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If one hidden layer is not enough, we can obviously extend the autoencoder to more hidden layers. Now our implementation uses 3 hidden layers instead of just one. Any of the hidden layers can be picked as the feature representation but we will make the network symmetrical and use the middle-most layer.
How can we constraint the reconstruction of autoencoders?
The input image is downsampled to give a latent representation of smaller dimensions and force the autoencoder to learn a compressed version of the images. There are other ways we can constraint the reconstruction of an autoencoder than to impose a hidden layer of smaller dimension than the input.
How does an Undercomplete autoencoder help a neural net?
Here, we see that we have an undercomplete autoencoder as the hidden layer dimension (64) is smaller than the input (784). This constraint will impose our neural net to learn a compressed representation of data. If one hidden layer is not enough, we can obviously extend the autoencoder to more hidden layers.
How to change the error term of an autoencoder?
Denoising autoencoder : Rather than adding a penalty to the loss function, we can obtain an autoencoder that learns something useful by changing the reconstruction error term of the loss function. This can be done by adding some noise of the input image and make the autoencoder learn to remove it.
How to train stacked auto encoders layer wise?
Layer-wise pre-training of stacked auto-encoders consists of the following steps: Train the bottommost auto-encoder. After training, remove the decoder layer, construct a new auto-encoder by taking the latent representationof the previous auto-encoder as input. Train the new auto-encoder.
Which is an example of an auto encoder?
Autoencoder, by design, reduces data dimensions by learning how to ignore the noise in the data. Here is an example of the input/output image from the MNIST dataset to an autoencoder. Autoencoders consists of 4 main parts: