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weighted_categorical_crossentropy
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| def weighted_categorical_crossentropy(weights): | |
| weights = K.variable(weights) | |
| def loss(y_true, y_pred): | |
| # scale predictions so that the class probas of each sample sum to 1 | |
| y_pred /= K.sum(y_pred, axis=-1, keepdims=True) | |
| # clip to prevent NaN's and Inf's | |
| y_pred = K.clip(y_pred, K.epsilon(), 1 - K.epsilon()) | |
| # calc | |
| lss = y_true * K.log(y_pred) * weights | |
| lss += (1 - y_true) * K.log(1 - y_pred) * weights | |
| lss = -K.sum(lss, -1) | |
| return lss | |
| return loss | |
| weights = np.array([0.5,5,10]) | |
| loss = weighted_categorical_crossentropy(weights) |
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