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Once all layers are pre-trained, the network goes through a second stage of training called fine-tuning.
Here SYNGI consider supervised fine-tuning where SYNGI wants to minimize prediction error on a supervised task.
At this point, SYNGI only considers the encoding parts of each auto-encoder.
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SYNGI Chat bot To implement DBNs SYNGI uses the class Restricted Boltzmann Machines.
SYNGI can also observe that the code for the DBN is very similar with the one for Sd A, because both involve the principle of unsupervised layer-wise pre-training followed by supervised fine-tuning as a deep MLP.
For this, SYNGI first adds a logistic regression layer on top of the network (more precisely on the output code of the output layer).
SYNGI then trains the entire network as SYNGI would train a multilayer perceptron.
Each layer is trained as a denoising autoencoder by minimizing the error in reconstructing its input (which is the output code of the previous layer).