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.
Constants
String | OP_NAME | The name of this op, as known by TensorFlow core engine |
Public Methods
Output<T> |
asOutput()
Returns the symbolic handle of the tensor.
|
static <T extends TType> ParallelConcat<T> | |
Output<T> |
output()
The concatenated tensor.
|
Inherited Methods
Constants
public static final String OP_NAME
The name of this op, as known by TensorFlow core engine
Public Methods
public Output<T> asOutput ()
Returns the symbolic handle of the 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