diff --git a/TicTacToe_AI/Net/pytorch_ai.py b/TicTacToe_AI/Net/pytorch_ai.py
index efea5ae..701918f 100644
--- a/TicTacToe_AI/Net/pytorch_ai.py
+++ b/TicTacToe_AI/Net/pytorch_ai.py
@@ -4,6 +4,9 @@ import torch.optim as optim
 from torch import nn
 import torch.nn.functional as F
 from tqdm import tqdm
+import wandb
+
+wandb.init(project="tictactoe")
 
 
 def to_set(raw_list):
@@ -46,7 +49,7 @@ def buildsets():
         testset = to_set(alllines[0:10000])
 
         print('Generating trainset...')
-        trainset = to_set(alllines[10001:200000])
+        trainset = to_set(alllines[10001:])
 
     return trainset, testset
 
@@ -60,6 +63,7 @@ def testnet(net, testset):
             if torch.argmax(output) == label[0]:
                 correct += 1
             total += 1
+    wandb.log({'test_accuracy': correct / total})
     print("Accuracy: ", round(correct / total, 3))
 
 
@@ -79,7 +83,15 @@ class Net(torch.nn.Module):
         return F.log_softmax(x, dim=1)
 
 
-net = torch.load('./nets/net_3.pt')
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('running on %s' % device)
+
+# net = torch.load('./nets/net_3.pt')
+
+net = Net()
+wandb.watch(net)
+
+net.to(device)
 
 optimizer = optim.Adam(net.parameters(), lr=0.001)
 
@@ -87,13 +99,16 @@ trainset, testset = buildsets()
 
 for epoch in range(100):
     print('Epoch: ' + str(epoch))
+    wandb.log({'epoch': epoch})
     for X, label in tqdm(trainset):
         net.zero_grad()
+        X.to(device)
         output = net(X)
+        output.cpu()
         loss = F.nll_loss(output.view(1, 10), label[0])
         loss.backward()
         optimizer.step()
+        wandb.log({'loss': loss})
 
-    print(loss)
-    torch.save(net, './nets/net_' + str(epoch + 3) + '.pt')
+    torch.save(net, './nets/gpunets/net_' + str(epoch) + '.pt')
     testnet(net, testset)