Exercise:Vectorization
From Ufldl
| Line 3: | Line 3: | ||
In the previous problem set, we implemented a sparse autoencoder for patches taken from natural images. In this problem set, you will adapt the sparse autoencoder to work on images of handwritten digits. You will be given a working but unvectorized implementation, and your task will be to vectorize a key step to improve its performance. | In the previous problem set, we implemented a sparse autoencoder for patches taken from natural images. In this problem set, you will adapt the sparse autoencoder to work on images of handwritten digits. You will be given a working but unvectorized implementation, and your task will be to vectorize a key step to improve its performance. | ||
| - | In the file <tt>vec_assign.zip</tt>, you will find MATLAB code implementing a sparse autoencoder. To run the code, you will need to download an additional data set from the [http://yann.lecun.com/exdb/mnist/|MNIST handwritten digit database]. Download the file <tt>train-images-idx3-ubyte.gz</tt> and decompress it to the <tt>MNIST/</tt> folder in the project path. You should then be able to run <tt>train.m</tt> and obtain some preliminary results. | + | In the file <tt>vec_assign.zip</tt>, you will find MATLAB code implementing a sparse autoencoder. To run the code, you will need to download an additional data set from the [http://yann.lecun.com/exdb/mnist/|MNIST handwritten digit database]. Download the file <tt>train-images-idx3-ubyte.gz</tt> and decompress it to the <tt>MNIST/</tt> folder in the project path. After obtaining the source images, we have [[[Using the MNIST Dataset | provided functions ]] help you load them up as Matlab matrices. |
| + | |||
| + | <!-- You should then be able to run <tt>train.m</tt> and obtain some preliminary results. --> | ||
=== MNIST === | === MNIST === | ||
