def main(params):
(Xtrain, ytrain), (Xval, yval), (Xtest, ytest) = df.datasets.mnist.data()
model = twinnet()
#criterion = df.ClassNLLCriterion()
criterion = df.BCECriterion()
optimiser = df.optimizers.AdaDelta(rho=0.95)
for epoch in range(100):
model.training()
train(Xtrain, ytrain, model, optimiser, criterion, epoch,
params['batch_size'], 'train')
if epoch % 3 == 0:
train(Xtrain, ytrain, model, optimiser, criterion, epoch,
params['batch_size'], 'stats')
model.evaluate()
validate(Xval, yval, model, epoch, params['batch_size'])
