def train():
train_set = torchvision.datasets.FashionMNIST(
root='./data/FashionMNIST',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor()
])
)
params = OrderedDict(
lr=[0.01, 0.001],
batch_size=[100, 1000],
shuffle=[True, False],
num_workers=[2]
)
runManager = RunManager()
for run in RunBuilder.get_runs(params):
network = finalModel()
loader = DataLoader(
train_set, batch_size=run.batch_size, shuffle=run.shuffle, num_workers=run.num_workers)
optimizer = optim.Adam(network.parameters(), lr=run.lr)
runManager.begin_run(run, network, loader)
for epoch in range(10):
runManager.begin_epoch()
for batch in loader:
images, labels = batch
# support computation based on device type
images = images.to(get_device_type())
labels = labels.to(get_device_type())
preds = network(images)
loss = F.cross_entropy(preds, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
runManager.track_loss(loss)
runManager.track_num_correct(preds, labels)
runManager.end_epoch()
runManager.end_run()
runManager.save('results')
print(f"{splits}")
train_set, val_set = dataset.random_split(init_train_data, splits)
for run in RunBuilder.get_runs(params):
network = MyConvNet()
loader = DataLoader(train_set,
batch_size=run.batch_size,
num_workers=run.num_workers,
shuffle=run.shuffle)
optimizer = optim.Adam(network.parameters(), lr=run.lr)
m.begin_run(run, network, loader)
for epoch in range(50):
m.begin_epoch()
for batch in loader:
print(m.epoch_count)
images, labels = batch
preds = network(images)
loss = F.cross_entropy(preds, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
m.track_loss(loss)
m.track_num_correct(preds, labels)
m.end_epoch()
m.end_run()
m.save('results')
