tf.contrib.distributions.Multinomial.log_prob()

tf.contrib.distributions.Multinomial.log_prob(value, name='log_prob')

Log probability density/mass function (depending on is_continuous).

Additional documentation from Multinomial:

For each batch of counts [n_1,...,n_k], P[counts] is the probability that after sampling n draws from this Multinomial distribution, the number of draws falling in class j is n_j. Note that different sequences of draws can result in the same counts, thus the probability includes a combinatorial coefficient.

Note that input "counts" must be a non-negative tensor with dtype dtype and whose shape can be broadcast with self.p and self.n. For fixed leading dimensions, the last dimension represents counts for the corresponding Multinomial distribution in self.p. counts is only legal if it sums up to n and its components are equal to integer values.

Args:

• value: float or double Tensor.
• name: The name to give this op.

Returns:

• log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.