Implemented Net based AI

TicTacToe_AI/Net/pytorch_ai.py

@@ -1,5 +1,8 @@
 import random
 import torch
+import torch.optim as optim
+from torch import nn
+import torch.nn.functional as F
 from tqdm import tqdm
 
 
@@ -10,9 +13,11 @@ def to_set(raw_list):
         raw_board, raw_label = line.split('|')[0], line.split('|')[1]
 
         # convert string label to tensor
-        label = torch.zeros([1, 9])
+        label = torch.zeros([1, 1], dtype=torch.long)
         if not (int(raw_label) is -1):
-            label[0][int(raw_label)] = 1
+            label[0][0] = int(raw_label)
+        else:
+            label[0][0] = 9
 
         # convert board to tensor
         raw_board = raw_board.split(',')
@@ -30,13 +35,65 @@ def to_set(raw_list):
     return out_set
 
 
-with open('boards.bds', 'r') as infile:
+def buildsets():
+    with open('boards.bds', 'r') as infile:
         print('Loading file...')
         alllines = infile.readlines()
+        print(len(alllines))
         random.shuffle(alllines)
 
         print('Generating testset...')
-    testset = to_set(alllines[0:50000])
+        testset = to_set(alllines[0:10000])
 
         print('Generating trainset...')
-    trainset = to_set(alllines[50001:])
+        trainset = to_set(alllines[10001:200000])
+
+    return trainset, testset
+
+
+def testnet(net, testset):
+    correct = 0
+    total = 0
+    with torch.no_grad():
+        for X, label in testset:
+            output = net(X)
+            if torch.argmax(output) == label[0]:
+                correct += 1
+            total += 1
+    print("Accuracy: ", round(correct / total, 3))
+
+
+class Net(torch.nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.fc1 = nn.Linear(9, 9)
+        self.fc2 = nn.Linear(9, 20)
+        self.fc3 = nn.Linear(20, 50)
+        self.fc4 = nn.Linear(50, 10)
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = F.relu(self.fc3(x))
+        x = self.fc4(x)
+        return F.log_softmax(x, dim=1)
+
+
+net = Net()
+
+optimizer = optim.Adam(net.parameters(), lr=0.001)
+
+trainset, testset = buildsets()
+
+for epoch in range(100):
+    print('Epoch: ' + str(epoch))
+    for X, label in tqdm(trainset):
+        net.zero_grad()
+        output = net(X)
+        loss = F.nll_loss(output.view(1, 10), label[0])
+        loss.backward()
+        optimizer.step()
+
+    print(loss)
+    torch.save(net, './nets/net_' + str(epoch) + '.pt')
+    testnet(net, testset)

mnist_classifier.py

@@ -14,7 +14,7 @@ test = datasets.MNIST('./datasets', train=False, download=True,
                           transforms.ToTensor()
                       ]))
 
-trainset = torch.utils.data.DataLoader(train, batch_size=10, shuffle=True)
+trainset = torch.utils.data.DataLoader(train, batch_size=15, shuffle=True)
 testset = torch.utils.data.DataLoader(test, batch_size=10, shuffle=False)