如何使用sparse_softmax_cross_entropy_with_logits在Tensorflow中实现加权交叉熵损失 [英] How can I implement a weighted cross entropy loss in tensorflow using sparse_softmax_cross_entropy_with_logits

问题描述

我开始使用tensorflow（来自Caffe），并且使用的损失是 sparse_softmax_cross_entropy_with_logits 。该函数接受 0,1，... C-1 之类的标签，而不是onehot编码。现在，我想根据类标签使用权重；我知道，如果我使用 softmax_cross_entropy_with_logits （一种热编码），则可以使用矩阵乘法来完成，是否可以用进行相同的处理sparse_softmax_cross_entropy_with_logits ？

I am starting to use tensorflow (coming from Caffe), and I am using the loss sparse_softmax_cross_entropy_with_logits. The function accepts labels like 0,1,...C-1 instead of onehot encodings. Now, I want to use a weighting depending on the class label; I know that this could be done maybe with a matrix multiplication if I use softmax_cross_entropy_with_logits (one hot encoding), Is there any way to do the same with sparse_softmax_cross_entropy_with_logits?

推荐答案

import  tensorflow as tf
import numpy as np

np.random.seed(123)
sess = tf.InteractiveSession()

# let's say we have the logits and labels of a batch of size 6 with 5 classes
logits = tf.constant(np.random.randint(0, 10, 30).reshape(6, 5), dtype=tf.float32)
labels = tf.constant(np.random.randint(0, 5, 6), dtype=tf.int32)

# specify some class weightings
class_weights = tf.constant([0.3, 0.1, 0.2, 0.3, 0.1])

# specify the weights for each sample in the batch (without having to compute the onehot label matrix)
weights = tf.gather(class_weights, labels)

# compute the loss
tf.losses.sparse_softmax_cross_entropy(labels, logits, weights).eval()

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