Package io.github.kirstenali.deepj.tensor

Class Tensor

java.lang.Object

io.github.kirstenali.deepj.tensor.Tensor

public class Tensor
extends Object

Field Summary

Fields

Modifier and Type	Field	Description
final int	cols
final double[][]	data
final int	rows

Constructor Summary

Constructors

Constructor	Description
Tensor(double[][] data)
Tensor(int rows, int cols)

Method Summary

Modifier and Type	Method	Description
static void	adamWUpdate(Tensor w, Tensor g, Tensor mt, Tensor vt, double lr, double beta1, double beta2, double eps, double weightDecay, double bc1, double bc2)
Tensor	add(Tensor other)
Tensor	addBroadcastCols(Tensor colVector)
Tensor	addBroadcastRows(Tensor rowVector)
Tensor	addInPlace(Tensor b)
Tensor	addRowVector(Tensor rowVector)
Tensor	addScalar(double s)
Tensor	addScalarInPlace(double s)
static TensorBackend	backend()
static Tensor	causalMask(int size)
Tensor	clamp(double min, double max)
Tensor	crossEntropyGradient(int[] targets)
double	crossEntropyLoss(int[] targets)
Tensor	divide(Tensor other)
Tensor	divideBroadcastCols(Tensor colVector)
Tensor	divideInPlace(Tensor b)
Tensor	divideScalar(double s)
Tensor	divideScalarInPlace(double s)
Tensor	exp()
Tensor	expInPlace()
static double[]	flattenTensor(Tensor t)
Tensor	geluActivation()
Tensor	geluBackward(Tensor gradOutput)
Tensor	geluInPlace()
double	get(int r, int c)
Object	getGpuTag()	Get the GPU handle (used by GPU backends).
Tensor	getRow(int row)
static Tensor	layerNormBackward(Tensor dXHat, Tensor xHat, Tensor std, int dim)
Tensor	log()
Tensor	logInPlace()
void	materialize()	Ensures this tensor's data[][] is up to date with any pending GPU computation.
Tensor	matmul(Tensor other)
Tensor	maxAlongRows()
Tensor	meanAlongRows()
Tensor	multiply(Tensor other)
Tensor	multiplyBroadcastCols(Tensor colVector)
Tensor	multiplyBroadcastRows(Tensor rowVector)
Tensor	multiplyInPlace(Tensor b)
Tensor	multiplyScalar(double s)
Tensor	multiplyScalarInPlace(double s)
Tensor	neg()
Tensor	negInPlace()
static Tensor	ones(int rows, int cols)
Tensor	pow(double exponent)
void	print(String label)
static Tensor	random(int rows, int cols, Random rand)
Tensor	reluActivation()
Tensor	reluBackward(Tensor gradOutput)
Tensor	reluInPlace()
static void	requireSameShape(Tensor a, Tensor b, String op)
static void	requireTargetsMatchRows(Tensor logits, int[] targets)
static Tensor	sampleRows(Tensor t, int n, Random rnd)
static void	scatterAddRows(Tensor target, int[] indices, Tensor grad)
void	set(int r, int c, double value)
static void	setBackend(TensorBackend backend)
void	setGpuTag(Object tag)	Set the GPU handle (used by GPU backends).
void	setRow(int row, Tensor source, int srcRow)
Tensor	sigmoidActivation()
Tensor	sigmoidInPlace()
static Tensor	sliceRows(Tensor t, int[] rowIndices, int cols)
Tensor	softmaxBackward(Tensor softmaxOut)
Tensor	softmaxRows()
Tensor	sqrt()
Tensor	sqrtInPlace()
Tensor	subtract(Tensor other)
Tensor	subtractBroadcastCols(Tensor colVector)
Tensor	subtractInPlace(Tensor b)
double	sum()
double	sumAbs()
Tensor	sumAlongCols()
Tensor	sumAlongRows()
Tensor	sumRows()
Tensor	tanhActivation()
Tensor	tanhInPlace()
Tensor	transpose()
static Tensor	unflattenToTensor(double[] flat, int rows, int cols)
Tensor	varianceAlongRows()
static Tensor	zeros(int rows, int cols)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Details

data

public final double[][] data

rows

public final int rows

cols

public final int cols

Constructor Details

Tensor

public Tensor(int rows,
 int cols)

Tensor

public Tensor(double[][] data)

Method Details

getGpuTag

public Object getGpuTag()

Get the GPU handle (used by GPU backends).

setGpuTag

public void setGpuTag(Object tag)

Set the GPU handle (used by GPU backends).

setBackend

public static void setBackend(TensorBackend backend)

backend

public static TensorBackend backend()

materialize

public void materialize()

Ensures this tensor's data[][] is up to date with any pending GPU computation. No-op if this tensor has no GPU handle or is already materialized. Call this before reading data[][] directly.

matmul

public Tensor matmul(Tensor other)

add

public Tensor add(Tensor other)

subtract

public Tensor subtract(Tensor other)

multiply

public Tensor multiply(Tensor other)

divide

public Tensor divide(Tensor other)

addRowVector

public Tensor addRowVector(Tensor rowVector)

addBroadcastCols

public Tensor addBroadcastCols(Tensor colVector)

divideBroadcastCols

public Tensor divideBroadcastCols(Tensor colVector)

subtractBroadcastCols

public Tensor subtractBroadcastCols(Tensor colVector)

multiplyBroadcastCols

public Tensor multiplyBroadcastCols(Tensor colVector)

addBroadcastRows

public Tensor addBroadcastRows(Tensor rowVector)

multiplyBroadcastRows

public Tensor multiplyBroadcastRows(Tensor rowVector)

multiplyScalar

public Tensor multiplyScalar(double s)

addScalar

public Tensor addScalar(double s)

divideScalar

public Tensor divideScalar(double s)

sumRows

public Tensor sumRows()

sumAlongRows

public Tensor sumAlongRows()

sumAlongCols

public Tensor sumAlongCols()

meanAlongRows

public Tensor meanAlongRows()

varianceAlongRows

public Tensor varianceAlongRows()

maxAlongRows

public Tensor maxAlongRows()

sum

public double sum()

sumAbs

public double sumAbs()

clamp

public Tensor clamp(double min,
 double max)

transpose

public Tensor transpose()

sqrt

public Tensor sqrt()

pow

public Tensor pow(double exponent)

neg

public Tensor neg()

exp

public Tensor exp()

log

public Tensor log()

tanhActivation

public Tensor tanhActivation()

sigmoidActivation

public Tensor sigmoidActivation()

reluActivation

public Tensor reluActivation()

reluBackward

public Tensor reluBackward(Tensor gradOutput)

geluActivation

public Tensor geluActivation()

geluBackward

public Tensor geluBackward(Tensor gradOutput)

addInPlace

public Tensor addInPlace(Tensor b)

subtractInPlace

public Tensor subtractInPlace(Tensor b)

multiplyInPlace

public Tensor multiplyInPlace(Tensor b)

divideInPlace

public Tensor divideInPlace(Tensor b)

multiplyScalarInPlace

public Tensor multiplyScalarInPlace(double s)

addScalarInPlace

public Tensor addScalarInPlace(double s)

divideScalarInPlace

public Tensor divideScalarInPlace(double s)

sqrtInPlace

public Tensor sqrtInPlace()

negInPlace

public Tensor negInPlace()

expInPlace

public Tensor expInPlace()

logInPlace

public Tensor logInPlace()

reluInPlace

public Tensor reluInPlace()

geluInPlace

public Tensor geluInPlace()

tanhInPlace

public Tensor tanhInPlace()

sigmoidInPlace

public Tensor sigmoidInPlace()

softmaxRows

public Tensor softmaxRows()

softmaxBackward

public Tensor softmaxBackward(Tensor softmaxOut)

crossEntropyLoss

public double crossEntropyLoss(int[] targets)

crossEntropyGradient

public Tensor crossEntropyGradient(int[] targets)

adamWUpdate

public static void adamWUpdate(Tensor w,
 Tensor g,
 Tensor mt,
 Tensor vt,
 double lr,
 double beta1,
 double beta2,
 double eps,
 double weightDecay,
 double bc1,
 double bc2)

layerNormBackward

public static Tensor layerNormBackward(Tensor dXHat,
 Tensor xHat,
 Tensor std,
 int dim)

get

public double get(int r,
 int c)

set

public void set(int r,
 int c,
 double value)

getRow

public Tensor getRow(int row)

setRow

public void setRow(int row,
 Tensor source,
 int srcRow)

sliceRows

public static Tensor sliceRows(Tensor t,
 int[] rowIndices,
 int cols)

scatterAddRows

public static void scatterAddRows(Tensor target,
 int[] indices,
 Tensor grad)

sampleRows

public static Tensor sampleRows(Tensor t,
 int n,
 Random rnd)

print

public void print(String label)

zeros

public static Tensor zeros(int rows,
 int cols)

ones

public static Tensor ones(int rows,
 int cols)

random

public static Tensor random(int rows,
 int cols,
 Random rand)

causalMask

public static Tensor causalMask(int size)

unflattenToTensor

public static Tensor unflattenToTensor(double[] flat,
 int rows,
 int cols)

flattenTensor

public static double[] flattenTensor(Tensor t)

requireSameShape

public static void requireSameShape(Tensor a,
 Tensor b,
 String op)

requireTargetsMatchRows

public static void requireTargetsMatchRows(Tensor logits,
 int[] targets)