Neural Network Softmax (cross-entropy) Backpropagation Derivation Calculus
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I am trying to derive the backpropagation gradients when using softmax in the output layer with Cross-entropy Loss function. Can someone please explain why we did a Summation in the partial Derivative of Softmax below ( why not a chain rule product ) ?
Thanks
neural-network backpropagation math
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$begingroup$
I am trying to derive the backpropagation gradients when using softmax in the output layer with Cross-entropy Loss function. Can someone please explain why we did a Summation in the partial Derivative of Softmax below ( why not a chain rule product ) ?
Thanks
neural-network backpropagation math
New contributor
$endgroup$
add a comment |
$begingroup$
I am trying to derive the backpropagation gradients when using softmax in the output layer with Cross-entropy Loss function. Can someone please explain why we did a Summation in the partial Derivative of Softmax below ( why not a chain rule product ) ?
Thanks
neural-network backpropagation math
New contributor
$endgroup$
I am trying to derive the backpropagation gradients when using softmax in the output layer with Cross-entropy Loss function. Can someone please explain why we did a Summation in the partial Derivative of Softmax below ( why not a chain rule product ) ?
Thanks
neural-network backpropagation math
neural-network backpropagation math
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edited 10 mins ago
Hamza El Bouatmani
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asked 2 hours ago
Hamza El BouatmaniHamza El Bouatmani
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Hamza El Bouatmani is a new contributor. Be nice, and check out our Code of Conduct.
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