stack
Class: Autoencoder
Stack encoders from several autoencoders together
Description
stackednet = stack(autoenc1,autoenc2,...) returns
a network object created by stacking the encoders
of the autoencoders, autoenc1, autoenc2,
and so on.
Input Arguments
Output Arguments
Examples
Create a Stacked Network
Load the training data.
[X,T] = iris_dataset;
Train an autoencoder with a hidden layer of size 5 and a linear transfer function for the decoder. Set the L2 weight regularizer to 0.001, sparsity regularizer to 4 and sparsity proportion to 0.05.
hiddenSize = 5; autoenc = trainAutoencoder(X, hiddenSize, ... 'L2WeightRegularization', 0.001, ... 'SparsityRegularization', 4, ... 'SparsityProportion', 0.05, ... 'DecoderTransferFunction','purelin');
Extract the features in the hidden layer.
features = encode(autoenc,X);
Train a softmax layer for classification using the features .
softnet = trainSoftmaxLayer(features,T);
Stack the encoder and the softmax layer to form a deep network.
stackednet = stack(autoenc,softnet);
View the stacked network.
view(stackednet);
Tips
The size of the hidden representation of one autoencoder must match the input size of the next autoencoder or network in the stack.
The first input argument of the stacked network is the input argument of the first autoencoder. The output argument from the encoder of the first autoencoder is the input of the second autoencoder in the stacked network. The output argument from the encoder of the second autoencoder is the input argument to the third autoencoder in the stacked network, and so on.
The stacked network object
stacknetinherits its training parameters from the final input argumentnet1.