The architecture of the network is flexible (number of layers, input and output units). The neural network classifies using regularized logistic regression. The gradients are computed with backpropagation and are checked numerically. The network is optimized with the   SciPy  nonlinear conjugate gradient algorithm. When several  regularization parameters are used, the optimization is parallelized. Finally, learning curves are computed to evaluate the performance of the neural network.