Computes log Poisson loss given log_input.
tf.nn.log_poisson_loss(
targets, log_input, compute_full_loss=False, name=None
)
Gives the log-likelihood loss between the prediction and the target under the
assumption that the target has a Poisson distribution.
Caveat: By default, this is not the exact loss, but the loss minus a
constant term [log(z!)]. That has no effect for optimization, but
does not play well with relative loss comparisons. To compute an
approximation of the log factorial term, specify
compute_full_loss=True to enable Stirling's Approximation.
For brevity, let c = log(x) = log_input, z = targets. The log Poisson
loss is
-log(exp(-x) * (x^z) / z!)
= -log(exp(-x) * (x^z)) + log(z!)
~ -log(exp(-x)) - log(x^z) [+ z * log(z) - z + 0.5 * log(2 * pi * z)]
[ Note the second term is the Stirling's Approximation for log(z!).
It is invariant to x and does not affect optimization, though
important for correct relative loss comparisons. It is only
computed when compute_full_loss == True. ]
= x - z * log(x) [+ z * log(z) - z + 0.5 * log(2 * pi * z)]
= exp(c) - z * c [+ z * log(z) - z + 0.5 * log(2 * pi * z)]
Args |
|---|
targets
|
A Tensor of the same type and shape as log_input.
|
log_input
|
A Tensor of type float32 or float64.
|
compute_full_loss
|
whether to compute the full loss. If false, a constant
term is dropped in favor of more efficient optimization.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor of the same shape as log_input with the componentwise
logistic losses.
|
Raises |
|---|
ValueError
|
If log_input and targets do not have the same shape.
|
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TensorFlow 1 version
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