defined loss function in tensorflow?

问题

In my project, the negative instance is far more than positive instance, so I want to give positive instance with a larger weight. my target is:

loss = 0.0
if y_label==1:loss += 100 * cross_entropy
else:loss += cross_entropy

How to realizate this in tensorflow[?]

回答1:

Let losses to be a vector (rank-1 tensor) of loss values for the examples in your batch. And let y be the the vector of corresponding labels. You could then achieve the result you want by

weights = w_pos*y + w_neg*(1.0-y)
loss = tf.reduce_mean(weights*losses)

Here, w_pos and w_neg are constant scalar values (w_pos=100.0 and w_neg=1.0 in your example). The vector weights then has a value of w_pos for examples where the label equals 1 and w_neg where it equals 0. You then multiply weights element-wise with losses to weigh the values in the losses according to the corresponding labels and then take the mean.

来源：https://stackoverflow.com/questions/43273677/defined-loss-function-in-tensorflow