1
$\begingroup$

I was following this basic TensorFlow Image Classification problem, where images of flowers have to be classified into one of 5 possible classes. The labels in the training set are not one-hot encoded, and are individual numbers: 1,2,3,4 or 5 (corresponding to 5 classes). The final layer of the ConvNet however has num_class number of units.

Wouldn't there be a dimension mismatch while computing the loss, since you are finding the difference between a [num_class, 1] (predicted label) dimensioned vector and a [1, 1] (true label) dimensioned vector?

Does the Keras backend automatically convert the labels into one-hot vectors?

Thank you in advance!

Improve this question
$\endgroup$

1 Answer 1

Reset to default
0
$\begingroup$

The loss function handles the conversion. TensorFlow has a SparseCategoricalCrossentropy and a CategoricalCrossentropy loss function. The first expect your labels to be provided as integers, where the latter expects one-hot encodings. In the given example, they use the SparseCategoricalCrossentropy loss function, therefore it is ok to supply your labels as integers.

Improve this answer
$\endgroup$
1
  • $\begingroup$ Thank you so much for that pointer! I did not realize that there were two types of these loss functions. $\endgroup$
    – commanderScraps
    Commented Nov 23, 2020 at 14:11

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.