Tag Archives: Softmax

A Simple Deep Network

During this spring break, I worked on building a simple deep network, which has two parts, sparse autoencoder and softmax regression. The method is exactly the same as the “Building Deep Networks for Classification” part in UFLDL tutorial. For better understanding it, I re-implemented it using C++ and OpenCV.  GENERAL OUTLINE Read dataset (including training data […]

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Softmax Regression (with OpenCV)

This is the same algorithm with the previous SOFTMAX REGRESSION post. Because I’m going to try to build deeper neural networks for images, so as a review of OpenCV programming, I rewrote the Softmax regression code using OpenCV mat, instead of Armadillo. I used Matlab, Octave, Armadillo a lot these days, it is kind of […]

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[UFLDL Exercise] Implement deep networks for digit classification

I’m learning Prof. Andrew Ng’s Unsupervised Feature Learning and Deep Learning tutorial, This is the 6th exercise, which is a combination of Sparse Autoencoder and Softmax regression algorithm, and fine-tuning algorithm. It builds a 2-hidden layers sparse autoencoder net and one layer Softmax regression, we first train this network layer by layer, from left to right, then […]

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[UFLDL Exercise] Self-Taught Learning

I’m learning Prof. Andrew Ng’s Unsupervised Feature Learning and Deep Learning tutorial, This is the 5th exercise, which is a combination of Sparse Autoencoder and Softmax regression algorithm. It uses the features trained by sparse autoencoder as training input of Softmax regression, and builds a classifier which have more accuracy than regular softmax regression. I’ll not go through […]

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Softmax Regression

WHAT IS SOFTMAX Softmax regression always as one module in a deep learning network, and most likely to be the last module, the output module. What is it? It is a generalized version of logistic regression. Just like logistic regression, it belongs to supervised learning, and the superiority is, the class label y can be more than […]

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