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# Go Games Dataset for PyTorch Neural Network Training
## Overview
This dataset contains Go game positions extracted from high-quality SGF files for training neural networks. The positions are organized into three strength categories based on game quality.
## Dataset Statistics
- **Total SGF Files Processed**: 61149
- **Valid SGF Files**: 0
- **Total Positions**: 29884
- **Processing Time**: 14.90 seconds
## Strength Categories
The dataset is divided into three strength categories:
- **Standard** (Quality 80-85): 2704 games, 9934 positions
- **Strong** (Quality 86-92): 3397 games, 9958 positions
- **Elite** (Quality 93-100): 55048 games, 9992 positions
## Directory Structure
```
dataset/
├── train/
│ ├── boards.pt # Board state tensors (N, C, H, W)
│ ├── moves.pt # Move labels (N,)
│ ├── colors.pt # Player colors (N,)
│ └── metadata.json # Additional information
├── val/
│ ├── boards.pt
│ ├── moves.pt
│ ├── colors.pt
│ └── metadata.json
├── test/
│ ├── boards.pt
│ ├── moves.pt
│ ├── colors.pt
│ └── metadata.json
├── stats.json # Processing statistics
└── README.md # This file
```
## Board Representation
The board state is represented as a tensor with 3 channels:
1. Black stones (1 where black stone is present, 0 elsewhere)
2. White stones (1 where white stone is present, 0 elsewhere)
3. Next player (all 1s if black to play, all 0s if white to play)
## Usage with PyTorch
```python
import torch
import json
import os
from torch.utils.data import Dataset, DataLoader
class GoDataset(Dataset):
def __init__(self, data_dir):
self.boards = torch.load(os.path.join(data_dir, "boards.pt"))
self.moves = torch.load(os.path.join(data_dir, "moves.pt"))
self.colors = torch.load(os.path.join(data_dir, "colors.pt"))
with open(os.path.join(data_dir, "metadata.json"), 'r', encoding='utf-8') as f:
self.metadata = json.load(f)
def __len__(self):
return len(self.moves)
def __getitem__(self, idx):
return {
'board': self.boards[idx],
'move': self.moves[idx],
'color': self.colors[idx]
}
# Create datasets
train_dataset = GoDataset('dataset/train')
val_dataset = GoDataset('dataset/val')
test_dataset = GoDataset('dataset/test')
# Create data loaders
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=64)
test_loader = DataLoader(test_dataset, batch_size=64)
```
## License
The dataset is intended for research and educational purposes only.
## Creation Date
This dataset was created on 2025.3.13
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