MATLAB Modules
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== MATLAB Modules == | == MATLAB Modules == | ||
| - | '''[[Exercise:Sparse_Autoencoder|Sparse autoencoder]]'' | + | '''[[Exercise:Sparse_Autoencoder|Sparse autoencoder]]''' | [http://ufldl.stanford.edu/wiki/resources/sparseae_exercise.zip sparseae_exercise.zip] |
| + | * checkNumericalGradient.m - Makes sure that computeNumericalGradient is implmented correctly | ||
| + | * computeNumericalGradient.m - Computes numerical gradient of a function (to be filled in) | ||
| + | * display_network.m - Visualizes images or filters for autoencoders as a grid | ||
| + | * initializeParameters.m - Initializes parameters for sparse autoencoder randomly | ||
| + | * sampleIMAGES.m - Samples 8x8 patches from an image matrix (to be filled in) | ||
| + | * sparseAutoencoderCost.m - Calculates cost and gradient of cost function of sparse autoencoder | ||
| + | * train.m - Framework for training and testing sparse autoencoder | ||
| - | |||
| - | '''[[ | + | '''[[Using the MNIST Dataset]]''' | [http://ufldl.stanford.edu/wiki/resources/mnistHelper.zip mnistHelper.zip] |
| + | * loadMNISTImages.m - Returns a matrix containing raw MNIST images | ||
| + | * loadMNISTLabels.m - Returns a matrix containing MNIST labels | ||
| - | |||
| - | '''[[Exercise:PCA_and_Whitening|PCA and Whitening]]''' | + | '''[[Exercise:PCA_and_Whitening|PCA and Whitening]]''' | [http://ufldl.stanford.edu/wiki/resources/pca_exercise.zip pca_exercise.zip] |
| + | * display_network.m - Visualizes images or filters for autoencoders as a grid | ||
| + | * pca_gen.m - Framework for whitening exercise | ||
| + | * sampleIMAGESRAW.m - Returns 8x8 raw unwhitened patches | ||
| - | [http://ufldl.stanford.edu/wiki/resources/ | + | |
| + | '''[[Exercise:Softmax_Regression|Softmax Regression]]''' | [http://ufldl.stanford.edu/wiki/resources/softmax_exercise.zip softmax_exercise.zip] | ||
| - | + | * checkNumericalGradient.m - Makes sure that computeNumericalGradient is implmented correctly | |
| - | + | * display_network.m - Visualizes images or filters for autoencoders as a grid | |
| - | + | * loadMNISTImages.m - Returns a matrix containing raw MNIST images | |
| + | * loadMNISTLabels.m - Returns a matrix containing MNIST labels | ||
| + | * softmaxCost.m - Computes cost and gradient of cost function of softmax | ||
| + | * softmaxTrain.m - Trains a softmax model with the given parameters | ||
| + | * train.m - Framework for this exercise | ||
