使用theano函数在keras中自定义损失函数

我想使用我自己的binary_crossentropy,而不是使用Keras库随附的库.这是我的自定义函数：

    import theano
    from keras import backend as K

    def elementwise_multiply(a, b): # a and b are tensors
       c = a * b
       return theano.function([a, b], c)

    def custom_objective(y_true, y_pred):  
       first_log = K.log(y_pred)
       first_log = elementwise_multiply(first_log, y_true)
       second_log = K.log(1 - y_pred)
       second_log = elementwise_multiply(second_log, (1 - y_true))
       result = second_log + first_log
       return K.mean(result, axis=-1)

note: This is for practice. I’m aware of
T.nnet.binary_crossentropy(y_pred, y_true)

但是,当我编译模型时：

sgd = SGD(lr=0.001)
model.compile(loss = custom_objective, optimizer = sgd)

我收到此错误：

————————————————————————— TypeError Traceback (most recent call
last) in ()
36
37 sgd = SGD(lr=0.001)
—> 38 model.compile(loss = custom_objective, optimizer = sgd)
39 # ==============================================

C:\Program Files (x86)\Anaconda3\lib\site-packages\keras\models.py in
compile(self, optimizer, loss, class_mode)
418 else:
419 mask = None
–> 420 train_loss = weighted_loss(self.y, self.y_train, self.weights, mask)
421 test_loss = weighted_loss(self.y, self.y_test, self.weights, mask)
422

C:\Program Files (x86)\Anaconda3\lib\site-packages\keras\models.py in
weighted(y_true, y_pred, weights, mask)
80 ”’
81 # score_array has ndim >= 2
—> 82 score_array = fn(y_true, y_pred)
83 if mask is not None:
84 # mask should have the same shape as score_array

in custom_objective(y_true, y_pred)
11 second_log = K.log(1 – K.clip(y_true, K.epsilon(), np.inf))
12 second_log = elementwise_multiply(second_log, (1-y_true))
—> 13 result = second_log + first_log
14 #result = np.multiply(result, y_pred)
15 return K.mean(result, axis=-1)

TypeError: unsupported operand type(s) for +: ‘Function’ and
‘Function’

当我用内联函数替换elementwise_multiply时：

def custom_objective(y_true, y_pred):  
    first_log = K.log(y_pred)    
    first_log = first_log * y_true
    second_log = K.log(1 - y_pred)
    second_log = second_log * (1-y_true)
    result = second_log + first_log
    return K.mean(result, axis=-1)

该模型可以编译,但损失值为nan：

Epoch 1/1 945/945 [==============================] – 62s – loss: nan –
acc: 0.0011 – val_loss: nan – val_acc: 0.0000e+00

有人可以帮我吗？

谢谢

解决方法:

我发现了问题.我必须将返回值乘以“ -1”,因为我使用随机梯度递归(sgd)作为优化器,而不是随机梯度上升！

这是代码,它像一个魅力一样工作：

import theano
from keras import backend as K

def custom_objective(y_true, y_pred):  
    first_log = K.log(y_pred)    
    first_log = first_log * y_true
    second_log = K.log(1 - y_pred)
    second_log = second_log * (1 - y_true)
    result = second_log + first_log
    return (-1 * K.mean(result)) 

标签：keras,theano,python
来源： https://codeday.me/bug/20191026/1939387.html