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Class GammaSoftplus
Gamma(concentration=1, rate=1 / mean)
where
Inherits From: ExponentialFamily
mean = softplus(matmul(X, weights))
.
__init__
__init__(name=None)
Creates the ExponentialFamily.
Args:
name
: Pythonstr
used as TF namescope for ops created by member functions. Default value:None
(i.e., the subclass name).
Properties
is_canonical
Returns True
when variance(r) == grad_mean(r)
for all r
.
name
Returns TF namescope prefixed to ops created by member functions.
Methods
__call__
__call__(
predicted_linear_response,
name=None
)
Computes mean(r), var(mean), d/dr mean(r)
for linear response, r
.
Here mean
and var
are the mean and variance of the sufficient statistic,
which may not be the same as the mean and variance of the random variable
itself. If the distribution's density has the form
p_Y(y) = h(y) Exp[dot(theta, T(y)) - A]
where theta
and A
are constants and h
and T
are known functions,
then mean
and var
are the mean and variance of T(Y)
. In practice,
often T(Y) := Y
and in that case the distinction doesn't matter.
Args:
predicted_linear_response
:float
-likeTensor
corresponding totf.matmul(model_matrix, weights)
.name
: Pythonstr
used as TF namescope for ops created by member functions. Default value:None
(i.e., 'call').
Returns:
mean
:Tensor
with shape and dtype ofpredicted_linear_response
representing the distribution prescribed mean, given the prescribed linear-response to mean mapping.variance
:Tensor
with shape and dtype ofpredicted_linear_response
representing the distribution prescribed variance, given the prescribed linear-response to mean mapping.grad_mean
:Tensor
with shape and dtype ofpredicted_linear_response
representing the gradient of the mean with respect to the linear-response and given the prescribed linear-response to mean mapping.
log_prob
log_prob(
response,
predicted_linear_response,
name=None
)
Computes D(param=mean(r)).log_prob(response)
for linear response, r
.
Args:
response
:float
-likeTensor
representing observed ("actual") responses.predicted_linear_response
:float
-likeTensor
corresponding totf.matmul(model_matrix, weights)
.name
: Pythonstr
used as TF namescope for ops created by member functions. Default value:None
(i.e., 'log_prob').
Returns:
log_prob
:Tensor
with shape and dtype ofpredicted_linear_response
representing the distribution prescribed log-probability of the observedresponse
s.