def load_model(self):
self.data_node = tf.placeholder(tf.float32, shape=(1, INPUT_WIDTH, INPUT_WIDTH, INPUT_DEPTH))
self.sess=tf.Session()
if self.is_value_net :
from valuenet import ValueNet
self.vnet=ValueNet()
self.keep_prob_node=tf.placeholder(tf.float32)
self.value=self.vnet.model(self.data_node, keep_prob_node=self.keep_prob_node)
self.position_values=np.ndarray(dtype=np.float32, shape=(BOARD_SIZE**2,))
else:
self.net = SLNetwork()
self.net.declare_layers(num_hidden_layer=5)
self.logit=self.net.model(self.data_node)
saver = tf.train.Saver()
saver.restore(self.sess, self.model_path)
def selfplay(self):
data_node=tf.placeholder(tf.float32, shape=(1, INPUT_WIDTH, INPUT_WIDTH, INPUT_DEPTH))
slnet=SLNetwork()
slnet.declare_layers(num_hidden_layer=8)
this_logits=slnet.model(data_node)
saver=tf.train.Saver(max_to_keep=MAX_NUM_MODEL_TO_KEEP)
print(this_logits.name)
sl_model = os.path.join(MODELS_DIR, SLMODEL_NAME)
sess = tf.Session()
saver.restore(sess, sl_model)
with tf.variable_scope("other_nn_player"):
slnet2=SLNetwork()
slnet2.declare_layers(num_hidden_layer=8)
other_logits=slnet2.model(data_node)
#use non-scoped name to restore those variables
var_dict2 = {slnet.input_layer.weight.op.name: slnet2.input_layer.weight,
slnet.input_layer.bias.op.name: slnet2.input_layer.bias}
for i in xrange(slnet2.num_hidden_layer):
var_dict2[slnet.conv_layer[i].weight.op.name] = slnet2.conv_layer[i].weight
var_dict2[slnet.conv_layer[i].bias.op.name] = slnet2.conv_layer[i].bias
saver2 = tf.train.Saver(var_list=var_dict2)
otherSess = tf.Session()
saver2.restore(otherSess, sl_model)
batch_game_size=128
batch_reward_node = tf.placeholder(dtype=np.float32, shape=(PG_STATE_BATCH_SIZE,))
batch_data_node = tf.placeholder(dtype=np.float32, shape=(PG_STATE_BATCH_SIZE, INPUT_WIDTH, INPUT_WIDTH, INPUT_DEPTH))
batch_label_node = tf.placeholder(shape=(PG_STATE_BATCH_SIZE,), dtype=np.int32)
batch_rewards = np.ndarray(dtype=np.float32, shape=(PG_STATE_BATCH_SIZE,))
batch_data = np.ndarray(dtype=np.float32, shape=(PG_STATE_BATCH_SIZE, INPUT_WIDTH, INPUT_WIDTH, INPUT_DEPTH))
batch_labels = np.ndarray(shape=(PG_STATE_BATCH_SIZE,), dtype=np.int32)
game_rewards=np.ndarray(shape=(batch_game_size,), dtype=np.float32)
tf.get_variable_scope().reuse_variables()
logit = slnet.model(batch_data_node)
entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logit, batch_label_node)
loss = tf.reduce_mean(tf.mul(batch_reward_node, entropy))
opt = tf.train.GradientDescentOptimizer(FLAGS.alpha / batch_game_size)
opt_op=opt.minimize(loss)
win1=0
win2=0
ite=0
g_step=0
if not os.path.exists(MODELS_DIR):
os.makedirs(MODELS_DIR)
print("no SL model? creating pg model dir")
else:
for f in os.listdir(MODELS_DIR):
if f.startswith(PGMODEL_NAME):
try:
os.remove(os.path.join(MODELS_DIR,f))
except OSError as e:
print(e.strerror)
print("removing old models in pg dir")
while ite < FLAGS.max_iterations:
start_batch_time = time.time()
games,_tmp1,_tmp2=self.play_one_batch_games(sess,otherSess, this_logits,other_logits,data_node,batch_game_size, game_rewards)
win1 += _tmp1
win2 += _tmp2
data_tool=data_util(games, PG_STATE_BATCH_SIZE, batch_data, batch_labels)
data_tool.disable_symmetry_checking()
offset1=0; offset2=0; nepoch=0
while nepoch <1 :
o1,o2,next_epoch=data_tool.prepare_batch(offset1,offset2)
if(next_epoch):
nepoch += 1
k=0
batch_rewards.fill(0)
new_o1, new_o2=0,0
while k < PG_STATE_BATCH_SIZE:
for i in range(offset1, batch_game_size):
R=game_rewards[i]
sign=1 if offset2 % 2 ==0 else -1
for j in range(offset2, len(games[i])-1):
batch_rewards[k]=R*sign*(FLAGS.gamma**(len(games[i])-2-j))
sign=-sign
k += 1
if(k>=PG_STATE_BATCH_SIZE):
new_o1=i
new_o2=j+1
break
offset2=0
if(k>=PG_STATE_BATCH_SIZE):
break
if k<PG_STATE_BATCH_SIZE:
offset1, offset2 = 0,0
assert(new_o1==o1 and new_o2==o2)
offset1, offset2=o1,o2
sess.run(opt_op, feed_dict={batch_data_node:batch_data, batch_label_node:batch_labels, batch_reward_node: batch_rewards})
print("time cost for one batch of %d games"%PG_GAME_BATCH_SIZE, time.time()-start_batch_time)
ite += 1
if ite % FLAGS.frequency==0:
saver.save(sess, os.path.join(MODELS_DIR, PGMODEL_NAME), global_step=g_step)
pg_model=self.select_model(MODELS_DIR)
saver2.restore(otherSess, pg_model)
g_step +=1
print("Replce opponenet with new model, ", g_step)
print(pg_model)
ite += 1
print("In total, this win", win1, "opponenet win", win2)
otherSess.close()
sess.close()
class NNAgent(object):
def __init__(self, model_location, name, is_value_net=False):
self.model_path=model_location
self.agent_name=name
self.is_value_net=is_value_net
self.initialize_game()
def initialize_game(self):
self.game_state=[]
self.boardtensor=np.zeros(dtype=np.float32, shape=(1, INPUT_WIDTH, INPUT_WIDTH, INPUT_DEPTH))
make_empty_board_tensor(self.boardtensor)
self.load_model()
def load_model(self):
self.data_node = tf.placeholder(tf.float32, shape=(1, INPUT_WIDTH, INPUT_WIDTH, INPUT_DEPTH))
self.sess=tf.Session()
if self.is_value_net :
from valuenet import ValueNet
self.vnet=ValueNet()
self.keep_prob_node=tf.placeholder(tf.float32)
self.value=self.vnet.model(self.data_node, keep_prob_node=self.keep_prob_node)
self.position_values=np.ndarray(dtype=np.float32, shape=(BOARD_SIZE**2,))
else:
self.net = SLNetwork()
self.net.declare_layers(num_hidden_layer=5)
self.logit=self.net.model(self.data_node)
saver = tf.train.Saver()
saver.restore(self.sess, self.model_path)
def reinitialize(self):
self.game_state = []
make_empty_board_tensor(self.boardtensor)
#0-black player, 1-white player
def play_move(self, intplayer, intmove):
update_tensor(self.boardtensor, intplayer, intmove)
self.game_state.append(intmove)
def generate_move(self, intplayer=None):
if self.is_value_net :
s=list(self.game_state)
empty_positions=[i for i in range(BOARD_SIZE**2) if i not in s]
self.position_values.fill(0.0)
for intmove in empty_positions:
update_tensor(self.boardtensor, intplayer, intmove)
v=self.sess.run(self.value, feed_dict={self.data_node:self.boardtensor})
undo_update_tensor(self.boardtensor,intplayer, intmove)
self.position_values[intmove]=v
im=softmax_selection(self.position_values, self.game_state, temperature=0.1)
#im=max_selection(self.position_values, self.game_state)
return im
else:
logits=self.sess.run(self.logit, feed_dict={self.data_node:self.boardtensor})
intmove=softmax_selection(logits, self.game_state)
#intmove=max_selection(logits, self.game_state)
raw_move=intmove_to_raw(intmove)
assert(ord('a') <= ord(raw_move[0]) <= ord('z') and 0<= int(raw_move[1:]) <BOARD_SIZE**2)
return raw_move
def close_all(self):
self.sess.close()
