MycroftAI
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mycroft-precise
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Make loss bias use module level variable

pull/19/head
Matthew Scholefield 2018-07-05 13:30:51 -05:00
parent 3fc1fe4789
commit 252abbeb15
1 changed files with 4 additions and 4 deletions
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8
precise/functions.py
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@ -13,6 +13,8 @@
# limitations under the License. # limitations under the License.
from typing import * from typing import *
LOSS_BIAS = 0.9 # [0..1] where 1 is inf bias
def weighted_log_loss(yt, yp) -> Any: def weighted_log_loss(yt, yp) -> Any:
""" """
@ -21,23 +23,21 @@ def weighted_log_loss(yt, yp) -> Any:
yp: Prediction yp: Prediction
""" """
from keras import backend as K from keras import backend as K
weight = 0.7 # [0..1] where 1 is inf bias
pos_loss = -(0 + yt) * K.log(0 + yp + K.epsilon()) pos_loss = -(0 + yt) * K.log(0 + yp + K.epsilon())
neg_loss = -(1 - yt) * K.log(1 - yp + K.epsilon()) neg_loss = -(1 - yt) * K.log(1 - yp + K.epsilon())
return weight * K.mean(neg_loss) + (1. - weight) * K.mean(pos_loss) return LOSS_BIAS * K.mean(neg_loss) + (1. - LOSS_BIAS) * K.mean(pos_loss)
def weighted_mse_loss(yt, yp) -> Any: def weighted_mse_loss(yt, yp) -> Any:
from keras import backend as K from keras import backend as K
weight = 0.9 # [0..1] where 1 is inf bias
total = K.sum(K.ones_like(yt)) total = K.sum(K.ones_like(yt))
neg_loss = total * K.sum(K.square(yp * (1 - yt))) / K.sum(1 - yt) neg_loss = total * K.sum(K.square(yp * (1 - yt))) / K.sum(1 - yt)
pos_loss = total * K.sum(K.square(1. - (yp * yt))) / K.sum(yt) pos_loss = total * K.sum(K.square(1. - (yp * yt))) / K.sum(yt)
return weight * neg_loss + (1. - weight) * pos_loss return LOSS_BIAS * neg_loss + (1. - LOSS_BIAS) * pos_loss
def false_pos(yt, yp) -> Any: def false_pos(yt, yp) -> Any: