Exercise:Convolution and Pooling
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Exercise:Convolution and Pooling
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== Convolution and Pooling == This problem set is divided into two parts. In the first part, you will implement a [[linear decoder]] to learn features on color images from the STL10 dataset. In the second part, you will use these learned features in convolution and pooling for classifying STL10 images. In the file <tt>...</tt> we have provided some starter code. You will need to modify '''<tt>...</tt>''' for this exercise. === Dependencies === The following additional files are required for this exercise: * STL10 dataset You will also need: * <tt>sparseAutoencoderCost.m</tt> (and related functions) from [[Exercise:Sparse Autoencoder]] * <tt>softmaxTrain.m</tt> (and related functions) from [[Exercise:Softmax Regression]] ''If you have not completed the exercises listed above, we strongly suggest you complete them first.'' === Part I: Linear decoder on color images === In all the exercise so far, you have been working only with grayscale images. In this exercise, you will get the opportunity to work with RGB color images for the first time. Conveniently, the fact that an image has three color channels (RGB), rather than a single gray channel, presents little difficulty for the sparse autoencoder. You can just combine the intensities from all the color channels for the pixels into one long vector, as if you were working with a grayscale image with 3x the number of pixels as the original image. === Part II: Convolution and pooling ===
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