tfp.stats.log_soosum_exp
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Computes the log-swap-one-out-sum of exp(logx)
.
tfp.stats.log_soosum_exp(
logx, axis, keepdims=False, name=None
)
The swapped out element logx[i]
is replaced with the log-leave-i
-out
geometric mean of logx
.
Args |
logx
|
Floating-type Tensor representing log(x) where x is some
positive value.
|
axis
|
The dimensions to sum across. If None (the default), reduces all
dimensions. Must be in the range [-rank(logx), rank(logx)] .
Default value: None (i.e., reduce over all dims).
|
keepdims
|
If true, retains reduced dimensions with length 1.
Default value: False (i.e., keep all dims in log_mean_x ).
|
name
|
Python str name prefixed to Ops created by this function.
Default value: None (i.e., "log_soomean_exp" ).
|
Returns |
log_soomean_x
|
logx.dtype Tensor characterized by the natural-log of the
sum of x except that the element logx[i]is replaced with the
log of the leave- i-out Geometric-average. The sum of the gradient of log_soosum_xis n, i.e., the number of reduced elements.
Mathematically log_soomean_x` is,
log_soomean_x[i] = log(Avg{h[j ; i] : j=0, ..., m-1})
h[j ; i] = { u[j] j!=i
{ GeometricAverage{u[k] : k != i} j==i
|
log_sum_x
|
logx.dtype Tensor corresponding to the natural-log of the
average of x . The sum of the gradient of log_mean_x is 1 . Has
reduced shape of logx (per axis and keepdims ).
|
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Last updated 2023-11-21 UTC.
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