Exercise: Implement deep networks for digit classification
From Ufldl
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The code you have already implemented will allow you to stack various layers and perform layer-wise training. However, to perform fine-tuning, you will need to implement back-propogation as well. We will see that fine-tuning significantly improves the model's performance. | The code you have already implemented will allow you to stack various layers and perform layer-wise training. However, to perform fine-tuning, you will need to implement back-propogation as well. We will see that fine-tuning significantly improves the model's performance. | ||
| - | In the file <tt>stacked_ae_exercise.zip</tt>, we have provided some starter code. You will need to edit <tt>stackedAECost.m</tt>. You should also read <tt>stackedAETrain.m</tt> and ensure that you understand the steps. | + | In the file <tt>stacked_ae_exercise.zip</tt>, we have provided some starter code [http://ufldl.stanford.edu/wiki/resources/sparseae_exercise.zip]. You will need to edit <tt>stackedAECost.m</tt>. You should also read <tt>stackedAETrain.m</tt> and ensure that you understand the steps. |
=== Step 0: Initialize constants and parameters === | === Step 0: Initialize constants and parameters === | ||
