Question

我正在尝试根据此paper（第19页的公式3.8）实现自定义损失函数。我到达了此实现：

import numpy as np
import keras.backend as T
from keras import models

def costume_loss(censored):
    '''costume loss function'''

    def loglikelihood_function(y_true,y_pred,censored):
        '''implements likelihood function as per equation 3.8 in using survival prediction techniques to learn consumer'''
        results=[]
        n=T.int_shape(y_pred)[0]  #number of instances
        K=T.int_shape(y_pred)[1]    #number of subintervals

        print(T.int_shape(y_pred)[0],T.int_shape(y_pred)[1])


        for i in range(n):
            if censored[i]==0:
                sum1=np.sum([y_pred[i][j]*y_true[i][j] for j in range(K)])
                sum2=np.sum([math.exp(np.sum([ y_pred[i][k]  for k in range(j,K)])) for j in range(K)])
                results.append(-(sum1-math.log(sum2)))      
            else:
                sum1=np.sum([y_true[i][j]*math.exp(np.sum([y_pred[i][k] for k in range(j,K)]))   for j in range(K)])
                sum2=np.sum([math.exp(np.sum([ y_pred[i][k]  for k in range(j,K)])) for j in range(K)])
            results.append(-(math.log(sum1)-math.log(sum2))) 

        x=tf.constant(np.array(results,dtype='float64'))   #convert into tensor
        return T.mean(x)

    def loss_function(y_true,y_pred):
        return loglikelihood_function(y_true,y_pred,censored)

    return loss_function

但是，当我尝试编译模型时：

model.compile(optimizer='rmsprop',loss=costume_loss(censored=c),metrics=['accuracy'])

我要

TypeError: 'NoneType' object cannot be interpreted as an integer

在计算时似乎未定义批次大小。有人可以指出我正确的方向吗？也许我需要使用张量操作来实现它？如果可以，怎么办？

谢谢

KERAS：来自对数似然的自定义损失函数

0 个答案: