def main(num_epochs=200,
learning_rate=0.005,
momentum=0.5,
log_interval=500,
*args,
**kwargs):
train_loader, test_loader = loaders.loader(batch_size_train=100,
batch_size_test=1000)
# Train the model
total_step = len(train_loader)
curr_lr1 = learning_rate
model1 = VGG().to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer1 = torch.optim.Adam(model1.parameters(), lr=learning_rate)
# Train the model
total_step = len(train_loader)
best_accuracy1 = 0
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.to(device)
labels = labels.to(device)
# Forward
outputs = model1(images)
loss1 = criterion(outputs, labels)
# Backward and optimize
optimizer1.zero_grad()
loss1.backward()
optimizer1.step()
if i == 499:
print(
"Ordinary Epoch [{}/{}], Step [{}/{}] Loss: {:.4f}".format(
epoch + 1, num_epochs, i + 1, total_step,
loss1.item()))
# Test the model
model1.eval()
with torch.no_grad():
correct1 = 0
total1 = 0
for images, labels in test_loader:
images = images.to(device)
labels = labels.to(device)
outputs = model1(images)
_, predicted = torch.max(outputs.data, 1)
total1 += labels.size(0)
correct1 += (predicted == labels).sum().item()
if best_accuracy1 >= correct1 / total1:
curr_lr1 = learning_rate * np.asscalar(
pow(np.random.rand(1), 3))
update_lr(optimizer1, curr_lr1)
print('Test Accuracy of NN: {} % Best: {} %'.format(
100 * correct1 / total1, 100 * best_accuracy1))
else:
best_accuracy1 = correct1 / total1
net_opt1 = model1
print('Test Accuracy of NN: {} % (improvement)'.format(
100 * correct1 / total1))
model1.train()
models = [ "VGG3D", "ResNet3D", "ResNet2D", "VGG"]
model_id = 0 # Change this to correspond to the model in the list
if model_id == 0:
model = VGG3D()
elif model_id == 1:
model = resnet34()
elif model_id == 2:
model = ResNet2D(3)
elif model_id == 3:
model = VGG(3)
train_size = 15
test_size = 15
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.train()
model = model.to(device)
model = torch.load("model_m.pt")
model.eval()
train_loader = get_data_loader(train = True, batch_size = train_size,
split = 'train', model = models[model_id])
test_loader = get_data_loader(train = False, batch_size = test_size,
split = 'test', model = models[model_id])
_ , train_acc = test(model, train_loader, device)
print("Final Train Accuracy: ", train_acc)
_ , test_acc = test(model, test_loader, device)
print("Final Accuracy: ", test_acc)