Scale_factor = 1 / float(np.count_nonzero(labels))įor c in range(len(labels)): # For each class Probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)įor r in range(bottom.num): # For each element in the batch Scores -= np.max(scores, axis=1, keepdims=True) The sum of 70%, 20%, and 10% is 100%, and the first entry is the most likely one. With Softmax, the model predicts a vector of probabilities. ![]() Usually, an activation function (Sigmoid/Softmax) is applied to the scores before the CE loss computation.
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