def sparse_add(sp_a, sp_b, thresh=0):
"""Adds two `SparseTensor` objects to produce another `SparseTensor`.
The input `SparseTensor` objects' indices are assumed ordered in standard
lexicographic order. If this is not the case, before this step run
`SparseReorder` to restore index ordering.
By default, if two values sum to zero at some index, the output `SparseTensor`
would still include that particular location in its index, storing a zero in
the corresponding value slot. To override this, callers can specify `thresh`,
indicating that if the sum has a magnitude strictly smaller than `thresh`, its
corresponding value and index would then not be included. In particular,
`thresh == 0.0` (default) means everything is kept and actual thresholding
happens only for a positive value.
For example, suppose the logical sum is (densified):
[ 2]
[.1 ]
[ 6 -.2]
Then,
- thresh == 0 (the default): all 4 index/value pairs will be returned.
- thresh == 0.11: only .1 will vanish, and the remaining three index/value
pairs will be returned.
- thresh == 0.21: both .1 and -.2 will vanish.
Args:
sp_a: The first input `SparseTensor`.
sp_b: The second input `SparseTensor`.
thresh: A 0-D `Tensor`. The magnitude threshold that determines if an
output value/index pair takes space. Its dtype should match that of the
values if they are real; if the latter are complex64/complex128, then the
dtype should be float32/float64, correspondingly.
Returns:
A `SparseTensor` with the same shape, representing the sum.
Raises:
TypeError: If either `sp_a` or `sp_b` is not a `SparseTensor`.
"""
if not all(isinstance(sp_input, ops.SparseTensor) for sp_input in [sp_a,
sp_b]):
raise TypeError("All inputs must be SparseTensors")
thresh = ops.convert_to_tensor(thresh, dtype=sp_a.values.dtype.real_dtype,
name="thresh")
output_ind, output_val, output_shape = (
gen_sparse_ops._sparse_add(sp_a.indices,
sp_a.values,
sp_a.shape,
sp_b.indices,
sp_b.values,
sp_b.shape,
thresh))
return ops.SparseTensor(output_ind, output_val, output_shape)
def sparse_add(sp_a, sp_b, thresh=0):
"""Adds two `SparseTensor` objects to produce another `SparseTensor`.
The input `SparseTensor` objects' indices are assumed ordered in standard
lexicographic order. If this is not the case, before this step run
`SparseReorder` to restore index ordering.
By default, if two values sum to zero at some index, the output `SparseTensor`
would still include that particular location in its index, storing a zero in
the corresponding value slot. To override this, callers can specify `thresh`,
indicating that if the sum has a magnitude strictly smaller than `thresh`, its
corresponding value and index would then not be included. In particular,
`thresh == 0.0` (default) means everything is kept and actual thresholding
happens only for a positive value.
For example, suppose the logical sum is (densified):
[ 2]
[.1 ]
[ 6 -.2]
Then,
- thresh == 0 (the default): all 4 index/value pairs will be returned.
- thresh == 0.11: only .1 will vanish, and the remaining three index/value
pairs will be returned.
- thresh == 0.21: both .1 and -.2 will vanish.
Args:
sp_a: The first input `SparseTensor`.
sp_b: The second input `SparseTensor`.
thresh: A 0-D `Tensor`. The magnitude threshold that determines if an
output value/index pair takes space. Its dtype should match that of the
values if they are real; if the latter are complex64/complex128, then the
dtype should be float32/float64, correspondingly.
Returns:
A `SparseTensor` with the same shape, representing the sum.
Raises:
TypeError: If either `sp_a` or `sp_b` is not a `SparseTensor`.
"""
if not all(
isinstance(sp_input, ops.SparseTensor)
for sp_input in [sp_a, sp_b]):
raise TypeError("All inputs must be SparseTensors")
thresh = ops.convert_to_tensor(thresh,
dtype=sp_a.values.dtype.real_dtype,
name="thresh")
output_ind, output_val, output_shape = (gen_sparse_ops._sparse_add(
sp_a.indices, sp_a.values, sp_a.shape, sp_b.indices, sp_b.values,
sp_b.shape, thresh))
return ops.SparseTensor(output_ind, output_val, output_shape)
def sparse_add(a, b, thresh=0):
"""Adds two tensors, at least one of each is a `SparseTensor`.
If one `SparseTensor` and one `Tensor` are passed in, returns a `Tensor`. If
both arguments are `SparseTensor`s, this returns a `SparseTensor`. The order
of arguments does not matter. Use vanilla `tf.add()` for adding two dense
`Tensor`s.
The indices of any input `SparseTensor` are assumed ordered in standard
lexicographic order. If this is not the case, before this step run
`SparseReorder` to restore index ordering.
If both arguments are sparse, we perform "clipping" as follows. By default,
if two values sum to zero at some index, the output `SparseTensor` would still
include that particular location in its index, storing a zero in the
corresponding value slot. To override this, callers can specify `thresh`,
indicating that if the sum has a magnitude strictly smaller than `thresh`, its
corresponding value and index would then not be included. In particular,
`thresh == 0.0` (default) means everything is kept and actual thresholding
happens only for a positive value.
For example, suppose the logical sum of two sparse operands is (densified):
[ 2]
[.1 0]
[ 6 -.2]
Then,
- thresh == 0 (the default): all 5 index/value pairs will be returned.
- thresh == 0.11: only .1 and 0 will vanish, and the remaining three
index/value pairs will be returned.
- thresh == 0.21: .1, 0, and -.2 will vanish.
Args:
a: The first operand; `SparseTensor` or `Tensor`.
b: The second operand; `SparseTensor` or `Tensor`. At least one operand
must be sparse.
thresh: A 0-D `Tensor`. The magnitude threshold that determines if an
output value/index pair takes space. Its dtype should match that of the
values if they are real; if the latter are complex64/complex128, then the
dtype should be float32/float64, correspondingly.
Returns:
A `SparseTensor` or a `Tensor`, representing the sum.
Raises:
TypeError: If both `a` and `b` are `Tensor`s. Use `tf.add()` instead.
"""
if not any(isinstance(inp, ops.SparseTensor) for inp in [a, b]):
raise TypeError("At least one input should be SparseTensor; do you mean to"
" use tf.add()?")
if all(isinstance(inp, ops.SparseTensor) for inp in [a, b]):
thresh = ops.convert_to_tensor(thresh, dtype=a.values.dtype.real_dtype,
name="thresh")
output_ind, output_val, output_shape = (
gen_sparse_ops._sparse_add(a.indices,
a.values,
a.shape,
b.indices,
b.values,
b.shape,
thresh))
return ops.SparseTensor(output_ind, output_val, output_shape)
else:
# swap to make `a` the SparseTensor
if isinstance(b, ops.SparseTensor):
a, b = b, a
return gen_sparse_ops._sparse_tensor_dense_add(
a.indices, a.values, a.shape, b)