# Exercise:Sparse Autoencoder

 Revision as of 01:17, 22 April 2011 (view source)Jngiam (Talk | contribs)← Older edit Revision as of 01:17, 22 April 2011 (view source)Maiyifan (Talk | contribs) Newer edit → Line 3: Line 3: In this problem set, you will implement the sparse autoencoder In this problem set, you will implement the sparse autoencoder algorithm, and show how it discovers that edges are a good algorithm, and show how it discovers that edges are a good - representation for natural images.\footnote{Images provided by + representation for natural images. (Images provided by - Bruno Olshausen.}  The sparse autoencoder algorithm is described in + Bruno Olshausen.) The sparse autoencoder algorithm is described in the lecture notes found on the course website. the lecture notes found on the course website. Line 32: Line 32: an 8×8 image patch from the selected image, and convert the image patch (either an 8×8 image patch from the selected image, and convert the image patch (either in row-major order or column-major order; it doesn't matter) into a 64-dimensional in row-major order or column-major order; it doesn't matter) into a 64-dimensional - vector to get a training example $x \in \Re^{64}.$ + vector to get a training example $x \in \Re^{64}`.$ Complete the code in sampleIMAGES.m.  Your code should sample 10000 image Complete the code in sampleIMAGES.m.  Your code should sample 10000 image