Stanford UFLDL教程 MATLAB Modules

2017年10月14日 由 Creater 留言 »

MATLAB Modules

Sparse autoencoder |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 8×8 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 |mnistHelper.zip

  • loadMNISTImages.m – Returns a matrix containing raw MNIST images
  • loadMNISTLabels.m – Returns a matrix containing MNIST labels

PCA and Whitening |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 8×8 raw unwhitened patches

Softmax Regression |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

from: http://ufldl.stanford.edu/wiki/index.php/MATLAB_Modules

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