificityAtSensitivity metric with a name of Class.getSimpleName() and SensitivitySpecificityBase.DEFAULT_NUM_THRESHOLDS for the number of thresholds

SpecificityAtSensitivity(String, float, int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SpecificityAtSensitivity

Creates a PrecisionRecall metric.

SpecificityAtSensitivity(String, float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SpecificityAtSensitivity

Creates a SpecificityAtSensitivity metric with SensitivitySpecificityBase.DEFAULT_NUM_THRESHOLDS for the number of thresholds

sqrt(Ops, Operand<T>) - Method in class org.tensorflow.framework.constraints.AbstractConstraint

Gets the element-wise square root.

SQUARED_ACCUMULATOR - Static variable in class org.tensorflow.framework.optimizers.AdaGradDA

squaredHinge(Ops, Operand<? extends TNumber>, Operand<T>) - Static method in class org.tensorflow.framework.losses.Losses

Computes the squared hinge loss between labels and predictions.

SquaredHinge - Class in org.tensorflow.framework.losses

Computes the squared hinge loss between labels and predictions.

SquaredHinge<T extends TNumber> - Class in org.tensorflow.framework.metrics

A metric that computes the squared hinge loss metric between labels and predictions.

SquaredHinge() - Constructor for class org.tensorflow.framework.losses.SquaredHinge

Creates a Squared Hinge AbstractLoss using Class.getSimpleName() as the loss name and a AbstractLoss Reduction of AbstractLoss.REDUCTION_DEFAULT

SquaredHinge(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SquaredHinge

Creates a SquaredHinge metric using Class.getSimpleName() for the metric name.

SquaredHinge(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.SquaredHinge

Creates a SquaredHinge metric

SquaredHinge(String, Reduction) - Constructor for class org.tensorflow.framework.losses.SquaredHinge

Creates a Squared Hinge

SquaredHinge(Reduction) - Constructor for class org.tensorflow.framework.losses.SquaredHinge

Creates a Squared Hinge AbstractLoss using Class.getSimpleName() as the loss name

STDDEV_DEFAULT - Static variable in class org.tensorflow.framework.initializers.RandomNormal

STDDEV_DEFAULT - Static variable in class org.tensorflow.framework.initializers.TruncatedNormal

Sum<T extends TNumber> - Class in org.tensorflow.framework.metrics

Computes the (weighted) sum of the given values.

Sum(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Sum

Creates a Sum metric with a name of Class.getSimpleName()

Sum(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.Sum

Creates a Sum metric.

SUM - org.tensorflow.framework.losses.Reduction

SUM - org.tensorflow.framework.metrics.MetricReduction

Scalar sum of weighted values.

SUM_OVER_BATCH_SIZE - org.tensorflow.framework.losses.Reduction

SUM_OVER_BATCH_SIZE - org.tensorflow.framework.metrics.MetricReduction

Scalar sum of weighted values divided by number of elements.

swish(Ops, Operand<T>) - Static method in class org.tensorflow.framework.activations.Swish

Applies the Swish activation function, swish(x) = x * sigmoid(x).

Swish - Class in org.tensorflow.framework.activations

Swish activation function.

Swish() - Constructor for class org.tensorflow.framework.activations.Swish

Creates a Swish activation, swish(x) = x * sigmoid(x).

Swish(Map<String, Object>) - Constructor for class org.tensorflow.framework.activations.Swish

Creates a new Swish from a configuration Map

SWISH - org.tensorflow.framework.activations.Activations

T

take(long) - Method in class org.tensorflow.framework.data.Dataset

Returns a new `Dataset` with only the first `count` elements from this dataset.

tanh(Ops, Operand<T>) - Static method in class org.tensorflow.framework.activations.Tanh

Applies the Hyperbolic tangent activation function, tanh(x) = sinh(x)/cosh(x) = ((exp(x) - exp(-x))/(exp(x) + exp(-x))).

Tanh - Class in org.tensorflow.framework.activations

Hyperbolic tangent activation function.

Tanh() - Constructor for class org.tensorflow.framework.activations.Tanh

Creates a Hyperbolic tangent activation.

Tanh(Map<String, Object>) - Constructor for class org.tensorflow.framework.activations.Tanh

Creates a new Tanh from a configuration Map

TANH - org.tensorflow.framework.activations.Activations

tensordot(Scope, Operand<T>, Operand<T>, int) - Static method in class org.tensorflow.framework.op.math.TensorDot

Tensor contraction of a and b along specified axes and outer product.

tensordot(Scope, Operand<T>, Operand<T>, int[]) - Static method in class org.tensorflow.framework.op.math.TensorDot

Tensor contraction of a and b along specified axes and outer product.

tensordot(Scope, Operand<T>, Operand<T>, int[][]) - Static method in class org.tensorflow.framework.op.math.TensorDot

Tensor contraction of a and b along specified axes and outer product.

tensordot(Scope, Operand<T>, Operand<T>, Operand<TInt32>) - Static method in class org.tensorflow.framework.op.math.TensorDot

Tensor contraction of a and b along specified axes and outer product.

tensordot(Scope, Operand<T>, Operand<T>, Operand<TInt32>, Operand<TInt32>) - Static method in class org.tensorflow.framework.op.math.TensorDot

Tensor contraction of a and b along specified axes and outer product.

tensordot(Operand<T>, Operand<T>, int) - Method in class org.tensorflow.framework.op.MathOps

Tensor contraction of a and b along specified axes and outer product.

tensordot(Operand<T>, Operand<T>, int[]) - Method in class org.tensorflow.framework.op.MathOps

Tensor contraction of a and b along specified axes and outer product.

tensordot(Operand<T>, Operand<T>, int[][]) - Method in class org.tensorflow.framework.op.MathOps

Tensor contraction of a and b along specified axes and outer product.

tensordot(Operand<T>, Operand<T>, Operand<TInt32>) - Method in class org.tensorflow.framework.op.MathOps

Tensor contraction of a and b along specified axes and outer product.

tensordot(Operand<T>, Operand<T>, Operand<TInt32>, Operand<TInt32>) - Method in class org.tensorflow.framework.op.MathOps

Tensor contraction of a and b along specified axes and outer product.

TensorDot - Class in org.tensorflow.framework.op.math

tensor contraction Operations

TensorDot() - Constructor for class org.tensorflow.framework.op.math.TensorDot

tensorflow.framework - module tensorflow.framework

textLineDataset(Ops, String, String, long) - Static method in class org.tensorflow.framework.data.Dataset

Creates a TextLineDataset from a file containing one recored per ling.

tf - Variable in class org.tensorflow.framework.activations.AbstractActivation

The TensorFlow Ops

tf - Variable in class org.tensorflow.framework.data.Dataset

tf - Variable in class org.tensorflow.framework.data.DatasetIterator

tf - Variable in class org.tensorflow.framework.data.DatasetOptional

tf - Variable in class org.tensorflow.framework.optimizers.Optimizer

The ops builder for the graph.

tfRecordDataset(Ops, String, String, long) - Static method in class org.tensorflow.framework.data.Dataset

Creates a TFRecordDataset from a file containing TFRecords

THRESHOLD_DEFAULT - Static variable in class org.tensorflow.framework.activations.ReLU

topKCategoricalAccuracy(Ops, Operand<? extends TNumber>, Operand<T>, long) - Static method in class org.tensorflow.framework.metrics.Metrics

Computes how often targets are in the top K predictions.

TopKCategoricalAccuracy<T extends TNumber> - Class in org.tensorflow.framework.metrics

Computes the poisson loss metric between labels and predictions.

TopKCategoricalAccuracy(int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.TopKCategoricalAccuracy

Creates a TopKCategoricalAccuracy metric using Class.getSimpleName() for the metric name.

TopKCategoricalAccuracy(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.TopKCategoricalAccuracy

Creates a TopKCategoricalAccuracy metric using TopKCategoricalAccuracy.DEFAULT_K for k, Number of top elements to look at for computing accuracy and using Class.getSimpleName() for the metric name.

TopKCategoricalAccuracy(String, int, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.TopKCategoricalAccuracy

Creates a TopKCategoricalAccuracy metric

TopKCategoricalAccuracy(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.TopKCategoricalAccuracy

Creates a TopKCategoricalAccuracy metric using TopKCategoricalAccuracy.DEFAULT_K for k, Number of top elements to look at for computing accuracy.

toShape(Scope, Operand<T>) - Static method in class org.tensorflow.framework.utils.ShapeUtils

Converts a shape operand to a Shape object

toString() - Method in class org.tensorflow.framework.data.Dataset

toString() - Method in class org.tensorflow.framework.optimizers.AdaDelta

toString() - Method in class org.tensorflow.framework.optimizers.AdaGrad

toString() - Method in class org.tensorflow.framework.optimizers.AdaGradDA

toString() - Method in class org.tensorflow.framework.optimizers.Adam

toString() - Method in class org.tensorflow.framework.optimizers.GradientDescent

toString() - Method in class org.tensorflow.framework.optimizers.Momentum

toString() - Method in class org.tensorflow.framework.optimizers.RMSProp

TOTAL - Static variable in class org.tensorflow.framework.metrics.MeanTensor

TOTAL_CONFUSION_MATRIX - Static variable in class org.tensorflow.framework.metrics.MeanIoU

TRUE_NEGATIVES - Static variable in class org.tensorflow.framework.metrics.AUC

TRUE_POSITIVES - Static variable in class org.tensorflow.framework.metrics.AUC

TRUE_POSITIVES - Static variable in class org.tensorflow.framework.metrics.Precision

TRUE_POSITIVES - Static variable in class org.tensorflow.framework.metrics.Recall

TrueNegatives<T extends TNumber> - Class in org.tensorflow.framework.metrics

Metric that calculates the number of true negatives.

TrueNegatives(float[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.TrueNegatives

Creates a TrueNegatives metric, using Class.getSimpleName() for the metric name

TrueNegatives(float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.TrueNegatives

Creates a TrueNegatives metric, using Class.getSimpleName() for the metric name

TrueNegatives(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.TrueNegatives

Creates a TrueNegatives metric, using Class.getSimpleName() for the metric name and a default threshold of ConfusionMatrixConditionCount.DEFAULT_THRESHOLD.

TrueNegatives(String, float[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.TrueNegatives

Creates a TrueNegatives metric

TrueNegatives(String, float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.TrueNegatives

Creates a TrueNegatives metric

TrueNegatives(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.TrueNegatives

Creates a TrueNegatives metric, using a default threshold of ConfusionMatrixConditionCount.DEFAULT_THRESHOLD.

TruePositives<T extends TNumber> - Class in org.tensorflow.framework.metrics

Metric that calculates the number of true positives.

TruePositives(float[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.TruePositives

Creates a TruePositives metric, using Class.getSimpleName() for the metric name

TruePositives(float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.TruePositives

Creates a TruePositives metric, using Class.getSimpleName() for the metric name

TruePositives(long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.TruePositives

Creates a TruePositives metric, using Class.getSimpleName() for the metric name and a default threshold of ConfusionMatrixConditionCount.DEFAULT_THRESHOLD.

TruePositives(String, float[], long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.TruePositives

Creates a TruePositives metric

TruePositives(String, float, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.TruePositives

Creates a TruePositives metric

TruePositives(String, long, Class<T>) - Constructor for class org.tensorflow.framework.metrics.TruePositives

Creates a TruePositives metric, using a default threshold of ConfusionMatrixConditionCount.DEFAULT_THRESHOLD.

TRUNCATED_NORMAL - org.tensorflow.framework.initializers.VarianceScaling.Distribution

TruncatedNormal<T extends TFloating> - Class in org.tensorflow.framework.initializers

Initializer that generates a truncated normal distribution.

TruncatedNormal(double, double, long) - Constructor for class org.tensorflow.framework.initializers.TruncatedNormal

Creates a TruncatedNormal Initializer.

TruncatedNormal(long) - Constructor for class org.tensorflow.framework.initializers.TruncatedNormal

Creates a TruncatedNormal Initializer using TruncatedNormal.MEAN_DEFAULT for the mean and TruncatedNormal.STDDEV_DEFAULT for the standard deviation.

U

UNIFORM - org.tensorflow.framework.initializers.VarianceScaling.Distribution

union(Scope, Operand<T>, Operand<T>) - Static method in class org.tensorflow.framework.op.sets.Sets

Computes set union of elements in last dimension of a and b.

union(Operand<T>, Operand<T>) - Method in class org.tensorflow.framework.op.SetOps

Computes set union of elements in last dimension of a and b.

UNION - org.tensorflow.framework.op.sets.Sets.Operation

UnitNorm - Class in org.tensorflow.framework.constraints

Constrains the weights to have unit norm.

UnitNorm() - Constructor for class org.tensorflow.framework.constraints.UnitNorm

Create a UnitNorm AbstractConstraint with the axis set to UnitNorm.AXIS_DEFAULT

UnitNorm(int) - Constructor for class org.tensorflow.framework.constraints.UnitNorm

Create a UnitNorm AbstractConstraint

UnitNorm(int[]) - Constructor for class org.tensorflow.framework.constraints.UnitNorm

Create a UnitNorm AbstractConstraint

updateState(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>) - Method in class org.tensorflow.framework.metrics.BaseMetric

Creates a NoOp Operation with control dependencies to update the metric state

updateState(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>) - Method in interface org.tensorflow.framework.metrics.Metric

Creates a NoOp Operation with control dependencies to update the metric state

updateState(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Operand<? extends TNumber>) - Method in class org.tensorflow.framework.metrics.BaseMetric

Creates a NoOp Operation with control dependencies to update the metric state

updateState(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Operand<? extends TNumber>) - Method in interface org.tensorflow.framework.metrics.Metric

Creates a NoOp Operation with control dependencies to update the metric state

updateStateList(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>) - Method in class org.tensorflow.framework.metrics.BaseMetric

Creates a List of Operations to update the metric state based on input values.

updateStateList(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>) - Method in class org.tensorflow.framework.metrics.MeanTensor

Accumulates statistics for computing the element-wise mean.

updateStateList(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>) - Method in interface org.tensorflow.framework.metrics.Metric

Creates a List of Operations to update the metric state based on input values.

updateStateList(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Operand<? extends TNumber>) - Method in class org.tensorflow.framework.metrics.AUC

Creates a List of Operations to update the metric state based on labels and predictions.

updateStateList(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Operand<? extends TNumber>) - Method in class org.tensorflow.framework.metrics.BaseMetric

Creates a List of Operations to update the metric state based on labels and predictions.

updateStateList(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Operand<? extends TNumber>) - Method in class org.tensorflow.framework.metrics.MeanIoU

Accumulates the confusion matrix statistics.

updateStateList(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Operand<? extends TNumber>) - Method in class org.tensorflow.framework.metrics.MeanRelativeError

Accumulates metric statistics.

updateStateList(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Operand<? extends TNumber>) - Method in interface org.tensorflow.framework.metrics.Metric

Creates a List of Operations to update the metric state based on labels and predictions.

updateStateList(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Operand<? extends TNumber>) - Method in class org.tensorflow.framework.metrics.Precision

Accumulates true positive and false positive statistics.

updateStateList(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Operand<? extends TNumber>) - Method in class org.tensorflow.framework.metrics.Recall

Accumulates true positive and false negative statistics.

updateStateList(Ops, Operand<? extends TNumber>, Operand<? extends TNumber>, Operand<? extends TNumber>) - Method in class org.tensorflow.framework.metrics.RootMeanSquaredError

Accumulates root mean squared error statistics.

V

valueOf(String) - Static method in enum org.tensorflow.framework.activations.Activations

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.tensorflow.framework.initializers.VarianceScaling.Distribution

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.tensorflow.framework.initializers.VarianceScaling.Mode

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.tensorflow.framework.losses.Reduction

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.tensorflow.framework.metrics.AUCCurve

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.tensorflow.framework.metrics.AUCSummationMethod

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.tensorflow.framework.metrics.MetricReduction

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.tensorflow.framework.op.sets.Sets.Operation

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.tensorflow.framework.optimizers.Optimizers

Returns the enum constant of this type with the specified name.

values() - Static method in enum org.tensorflow.framework.activations.Activations

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.tensorflow.framework.initializers.VarianceScaling.Distribution

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.tensorflow.framework.initializers.VarianceScaling.Mode

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.tensorflow.framework.losses.Reduction

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.tensorflow.framework.metrics.AUCCurve

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.tensorflow.framework.metrics.AUCSummationMethod

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.tensorflow.framework.metrics.MetricReduction

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.tensorflow.framework.op.sets.Sets.Operation

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.tensorflow.framework.optimizers.Optimizers

Returns an array containing the constants of this enum type, in the order they are declared.

VARIABLE_V2 - Static variable in class org.tensorflow.framework.optimizers.Optimizer

VarianceScaling<T extends TFloating> - Class in org.tensorflow.framework.initializers

Initializer capable of adapting its scale to the shape of weights tensors.

VarianceScaling(double, VarianceScaling.Mode, VarianceScaling.Distribution, long) - Constructor for class org.tensorflow.framework.initializers.VarianceScaling

Creates a VarianceScaling Initializer

VarianceScaling(long) - Constructor for class org.tensorflow.framework.initializers.VarianceScaling

Creates a VarianceScaling Initializer

VarianceScaling.Distribution - Enum in org.tensorflow.framework.initializers

The random distribution to use when initializing the values.

VarianceScaling.Mode - Enum in org.tensorflow.framework.initializers

The mode to use for calculating the fan values.

W

WEIGHTED_MEAN - org.tensorflow.framework.metrics.MetricReduction

Scalar sum of weighted values divided by sum of weights.

withControlDependencies(Iterable<Op>) - Method in class org.tensorflow.framework.op.FrameworkOps

Returns an API that adds operations to the graph with the provided control dependencies.

withDevice(DeviceSpec) - Method in class org.tensorflow.framework.op.FrameworkOps

Returns an API that places the created operations on the device(s) matching the provided spec.

withInitScope() - Method in class org.tensorflow.framework.op.FrameworkOps

Returns an FrameworkOps that builds init operations.

withName(String) - Method in class org.tensorflow.framework.op.FrameworkOps

Returns an API that uses the provided name for an op.

withSubScope(String) - Method in class org.tensorflow.framework.op.FrameworkOps

Returns an API that builds operations with the provided name prefix.

Z

Zeros<T extends TType> - Class in org.tensorflow.framework.initializers

Creates an Initializer that sets all values to zero.

Zeros() - Constructor for class org.tensorflow.framework.initializers.Zeros

Creates an Initializer that sets all values to one.

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