Stacked Autoencoders
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===Training=== | ===Training=== | ||
| - | A good way to obtain good parameters for a stacked autoencoder is to use greedy layer-wise training. To do this, first train the first layer on raw input to obtain parameters | + | A good way to obtain good parameters for a stacked autoencoder is to use greedy layer-wise training. To do this, first train the first layer on raw input to obtain parameters <math>W^{(1,1)}, W^{(1,2)}, b^{(1,1)}, b^{(1,2)}</math>. Use the first layer to transform the raw input into a vector consisting of activation of the hidden units, A. Train the second layer on this vector to obtain parameters <math>W^{(2,1)}, W^{(2,2)}, b^{(2,1)}, b^{(2,2)}</math>. Repeat for subsequent layers, using the output of each layer as input for the subsequent layer. |
This method trains the parameters of each layer individually while freezing parameters for the remainder of the model. To produce better results, after this phase of training is complete, [[Fine-tuning Stacked AEs | fine-tuning]] using backpropagation can be used to improve the results by tuning the parameters of all layers are changed at the same time. | This method trains the parameters of each layer individually while freezing parameters for the remainder of the model. To produce better results, after this phase of training is complete, [[Fine-tuning Stacked AEs | fine-tuning]] using backpropagation can be used to improve the results by tuning the parameters of all layers are changed at the same time. | ||
