Denote this distribution (self) by P and the other distribution by Q.
Assuming P, Q are absolutely continuous with respect to one another and permit densities
p(x) dr(x) and q(x) dr(x), (Shannon) cross entropy is defined as:
H[P, Q] = E_p[log q(X)] = int_F p(x) log q(x) dr(x)
where F denotes the support of the random variable X ~ P
.
tfd_cross_entropy(distribution, other, name = "cross_entropy")
distribution  The distribution being used. 

other 

name  String prepended to names of ops created by this function. 
cross_entropy: self.dtype Tensor with shape [B1, ..., Bn]
representing n different calculations of (Shannon) cross entropy.
Other distribution_methods:
tfd_cdf()
,
tfd_covariance()
,
tfd_entropy()
,
tfd_kl_divergence()
,
tfd_log_cdf()
,
tfd_log_prob()
,
tfd_log_survival_function()
,
tfd_mean()
,
tfd_mode()
,
tfd_prob()
,
tfd_quantile()
,
tfd_sample()
,
tfd_stddev()
,
tfd_survival_function()
,
tfd_variance()
# \donttest{ d1 < tfd_normal(loc = 1, scale = 1) d2 < tfd_normal(loc = 2, scale = 1) d1 %>% tfd_cross_entropy(d2)#> tf.Tensor(1.9189385, shape=(), dtype=float32)# }