69 lines
2.0 KiB
Python
69 lines
2.0 KiB
Python
# Copyright 2018 Mycroft AI Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import *
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LOSS_BIAS = 0.9 # [0..1] where 1 is inf bias
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def set_loss_bias(bias: float):
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"""
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Near 1.0 reduces false positives
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Near 0.0 reduces false negatives
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"""
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global LOSS_BIAS
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LOSS_BIAS = bias
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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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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 LOSS_BIAS * K.mean(neg_loss) + (1. - LOSS_BIAS) * K.mean(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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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 LOSS_BIAS * neg_loss + (1. - LOSS_BIAS) * 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.maximum(1.0, 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.maximum(1.0, 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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