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@xuhdev
Created May 5, 2020 02:39
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for epoch in range(1): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
# zero the parameter gradients
optimizer.zero_grad()
# forward
outputs = net(inputs)
loss = criterion(outputs, labels)
# backward (differentiate)
loss.backward()
# optimize (update)
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 3000 == 2999:
# print every 3000 mini-batches
print(f'Epoch: {epoch + 1}, Iteration: {i + 1}, loss: {running_loss / 3000}')
running_loss = 0.0
# Test accuracy
correct = 0
total = 0
with torch.no_grad():
for data in test_loader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1) # The label with the maximum probability is predicted
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f'Accuracy of the network on the test images: {(100 * correct / total)} %')
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