(Xtrain, ytrain), (Xval, yval), (Xtest, ytest) = load_mnist()
criterion = df.ClassNLLCriterion()
def run(optim):
progress = make_progressbar('Training with ' + str(optim), 5)
progress.start()
model = net()
model.training()
for epoch in range(5):
train(Xtrain, ytrain, model, optim, criterion, batch_size, 'train')
train(Xtrain, ytrain, model, optim, criterion, batch_size, 'stats')
progress.update(epoch + 1)
progress.finish()
model.evaluate()
nll, _ = test(Xtrain, ytrain, model, batch_size)
_, nerr = test(Xval, yval, model, batch_size)
print("Trainset NLL: {:.2f}".format(nll))
print("Testset errors: {}".format(nerr))
run(df.SGD(lr=1e-1))
run(df.Momentum(lr=1e-2, momentum=0.95))
run(df.Nesterov(lr=1e-2, momentum=0.90))
run(df.AdaGrad(lr=1e-2, eps=1e-4))
run(df.RMSProp(lr=1e-3, rho=0.90, eps=1e-5))
run(df.AdaDelta(rho=0.99, lr=5e-1, eps=1e-4))
model.add(df.Linear(512, 9))
model.add(df.SoftMax())
return model
if __name__ == "__main__":
if __package__ is None: # PEP366
__package__ = "DeepFried2.examples.KaggleOtto"
train_data_x, train_data_y = load_train_data()
train_data_x, valid_data_x, train_data_y, valid_data_y = train_test_split(
train_data_x, train_data_y, train_size=0.85)
model = nnet()
criterion = df.ClassNLLCriterion()
optimiser = df.Momentum(lr=0.01, momentum=0.9)
for epoch in range(1, 1001):
model.training()
if epoch % 100 == 0:
optimiser.hyperparams['lr'] /= 10
train(train_data_x, train_data_y, model, optimiser, criterion, epoch,
100, 'train')
train(train_data_x, train_data_y, model, optimiser, criterion, epoch,
100, 'stats')
model.evaluate()
validate(valid_data_x, valid_data_y, model, epoch, 100)
