[英]TF/KERAS : passing a list as loss for single output
我的模型只有一個輸出,但我想結合兩個不同的損失函數,(注意:類數 = 24 )。
c = 0.8
lamda = 32
# My personalized loss function
def selective_loss(y_true, y_pred): .
loss = K.categorical_crossentropy(
K.repeat_elements(y_pred[:, -1:], CLASSES, axis=1) * y_true[:, :-1],
y_pred[:, :-1]) + lamda * K.maximum(-K.mean(y_pred[:, -1]) + c, 0) ** 2
return loss
p = np.ones(CLASSES) / CLASSES#The weights of class.
#And doing de model compile.
model.compile(loss = ['categorical_crossentropy', selective_loss],
loss_weights = p,
optimizer= sgd,
metrics = ['accuracy'])
但它抱怨我需要兩個輸出,因為我定義了兩個損失:
ValueError: When passing a list as loss, it should have one entry per model outputs. The model has 1 outputs, but you passed loss=['categorical_crossentropy', <function selective_loss at 0x7fcfb68daa60>]
您是否必須將兩種損失合二為一? 如果是這樣,你會怎么做?
還是最好有兩個輸出? 這會影響預測嗎? 會怎樣?
我在兩個損失函數之間進行加權,使用 alpha 0.5 但其他浮點數也有效:
#Private loss is the selective_loss.
def total_loss(y_true, y_pred):
alpha = 0.5
return (1-alpha)*categorical_crossentropy(y_true, y_pred) + alpha*selective_loss(y_true, y_pred)
#Compile the model with weighting loss.
model.compile(loss = total_loss,
loss_weights = p,
optimizer= sgd,
metrics = ['accuracy'])
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