def main(params):
train_set, valid_set, test_set = df.datasets.mnist.data()
train_set_x, train_set_y = train_set
test_set_x, test_set_y = test_set
model = lenet()
criterion = df.ClassNLLCriterion()
optimiser = df.SGD(lr=params['lr'])
for epoch in range(100):
model.training()
train(train_set_x, train_set_y, model, optimiser, criterion, epoch,
params['batch_size'], 'train')
train(train_set_x, train_set_y, model, optimiser, criterion, epoch,
params['batch_size'], 'stats')
model.evaluate()
validate(test_set_x, test_set_y, model, epoch, params['batch_size'])
from test import test
from model import net, lenet2
if __name__ == "__main__":
print("THIS IS JUST AN EXAMPLE.")
print("Please don't take these numbers as a benchmark.")
print("While the optimizer's parameters have been grid-searched,")
print(
"a fair comparison would run all experiments multiple times AND RUN MORE THAN FIVE EPOCHS."
)
batch_size = 64
(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()
