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