Exercise:Softmax Regression

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(Step 4: Learning parameters)
(Step 4: Learning parameters)
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=== Step 4: Learning parameters ===
=== Step 4: Learning parameters ===
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Now that you've verified that your gradients are correct, you can train your softmax model using the function <tt>softmaxTrain</tt> in softmaxTrain.m. <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 3 minutes for 100 iterations.
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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 3 minutes for 100 iterations.
=== Step 5: Cross-validation ===
=== Step 5: Cross-validation ===

Revision as of 01:07, 25 April 2011

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