Self-Taught Learning
From Ufldl
(→Overview) |
(→Learning features) |
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[[File:STL_SparseAE.png]] | [[File:STL_SparseAE.png]] | ||
| - | Having trained the parameters <math>\textstyle W^{(1)}, b^{(1)}, W^{(2)} b^{(2)}</math> of this model, | + | Having trained the parameters <math>\textstyle W^{(1)}, b^{(1)}, W^{(2)}, b^{(2)}</math> of this model, |
given any new input <math>\textstyle x</math>, we can now compute the corresponding vector of | given any new input <math>\textstyle x</math>, we can now compute the corresponding vector of | ||
activations <math>\textstyle a</math> of the hidden units. As we saw previously, this often gives a | activations <math>\textstyle a</math> of the hidden units. As we saw previously, this often gives a | ||
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Given a test example <math>\textstyle x_{\rm test}</math>, we would then follow the same procedure: | Given a test example <math>\textstyle x_{\rm test}</math>, we would then follow the same procedure: | ||
For feed it to the autoencoder to get <math>\textstyle a_{\rm test}</math>. Then, feed | For feed it to the autoencoder to get <math>\textstyle a_{\rm test}</math>. Then, feed | ||
| - | either <math>\textstyle a_{\rm test}</math> or <math>\textstyle (x_{\rm test}, a_{\rm test})</math> to the trained classifier to get a prediction. | + | either <math>\textstyle a_{\rm test}</math> or <math>\textstyle (x_{\rm test}, a_{\rm test})</math> to the trained classifier to get a prediction. |
== On pre-processing the data == | == On pre-processing the data == | ||
