# SparseCategoricalcrossEntropy(from_logits=True) internally apply softmax?

Regarding Tensorflow/Keras SparseCategoricalcrossEntropy.

SparseCategoricalcrossEntropy(from_logits=True) expects the logits that has not been normalized by softmax. Then does SparseCategoricalcrossEntropy applies softmax internally?

I suppose applying softmax would not be required internally as taking argmax would give the index expected.

Yes, Softmax function is called when logit=True
Infact, if we check the keras code [Link], the softmax output is ignored in every condition and tf.nn.sparse_softmax_cross_entropy_with_logits is called. This function calculate softmax prior to cross_entropy as explained [Here]