Why do people use CrossEntropyLoss and not just a softmax probability as the loss?
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I don't understand why one would add additional complexity to log, probabilities for the loss function of a classification Neural Network. What benefit does that have, as opposed to just using the 0-1.0 values(probabilities of a class) you get from the softmax function at the final layer?
Does this add extra non-linearity that we don't understand why it does good, but just happens to do good a lot of times since we give the Neural Net some more complexity?
neural-network classification multiclass-classification loss-function probability
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katiex7katiex7
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$begingroup$
I don't understand why one would add additional complexity to log, probabilities for the loss function of a classification Neural Network. What benefit does that have, as opposed to just using the 0-1.0 values(probabilities of a class) you get from the softmax function at the final layer?
Does this add extra non-linearity that we don't understand why it does good, but just happens to do good a lot of times since we give the Neural Net some more complexity?
neural-network classification multiclass-classification loss-function probability
asked 5 mins ago
katiex7katiex7
101
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katiex7 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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$endgroup$
add a comment |
$begingroup$
I don't understand why one would add additional complexity to log, probabilities for the loss function of a classification Neural Network. What benefit does that have, as opposed to just using the 0-1.0 values(probabilities of a class) you get from the softmax function at the final layer?
Does this add extra non-linearity that we don't understand why it does good, but just happens to do good a lot of times since we give the Neural Net some more complexity?
neural-network classification multiclass-classification loss-function probability
asked 5 mins ago
katiex7katiex7
101
New contributor
katiex7 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I don't understand why one would add additional complexity to log, probabilities for the loss function of a classification Neural Network. What benefit does that have, as opposed to just using the 0-1.0 values(probabilities of a class) you get from the softmax function at the final layer?
Does this add extra non-linearity that we don't understand why it does good, but just happens to do good a lot of times since we give the Neural Net some more complexity?
neural-network classification multiclass-classification loss-function probability
neural-network classification multiclass-classification loss-function probability
asked 5 mins ago
katiex7katiex7
101
New contributor
katiex7 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
katiex7katiex7
101
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asked 5 mins ago
katiex7katiex7
101
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asked 5 mins ago
katiex7katiex7
101
asked 5 mins ago
katiex7katiex7
101
101
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