84
edits
Changes
→Implementation of a Neural Network
optimizer.step()
print(loss)
''' 6. Iterate through the trainset data again to verify current accuracy rating of our neural network using the training data
''' we just trained on.
correct = 0
total = 0
with torch.no_grad():
for data in trainset:
X, y = data
output = net(X.view(-1,784))
for idx,i in enumerate(output):
if torch.argmax(i) == y[idx]:
correct += 1
total += 1
print("Accuracy: ", round(correct/total, 3))
== Getting Started With Jupyter ==
