Last active
March 25, 2020 03:00
-
-
Save yano/3a072e5e2b7a55703028751820bfacbf to your computer and use it in GitHub Desktop.
Keras学習時にPrecision, Recall, F-measureを表示するサンプル
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
#coding:utf-8 | |
import keras.backend as K | |
# ... kerasのコードなんとかかんとか | |
# precision, recall, f-measureを定義する | |
# 0.20というのが閾値になっているので適宜変更する | |
#precision | |
def P(y_true, y_pred): | |
true_positives = K.sum(K.cast(K.greater(K.clip(y_true * y_pred, 0, 1), 0.20), 'float32')) | |
pred_positives = K.sum(K.cast(K.greater(K.clip(y_pred, 0, 1), 0.20), 'float32')) | |
precision = true_positives / (pred_positives + K.epsilon()) | |
return precision | |
#recall | |
def R(y_true, y_pred): | |
true_positives = K.sum(K.cast(K.greater(K.clip(y_true * y_pred, 0, 1), 0.20), 'float32')) | |
poss_positives = K.sum(K.cast(K.greater(K.clip(y_true, 0, 1), 0.20), 'float32')) | |
recall = true_positives / (poss_positives + K.epsilon()) | |
return recall | |
#f-measure | |
def F(y_true, y_pred): | |
p_val = P(y_true, y_pred) | |
r_val = R(y_true, y_pred) | |
f_val = 2*p_val*r_val / (p_val + r_val) | |
return f_val | |
# ... kerasのコードなんとかかんとか | |
# metricsで学習時にP,R,Fを表示するようにする | |
model.compile(optimizer=rms_prop, loss="binary_crossentropy", metrics=[P, R, F]) | |
# ... kerasのコードなんとかかんとか | |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment