Exercise:Self-Taught Learning
From Ufldl
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===Step One: Generate the input and test data sets=== | ===Step One: Generate the input and test data sets=== | ||
| - | Download and decompress <tt>stl_exercise.zip</tt>, which contains starter code for this exercise. Additionally, you will need to download the datasets from the MNIST Handwritten Digit Database for this project. | + | Download and decompress <tt>[http://ufldl.stanford.edu/wiki/resources/stl_exercise.zip stl_exercise.zip]</tt>, which contains starter code for this exercise. Additionally, you will need to download the datasets from the MNIST Handwritten Digit Database for this project. |
To separate the the cases, you will need to fill in <tt>filterData.m</tt>, which should return a subset of the data set containing examples with labels within a certain bound. After implementing this function, run this step. To test your function, the script will output the number of examples in the supervised training and the STL training data set, which should be 60000 and 30596 respectively. | To separate the the cases, you will need to fill in <tt>filterData.m</tt>, which should return a subset of the data set containing examples with labels within a certain bound. After implementing this function, run this step. To test your function, the script will output the number of examples in the supervised training and the STL training data set, which should be 60000 and 30596 respectively. | ||
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===Step Four: Training and testing the logistic regression model=== | ===Step Four: Training and testing the logistic regression model=== | ||
| - | After completing these steps, running the entire script in <tt>trainSelfTaught.m</tt> will use your sparse autoencoder to train the logistic model, then measure how well this system performs on the test set. Statistics about the model will be displayed afterwards. If you've done all the steps correctly, you should get an accuracy of about | + | After completing these steps, running the entire script in <tt>trainSelfTaught.m</tt> will use your sparse autoencoder to train the logistic model, then measure how well this system performs on the test set. Statistics about the model will be displayed afterwards. If you've done all the steps correctly, you should get an accuracy of about X percent. |
[[Category:Exercises]] | [[Category:Exercises]] | ||
