Pownie/backgammon
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fixed global step, now using exp decay

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Alexander Munch-Hansen 2018-04-19 16:01:19 +02:00
parent cba0f67ae2
commit 66589dfde3
1 changed files with 4 additions and 5 deletions
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9
network.py
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@ -38,10 +38,10 @@ class Network:
# Can't remember the best learning_rate, look this up
self.max_learning_rate = 0.1
self.min_learning_rate = 0.001
self.learning_rate = 0.01
# self.learning_rate = 0.01
self.global_step = tf.Variable(0, trainable=False, name="global_step")
# self.learning_rate = tf.maximum(self.min_learning_rate, tf.train.exponential_decay(self.max_learning_rate, self.global_step, 50000, 0.96, staircase=True), name="learning_rate")
self.learning_rate = tf.maximum(self.min_learning_rate, tf.train.exponential_decay(self.max_learning_rate, self.global_step, 50000, 0.96, staircase=True), name="learning_rate")
@ -164,11 +164,10 @@ class Network:
# Restore trained episode count for model
episode_count_path = os.path.join(self.checkpoint_path, "episodes_trained")
if os.path.isfile(episode_count_path):
p if os.path.isfile(episode_count_path):
with open(episode_count_path, 'r') as f:
self.config['start_episode'] = int(f.read())
else:
assert False
def make_move(self, sess, board, roll, player):
# print(Board.pretty(board))