时间戳

首页 > 解决方案 > Output of custom loss in Keras

python - Output of custom loss in Keras

问题描述

I know there are many questions treating custom loss functions in Keras but I've been unable to answer this even after 3 hours of googling.

Here is a very simplified example of my problem. I realize this example is pointless but I provide it for simplicity, I obviously need to implement something more complicated. 

from keras.backend import binary_crossentropy
from keras.backend import mean
def custom_loss(y_true, y_pred):

    zeros = tf.zeros_like(y_true)
    index_of_zeros = tf.where(tf.equal(zeros, y_true))
    ones = tf.ones_like(y_true)
    index_of_ones = tf.where(tf.equal(ones, y_true))

    zero = tf.gather(y_pred, index_of_zeros)
    one = tf.gather(y_pred, index_of_ones)

    loss_0 = binary_crossentropy(tf.zeros_like(zero), zero)
    loss_1 = binary_crossentropy(tf.ones_like(one), one)

    return mean(tf.concat([loss_0, loss_1], axis=0))

I do not understand why training the network with the above loss function on a two class dataset does not yield the same result as training with the built in binary-crossentropy loss function. Thank you!

EDIT: I edited the code snippet to include the mean as per comments below. I still get the same behavior however.

标签： pythontensorflowkerasloss-functioncross-entropy

解决方案

我终于弄明白了。tf.where当形状为“未知”时，该函数的行为非常不同。要修复上面的代码片段，只需在声明函数后插入以下行：

y_pred = tf.reshape(y_pred, [-1])
y_true = tf.reshape(y_true, [-1])

推荐阅读