Added bias learning to backprop
and modified softmax layer act function
hanged the cost function in backprop.py (line 22) to use cross-entropy instead of squared error
Started gradcheck
Added regularisation to backprop and errors.
Made RBM a subclass of NeuralNet

- Moved feature_map() to NeuralNet
- Moved draw() to network.py
- RBM now extends its non-specific functionality from RBM
Made RBM a subclass of NeuralNet
Regularisation moved out of backprop, into train. Default decay rate is now 0, i.e no regularisation.
Finished gradient checking. To use it just run testNet() and it'll get the derivatives of a small net from backprop and gradcheck for you to compare. Also added a setter in network.py
Moved some more functionality to network.py and added BinaryStochasticLayer to layer.py
Created add_layer method in NeuralNet and moved random initialization heuristic to its own function
Created DBN class and added simple methods to RBM
Started metrics module. Learn curve is done, moved gradcheck and error from backprop to metrics.
Minor change. Added tanh layer class to layer module. Pretty sure the derivative's correct.
Repurposed rbmmachine to rbmstack and added switch_type to Layer
Additions to metrics module.
Added load_from_matfile to rbmstack
Added dbntest module
Wake-Sleep and Contrastive wake-sleep. I have no doubts that it's full of bugs, since I still didn't get around to testing it. Just thought I'd upload what I've done
Fixed superficial errors in __untie_weights__ and wake_sleep
Made probs var in binarystochastic layer