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connect our own network to mcts

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This commit is contained in:
Dong Yan 2017-11-12 22:40:58 +08:00
parent ab1b3775e7
commit d9368c9a78
2 changed files with 46 additions and 30 deletions
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2
AlphaGo/.gitignore vendored Normal file
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@ -0,0 +1,2 @@
data
checkpoints

74
AlphaGo/Network.py
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@ -1,14 +1,16 @@
import os
import time
import sys
import numpy as np
import tensorflow as tf
import tensorflow.contrib.layers as layers
import multi_gpu
import time
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
#os.environ["CUDA_VISIBLE_DEVICES"] = "1"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
def residual_block(input, is_training):
normalizer_params = {'is_training': is_training,
@ -127,34 +129,46 @@ def train():
saver.save(sess, result_path + save_path)
del data, boards, wins, ps
def forward(call_number):
#checkpoint_path = "/home/yama/rl/tianshou/AlphaGo/checkpoints"
checkpoint_path = "/home/yama/rl/tianshou/AlphaGo/checkpoints/jialian"
board_file = np.genfromtxt("/home/yama/rl/tianshou/leela-zero/src/mcts_nn_files/board_" + call_number, dtype='str');
human_board = np.zeros((17, 19, 19))
#TODO : is it ok to ignore the last channel?
for i in range(17):
human_board[i] = np.array(list(board_file[i])).reshape(19, 19)
feed_board = human_board.transpose(1, 2, 0).reshape(1, 19, 19, 17)
#print(feed_board.shape)
def forward(board):
result_path = "./results/"
itflag = False
res = None
if board is None:
board = np.load("/home/yama/tongzheng/AG/self_play_204/d7d7d552b7be4b51883de99d74a8e51b.npz")
board = board["boards"][100].reshape(-1, 19, 19, 17)
result_path = "../parameters/checkpoints"
itflag = True
with multi_gpu.create_session() as sess:
sess.run(tf.global_variables_initializer())
ckpt_file = tf.train.latest_checkpoint(result_path)
if ckpt_file is not None:
print('Restoring model from {}...'.format(ckpt_file))
saver.restore(sess, ckpt_file)
else:
raise ValueError("No model loaded")
res = sess.run([tf.nn.softmax(p), v], feed_dict={x: board, is_training: itflag})
# res = sess.run([tf.nn.softmax(p),v], feed_dict={x:fix_board["boards"][300].reshape(-1, 19, 19, 17), is_training:False})
# res = sess.run([tf.nn.softmax(p),v], feed_dict={x:fix_board["boards"][50].reshape(-1, 19, 19, 17), is_training:True})
print(res)
# print(res[0].tolist()[0])
# print(np.argmax(res[0]))
return res
#npz_board = np.load("/home/yama/rl/tianshou/AlphaGo/data/7f83928932f64a79bc1efdea268698ae.npz")
#print(npz_board["boards"].shape)
#feed_board = npz_board["boards"][10].reshape(-1, 19, 19, 17)
##print(feed_board)
#show_board = feed_board[0].transpose(2, 0, 1)
#print("board shape : ", show_board.shape)
#print(show_board)
itflag = True
with multi_gpu.create_session() as sess:
sess.run(tf.global_variables_initializer())
ckpt_file = tf.train.latest_checkpoint(checkpoint_path)
if ckpt_file is not None:
#print('Restoring model from {}...'.format(ckpt_file))
saver.restore(sess, ckpt_file)
else:
raise ValueError("No model loaded")
res = sess.run([tf.nn.softmax(p),v], feed_dict={x:feed_board, is_training:itflag})
#res = sess.run([tf.nn.softmax(p),v], feed_dict={x:fix_board["boards"][300].reshape(-1, 19, 19, 17), is_training:False})
#res = sess.run([tf.nn.softmax(p),v], feed_dict={x:fix_board["boards"][50].reshape(-1, 19, 19, 17), is_training:True})
#print(np.argmax(res[0]))
np.savetxt(sys.stdout, res[0][0], fmt="%.6f", newline=" ")
np.savetxt(sys.stdout, res[1][0], fmt="%.6f", newline=" ")
pv_file = "/home/yama/rl/tianshou/leela-zero/src/mcts_nn_files/policy_value"
np.savetxt(pv_file, np.concatenate((res[0][0], res[1][0])), fmt="%.6f", newline=" ")
#np.savetxt(pv_file, res[1][0], fmt="%.6f", newline=" ")
return res
if __name__ == '__main__':
train()
# if sys.argv[1] == "test":
# forward(None)
if __name__=='__main__':
np.set_printoptions(threshold='nan')
#time.sleep(2)
forward(sys.argv[1])