Question

我想在keras中定义自己的损失函数，并将bce_loss与变量W相乘。实际上，W与张量bce_loss的形状相同。

如果我打印张量bce_loss，也许可以显示如下：

Tensor("loss_8/activation_14_loss/logistic_loss:0", shape=(?, 3), dtype=float32)

现在我不知道如何获得bce_loss的形状，并使变量W具有与bce_loss相同的形状。

我的代码：

def myLoss(y_true, y_pred):
    bce_loss = K.binary_crossentropy(y_true, y_pred)
    # want to get a variable W with the same shape of bce_loss
    # And W is initialized with normal distribution.
    val = np.random.normal(0, 0.05, size= bce_loss.size()) 
    W = keras.variable( val )
    return K.mean(self.W*bce_loss, axis = -1)

Answer 1

您可以这样定义损失函数：

from keras import backend as K
import numpy as np

def myLoss(y_true, y_pred):
    bce_loss = K.binary_crossentropy(y_true, y_pred)
    w = K.random_normal(K.shape(bce_loss), 0, 0.05)
    return K.mean(w * bce_loss, axis=-1)

y_t = K.placeholder((1,2))
y_p = K.placeholder((1,2))
loss = myLoss(y_t, y_p)
print(K.get_session().run(loss, {y_t: np.array([[1,1]]), y_p: np.array([[0.5, 0.2]])}))

如何在Keras中定义具有张量相同形状的变量？

1 个答案: