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|
Computes log(mean(exp(input_tensor))).
tfp.math.reduce_logmeanexp(
input_tensor, axis=None, keepdims=False, experimental_named_axis=None, name=None
)
Reduces input_tensor along the dimensions given in axis. Unless
keepdims is true, the rank of the tensor is reduced by 1 for each entry in
axis. If keepdims is true, the reduced dimensions are retained with length
1.
If axis has no entries, all dimensions are reduced, and a tensor with a
single element is returned.
This function is more numerically stable than log(reduce_mean(exp(input))).
It avoids overflows caused by taking the exp of large inputs and underflows
caused by taking the log of small inputs.
Args | |
|---|---|
input_tensor
|
The tensor to reduce. Should have numeric type. |
axis
|
The dimensions to reduce. If None (the default), reduces all
dimensions. Must be in the range [-rank(input_tensor),
rank(input_tensor)).
|
keepdims
|
Boolean. Whether to keep the axis as singleton dimensions.
Default value: False (i.e., squeeze the reduced dimensions).
|
experimental_named_axis
|
A str or list ofstraxis names to additionally
reduce over. ProvidingNonewill not reduce over any axes.
</td>
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<td>name</td>
<td>
Pythonstrname prefixed to Ops created by this function.
Default value:None(i.e.,'reduce_logmeanexp'`).
|
Returns | |
|---|---|
log_mean_exp
|
The reduced tensor. |