简单来说,只需将以下函数传递给model.compile函数:
from keras import backend as K
def recall_m(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def precision_m(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
def f1_m(y_true, y_pred):
precision = precision_m(y_true, y_pred)
recall = recall_m(y_true, y_pred)
return 2*((precision*recall)/(precision+recall+K.epsilon()))
def TP(y_true, y_pred):
tp = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
y_pos = K.round(K.clip(y_true, 0, 1))
n_pos = K.sum(y_pos)
y_neg = 1 - y_pos
n_neg = K.sum(y_neg)
n = n_pos + n_neg
return tp/n
def TN(y_true, y_pred):
y_pos = K.round(K.clip(y_true, 0, 1))
n_pos = K.sum(y_pos)
y_neg = 1 - y_pos
n_neg = K.sum(y_neg)
n = n_pos + n_neg
y_pred_pos = K.round(K.clip(y_pred, 0, 1))
y_pred_neg = 1 - y_pred_pos
tn = K.sum(K.round(K.clip(y_neg * y_pred_neg, 0, 1)))
return tn/n
def FP(y_true, y_pred):
y_pos = K.round(K.clip(y_true, 0, 1))
n_pos = K.sum(y_pos)
y_neg = 1 - y_pos
n_neg = K.sum(y_neg)
n = n_pos + n_neg
tn = K.sum(K.round(K.clip(y_neg * y_pred, 0, 1)))
return tn/n
def FN(y_true, y_pred):
y_pos = K.round(K.clip(y_true, 0, 1))
n_pos = K.sum(y_pos)
y_neg = 1 - y_pos
n_neg = K.sum(y_neg)
n = n_pos + n_neg
y_pred_pos = K.round(K.clip(y_pred, 0, 1))
y_pred_neg = 1 - y_pred_pos
tn = K.sum(K.round(K.clip(y_true * y_pred_neg, 0, 1)))
return tn/n
然后,
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=lr),
metrics=['accuracy',f1_m,precision_m, recall_m, TP, TN, FP, FN])