Exercise:Softmax Regression
From Ufldl
(→Step 5: Testing) |
(→Step 4: Learning parameters) |
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=== Step 4: Learning parameters === | === Step 4: Learning parameters === | ||
| - | Now that you've verified that your gradients are correct, you can train your softmax model using the function <tt>softmaxTrain</tt> in <tt>softmaxTrain.m</tt>. <tt>softmaxTrain</tt> which uses the L-BFGS algorithm, in the function <tt>minFunc</tt>. Training the model on the entire MNIST training set of 60000 28x28 images should be rather quick, and take less than | + | Now that you've verified that your gradients are correct, you can train your softmax model using the function <tt>softmaxTrain</tt> in <tt>softmaxTrain.m</tt>. <tt>softmaxTrain</tt> which uses the L-BFGS algorithm, in the function <tt>minFunc</tt>. Training the model on the entire MNIST training set of 60000 28x28 images should be rather quick, and take less than 5 minutes for 100 iterations. |
Factoring <tt>softmaxTrain</tt> out as a function means that you will be able to easily reuse it to train softmax models on other data sets in the future by invoking the function with different parameters. | Factoring <tt>softmaxTrain</tt> out as a function means that you will be able to easily reuse it to train softmax models on other data sets in the future by invoking the function with different parameters. | ||
