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1-ply runs even faster.

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Alexander Munch-Hansen 2018-05-15 19:29:27 +02:00
parent 260c32d909
commit a77c13a0a4
1 changed files with 3 additions and 22 deletions
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25
network.py
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@ -327,12 +327,6 @@ class Network:
list_of_lengths = [len(board) for board in list_of_moves]
start = time.time()
for i in range(len(test_list)):
self.model.predict_on_batch(np.array([state]))
print("Indiviual rolls:", time.time() - start)
all_scores = [self.model.predict_on_batch(board) for board in list_of_moves]
start = time.time()
all_scores_legit = self.model.predict_on_batch(np.array(test_list))
@ -343,23 +337,10 @@ class Network:
split_scores.append(all_scores_legit[from_idx:from_idx+length])
from_idx += length
transformed_splits = [tf.reduce_mean(scores) for scores in split_scores]
means_splits = [tf.reduce_mean(scores) for scores in split_scores]
transformed_means_splits = [x if player == 1 else (1-x) for x in means_splits]
print(transformed_splits)
print("All in one:", time.time() - start)
scores_means = [tf.reduce_mean(score) for score in all_scores]
print(scores_means)
transformed_means = [x if player == 1 else (1-x) for x in scores_means]
# print(time.time() - start)
return ([scores_means, transformed_means])
return ([means_splits, transformed_means_splits])
def calc_n_ply(self, n_init, sess, board, player, roll):