Change the way coffee breaks work and add statistics accumulator.
This commit changes the way coffee breaks work -> each break now returns a dict in which statistics are recorded.
These dicts can then be recoreded by the AccumulateStatistics break and saved to a hdf5 file.
This is quite useful for monitoring training.
add a new tool: plot_statistics.jl
it can be used to plot data that was written by the AccumulateStatistics break
and even works during training.
This is analogous to the plot_monitor.py script of pylearn2.
add an example script that reproduces the 105 errors on mnist fom Nitishs dropout paper.
This is a first quick sketch that gives the desired result of ~100 errors on MNIST
using a fully connected network of 2 layers a 1200 units with dropout.
NOTE: this is still missing the constraint on the L2 norm of the weights, will add them in a later comit.
add comments to dropout example, plot_statistics.jl now supports multiple files for plotting
Merge pull request #6 from stokasto/accumulate_statistics

Add coffee break to allow for accumulation of statistics and reproduce ~100 errors on MNIST