def SecureSum(input_tensor,
axis=None,
keepdims=None,
name=None,
reduction_indices=None,
keep_dims=None):
keepdims = False if keepdims is None else keepdims
axis = math_ops._ReductionDims(input_tensor, axis)
return _secure_ops.secure_reduce_sum(input_tensor, reduction_indices=axis, name=name, keep_dims=keepdims)
def SecureSum(input_tensor,
axis=None,
keepdims=None,
name=None,
reduction_indices=None,
keep_dims=None):
keepdims = deprecation.deprecated_argument_lookup("keepdims", keepdims,
"keep_dims", keep_dims)
keepdims = False if keepdims is None else keepdims
axis = math_ops._ReductionDims(input_tensor, axis)
return _secure_ops.secure_reduce_sum(input_tensor, reduction_indices=axis, name=name, keep_dims=keepdims)
def rtt_mean(
input_tensor,
axis=None,
keepdims=None,
name=None,
reduction_indices=None,
keep_dims=None,
):
"""Computes the mean of elements across dimensions of a tensor."""
keepdims = False if keepdims is None else keepdims
axis = math_ops._ReductionDims(input_tensor, axis)
input_tensor = rtt_ts.convert_to_rtttensor(input_tensor)
_result = rtt_ts.rtt_ops.rtt_reduce_mean(input_tensor,
reduction_indices=axis,
name=name,
keep_dims=keepdims)
return rtt_ts.RttTensor(_result)
def MpcMean(input_tensor,
axis=None,
keepdims=None,
name=None,
reduction_indices=None,
keep_dims=None):
# if axis is None:
# axis = reduction_indices
# if axis is None:
# axis = -1
keepdims = False if keepdims is None else keepdims
axis = math_ops._ReductionDims(input_tensor, axis)
return _mpcops.mpc_mean(input_tensor,
reduction_indices=axis,
name=name,
keep_dims=keepdims)
def sparse_reduce_sum(sp_input, reduction_axes=None, keep_dims=False):
"""Computes the sum of elements across dimensions of a SparseTensor.
This Op takes a SparseTensor and is the sparse counterpart to
`tf.reduce_sum()`. In particular, this Op also returns a dense `Tensor`
instead of a sparse one.
Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless
`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
with length 1.
If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
with a single element is returned.
For example:
```python
# 'x' represents [[1, ?, 1]
# [?, 1, ?]]
# where ? is implictly-zero.
tf.sparse_reduce_sum(x) ==> 3
tf.sparse_reduce_sum(x, 0) ==> [1, 1, 1]
tf.sparse_reduce_sum(x, 1) ==> [2, 1]
tf.sparse_reduce_sum(x, 1, keep_dims=True) ==> [[2], [1]]
tf.sparse_reduce_sum(x, [0, 1]) ==> 3
```
Args:
sp_input: The SparseTensor to reduce. Should have numeric type.
reduction_axes: The dimensions to reduce; list or scalar. If `None` (the
default), reduces all dimensions.
keep_dims: If true, retain reduced dimensions with length 1.
Returns:
The reduced Tensor.
"""
return gen_sparse_ops.sparse_reduce_sum(sp_input.indices,
sp_input.values,
sp_input.shape,
math_ops._ReductionDims(
sp_input, reduction_axes),
keep_dims)
def rtt_sum(
input_tensor,
axis=None,
keepdims=None,
name=None,
reduction_indices=None,
keep_dims=None,
):
"""Computes the sum of elements across dimensions of a tensor."""
keepdims = deprecation.deprecated_argument_lookup("keepdims", keepdims,
"keep_dims", keep_dims)
keepdims = False if keepdims is None else keepdims
axis = math_ops._ReductionDims(input_tensor, axis)
input_tensor = rtt_ts.convert_to_rtttensor(input_tensor)
_result = rtt_ts.rtt_ops.rtt_reduce_sum(input_tensor,
reduction_indices=axis,
name=name,
keep_dims=keepdims)
return rtt_ts.RttTensor(_result)
