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rework network

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This commit is contained in:
Christoffer Müller Madsen 2018-03-22 15:30:47 +01:00
parent b7e6dd10af
commit 98c9af72e7
3 changed files with 151 additions and 127 deletions
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46
main.py
View File

@ -102,29 +102,29 @@ if args.list_models:
sys.stderr.write(" {name}: {eps_trained}\n".format(name = model[0], eps_trained = model[1]))
exit()
# Set up network
from network import Network
network = Network(config, config['model'])
eps = config['start_episode']
# Set up variables
episode_count = config['episode_count']
if __name__ == "__main__":
# Set up network
from network import Network
network = Network(config, config['model'])
start_episode = network.episodes_trained
# Set up variables
episode_count = config['episode_count']
if args.train:
while True:
train_outcome = network.train_model(episodes = episode_count, trained_eps = eps)
eps += episode_count
log_train_outcome(train_outcome, trained_eps = eps)
if config['eval_after_train']:
eval_outcomes = network.eval(trained_eps = eps)
log_eval_outcomes(eval_outcomes, trained_eps = eps)
if not config['train_perpetually']:
break
elif args.eval:
eps = config['start_episode']
outcomes = network.eval()
log_eval_outcomes(outcomes, trained_eps = eps)
#elif args.play:
# g.play(episodes = episode_count)
if args.train:
while True:
train_outcome = network.train_model(episodes = episode_count, trained_eps = start_episode)
start_episode += episode_count
log_train_outcome(train_outcome, trained_eps = start_episode)
if config['eval_after_train']:
eval_outcomes = network.eval(trained_eps = start_episode)
log_eval_outcomes(eval_outcomes, trained_eps = start_episode)
if not config['train_perpetually']:
break
elif args.eval:
outcomes = network.eval()
log_eval_outcomes(outcomes, trained_eps = start_episode)
# elif args.play:
# g.play(episodes = episode_count)

230
network.py
View File

@ -13,7 +13,7 @@ class Network:
input_size = 26
output_size = 1
# Can't remember the best learning_rate, look this up
learning_rate = 0.1
learning_rate = 0.05
# TODO: Actually compile tensorflow properly
#os.environ["TF_CPP_MIN_LOG_LEVEL"]="2"
@ -23,12 +23,20 @@ class Network:
def __init__(self, config, name):
self.config = config
self.session = tf.Session()
self.checkpoint_path = config['model_path']
self.name = name
# Restore trained episode count for model
episode_count_path = os.path.join(self.checkpoint_path, "episodes_trained")
if os.path.isfile(episode_count_path):
with open(episode_count_path, 'r') as f:
self.episodes_trained = int(f.read())
else:
self.episodes_trained = 0
# input = x
self.x = tf.placeholder('float', [1, Network.input_size], name='x')
self.x = tf.placeholder('float', [1, Network.input_size], name='input')
self.value_next = tf.placeholder('float', [1, Network.output_size], name="value_next")
xavier_init = tf.contrib.layers.xavier_initializer()
@ -43,20 +51,22 @@ class Network:
b_2 = tf.get_variable("b_2", (Network.output_size,),
initializer=tf.zeros_initializer)
value_after_input = self.custom_tanh(tf.matmul(self.x, W_1) + b_1, name='hidden_layer')
normalized_input = tf.nn.l2_normalize(self.x)
value_after_input = tf.sigmoid(tf.matmul(normalized_input, W_1) + b_1, name='hidden_layer')
self.value = self.custom_tanh(tf.matmul(value_after_input, W_2) + b_2, name='output_layer')
self.value = tf.sigmoid(tf.matmul(value_after_input, W_2) + b_2, name='output_layer')
# tf.reduce_sum basically finds the sum of its input, so this gives the
# difference between the two values, in case they should be lists, which
# they might be if our input changes
# TODO: Alexander thinks that self.value will be computed twice (instead of once)
difference_in_values = tf.reduce_sum(self.value_next - self.value, name='difference')
difference_in_values = tf.reshape(tf.subtract(self.value_next, self.value, name='difference_in_values'), [])
tf.summary.scalar("difference_in_values", tf.abs(difference_in_values))
trainable_vars = tf.trainable_variables()
gradients = tf.gradients(self.value, trainable_vars)
apply_gradients = []
with tf.variable_scope('apply_gradients'):
@ -67,13 +77,10 @@ class Network:
apply_gradients.append(grad_apply)
self.training_op = tf.group(*apply_gradients, name='training_op')
self.saver = tf.train.Saver(max_to_keep=1)
self.session.run(tf.global_variables_initializer())
self.restore_model()
self.saver = tf.train.Saver(max_to_keep=1)
def eval_state(self, state):
def eval_state(self, sess, state):
# Run state through a network
# Remember to create placeholders for everything because wtf tensorflow
@ -107,25 +114,25 @@ class Network:
# print("Network is evaluating")
val = self.session.run(self.value, feed_dict={self.x: state})
#print("eval ({})".format(self.name), state, val, sep="\n")
return val
return sess.run(self.value, feed_dict={self.x: state})
def save_model(self, episode_count):
self.saver.save(self.session, os.path.join(self.checkpoint_path, 'model.ckpt'))
def save_model(self, sess, episode_count):
self.saver.save(sess, os.path.join(self.checkpoint_path, 'model.ckpt'))
with open(os.path.join(self.checkpoint_path, "episodes_trained"), 'w+') as f:
print("[NETWK] ({name}) Saving model to:".format(name = self.name),
os.path.join(self.checkpoint_path, 'model.ckpt'))
f.write(str(episode_count) + "\n")
def restore_model(self):
def restore_model(self, sess):
if os.path.isfile(os.path.join(self.checkpoint_path, 'model.ckpt.index')):
latest_checkpoint = tf.train.latest_checkpoint(self.checkpoint_path)
print("[NETWK] ({name}) Restoring model from:".format(name = self.name),
str(latest_checkpoint))
self.saver.restore(self.session, latest_checkpoint)
self.saver.restore(sess, latest_checkpoint)
variables_names = [v.name for v in tf.trainable_variables()]
values = self.session.run(variables_names)
values = sess.run(variables_names)
for k, v in zip(variables_names, values):
print("Variable: ", k)
print("Shape: ", v.shape)
@ -137,26 +144,10 @@ class Network:
with open(episode_count_path, 'r') as f:
self.config['start_episode'] = int(f.read())
# Have a circular dependency, #fuck, need to rewrite something
def adjust_weights(self, board, v_next):
# print("lol")
board = np.array(board).reshape((1,26))
self.session.run(self.training_op, feed_dict = { self.x: board,
self.value_next: v_next })
# while game isn't done:
#x_next = g.next_move()
#value_next = network.eval_state(x_next)
#self.session.run(self.training_op, feed_dict={self.x: x, self.value_next: value_next})
#x = x_next
def make_move(self, board, roll):
def make_move(self, sess, board, roll):
# print(Board.pretty(board))
legal_moves = Board.calculate_legal_states(board, 1, roll)
moves_and_scores = [ (move, self.eval_state(np.array(move).reshape(1,26))) for move in legal_moves ]
moves_and_scores = [ (move, self.eval_state(sess, np.array(move).reshape(1,26))) for move in legal_moves ]
scores = [ x[1] for x in moves_and_scores ]
best_score_index = np.array(scores).argmax()
best_move_pair = moves_and_scores[best_score_index]
@ -165,73 +156,101 @@ class Network:
def train_model(self, episodes=1000, save_step_size = 100, trained_eps = 0):
start_time = time.time()
with tf.Session() as sess:
writer = tf.summary.FileWriter("/tmp/log/tf", sess.graph)
sess.run(tf.global_variables_initializer())
self.restore_model(sess)
variables_names = [v.name for v in tf.trainable_variables()]
values = sess.run(variables_names)
for k, v in zip(variables_names, values):
print("Variable: ", k)
print("Shape: ", v.shape)
print(v)
def print_time_estimate(eps_completed):
cur_time = time.time()
time_diff = cur_time - start_time
eps_per_sec = eps_completed / time_diff
secs_per_ep = time_diff / eps_completed
eps_remaining = (episodes - eps_completed)
sys.stderr.write("[TRAIN] Averaging {per_sec} episodes per second\n".format(per_sec = round(eps_per_sec, 2)))
sys.stderr.write("[TRAIN] {eps_remaining} episodes remaining; approx. {time_remaining} seconds remaining\n".format(eps_remaining = eps_remaining, time_remaining = int(eps_remaining * secs_per_ep)))
start_time = time.time()
def print_time_estimate(eps_completed):
cur_time = time.time()
time_diff = cur_time - start_time
eps_per_sec = eps_completed / time_diff
secs_per_ep = time_diff / eps_completed
eps_remaining = (episodes - eps_completed)
sys.stderr.write("[TRAIN] Averaging {per_sec} episodes per second\n".format(per_sec = round(eps_per_sec, 2)))
sys.stderr.write("[TRAIN] {eps_remaining} episodes remaining; approx. {time_remaining} seconds remaining\n".format(eps_remaining = eps_remaining, time_remaining = int(eps_remaining * secs_per_ep)))
sys.stderr.write("[TRAIN] Training {} episodes and save_step_size {}\n".format(episodes, save_step_size))
outcomes = []
for episode in range(1, episodes + 1):
sys.stderr.write("[TRAIN] Episode {}".format(episode + trained_eps))
# TODO decide which player should be here
player = 1
sys.stderr.write("[TRAIN] Training {} episodes and save_step_size {}\n".format(episodes, save_step_size))
outcomes = []
for episode in range(1, episodes + 1):
sys.stderr.write("[TRAIN] Episode {}".format(episode + trained_eps))
# TODO decide which player should be here
player = 1
roll = (random.randrange(1,7), random.randrange(1,7))
prev_board, _ = self.make_move(Board.flip(Board.initial_state) if player == -1 else Board.initial_state, roll)
if player == -1:
prev_board = Board.flip(prev_board)
# find the best move here, make this move, then change turn as the
# first thing inside of the while loop and then call
# best_move_and_score to get V_t+1
# i = 0
while Board.outcome(prev_board) is None:
# print("-"*30)
# print(i)
# print(roll)
# print(Board.pretty(prev_board))
# print("/"*30)
# i += 1
player *= -1
roll = (random.randrange(1,7), random.randrange(1,7))
cur_board, cur_board_value = self.make_move(Board.flip(prev_board) if player == -1 else prev_board, roll)
prev_board, _ = self.make_move(sess, Board.flip(Board.initial_state) if player == -1 else Board.initial_state, roll)
if player == -1:
cur_board = Board.flip(cur_board)
self.adjust_weights(prev_board, cur_board_value)
prev_board = cur_board
final_board = prev_board
sys.stderr.write("\t outcome {}".format(Board.outcome(final_board)[1]))
outcomes.append(Board.outcome(final_board)[1])
final_score = np.array([ Board.outcome(final_board)[1] ])
self.adjust_weights(prev_board, final_score.reshape((1, 1)))
sys.stderr.write("\n")
prev_board = Board.flip(prev_board)
if episode % min(save_step_size, episodes) == 0:
sys.stderr.write("[TRAIN] Saving model...\n")
self.save_model(episode+trained_eps)
# find the best move here, make this move, then change turn as the
# first thing inside of the while loop and then call
# best_move_and_score to get V_t+1
if episode % 50 == 0:
print_time_estimate(episode)
# i = 0
while Board.outcome(prev_board) is None:
# print("-"*30)
# print(i)
# print(roll)
# print(Board.pretty(prev_board))
# print("/"*30)
# i += 1
player *= -1
roll = (random.randrange(1,7), random.randrange(1,7))
sys.stderr.write("[TRAIN] Saving model for final episode...\n")
self.save_model(episode+trained_eps)
cur_board, cur_board_value = self.make_move(sess, Board.flip(prev_board) if player == -1 else prev_board, roll)
if player == -1:
cur_board = Board.flip(cur_board)
# print("cur_board_value:", cur_board_value)
# adjust weights
sess.run(self.training_op,
feed_dict = { self.x: np.array(prev_board).reshape((1,26)),
self.value_next: cur_board_value })
prev_board = cur_board
final_board = prev_board
sys.stderr.write("\t outcome {}".format(Board.outcome(final_board)[1]))
outcomes.append(Board.outcome(final_board)[1])
final_score = np.array([ Board.outcome(final_board)[1] ])
scaled_final_score = ((final_score + 2) / 4)
# print("scaled_final_score",scaled_final_score)
with tf.name_scope("final"):
merged = tf.summary.merge_all()
summary, _ = sess.run([merged, self.training_op],
feed_dict = { self.x: np.array(prev_board).reshape((1,26)),
self.value_next: scaled_final_score.reshape((1, 1)) })
writer.add_summary(summary, episode + trained_eps)
sys.stderr.write("\n")
if episode % min(save_step_size, episodes) == 0:
sys.stderr.write("[TRAIN] Saving model...\n")
self.save_model(sess, episode+trained_eps)
if episode % 50 == 0:
print_time_estimate(episode)
sys.stderr.write("[TRAIN] Saving model for final episode...\n")
self.save_model(sess, episode+trained_eps)
writer.close()
return outcomes
return outcomes
# take turn, which finds the best state and picks it, based on the current network
@ -244,7 +263,7 @@ class Network:
def eval(self, trained_eps = 0):
def do_eval(method, episodes = 1000, trained_eps = 0):
def do_eval(sess, method, episodes = 1000, trained_eps = 0):
start_time = time.time()
def print_time_estimate(eps_completed):
@ -265,7 +284,7 @@ class Network:
board = Board.initial_state
while Board.outcome(board) is None:
roll = (random.randrange(1,7), random.randrange(1,7))
board = (self.p1.make_move(board, self.p1.get_sym(), roll))[0]
board = (self.p1.make_move(sess, board, self.p1.get_sym(), roll))[0]
roll = (random.randrange(1,7), random.randrange(1,7))
board = Board.flip(Eval.make_random_move(Board.flip(board), 1, roll))
sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
@ -288,7 +307,7 @@ class Network:
#print(roll)
prev_board = tuple(board)
board = (self.make_move(board, roll))[0]
board = (self.make_move(sess, board, roll))[0]
#print("post p1:", board, sep="\n")
#print("."*30)
@ -336,9 +355,14 @@ class Network:
else:
sys.stderr.write("[EVAL ] Evaluation method '{}' is not defined\n".format(method))
return [0]
return [ (method, do_eval(method,
self.config['episode_count'],
trained_eps = trained_eps))
for method
in self.config['eval_methods'] ]
with tf.Session() as session:
session .run(tf.global_variables_initializer())
self.restore_model(session)
outcomes = [ (method, do_eval(session,
method,
self.config['episode_count'],
trained_eps = trained_eps))
for method
in self.config['eval_methods'] ]
return outcomes

2
plot.py
View File

@ -44,7 +44,7 @@ if __name__ == '__main__':
plt.show()
while True:
df = dataframes('default')['eval']
df = dataframes('a')['eval']
print(df)