the_code/Human/data/chrombpnet/chrombpnet_utils/losses.py

import tensorflow as tf
import tensorflow_probability as tfp


#from https://github.com/kundajelab/basepair/blob/cda0875571066343cdf90aed031f7c51714d991a/basepair/losses.py#L87
def multinomial_nll(true_counts, logits):
    """Compute the multinomial negative log-likelihood
    Args:
      true_counts: observed count values
      logits: predicted logit values
    """
    counts_per_example = tf.reduce_sum(true_counts, axis=-1)
    dist = tfp.distributions.Multinomial(total_count=counts_per_example,
                                         logits=logits)
    return (-tf.reduce_sum(dist.log_prob(true_counts)) / 
            tf.cast(tf.shape(true_counts)[0], dtype=tf.float32))