47 lines
1.3 KiB
Python
47 lines
1.3 KiB
Python
# Copyright (c) 2017 Mycroft AI Inc.
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from typing import *
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def weighted_log_loss(yt, yp) -> Any:
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"""
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Binary crossentropy with a bias towards false negatives
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yt: Target
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yp: Prediction
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"""
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from keras import backend as K
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weight = 0.99 # [0..1] where 1 is inf bias
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pos_loss = -(0 + yt) * K.log(0 + yp + K.epsilon())
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neg_loss = -(1 - yt) * K.log(1 - yp + K.epsilon())
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return weight * K.sum(neg_loss) + (1. - weight) * K.sum(pos_loss)
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def weighted_mse_loss(yt, yp) -> Any:
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from keras import backend as K
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weight = 0.9 # [0..1] where 1 is inf bias
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total = K.sum(K.ones_like(yt))
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neg_loss = total * K.sum(K.square(yp * (1 - yt))) / K.sum(1 - yt)
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pos_loss = total * K.sum(K.square(1. - (yp * yt))) / K.sum(yt)
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return weight * neg_loss + (1. - weight) * pos_loss
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def false_pos(yt, yp) -> Any:
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from keras import backend as K
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return K.sum(K.cast(yp * (1 - yt) > 0.5, 'float')) / K.sum(1 - yt)
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def false_neg(yt, yp) -> Any:
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from keras import backend as K
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return K.sum(K.cast((1 - yp) * (0 + yt) > 0.5, 'float')) / K.sum(0 + yt)
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def load_keras() -> Any:
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import keras
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keras.losses.weighted_log_loss = weighted_log_loss
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keras.metrics.false_pos = false_pos
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keras.metrics.false_positives = false_pos
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keras.metrics.false_neg = false_neg
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return keras
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