Artificial Intelligence, Deep Learning, Machine Learning, Optimization.
np.array([[0.25,0.25,0.25,0.25], [0.01,0.01,0.01,0.96]]) targets = np.array([[0,0,0,1], [0,0,0,1]]) def cross_entropy(predictions, targets, epsilon=1e-10): predictions = np.clip(predictions, epsilon, 1. - epsilon) N = predictions.shape[0] ce_loss = -np.sum(np.sum(targets * np.log(predictions +