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TensorScatterSub

public final class TensorScatterSub

Subtracts sparse `updates` from an existing tensor according to `indices`.

This operation creates a new tensor by subtracting sparse `updates` from the passed in `tensor`. This operation is very similar to `tf.scatter_nd_sub`, except that the updates are subtracted from an existing tensor (as opposed to a variable). If the memory for the existing tensor cannot be re-used, a copy is made and updated.

`indices` is an integer tensor containing indices into a new tensor of shape `shape`. The last dimension of `indices` can be at most the rank of `shape`:

indices.shape[-1] <= shape.rank

The last dimension of `indices` corresponds to indices into elements (if `indices.shape[-1] = shape.rank`) or slices (if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of `shape`. `updates` is a tensor with shape

indices.shape[:-1] + shape[indices.shape[-1]:]

The simplest form of tensor_scatter_sub is to subtract individual elements from a tensor by index. For example, say we want to insert 4 scattered elements in a rank-1 tensor with 8 elements.

In Python, this scatter subtract operation would look like this:

indices = tf.constant([[4], [3], [1], [7]])
     updates = tf.constant([9, 10, 11, 12])
     tensor = tf.ones([8], dtype=tf.int32)
     updated = tf.tensor_scatter_nd_sub(tensor, indices, updates)
     print(updated)
 
The resulting tensor would look like this:

[1, -10, 1, -9, -8, 1, 1, -11]

We can also, insert entire slices of a higher rank tensor all at once. For example, if we wanted to insert two slices in the first dimension of a rank-3 tensor with two matrices of new values.

In Python, this scatter add operation would look like this:

indices = tf.constant([[0], [2]])
     updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
                             [7, 7, 7, 7], [8, 8, 8, 8]],
                            [[5, 5, 5, 5], [6, 6, 6, 6],
                             [7, 7, 7, 7], [8, 8, 8, 8]]])
     tensor = tf.ones([4, 4, 4],dtype=tf.int32)
     updated = tf.tensor_scatter_nd_sub(tensor, indices, updates)
     print(updated)
 
The resulting tensor would look like this:

[[[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]], [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], [[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]], [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]]

Note that on CPU, if an out of bound index is found, an error is returned. On GPU, if an out of bound index is found, the index is ignored.

Public Methods

Output<T>
asOutput()
Returns the symbolic handle of a tensor.
static <T, U extends Number> TensorScatterSub<T>
create(Scope scope, Operand<T> tensor, Operand<U> indices, Operand<T> updates)
Factory method to create a class wrapping a new TensorScatterSub operation.
Output<T>
output()
A new tensor copied from tensor and updates subtracted according to the indices.

Inherited Methods

Public Methods

public Output<T> asOutput ()

Returns the symbolic handle of a tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static TensorScatterSub<T> create (Scope scope, Operand<T> tensor, Operand<U> indices, Operand<T> updates)

Factory method to create a class wrapping a new TensorScatterSub operation.

Parameters
scope current scope
tensor Tensor to copy/update.
indices Index tensor.
updates Updates to scatter into output.
Returns
  • a new instance of TensorScatterSub

public Output<T> output ()

A new tensor copied from tensor and updates subtracted according to the indices.