Exercise:Convolution and Pooling

From Ufldl

Jump to: navigation, search
(Step 3a: Convolution)
(Step 5: Test classifier)
Line 164: Line 164:
=== Step 5: Test classifier ===
=== Step 5: Test classifier ===
-
Now that you have a trained softmax classifier, you can see how well it performs on the test set. This section contains code that will load the test set (which is a smaller part of the STL10 dataset, specifically, 3200 rescaled 64x64 images from 4 different classes) and obtain the pooled, convolved features for the images using the functions <tt>cnnConvolve</tt> and <tt>cnnPool</tt> which you wrote earlier, as well as the preprocessing matrices <tt>ZCAWhite</tt> and <tt>meanImage</tt> which were computed earlier in preprocessing the training images. These pooled features will then be run through the softmax classifier, and the accuracy of the predictions will be computed. Because object recognition is a difficult task, the accuracy will be relatively low - we obtained an accuracy of around XX%.
+
Now that you have a trained softmax classifier, you can see how well it performs on the test set. This section contains code that will load the test set (which is a smaller part of the STL10 dataset, specifically, 3200 rescaled 64x64 images from 4 different classes) and obtain the pooled, convolved features for the images using the functions <tt>cnnConvolve</tt> and <tt>cnnPool</tt> which you wrote earlier, as well as the preprocessing matrices <tt>ZCAWhite</tt> and <tt>meanImage</tt> which were computed earlier in preprocessing the training images. These pooled features will then be run through the softmax classifier, and the accuracy of the predictions will be computed. You should expect to get an accuracy of around 77-78%.

Revision as of 07:58, 20 May 2011

Personal tools