使用theano函数在keras中自定义损失函数
作者:互联网
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)
422C:\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_arrayin 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