Fine-tuning Stacked AEs

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(Recap of the Backpropagation Algorithm)
(Recap of the Backpropagation Algorithm)
Line 16: Line 16:
= - (\nabla_{a^{n_l}}J) \bullet f'(z^{(n_l)})
= - (\nabla_{a^{n_l}}J) \bullet f'(z^{(n_l)})
\end{align}</math>
\end{align}</math>
-
::For the softmax layer, we have <math>\delta^{n_l} = \theta^T(I-P)</math> where <math>I</math> is the input labels and <math>P</math> is the predicted labels.
+
::For the softmax layer, we have <math>\nabla J = \theta^T(I-P)</math> where <math>I</math> is the input labels and <math>P</math> is the predicted labels.
: 3. For <math>\textstyle l = n_l-1, n_l-2, n_l-3, \ldots, 2</math>  
: 3. For <math>\textstyle l = n_l-1, n_l-2, n_l-3, \ldots, 2</math>  
::Set
::Set

Revision as of 01:33, 22 April 2011

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