training_matrix_N=1e4_Ninput=100: An example training set of images for training sparse...

Description Usage Format Examples

Description

This is an example set training.matrix of 5000 image patches of 10 by 10 pixels, randomly cropped from a set of 10 decoloured nature photos. The rows of training.matrix correspond to the training examples, the columns correspond to pixels of the image patches.

Usage

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data('training_matrix_N=5e3_Ninput=100')

Format

The format is: chr "training_matrix_N=5e3_Ninput=100"

Examples

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data('training_matrix_N=5e3_Ninput=100') ## load the example training.matrix

## Set up the autoencoder architecture:
nl=3                          ## number of layers (default is 3: input, hidden, output)
unit.type = "logistic"        ## specify the network unit type, i.e., the unit's 
                              ## activation function ("logistic" or "tanh")
Nx.patch=10                   ## width of training image patches, in pixels
Ny.patch=10                   ## height of training image patches, in pixels
N.input = Nx.patch*Ny.patch   ## number of units (neurons) in the input layer (one unit per pixel)
N.hidden = 10*10              ## number of units in the hidden layer
lambda = 0.0002               ## weight decay parameter     
beta = 6                      ## weight of sparsity penalty term 
rho = 0.01                    ## desired sparsity parameter
epsilon <- 0.001              ## a small parameter for initialization of weights 
                              ## as small gaussian random numbers sampled from N(0,epsilon^2)
max.iterations = 2000         ## number of iterations in optimizer

## Train the autoencoder on training.matrix using BFGS optimization method 
## (see help('optim') for details):

## Not run: 
autoencoder.object <- autoencode(X.train=training.matrix,nl=nl,N.hidden=N.hidden,
          unit.type=unit.type,lambda=lambda,beta=beta,rho=rho,epsilon=epsilon,
          optim.method="BFGS",max.iterations=max.iterations,
          rescale.flag=TRUE,rescaling.offset=0.001)
          
## End(Not run)

          
## Extract weights W and biases b from autoencoder.object:
W <- autoencoder.object$W
b <- autoencoder.object$b
## Visualize learned features of the autoencoder:
visualize.hidden.units(autoencoder.object,Nx.patch,Ny.patch)

autoencoder documentation built on May 29, 2017, 6:44 p.m.

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