BooleanMaskUpdate

public abstract class BooleanMaskUpdate

Nested Classes

class BooleanMaskUpdate.Options Optional attributes for BooleanMaskUpdate

Public Constructors

BooleanMaskUpdate()

Public Methods

static BooleanMaskUpdate.Options	axis(Integer axis) Used to indicate the axis to mask from.
static BooleanMaskUpdate.Options	broadcast(Boolean broadcast) Whether to try broadcasting update.
static <T extends TType> Operand<T>	create(Scope scope, Operand<T> tensor, Operand<TBool> mask, Operand<T> updates, Options... options) Updates a tensor at the masked values, and returns the updated tensor.

Inherited Methods

From class java.lang.Object

boolean	equals(Object arg0)
final Class<?>	getClass()
int	hashCode()
final void	notify()
final void	notifyAll()
String	toString()
final void	wait(long arg0, int arg1)
final void	wait(long arg0)
final void	wait()

Public Constructors

public BooleanMaskUpdate ()

Public Methods

public static BooleanMaskUpdate.Options axis (Integer axis)

Used to indicate the axis to mask from. axis + dim(mask) <= dim(tensor) and mask's shape must match the first axis + dim(mask) dimensions of tensor's shape.

Parameters

axis	the axis to mask from. Uses 0 if null.

public static BooleanMaskUpdate.Options broadcast (Boolean broadcast)

Whether to try broadcasting update. True by default.

public static Operand<T> create (Scope scope, Operand<T> tensor, Operand<TBool> mask, Operand<T> updates, Options... options)

Updates a tensor at the masked values, and returns the updated tensor. Does not mutate the input tensors. updates will be broadcasted by default

Numpy equivalent is `tensor[mask] = updates`.

In general, 0 < dim(mask) = K <= dim(tensor), and mask's shape must match the first K dimensions of tensor's shape. We then have: booleanMask(tensor, mask)[i, j1,...,jd] = tensor[i1,...,iK,j1,...,jd] where (i1,...,iK) is the ith true entry of mask (row-major order).

The axis could be used with mask to indicate the axis to mask from (it's 0 by default). In that case, axis + dim(mask) <= dim(tensor) and mask's shape must match the first axis + dim(mask) dimensions of tensor's shape.

The shape of updates should be [n, t_1, t_2, ...] where n is the number of true values in mask and t_i is the ith dimension of tensor after axis and mask. updates will be broadcasted to this shape by default, which can be disabled using options.

Parameters

tensor	The tensor to mask.
mask	The mask to apply.
updates	the new values
options	carries optional attributes values

Returns

The masked tensor.