tf.nn.log_poisson_loss( targets, log_input, compute_full_loss=False, name=None )
Defined in tensorflow/python/ops/nn_impl.py
.
See the guide: Neural Network > Losses
Computes log Poisson loss given log_input
.
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)]
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).A Tensor
of the same shape as log_input
with the componentwise logistic losses.
ValueError
: If log_input
and targets
do not have the same shape.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/nn/log_poisson_loss