class ValueNet(object):
def __init__(self, brain_dir, summary_dir):
self.brain_dir = brain_dir
self.brain_file = os.path.join(self.brain_dir, 'model.ckpt')
self.summary_dir = summary_dir
self._has_more_data = True
self.ds_train = None
self.ds_test = None
self.graph = tf.Graph()
with self.graph.as_default():
self.states_pl, self.rewards_pl = self.placeholder_inputs()
self.value_outputs, self.opt_op, self.global_step, self.mse = self.model(
self.states_pl, self.rewards_pl)
self.summary_op = tf.merge_all_summaries()
init = tf.initialize_all_variables()
self.saver = tf.train.Saver(
tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope="value_net"))
self.summary_writer = tf.train.SummaryWriter(self.summary_dir,
self.graph)
self.sess = tf.Session(graph=self.graph)
self.sess.run(init)
def get_input_shape(self):
NUM_CHANNELS = 4
return Board.BOARD_SIZE, Board.BOARD_SIZE, NUM_CHANNELS
def placeholder_inputs(self):
h, w, c = self.get_input_shape()
states = tf.placeholder(tf.float32, [None, h, w, c]) # NHWC
rewards = tf.placeholder(tf.float32, shape=[None])
return states, rewards
def weight_variable(self, shape):
initial = tf.truncated_normal(shape, stddev=0.01)
return tf.Variable(initial)
def bias_variable(self, shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def create_value_net(self, states_pl):
NUM_CHANNELS = 4
ch1 = 32
W_1 = self.weight_variable([3, 3, NUM_CHANNELS, ch1])
b_1 = self.bias_variable([ch1])
ch = 32
W_2 = self.weight_variable([3, 3, ch1, ch])
b_2 = self.bias_variable([ch])
W_21 = self.weight_variable([3, 3, ch, ch])
b_21 = self.bias_variable([ch])
W_22 = self.weight_variable([3, 3, ch, ch])
b_22 = self.bias_variable([ch])
# W_23 = self.weight_variable([1, 1, ch, 1])
# b_23 = self.bias_variable([1])
h_conv1 = tf.nn.relu(
tf.nn.conv2d(states_pl, W_1, [1, 1, 1, 1], padding='SAME') + b_1)
h_conv2 = tf.nn.relu(
tf.nn.conv2d(h_conv1, W_2, [1, 1, 1, 1], padding='SAME') + b_2)
h_conv21 = tf.nn.relu(
tf.nn.conv2d(h_conv2, W_21, [1, 1, 1, 1], padding='SAME') + b_21)
h_conv22 = tf.nn.relu(
tf.nn.conv2d(h_conv21, W_22, [1, 1, 1, 1], padding='SAME') + b_22)
# h_conv23 = tf.nn.relu(tf.nn.conv2d(h_conv22, W_23, [1, 1, 1, 1], padding='SAME') + b_23)
conv_out_dim = h_conv22.get_shape()[1:].num_elements()
conv_out = tf.reshape(h_conv22, [-1, conv_out_dim])
num_hidden = 1
W_3 = tf.Variable(tf.zeros([conv_out_dim, num_hidden], tf.float32))
b_3 = tf.Variable(tf.zeros([num_hidden], tf.float32))
# W_4 = tf.Variable(tf.zeros([num_hidden, 1], tf.float32))
# b_4 = tf.Variable(tf.zeros([1], tf.float32))
# hidden = tf.nn.relu(tf.matmul(conv_out, W_3) + b_3)
# fc_out = tf.matmul(hidden, W_4) + b_4
fc_out = tf.tanh(tf.matmul(conv_out, W_3) + b_3)
return fc_out
def model(self, states_pl, rewards_pl):
global_step = tf.Variable(0, name='global_step', trainable=False)
with tf.variable_scope("value_net"):
value_outputs = self.create_value_net(states_pl)
value_net_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope="value_net")
mean_square_loss = tf.reduce_mean(
tf.squared_difference(rewards_pl, value_outputs))
value_reg_loss = tf.reduce_sum(
[tf.reduce_sum(tf.square(x)) for x in value_net_vars])
value_loss = mean_square_loss + 0.001 * value_reg_loss
optimizer = tf.train.AdamOptimizer(0.0001)
value_opt_op = optimizer.minimize(value_loss, global_step=global_step)
tf.scalar_summary("raw_value_loss", mean_square_loss)
tf.scalar_summary("reg_value_loss", value_reg_loss)
tf.scalar_summary("all_value_loss", value_loss)
return value_outputs, value_opt_op, global_step, mean_square_loss
def get_state_values(self, states, players):
h, w, c = self.get_input_shape()
ss = []
for s, p in zip(states, players):
img, _ = self.adapt_state(s, p)
ss.append(img)
ss = np.array(ss)
feed_dict = {
self.states_pl: ss.reshape((-1, h, w, c)),
}
return self.sess.run(self.value_outputs, feed_dict=feed_dict)
def save(self):
self.saver.save(self.sess, self.brain_file)
def load(self):
ckpt = tf.train.get_checkpoint_state(self.brain_dir)
if ckpt and ckpt.model_checkpoint_path:
self.saver.restore(self.sess, ckpt.model_checkpoint_path)
def close(self):
self.sess.close()
def train(self, train_dat_file, test_dat_file):
self.loader_train = DatasetLoader(train_dat_file)
self.loader_test = DatasetLoader(test_dat_file)
epoch = 0
while True:
print('epoch:', epoch)
epoch += 1
ith_part = 0
while self._has_more_data:
ith_part += 1
self.adapt()
self.train_part(ith_part)
# if ith_part >= 1:
# break
self._has_more_data = True
# if epoch >= 1:
# break
def fill_feed_dict(self, data_set, states_pl, rewards_pl, batch_size=None):
batch_size = batch_size or BATCH_SIZE
states_feed, rewards_feed = data_set.next_batch(batch_size)
feed_dict = {states_pl: states_feed, rewards_pl: rewards_feed}
return feed_dict
def train_part(self, ith_part):
NUM_STEPS = self.ds_train.num_examples // BATCH_SIZE
print('total num steps:', NUM_STEPS)
start_time = time.time()
train_mse = 0.
for step in range(1, NUM_STEPS + 1):
feed_dict = self.fill_feed_dict(self.ds_train, self.states_pl,
self.rewards_pl)
_, train_mse = self.sess.run([self.opt_op, self.mse],
feed_dict=feed_dict)
if step % 1000 == 0:
summary_str, gstep = self.sess.run(
[self.summary_op, self.global_step], feed_dict=feed_dict)
self.summary_writer.add_summary(summary_str, gstep)
self.summary_writer.flush()
if step == NUM_STEPS:
self.saver.save(self.sess,
self.brain_file,
global_step=self.global_step)
duration = time.time() - start_time
test_mse = self.do_eval(self.mse, self.states_pl, self.rewards_pl,
self.ds_test)
print(
'part: %d, acc_train: %.3f, test accuracy: %.3f, time cost: %.3f sec'
% (ith_part, train_mse, test_mse, duration))
def do_eval(self, mse, states_pl, rewards_pl, data_set):
accum_mse = 0.
batch_size = BATCH_SIZE
assert batch_size != 0
steps_per_epoch = math.ceil(data_set.num_examples / batch_size)
for _ in range(steps_per_epoch):
feed_dict = self.fill_feed_dict(data_set, states_pl, rewards_pl,
batch_size)
accum_mse += self.sess.run(mse, feed_dict=feed_dict)
avg_mse = accum_mse / (steps_per_epoch or 1)
return avg_mse
def forge(self, row):
board = row[:Board.BOARD_SIZE_SQ]
player = row[-2]
image, _ = self.adapt_state(board, player)
reward = row[-1]
return image, reward
def adapt_state(self, board, player):
black = (board == Board.STONE_BLACK).astype(float)
white = (board == Board.STONE_WHITE).astype(float)
empty = (board == Board.STONE_EMPTY).astype(float)
is_black_move = np.ones_like(
black, float) if player == Board.STONE_BLACK else np.zeros_like(
black, float)
image = np.dstack((black, white, empty, is_black_move)).ravel()
legal = empty.astype(bool)
return image, legal
def adapt(self):
gc.collect()
if self.ds_train is not None and not self.loader_train.is_wane:
self.ds_train = None
if self.ds_test is not None and not self.loader_test.is_wane:
self.ds_test = None
gc.collect()
h, w, c = self.get_input_shape()
def f(dat):
ds = []
for row in dat:
s, r = self.forge(row)
ds.append((s, r))
ds = np.array(ds)
return DataSet(
np.vstack(ds[:, 0]).reshape((-1, h, w, c)), ds[:, 1])
if self.ds_train is None:
ds_train, self._has_more_data = self.loader_train.load(
DATASET_CAPACITY)
self.ds_train = f(ds_train)
if self.ds_test is None:
ds_test, _ = self.loader_test.load(DATASET_CAPACITY // 2)
self.ds_test = f(ds_test)
print(self.ds_train.images.shape, self.ds_train.labels.shape)
print(self.ds_test.images.shape, self.ds_test.labels.shape)
class DCNN3(Pre):
def __init__(self, is_train=True, is_revive=False, is_rl=False):
super(DCNN3, self).__init__(is_train, is_revive, is_rl)
self.loader_train = DatasetLoader(Pre.DATA_SET_TRAIN)
self.loader_valid = DatasetLoader(Pre.DATA_SET_VALID)
self.loader_test = DatasetLoader(Pre.DATA_SET_TEST)
def placeholder_inputs(self):
h, w, c = self.get_input_shape()
states = tf.placeholder(tf.float32, [None, h, w, c]) # NHWC
actions = tf.placeholder(tf.float32, [None, Pre.NUM_ACTIONS])
return states, actions
def model(self, states_pl, actions_pl):
with tf.variable_scope("policy_net"):
self.predictions = self.create_policy_net(states_pl)
with tf.variable_scope("value_net"):
self.value_outputs = self.create_value_net(states_pl)
self.policy_net_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="policy_net")
pg_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=self.predictions, labels=actions_pl))
reg_loss = tf.reduce_sum([tf.reduce_sum(tf.square(x)) for x in self.policy_net_vars])
self.loss = pg_loss + 0.001 * reg_loss
# tf.scalar_summary("raw_policy_loss", pg_loss)
# tf.scalar_summary("reg_policy_loss", reg_loss)
# tf.scalar_summary("all_policy_loss", self.loss)
self.optimizer = tf.train.AdamOptimizer(0.0001)
self.opt_op = self.optimizer.minimize(self.loss)
self.predict_probs = tf.nn.softmax(self.predictions)
eq = tf.equal(tf.argmax(self.predict_probs, 1), tf.argmax(actions_pl, 1))
# best_move = tf.argmax(actions_pl, 1)
# eq = tf.nn.in_top_k(self.predict_probs, best_move, 3)
self.eval_correct = tf.reduce_sum(tf.cast(eq, tf.int32))
self.rl_op(actions_pl)
def create_conv_net(self, states_pl):
ch1 = 32
W_1 = self.weight_variable([3, 3, Pre.NUM_CHANNELS, ch1])
b_1 = self.bias_variable([ch1])
ch = 32
W_2 = self.weight_variable([3, 3, ch1, ch])
b_2 = self.bias_variable([ch])
W_21 = self.weight_variable([3, 3, ch, ch])
b_21 = self.bias_variable([ch])
W_22 = self.weight_variable([3, 3, ch, ch])
b_22 = self.bias_variable([ch])
W_23 = self.weight_variable([1, 1, ch, 1024])
b_23 = self.bias_variable([1024])
h_conv1 = tf.nn.relu(tf.nn.conv2d(states_pl, W_1, [1, 1, 1, 1], padding='VALID') + b_1)
h_conv2 = tf.nn.relu(tf.nn.conv2d(h_conv1, W_2, [1, 1, 1, 1], padding='SAME') + b_2)
h_conv21 = tf.nn.relu(tf.nn.conv2d(h_conv2, W_21, [1, 1, 1, 1], padding='SAME') + b_21)
h_conv22 = tf.nn.relu(tf.nn.conv2d(h_conv21, W_22, [1, 1, 1, 1], padding='SAME') + b_22)
h_conv23 = tf.nn.relu(tf.nn.conv2d(h_conv22, W_23, [1, 1, 1, 1], padding='SAME') + b_23)
self.conv_out_dim = h_conv23.get_shape()[1:].num_elements()
conv_out = tf.reshape(h_conv23, [-1, self.conv_out_dim])
return conv_out
def create_policy_net(self, states_pl):
conv = self.create_conv_net(states_pl)
conv = tf.identity(conv, 'policy_net_conv')
W_3 = self.weight_variable([self.conv_out_dim, Pre.NUM_ACTIONS])
b_3 = self.bias_variable([Pre.NUM_ACTIONS])
fc_out = tf.matmul(conv, W_3) + b_3
return fc_out
def create_value_net(self, states_pl):
conv = self.create_conv_net(states_pl)
conv = tf.identity(conv, 'value_net_conv')
num_hidden = 128
W_3 = tf.Variable(tf.zeros([self.conv_out_dim, num_hidden], tf.float32))
b_3 = tf.Variable(tf.zeros([num_hidden], tf.float32))
W_4 = tf.Variable(tf.zeros([num_hidden, 1], tf.float32))
b_4 = tf.Variable(tf.zeros([1], tf.float32))
hidden = tf.nn.relu(tf.matmul(conv, W_3) + b_3)
fc_out = tf.matmul(hidden, W_4) + b_4
return fc_out
def forge(self, row):
board = row[:Board.BOARD_SIZE_SQ]
image, _ = self.adapt_state(board)
visit = row[Board.BOARD_SIZE_SQ::2]
# visit[visit == 0] = 1
# win = row[Board.BOARD_SIZE_SQ+1::2]
win_rate = visit
s = np.sum(win_rate)
win_rate /= s
return image, win_rate
def adapt(self, filename):
# proc = psutil.Process(os.getpid())
gc.collect()
# mem0 = proc.memory_info().rss
if self.ds_train is not None and not self.loader_train.is_wane:
self.ds_train = None
if self.ds_valid is not None and not self.loader_valid.is_wane:
self.ds_valid = None
if self.ds_test is not None and not self.loader_test.is_wane:
self.ds_test = None
gc.collect()
# mem1 = proc.memory_info().rss
# print('gc(M):', (mem1 - mem0) / 1024 ** 2)
h, w, c = self.get_input_shape()
def f(dat):
ds = []
for row in dat:
s, a = self.forge(row)
ds.append((s, a))
ds = np.array(ds)
return DataSet(np.vstack(ds[:, 0]).reshape((-1, h, w, c)), np.vstack(ds[:, 1]))
if self.ds_train is None:
ds_train, self._has_more_data = self.loader_train.load(Pre.DATASET_CAPACITY)
self.ds_train = f(ds_train)
if self.ds_valid is None:
ds_valid, _ = self.loader_valid.load(Pre.DATASET_CAPACITY // 2)
self.ds_valid = f(ds_valid)
if self.ds_test is None:
ds_test, _ = self.loader_test.load(Pre.DATASET_CAPACITY // 2)
self.ds_test = f(ds_test)
print(self.ds_train.images.shape, self.ds_train.labels.shape)
print(self.ds_valid.images.shape, self.ds_valid.labels.shape)
print(self.ds_test.images.shape, self.ds_test.labels.shape)
def get_input_shape(self):
return Board.BOARD_SIZE, Board.BOARD_SIZE, Pre.NUM_CHANNELS
def mid_vis(self, feed_dict):
pass
class ValueNet(object):
def __init__(self, brain_dir, summary_dir):
self.brain_dir = brain_dir
self.brain_file = os.path.join(self.brain_dir, 'model.ckpt')
self.summary_dir = summary_dir
self._has_more_data = True
self.ds_train = None
self.ds_test = None
self.graph = tf.Graph()
with self.graph.as_default():
self.states_pl, self.rewards_pl = self.placeholder_inputs()
self.value_outputs, self.opt_op, self.global_step, self.mse = self.model(self.states_pl, self.rewards_pl)
self.summary_op = tf.summary.merge_all()
init = tf.initialize_all_variables()
self.saver = tf.train.Saver(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="value_net"))
self.summary_writer = tf.summary.FileWriter(self.summary_dir, self.graph)
self.sess = tf.Session(graph=self.graph)
self.sess.run(init)
def get_input_shape(self):
NUM_CHANNELS = 4
return Board.BOARD_SIZE, Board.BOARD_SIZE, NUM_CHANNELS
def placeholder_inputs(self):
h, w, c = self.get_input_shape()
states = tf.placeholder(tf.float32, [None, h, w, c]) # NHWC
rewards = tf.placeholder(tf.float32, shape=[None])
return states, rewards
def weight_variable(self, shape):
initial = tf.truncated_normal(shape, stddev=0.01)
return tf.Variable(initial)
def bias_variable(self, shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def create_value_net(self, states_pl):
NUM_CHANNELS = 4
ch1 = 32
W_1 = self.weight_variable([3, 3, NUM_CHANNELS, ch1])
b_1 = self.bias_variable([ch1])
ch = 32
W_2 = self.weight_variable([3, 3, ch1, ch])
b_2 = self.bias_variable([ch])
W_21 = self.weight_variable([3, 3, ch, ch])
b_21 = self.bias_variable([ch])
W_22 = self.weight_variable([3, 3, ch, ch])
b_22 = self.bias_variable([ch])
# W_23 = self.weight_variable([1, 1, ch, 1])
# b_23 = self.bias_variable([1])
h_conv1 = tf.nn.relu(tf.nn.conv2d(states_pl, W_1, [1, 1, 1, 1], padding='SAME') + b_1)
h_conv2 = tf.nn.relu(tf.nn.conv2d(h_conv1, W_2, [1, 1, 1, 1], padding='SAME') + b_2)
h_conv21 = tf.nn.relu(tf.nn.conv2d(h_conv2, W_21, [1, 1, 1, 1], padding='SAME') + b_21)
h_conv22 = tf.nn.relu(tf.nn.conv2d(h_conv21, W_22, [1, 1, 1, 1], padding='SAME') + b_22)
# h_conv23 = tf.nn.relu(tf.nn.conv2d(h_conv22, W_23, [1, 1, 1, 1], padding='SAME') + b_23)
conv_out_dim = h_conv22.get_shape()[1:].num_elements()
conv_out = tf.reshape(h_conv22, [-1, conv_out_dim])
num_hidden = 1
W_3 = tf.Variable(tf.zeros([conv_out_dim, num_hidden], tf.float32))
b_3 = tf.Variable(tf.zeros([num_hidden], tf.float32))
# W_4 = tf.Variable(tf.zeros([num_hidden, 1], tf.float32))
# b_4 = tf.Variable(tf.zeros([1], tf.float32))
# hidden = tf.nn.relu(tf.matmul(conv_out, W_3) + b_3)
# fc_out = tf.matmul(hidden, W_4) + b_4
fc_out = tf.tanh(tf.matmul(conv_out, W_3) + b_3)
return fc_out
def model(self, states_pl, rewards_pl):
global_step = tf.Variable(0, name='global_step', trainable=False)
with tf.variable_scope("value_net"):
value_outputs = self.create_value_net(states_pl)
value_net_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="value_net")
mean_square_loss = tf.reduce_mean(tf.squared_difference(rewards_pl, value_outputs))
value_reg_loss = tf.reduce_sum([tf.reduce_sum(tf.square(x)) for x in value_net_vars])
value_loss = mean_square_loss + 0.001 * value_reg_loss
optimizer = tf.train.AdamOptimizer(0.0001)
value_opt_op = optimizer.minimize(value_loss, global_step=global_step)
tf.summary.scalar("raw_value_loss", mean_square_loss)
tf.summary.scalar("reg_value_loss", value_reg_loss)
tf.summary.scalar("all_value_loss", value_loss)
return value_outputs, value_opt_op, global_step, mean_square_loss
def get_state_values(self, states, players):
h, w, c = self.get_input_shape()
ss = []
for s, p in zip(states, players):
img, _ = self.adapt_state(s, p)
ss.append(img)
ss = np.array(ss)
feed_dict = {
self.states_pl: ss.reshape((-1, h, w, c)),
}
return self.sess.run(self.value_outputs, feed_dict=feed_dict)
def save(self):
self.saver.save(self.sess, self.brain_file)
def load(self):
ckpt = tf.train.get_checkpoint_state(self.brain_dir)
if ckpt and ckpt.model_checkpoint_path:
self.saver.restore(self.sess, ckpt.model_checkpoint_path)
def close(self):
self.sess.close()
def train(self, train_dat_file, test_dat_file):
self.loader_train = DatasetLoader(train_dat_file)
self.loader_test = DatasetLoader(test_dat_file)
epoch = 0
while True:
print('epoch:', epoch)
epoch += 1
ith_part = 0
while self._has_more_data:
ith_part += 1
self.adapt()
self.train_part(ith_part)
# if ith_part >= 1:
# break
self._has_more_data = True
# if epoch >= 1:
# break
def fill_feed_dict(self, data_set, states_pl, rewards_pl, batch_size=None):
batch_size = batch_size or BATCH_SIZE
states_feed, rewards_feed = data_set.next_batch(batch_size)
feed_dict = {
states_pl: states_feed,
rewards_pl: rewards_feed
}
return feed_dict
def train_part(self, ith_part):
NUM_STEPS = self.ds_train.num_examples // BATCH_SIZE
print('total num steps:', NUM_STEPS)
start_time = time.time()
train_mse = 0.
for step in range(1, NUM_STEPS + 1):
feed_dict = self.fill_feed_dict(self.ds_train, self.states_pl, self.rewards_pl)
_, train_mse = self.sess.run([self.opt_op, self.mse], feed_dict=feed_dict)
if step % 1000 == 0:
summary_str, gstep = self.sess.run([self.summary_op, self.global_step], feed_dict=feed_dict)
self.summary_writer.add_summary(summary_str, gstep)
self.summary_writer.flush()
if step == NUM_STEPS:
self.saver.save(self.sess, self.brain_file, global_step=self.global_step)
duration = time.time() - start_time
test_mse = self.do_eval(self.mse, self.states_pl, self.rewards_pl, self.ds_test)
print('part: %d, acc_train: %.3f, test accuracy: %.3f, time cost: %.3f sec' %
(ith_part, train_mse, test_mse, duration))
def do_eval(self, mse, states_pl, rewards_pl, data_set):
accum_mse = 0.
batch_size = BATCH_SIZE
assert batch_size != 0
steps_per_epoch = math.ceil(data_set.num_examples / batch_size)
for _ in range(steps_per_epoch):
feed_dict = self.fill_feed_dict(data_set, states_pl, rewards_pl, batch_size)
accum_mse += self.sess.run(mse, feed_dict=feed_dict)
avg_mse = accum_mse / (steps_per_epoch or 1)
return avg_mse
def forge(self, row):
board = row[:Board.BOARD_SIZE_SQ]
player = row[-2]
image, _ = self.adapt_state(board, player)
reward = row[-1]
return image, reward
def adapt_state(self, board, player):
black = (board == Board.STONE_BLACK).astype(float)
white = (board == Board.STONE_WHITE).astype(float)
empty = (board == Board.STONE_EMPTY).astype(float)
is_black_move = np.ones_like(black, float) if player == Board.STONE_BLACK else np.zeros_like(black, float)
image = np.dstack((black, white, empty, is_black_move)).ravel()
legal = empty.astype(bool)
return image, legal
def adapt(self):
gc.collect()
if self.ds_train is not None and not self.loader_train.is_wane:
self.ds_train = None
if self.ds_test is not None and not self.loader_test.is_wane:
self.ds_test = None
gc.collect()
h, w, c = self.get_input_shape()
def f(dat):
ds = []
for row in dat:
s, r = self.forge(row)
ds.append((s, r))
ds = np.array(ds)
return DataSet(np.vstack(ds[:, 0]).reshape((-1, h, w, c)), ds[:, 1])
if self.ds_train is None:
ds_train, self._has_more_data = self.loader_train.load(DATASET_CAPACITY)
self.ds_train = f(ds_train)
if self.ds_test is None:
ds_test, _ = self.loader_test.load(DATASET_CAPACITY // 2)
self.ds_test = f(ds_test)
print(self.ds_train.images.shape, self.ds_train.labels.shape)
print(self.ds_test.images.shape, self.ds_test.labels.shape)
class DCNN2(Pre):
def __init__(self, is_train=True, is_revive=False):
super().__init__(is_train, is_revive)
self.loader_train = DatasetLoader(Pre.DATA_SET_TRAIN)
self.loader_valid = DatasetLoader(Pre.DATA_SET_VALID)
self.loader_test = DatasetLoader(Pre.DATA_SET_TEST)
def diags(self, a):
assert len(a.shape) == 2 and a.shape[0] == a.shape[1]
valid = a.shape[0] - 5
vecs = [a.diagonal(i) for i in range(-valid, valid + 1)]
c = np.zeros((len(vecs), a.shape[0]))
c[:, :] = -1
for i, v in enumerate(vecs):
c[i, :v.shape[0]] = v
return c
def regulate(self, a):
md = self.diags(a)
ad = self.diags(np.rot90(a))
m = np.vstack((a, a.T, md, ad))
return m
def placeholder_inputs(self):
h, w, c = self.get_input_shape()
states = tf.placeholder(tf.float32, [None, h, w, c]) # NHWC
actions = tf.placeholder(tf.float32, [None, Pre.NUM_ACTIONS])
return states, actions
def model(self, states_pl, actions_pl):
ch1 = 32
W_1 = self.weight_variable([1, 5, Pre.NUM_CHANNELS, ch1])
b_1 = self.bias_variable([ch1])
ch = 32
W_2 = self.weight_variable([3, 3, ch1, ch])
b_2 = self.bias_variable([ch])
W_21 = self.weight_variable([3, 3, ch, ch])
b_21 = self.bias_variable([ch])
self.h_conv1 = tf.nn.relu(
tf.nn.conv2d(states_pl, W_1, [1, 1, 1, 1], padding='VALID') + b_1)
self.h_conv2 = tf.nn.relu(
tf.nn.conv2d(self.h_conv1, W_2, [1, 1, 1, 1], padding='SAME') +
b_2)
self.h_conv21 = tf.nn.relu(
tf.nn.conv2d(self.h_conv2, W_21, [1, 1, 1, 1], padding='SAME') +
b_21)
shape = self.h_conv21.get_shape().as_list()
dim = np.cumprod(shape[1:])[-1]
h_conv_out = tf.reshape(self.h_conv21, [-1, dim])
num_hidden = 128
W_3 = self.weight_variable([dim, num_hidden])
b_3 = self.bias_variable([num_hidden])
W_4 = self.weight_variable([num_hidden, Pre.NUM_ACTIONS])
b_4 = self.bias_variable([Pre.NUM_ACTIONS])
self.hidden = tf.matmul(h_conv_out, W_3) + b_3
self.predictions = tf.matmul(self.hidden, W_4) + b_4
self.cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
self.predictions, actions_pl)
# self.loss = tf.reduce_mean(self.cross_entropy)
self.loss = tf.reduce_mean(
-tf.reduce_sum(tf.nn.log_softmax(self.predictions) * actions_pl,
reduction_indices=1))
print("states_pl shape:", states_pl.get_shape())
print("actions_pl shape:", actions_pl.get_shape())
print("predictions shape:", self.predictions.get_shape())
print("cross_entropy shape:", self.cross_entropy.get_shape())
print("loss shape:", self.loss.get_shape())
tf.scalar_summary("loss", self.loss)
self.optimizer = tf.train.AdamOptimizer()
self.opt_op = self.optimizer.minimize(self.loss)
self.predict_probs = tf.nn.softmax(self.predictions)
eq = tf.equal(tf.argmax(self.predict_probs, 1),
tf.argmax(actions_pl, 1))
self.eval_correct = tf.reduce_sum(tf.cast(eq, tf.int32))
self.rl_op(actions_pl)
def forge(self, row):
board = row[:Board.BOARD_SIZE_SQ]
image, _ = self.adapt_state(board)
visit = row[Board.BOARD_SIZE_SQ::2]
# visit[visit == 0] = 1
# win = row[Board.BOARD_SIZE_SQ+1::2]
win_rate = visit
s = np.sum(win_rate)
win_rate /= s
return image, win_rate
def adapt(self, filename):
# proc = psutil.Process(os.getpid())
gc.collect()
# mem0 = proc.memory_info().rss
if self.ds_train is not None and not self.loader_train.is_wane:
self.ds_train = None
if self.ds_valid is not None and not self.loader_valid.is_wane:
self.ds_valid = None
if self.ds_test is not None and not self.loader_test.is_wane:
self.ds_test = None
gc.collect()
# mem1 = proc.memory_info().rss
# print('gc(M):', (mem1 - mem0) / 1024 ** 2)
h, w, c = self.get_input_shape()
def f(dat):
ds = []
for row in dat:
s, a = self.forge(row)
ds.append((s, a))
ds = np.array(ds)
return DataSet(
np.vstack(ds[:, 0]).reshape((-1, h, w, c)), np.vstack(ds[:,
1]))
if self.ds_train is None:
ds_train, self._has_more_data = self.loader_train.load(
Pre.DATASET_CAPACITY)
self.ds_train = f(ds_train)
if self.ds_valid is None:
ds_valid, _ = self.loader_valid.load(Pre.DATASET_CAPACITY // 2)
self.ds_valid = f(ds_valid)
if self.ds_test is None:
ds_test, _ = self.loader_test.load(Pre.DATASET_CAPACITY // 2)
self.ds_test = f(ds_test)
print(self.ds_train.images.shape, self.ds_train.labels.shape)
print(self.ds_valid.images.shape, self.ds_valid.labels.shape)
print(self.ds_test.images.shape, self.ds_test.labels.shape)
def adapt_state(self, board):
board = board.reshape(-1, Board.BOARD_SIZE)
board = self.regulate(board)
return super(DCNN2, self).adapt_state(board)
def get_input_shape(self):
assert Board.BOARD_SIZE >= 5
height = 6 * Board.BOARD_SIZE - 18 # row vecs + col vecs + valid(len>=5) main diag vecs + valid(len>=5) anti diag vecs
return height, Board.BOARD_SIZE, Pre.NUM_CHANNELS
def mid_vis(self, feed_dict):
pass
class DCNN2(Pre):
def __init__(self, is_train=True, is_revive=False):
super().__init__(is_train, is_revive)
self.loader_train = DatasetLoader(Pre.DATA_SET_TRAIN)
self.loader_valid = DatasetLoader(Pre.DATA_SET_VALID)
self.loader_test = DatasetLoader(Pre.DATA_SET_TEST)
def diags(self, a):
assert len(a.shape) == 2 and a.shape[0] == a.shape[1]
valid = a.shape[0] - 5
vecs = [a.diagonal(i) for i in range(-valid, valid + 1)]
c = np.zeros((len(vecs), a.shape[0]))
c[:, :] = -1
for i, v in enumerate(vecs):
c[i, :v.shape[0]] = v
return c
def regulate(self, a):
md = self.diags(a)
ad = self.diags(np.rot90(a))
m = np.vstack((a, a.T, md, ad))
return m
def placeholder_inputs(self):
h, w, c = self.get_input_shape()
states = tf.placeholder(tf.float32, [None, h, w, c]) # NHWC
actions = tf.placeholder(tf.float32, [None, Pre.NUM_ACTIONS])
return states, actions
def model(self, states_pl, actions_pl):
ch1 = 32
W_1 = self.weight_variable([1, 5, Pre.NUM_CHANNELS, ch1])
b_1 = self.bias_variable([ch1])
ch = 32
W_2 = self.weight_variable([3, 3, ch1, ch])
b_2 = self.bias_variable([ch])
W_21 = self.weight_variable([3, 3, ch, ch])
b_21 = self.bias_variable([ch])
self.h_conv1 = tf.nn.relu(tf.nn.conv2d(states_pl, W_1, [1, 1, 1, 1], padding='VALID') + b_1)
self.h_conv2 = tf.nn.relu(tf.nn.conv2d(self.h_conv1, W_2, [1, 1, 1, 1], padding='SAME') + b_2)
self.h_conv21 = tf.nn.relu(tf.nn.conv2d(self.h_conv2, W_21, [1, 1, 1, 1], padding='SAME') + b_21)
shape = self.h_conv21.get_shape().as_list()
dim = np.cumprod(shape[1:])[-1]
h_conv_out = tf.reshape(self.h_conv21, [-1, dim])
num_hidden = 128
W_3 = self.weight_variable([dim, num_hidden])
b_3 = self.bias_variable([num_hidden])
W_4 = self.weight_variable([num_hidden, Pre.NUM_ACTIONS])
b_4 = self.bias_variable([Pre.NUM_ACTIONS])
self.hidden = tf.matmul(h_conv_out, W_3) + b_3
self.predictions = tf.matmul(self.hidden, W_4) + b_4
self.cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=actions_pl, logits=self.predictions)
# self.loss = tf.reduce_mean(self.cross_entropy)
self.loss = tf.reduce_mean(-tf.reduce_sum(tf.nn.log_softmax(self.predictions) * actions_pl, axis=1))
print("states_pl shape:", states_pl.get_shape())
print("actions_pl shape:", actions_pl.get_shape())
print("predictions shape:", self.predictions.get_shape())
print("cross_entropy shape:", self.cross_entropy.get_shape())
print("loss shape:", self.loss.get_shape())
tf.summary.scalar("loss", self.loss)
self.optimizer = tf.train.AdamOptimizer()
self.opt_op = self.optimizer.minimize(self.loss)
self.predict_probs = tf.nn.softmax(self.predictions)
eq = tf.equal(tf.argmax(self.predict_probs, 1), tf.argmax(actions_pl, 1))
self.eval_correct = tf.reduce_sum(tf.cast(eq, tf.int32))
self.rl_op(actions_pl)
def forge(self, row):
board = row[:Board.BOARD_SIZE_SQ]
image, _ = self.adapt_state(board)
visit = row[Board.BOARD_SIZE_SQ::2]
# visit[visit == 0] = 1
# win = row[Board.BOARD_SIZE_SQ+1::2]
win_rate = visit
s = np.sum(win_rate)
win_rate /= s
return image, win_rate
def adapt(self, filename):
# proc = psutil.Process(os.getpid())
gc.collect()
# mem0 = proc.memory_info().rss
if self.ds_train is not None and not self.loader_train.is_wane:
self.ds_train = None
if self.ds_valid is not None and not self.loader_valid.is_wane:
self.ds_valid = None
if self.ds_test is not None and not self.loader_test.is_wane:
self.ds_test = None
gc.collect()
# mem1 = proc.memory_info().rss
# print('gc(M):', (mem1 - mem0) / 1024 ** 2)
h, w, c = self.get_input_shape()
def f(dat):
ds = []
for row in dat:
s, a = self.forge(row)
ds.append((s, a))
ds = np.array(ds)
return DataSet(np.vstack(ds[:, 0]).reshape((-1, h, w, c)), np.vstack(ds[:, 1]))
if self.ds_train is None:
ds_train, self._has_more_data = self.loader_train.load(Pre.DATASET_CAPACITY)
self.ds_train = f(ds_train)
if self.ds_valid is None:
ds_valid, _ = self.loader_valid.load(Pre.DATASET_CAPACITY // 2)
self.ds_valid = f(ds_valid)
if self.ds_test is None:
ds_test, _ = self.loader_test.load(Pre.DATASET_CAPACITY // 2)
self.ds_test = f(ds_test)
print(self.ds_train.images.shape, self.ds_train.labels.shape)
print(self.ds_valid.images.shape, self.ds_valid.labels.shape)
print(self.ds_test.images.shape, self.ds_test.labels.shape)
def adapt_state(self, board):
board = board.reshape(-1, Board.BOARD_SIZE)
board = self.regulate(board)
return super(DCNN2, self).adapt_state(board)
def get_input_shape(self):
assert Board.BOARD_SIZE >= 5
height = 6 * Board.BOARD_SIZE - 18 # row vecs + col vecs + valid(len>=5) main diag vecs + valid(len>=5) anti diag vecs
return height, Board.BOARD_SIZE, Pre.NUM_CHANNELS
def mid_vis(self, feed_dict):
pass