How can I implement a weighted cross entropy loss in tensorflow using sparse_softmax_cross_entropy_with_logits

Question

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,

Answer 1

The class weights are multiplied by the logits, so that still works for sparse_softmax_cross_entropy_with_logits. Refer to this solution for "Loss function for class imbalanced binary classifier in Tensor flow."

As a side note, you can pass weights directly into sparse_softmax_cross_entropy

tf.contrib.losses.sparse_softmax_cross_entropy(logits, labels, weight=1.0, scope=None)

This method is for cross-entropy loss using

tf.nn.sparse_softmax_cross_entropy_with_logits.

Weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weight is a tensor of size [batch_size], then the loss weights apply to each corresponding sample.