Exercise:Vectorization
From Ufldl
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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/ | + | 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. |
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