Multilabel image classification with sparse labels in TensorFlow?

Question

I want to perform a multilabel image classification task for n classes. I've got sparse label vectors for each image and each dimension of each label vector is currently encoded in this way:

1.0 ->Label true / Image belongs to this class

-1.0 ->Label false / Image does not contain to this class.

0.0 ->missing value/label

E.g.: V= {1.0,-1.0,1.0, 0.0}

For this example V the model should learn, that the corresponding image should be classified in the first and third class.

My problem is currently how to handle the missing values/labels. I've searched through the issues and found this issue: tensorflow/skflow#113 found here

So could do multilable image classification with: tf.nn.sigmoid_cross_entropy_with_logits(logits, targets, name=None)

but TensorFlow has this error function for sparse softmax, which is used for exclusive classification: tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels, name=None)

So is there something like sparse sigmoid cross entropy? (Couldn't find something) or any suggestions how can I handle my multilabel classification problem with sparse labels.

Answer 1

I used weighted_cross_entropy_with_logits as the loss function with positive weights for 1s.

In my case, all the labels are equally important. But 0 was ten times more likely to be appeared as the value of any label than 1.

So I weighed all the 1s by calling the pos_weight parameter of the aforementioned loss function. I used a pos_weight (= weight on positive values) of 10. By the way, I do not recommend any strategy to calculate the pos_weight. I think it will depend explicitly on the data in hand.

if real label = 1, weighted_cross_entropy = pos_weight * sigmoid_cross_entropy

Weighted cross entropy with logits is same as the Sigmoid cross entropy with logits, except for the extra weight value multiplied to all the targets with a positive real value i.e.; 1.

Theoretically, it should do the job. I am still tuning other parameters to optimize the performance. Will update with performance statistics later.