# Cross entropy versus Mean of Cross Entropy [duplicate]

In many neural network applications, people are prone to define loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels,logits) [tensorflow functions] as a loss function. Why add tf.reduce_mean (compute the expected value)?