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E

EmptyMatrix - Class in mklab.JGNN.core.empty

A Matrix without data that contains only the correct dimension names and sizes.

EmptyMatrix(long, long) - Constructor for class mklab.JGNN.core.empty.EmptyMatrix

Initializes an EmptyMatrix of given dimensions.

EmptyTensor - Class in mklab.JGNN.core.empty

A Tensor without data that contains only the correct dimension names and sizes.

EmptyTensor() - Constructor for class mklab.JGNN.core.empty.EmptyTensor

Initializes an EmptyTensor of zero size.

EmptyTensor(long) - Constructor for class mklab.JGNN.core.empty.EmptyTensor

Initializes an EmptyTensor of the given size.

endTape() - Method in class mklab.JGNN.nn.operations.LSTM

enter() - Method in class mklab.JGNN.core.Memory.Scope

epochs - Variable in class mklab.JGNN.adhoc.ModelTraining

estimateNumNonZeroElements() - Method in class mklab.JGNN.core.matrix.AccessCol

estimateNumNonZeroElements() - Method in class mklab.JGNN.core.matrix.AccessRow

estimateNumNonZeroElements() - Method in class mklab.JGNN.core.matrix.Diagonal

estimateNumNonZeroElements() - Method in class mklab.JGNN.core.matrix.SparseMatrix

estimateNumNonZeroElements() - Method in class mklab.JGNN.core.matrix.TransposedMatrix

estimateNumNonZeroElements() - Method in class mklab.JGNN.core.matrix.WrapCols

estimateNumNonZeroElements() - Method in class mklab.JGNN.core.matrix.WrapRows

estimateNumNonZeroElements() - Method in class mklab.JGNN.core.Tensor

Provides an estimation for the non-zero number of elements stored in the tensor, where this number is equal to the size for dense tensors, but equal to the actual number of non-zero elements for sparse tensors.

estimateNumNonZeroElements() - Method in class mklab.JGNN.core.tensor.SparseTensor

evaluate(Tensor, Tensor) - Method in class mklab.JGNN.nn.loss.Accuracy

evaluate(Tensor, Tensor) - Method in class mklab.JGNN.nn.loss.BinaryCrossEntropy

evaluate(Tensor, Tensor) - Method in class mklab.JGNN.nn.loss.CategoricalCrossEntropy

evaluate(Tensor, Tensor) - Method in class mklab.JGNN.nn.Loss

Provides a numerical evaluation of a loss function, so that lower values correspond to better predictions.

evaluate(Tensor, Tensor) - Method in class mklab.JGNN.nn.loss.report.VerboseLoss

exit() - Method in class mklab.JGNN.core.Memory.Scope

Exp - Class in mklab.JGNN.nn.activations

Implements a NNOperation that performs an element-by-element exponential transformation of its one input tensor.

Exp() - Constructor for class mklab.JGNN.nn.activations.Exp

expMinusOne() - Method in class mklab.JGNN.core.tensor.DenseTensor

expMinusOne() - Method in class mklab.JGNN.core.Tensor

Computes the exponential minus 1 of tensor elements.

external(Tensor, Tensor) - Static method in class mklab.JGNN.core.Matrix

Produces the external product of two tensors.

eye(long) - Static method in class mklab.JGNN.core.Matrix

Creates a sparse unit matrix.

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