neural net for tic-tac-toe using pytorch in python

Here's an example of how to create a neural network for tic-tac-toe using PyTorch in Python.

First, let's import the necessary libraries:

main.py
import torch
import torch.nn as nn
import torch.optim as optim

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Next, we'll define the neural network architecture. Our neural network will have an input layer of 9 (for the 9 squares in tic-tac-toe), 2 hidden layers of 128 neurons each, and an output layer of 9 (for the 9 possible moves).

main.py
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(9, 128)
        self.fc2 = nn.Linear(128, 128)
        self.fc3 = nn.Linear(128, 9)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

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Now we'll create an instance of the neural network:

main.py
net = Net()

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Next, we'll define the loss function and optimizer:

main.py
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

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We'll also define a function to convert our input data (a tic-tac-toe board) to a tensor:

main.py
def board_to_tensor(board):
    tensor = torch.zeros(9, dtype=torch.float32)
    for i in range(3):
        for j in range(3):
            if board[i][j] == 'X':
                tensor[i * 3 + j] = -1
            elif board[i][j] == 'O':
                tensor[i * 3 + j] = 1
    return tensor

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Now we can train our neural network. Here's an example of how to train the neural network for one epoch:

main.py
for i in range(len(training_data)):
    board, move = training_data[i]
    inputs = board_to_tensor(board)
    targets = torch.tensor(move, dtype=torch.long)

    optimizer.zero_grad()

    outputs = net(inputs)
    loss = criterion(outputs.unsqueeze(0), targets.unsqueeze(0))
    loss.backward()
    optimizer.step()

    running_loss += loss.item()

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In this example, training_data is a list of tuples where the first element is a tic-tac-toe board and the second element is the index of the move that was made on that board.

After training, we can use the neural network to make predictions. Here's an example of how to use the neural network to predict the best move for a given board:

main.py
def predict_move(board):
    inputs = board_to_tensor(board)
    outputs = net(inputs)
    _, predicted = torch.max(outputs, 0)
    return predicted.item()

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This function takes a tic-tac-toe board as input and returns the index of the best move.

And that's it! With this neural network, you can create a simple tic-tac-toe game that uses machine learning to make intelligent moves.

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pytorch
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