Exercise: Implement deep networks for digit classification
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| - | == | + | ===Overview=== |
| - | In this | + | In this exercise, you will use a stacked autoencoder for digit classification. This exercise is very similar to the self-taught learning exercise, in which we trained a digit classifier using a autoencoder layer followed by a softmax layer. The only difference in this exercise is that we will be using two autoencoder layers instead of one. |
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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. | ||
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. You will need to edit <tt>stackedAECost.m</tt>. You should also read <tt>stackedAETrain.m</tt> and ensure that you understand the steps. | ||
