FCNN4R-package: Fast Compressed Neural Networks for R

In FCNN4R: Fast Compressed Neural Networks for R

Description

Provides an interface to kernel routines from the FCNN C++ library. FCNN is based on a completely new Artificial Neural Network representation that offers unmatched efficiency, modularity, and extensibility. FCNN4R provides standard teaching (backpropagation, Rprop, simulated annealing, stochastic gradient) and pruning algorithms (minimum magnitude, Optimal Brain Surgeon), but it is first and foremost an efficient computational engine. Users can easily implement their algorithms by taking advantage of fast gradient computing routines, as well as network reconstruction functionality (removing weights and redundant neurons, reordering inputs, merging networks). Networks can be exported to C functions in order to integrate them into virtually any software solution.

Author(s)

Grzegorz Klima <[e-mail]>

References

G. Klima. A new approach towards implementing artificial neural networks. Technical Report, http://fcnn.sourceforge.net/, 2013.

Examples

# set up the XOR problem inputs and outputs
inp <- c(0, 0, 1, 1, 0, 1, 0, 1)
dim(inp) <- c(4, 2)
outp <- c(0, 1, 1, 0)
dim(outp) <- c(4, 1)
# create a 2-6-1 network
net <- mlp_net(c(2, 6, 1))
# set activation function in all layers
net <- mlp_set_activation(net, layer = "a", "sigmoid")
# randomise weights
net <- mlp_rnd_weights(net)
# tolerance level
tol <- 0.5e-4
# teach using Rprop, assign trained network and plot learning history
netmse <- mlp_teach_rprop(net, inp, outp, tol_level = tol,
                          max_epochs = 500, report_freq = 10)
net <- netmse$net
plot(netmse$mse, type = 'l')
# plot network with weights
mlp_plot(net, TRUE)
# if the algorithm had converged, prune using Optimal Brain Surgeon and plot
if (mlp_mse(net, inp, outp) <= tol) {
    net <- mlp_prune_obs(net, inp, outp, tol_level = tol,
                         max_reteach_epochs = 500, report = TRUE)[[1]]
    mlp_plot(net, TRUE)
}
# check network output
round(mlp_eval(net, inp), digits = 3)

FCNN4R documentation built on May 29, 2017, 4:26 p.m.