Exercise: Implement deep networks for digit classification

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(Overview)
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===Overview===
===Overview===
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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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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 and further finetune the two layers.
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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.
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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 backpropogation through both layers. We will see that fine-tuning significantly improves the model's performance.
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In the file [http://ufldl.stanford.edu/wiki/resources/stackedae_exercise.zip stackedae_exercise.zip], 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.
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In the file [http://ufldl.stanford.edu/wiki/resources/stackedae_exercise.zip stackedae_exercise.zip], we have provided some starter code. You will need to complete the code in  '''<tt>stackedAECost.m</tt>''', '''<tt>stackedAEPredict.m</tt>''' and '''<tt>stackedAEExercise.m</tt>'''. We have also provided <tt>params2stack.m</tt> and <tt>stack2params.m</tt> which you might find helpful in constructing deep networks.
=== Dependencies ===
=== Dependencies ===

Revision as of 05:02, 10 May 2011

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