images = images.to(device)
labels = labels.to(device)
outputs = model(images).to(device)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Print Loss for Tracking Training
if (i+1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
test_image, test_label = next(iter(test_loader))
_, test_predicted = torch.max(model(test_image.to(device)).data, 1)
# Test after Training is done
model.eval() # Set model to Evaluation Mode (Batchnorm uses moving mean/var instead of mini-batch mean/var)
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the {} test images: {} %'.format(len(test_loader)*batch_size, 100 * correct / total))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Prepare Test Data
test_data = torchvision.datasets.MNIST(root='./datasets',
train=False,
transform=transforms.ToTensor())
# Define Test Dataloader
test_loader = torch.utils.data.DataLoader(dataset=test_data,
batch_size=100,
shuffle=True)
# Load Model and Trained Parameters
model_test = ConvNet(10).to(device)
model_test.load_state_dict(torch.load('./model.pth', map_location=device))
model_test.eval()
# Evaluate
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.to(device)
labels = labels.to(device)
outputs = model_test(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the {} test images: {} %'.format(len(test_loader)*100, 100 * correct / total))