Instructions to use doraking/AlphaQuoridor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use doraking/AlphaQuoridor with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://doraking/AlphaQuoridor") - Notebooks
- Google Colab
- Kaggle
File size: 825 Bytes
2437f34 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | # ====================
# Execution of Learning Cycle
# ====================
# Importing packages
from dual_network import dual_network
from self_play import self_play
from train_network import train_network
from evaluate_network import evaluate_network
from evaluate_best_player import evaluate_best_player
# Number of NUM_EPOCH
NUM_TRAIN_CYCLE = 3
# Main function
if __name__ == '__main__':
# Creating the dual network
dual_network()
for i in range(NUM_TRAIN_CYCLE):
print('Train', i, '====================')
# self-play part
self_play()
# parameter update part
train_network()
# Evaluating new parameters
update_best_player = evaluate_network()
# Evaluating the best player
if update_best_player:
evaluate_best_player()
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