Warning: This API is deprecated and will be removed in a future
version of TensorFlow after
the replacement is stable.
ParallelConcat
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Concatenates a list of `N` tensors along the first dimension.
The input tensors are all required to have size 1 in the first dimension.
For example:
# 'x' is [[1, 4]]
# 'y' is [[2, 5]]
# 'z' is [[3, 6]]
parallel_concat([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim.
The difference between concat and parallel_concat is that concat requires all
of the inputs be computed before the operation will begin but doesn't require
that the input shapes be known during graph construction. Parallel concat
will copy pieces of the input into the output as they become available, in
some situations this can provide a performance benefit.
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 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
ParallelConcat<T>
create
(Scope scope, Iterable<Operand<T>> values, Shape shape)
Factory method to create a class wrapping a new ParallelConcat operation.
Parameters
scope |
current scope |
values |
Tensors to be concatenated. All must have size 1 in the first dimension
and same shape. |
shape |
the final shape of the result; should be equal to the shapes of any input
but with the number of input values in the first dimension. |
Returns
- a new instance of ParallelConcat
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Last updated 2022-02-12 UTC.
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