Combined tensorflow loss to avoid negative outputs

gistfile1.py

# custom combined loss cf https://towardsdatascience.com/custom-tensorflow-loss-functions-for-advanced-machine-learning-f13cdd1d188a
loss_1 = tf.losses.huber_loss

def loss_2(labels, predictions):
    k = tf.clip_by_value(predictions, -np.inf, 0)
#         k = tf.cond(predictions < 0, lambda: 1 * -predictions, lambda: 0)
    loss = tf.reduce_sum(-k)
    return loss

negativity_loss_rate = 0.1

def loss_combined(labels, predictions):
    # can also normalize the losses for stability but not done in this case
    norm = 1 #tf.reduce_sum(loss_1 + loss_2)
    loss = loss_1(labels, predictions) / norm + negativity_loss_rate * loss_2(labels, predictions) / norm
    return loss