import nolearn
from lasagne.layers import DenseLayer, InputLayer, DropoutLayer
from lasagne.nonlinearities import softmax, rectify, sigmoid
from lasagne.updates import nesterov_momentum, rmsprop, adagrad, sgd, adadelta
from nolearn.lasagne import NeuralNet, TrainSplit


layers0 = [('input', InputLayer),
           ('hidden1', DenseLayer),
           ('dropout1', DropoutLayer),
           ('hidden2', DenseLayer),
           ('dropout2', DropoutLayer),
           ('output', DenseLayer)]

net0 = NeuralNet(layers=layers0,
                 custom_score = custom_score,
                 input_shape=(None, train.shape[1]),
                 hidden1_num_units=50,
                 dropout1_p=0.1,
                 hidden2_num_units=300,
                 dropout2_p=0.3,

                 output_num_units=num_classes,
                 output_nonlinearity=softmax,

                 update=sgd,
                 update_learning_rate=0.01,

                 train_split = TrainSplit(0.2),
                 verbose=1,
                 regression=True,
                 max_epochs=50)

net0.fit(train, y)