127 lines
3.7 KiB
Diff
127 lines
3.7 KiB
Diff
diff --git a/TicTacToe_AI/Net/pytorch_ai.py b/TicTacToe_AI/Net/pytorch_ai.py
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index efea5ae..dd5e54d 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 +4,11 @@ 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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+
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+BATCH_SIZE = 15
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def to_set(raw_list):
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@@ -35,6 +40,39 @@ def to_set(raw_list):
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return out_set
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+def to_batched_set(raw_list):
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+ counter = 0
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+ out_set = []
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+ boardtensor = torch.zeros((BATCH_SIZE, 1, 9))
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+ labeltensor = torch.zeros(BATCH_SIZE)
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+ for line in tqdm(raw_list):
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+ line = line.replace('\n', '')
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+ raw_board, raw_label = line.split('|')[0], line.split('|')[1]
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+
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+ if not (int(raw_label) is -1):
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+ labeltensor[counter] = int(raw_label)
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+ else:
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+ labeltensor[counter] = 9
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+
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+ for n, block in enumerate(raw_board):
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+ if int(block) is -1:
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+ boardtensor[counter][0][n] = 0
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+ elif int(block) is 0:
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+ boardtensor[counter][0][n] = 0.5
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+ elif int(block) is 1:
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+ boardtensor[counter][0][n] = 1
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+
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+ if counter == (BATCH_SIZE - 1):
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+ out_set.append([boardtensor, labeltensor])
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+ boardtensor = torch.zeros((BATCH_SIZE, 1, 9))
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+ labeltensor = torch.zeros(BATCH_SIZE)
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+ counter = 0
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+ else:
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+ counter += 1
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+
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+ return out_set
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+
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+
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def buildsets():
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with open('boards.bds', 'r') as infile:
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print('Loading file...')
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@@ -46,7 +84,7 @@ 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[10001:100000])
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return trainset, testset
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@@ -60,6 +98,7 @@ 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 +118,15 @@ 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 +134,16 @@ 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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diff --git a/other_scripts/setcounter.py b/other_scripts/setcounter.py
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index 9735f20..e9eb00c 100644
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--- a/other_scripts/setcounter.py
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+++ b/other_scripts/setcounter.py
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@@ -7,9 +7,12 @@ data = datasets.MNIST('../datasets', train=True, download=True,
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transforms.ToTensor()
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]))
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-loader = torch.utils.data.DataLoader(data, batch_size=1, shuffle=False)
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+loader = torch.utils.data.DataLoader(data, batch_size=15, shuffle=False)
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set = {'0': 0, '1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
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+for data in loader:
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+ print(data[1].shape)
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+
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for _, label in tqdm(loader):
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set[str(label[0].item())] += 1
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