Uses of Class
deepnetts.tensor.Tensor2D

Packages that use Tensor2D

Package	Description
deepnetts.accl.spi
deepnetts.net.layers	Neural network layers, which are main building blocks of a neural network.
deepnetts.tensor

Uses of Tensor2D in deepnetts.accl.spi

Methods in deepnetts.accl.spi that return Tensor2D

Modifier and Type	Method	Description
Tensor2D	TensorVectorizationProvider.addVectorized(Tensor2D addTo, Tensor1D toAdd)
Tensor2D	TensorVectorizationProvider.matMulFma(Tensor2D matrixA, Tensor2D matrixB, Tensor2D result)
Tensor2D	TensorVectorizationProvider.matMulFmaParallel(Tensor2D matrixA, Tensor2D matrixB, Tensor2D result)
Tensor2D	TensorVectorizationProvider.matMulWithAddVector(Tensor2D matrixA, Tensor2D matrixB, Tensor2D result)
Tensor2D	TensorVectorizationProvider.matMulWithAddVectorParallel(Tensor2D matrixA, Tensor2D matrixB, Tensor2D result)

Methods in deepnetts.accl.spi with parameters of type Tensor2D

Modifier and Type	Method	Description
Tensor2D	TensorVectorizationProvider.addVectorized(Tensor2D addTo, Tensor1D toAdd)
void	ActivationVectorizationProvider.applySoftmaxBatchVectorized(Tensor2D logits)
Tensor1D	TensorVectorizationProvider.matMulFma(Tensor2D matrixA, Tensor1D vector, Tensor1D result)
Tensor2D	TensorVectorizationProvider.matMulFma(Tensor2D matrixA, Tensor2D matrixB, Tensor2D result)
Tensor1D	TensorVectorizationProvider.matMulFmaParallel(Tensor2D matrixA, Tensor1D vector, Tensor1D result)
Tensor2D	TensorVectorizationProvider.matMulFmaParallel(Tensor2D matrixA, Tensor2D matrixB, Tensor2D result)
Tensor1D	TensorVectorizationProvider.matMulWithAddVector(Tensor2D matrixA, Tensor1D vector, Tensor1D result)
Tensor2D	TensorVectorizationProvider.matMulWithAddVector(Tensor2D matrixA, Tensor2D matrixB, Tensor2D result)
Tensor1D	TensorVectorizationProvider.matMulWithAddVectorParallel(Tensor2D matrixA, Tensor1D vector, Tensor1D result)
Tensor2D	TensorVectorizationProvider.matMulWithAddVectorParallel(Tensor2D matrixA, Tensor2D matrixB, Tensor2D result)
void	TensorVectorizationProvider.outerProductVectorized(Tensor1D firstTensor, Tensor1D otherTensor, Tensor2D result)

Uses of Tensor2D in deepnetts.net.layers

Subclasses with type arguments of type Tensor2D in deepnetts.net.layers

Modifier and Type	Class	Description
class	CBOWEmbeddingLayer
class	EmbeddingLayer
class	FlatEmbeddingLayer
class	FullyConnectedLayer	Fully connected layer is used as a hidden layer in a neural network, and it has a single row of units/nodes/neurons connected to all neurons in previous and next layer.
class	OutputLayer	Output layer of a neural network.
class	SkipGramEmbeddingLayer

Methods in deepnetts.net.layers with parameters of type Tensor2D

Modifier and Type	Method	Description
void	SoftmaxOutputLayer.applySoftmaxBatch(Tensor2D inputs2D, Tensor2D outputs2D)

Uses of Tensor2D in deepnetts.tensor

Methods in deepnetts.tensor that return Tensor2D

Modifier and Type	Method	Description
final Tensor2D	Tensor2D.add(Tensor1D toAdd)
static Tensor2D	Tensors.create(int rows, int cols, float[] values)	Factory method for creating tensor instance,
static Tensor2D	Tensor2D.fromFile(String filePath)
Tensor2D	Tensor2D.getTransposed()
Tensor2D	Tensor2D.matMul(Tensor2D matrixB, Tensor2D result)	Performs dot product operation on this tensor and matrib, and stores results in result tensor.
Tensor2D	Tensor2D.matMulMT(Tensor2D matrixB, Tensor2D result)
Tensor2D	Tensor2D.matMulVectorized(Tensor2D matrixB, Tensor2D result)
static Tensor2D	Tensors.random(int rows, int cols)	Create and return a tensor with specified number of rows and cols filled with random values.
Tensor2D	Tensor2D.transposeInto(Tensor2D transposed)

Methods in deepnetts.tensor with parameters of type Tensor2D

Modifier and Type	Method	Description
static Tensor1D	Tensors.dotProduct(Tensor2D matrixA, Tensor1D vectorB, Tensor1D resultC)	Dot product matrix vector multiplication C = A .
static void	Tensors.dotProduct(Tensor2D matrixA, Tensor2D matrixB, Tensor2D result)
static void	Tensors.dotProductBuffered(Tensor2D matrixA, Tensor1D vectorB, Tensor1D resultC)	Matrix vector dot product.
static void	Tensors.dotProductBuffered(Tensor2D matrixA, Tensor2D matrixB, Tensor2D result)
Tensor2D	Tensor2D.matMul(Tensor2D matrixB, Tensor2D result)	Performs dot product operation on this tensor and matrib, and stores results in result tensor.
Tensor2D	Tensor2D.matMulMT(Tensor2D matrixB, Tensor2D result)
Tensor2D	Tensor2D.matMulVectorized(Tensor2D matrixB, Tensor2D result)
void	Tensor1D.outerProduct(Tensor1D otherTensor, Tensor2D result)
void	Tensor2D.outerProductAccumulate(Tensor2D inputs, Tensor2D result)
Tensor2D	Tensor2D.transposeInto(Tensor2D transposed)