def run(architecture_file, model_file, num_batches=10, pattern="centralModel-*"):
print "evaluating Q values..."
redisInstance = Redis(host='localhost', port=6379, db=0)
model_keys = redisInstance.keys(pattern)
results = {}
net = BaristaNet(architecture_file, model_file, None)
replay_dataset = ReplayDataset("temp-q-converge-dset.hdf5",
net.state[0].shape,
dset_size=1000,
overwrite=True)
net.add_dataset(replay_dataset)
game = SnakeGame()
preprocessor = generate_preprocessor(net.state.shape[2:], gray_scale)
exp_gain = ExpGain(net, ['w', 'a', 's', 'd'], preprocessor, game.cpu_play,
replay_dataset, game.encode_state())
print "Generating new experiences..."
for _ in xrange(100):
exp_gain.generate_experience(1e5)
print "Done"
for key in model_keys:
print "Evaluating model:", key
model = dict(redisC.Dict(key=key, redis=redisInstance))
q_avg = evaluate_model(net, model, num_batches)
results[key] = q_avg
for key in sorted(results.keys()):
print key.ljust(25) + "%0.4f" % results[key]
def main():
args = get_args()
caffe.set_phase_test()
if args.mode == "cpu":
caffe.set_mode_cpu()
else:
caffe.set_mode_gpu()
if not os.path.isfile(args.model):
if not args.solver:
print "Error: Model does not exist. No solver specified."
sys.exit(1)
print "Warning: model %s does not exist. Creating..."
solver = SGDSolver(args.solver)
solver.net.save(args.model)
# Initialize objects
net = BaristaNet(args.architecture, args.model, args.driver,
reset_log=True)
replay_dataset = ReplayDataset(args.dataset, net.state[0].shape,
dset_size=args.dset_size,
overwrite=args.overwrite)
net.add_dataset(replay_dataset)
game = SnakeGame()
preprocessor = generate_preprocessor(net.state.shape[2:], gray_scale)
exp_gain = ExpGain(net, ['w', 'a', 's', 'd'], preprocessor, game.cpu_play,
replay_dataset, game.encode_state())
if(args.overwrite):
for _ in xrange(min(args.initial_replay, args.dset_size)):
exp_gain.generate_experience(0)
# Start server loop
serversocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
serversocket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
serversocket.bind(('127.0.0.1', args.port))
serversocket.listen(5)
print
print "*"*80
print "* Starting BARISTA server: listening on port %d." % args.port
print "*"*80
# Signal Spark Executor that Barista is ready to receive connections
issue_ready_signal(args.port)
while True:
(clientsocket, address) = serversocket.accept()
if args.debug:
handler = debug_process_connection
else:
handler = process_connection
client_thread = threading.Thread(
target=handler,
args=(clientsocket, net, exp_gain))
client_thread.run()
def main():
args = get_args()
# Instantiate network
bnet = baristanet.BaristaNet(args.architecture, args.model, None)
# load parameters from checkpoint into the model
# params = load_saved_checkpoint(args.checkpoint)
# netutils.set_net_params(bnet.net, params)
# Initialize game player
replay_dataset = ReplayDataset("temp-dset.hdf5", bnet.state[0].shape,
dset_size=300*args.num_games,
overwrite=True)
game = SnakeGame()
preprocessor = generate_preprocessor(bnet.state.shape[2:], gray_scale)
exp_gain = ExpGain(bnet, ['w', 'a', 's', 'd'], preprocessor, game.cpu_play,
replay_dataset, game.encode_state())
if not os.path.isdir(args.output_dir):
os.makedirs(args.output_dir)
# Generate experiences
frame_index = 0
num_games_played = 0
state = exp_gain.get_preprocessed_state()
while num_games_played < args.num_games:
# select action
if random.random() < args.epsilon:
action = random.choice(exp_gain.actions)
else:
idx = bnet.select_action([state])
action = exp_gain.actions[idx]
exp_gain.play_action(action)
# Render frame
frame = gray_scale(exp_gain.sequence[-1].reshape((1,)+exp_gain.sequence[-1].shape))[-1]
big_frame = cv2.resize(frame, (0,0), fx=10, fy=10, interpolation=cv2.INTER_NEAREST)
cv2.imwrite(os.path.join(args.output_dir, "frame-%d.png" % frame_index), big_frame)
frame_index += 1
# cv2.imshow("Game", frame)
# cv2.waitKey(33)
# Check if Snake has died
if exp_gain.game_over:
print "Game Over"
exp_gain.reset_game()
num_games_played += 1
# Get next state
state = exp_gain.get_preprocessed_state()
def main():
args = get_args()
# Instantiate network
bnet = baristanet.BaristaNet(args.architecture, args.model, None)
# load parameters from checkpoint into the model
params = load_saved_checkpoint(args.checkpoint)
netutils.set_net_params(bnet.net, params)
# Initialize game player
replay_dataset = ReplayDataset("temp-dset.hdf5", bnet.state[0].shape,
dset_size=300*args.num_games,
overwrite=True)
game = SnakeGame()
preprocessor = generate_preprocessor(bnet.state.shape[2:], gray_scale)
exp_gain = ExpGain(bnet, ['w', 'a', 's', 'd'], preprocessor, game.cpu_play,
replay_dataset, game.encode_state())
if not os.path.isdir(args.output_dir):
os.makedirs(args.output_dir)
# Generate experiences
frame_index = 0
num_games_played = 0
state = exp_gain.get_preprocessed_state()
while num_games_played < args.num_games:
# select action
if random.random() < args.epsilon:
action = random.choice(exp_gain.actions)
else:
idx = bnet.select_action([state])
action = exp_gain.actions[idx]
exp_gain.play_action(action)
# Render frame
frame = gray_scale(exp_gain.sequence[-1].reshape((1,)+exp_gain.sequence[-1].shape))[-1]
big_frame = cv2.resize(frame, (0,0), fx=10, fy=10, interpolation=cv2.INTER_NEAREST)
cv2.imwrite(os.path.join(args.output_dir, "frame-%d.png" % frame_index), big_frame)
frame_index += 1
# cv2.imshow("Game", frame)
# cv2.waitKey(33)
# Check if Snake has died
if exp_gain.game_over:
print "Game Over"
exp_gain.reset_game()
num_games_played += 1
# Get next state
state = exp_gain.get_preprocessed_state()
def __init__(self, architecture_file, model_file):
# Initialize objects
self.net = BaristaNet(architecture_file, model_file, None)
self.batch_size = self.net.batch_size
game = SnakeGame()
preprocessor = generate_preprocessor(self.net.state.shape[2:],
gray_scale)
self.engines = [ExpGain(self.net, ['w', 'a', 's', 'd'],
preprocessor, game.cpu_play,
None, game.encode_state())
for _ in range(self.batch_size)]
def main():
args = get_args()
caffe.set_phase_test()
if args.mode == "cpu":
caffe.set_mode_cpu()
else:
caffe.set_mode_gpu()
if not os.path.isfile(args.model):
if not args.solver:
print "Error: Model does not exist. No solver specified."
sys.exit(1)
print "Warning: model %s does not exist. Creating..."
solver = SGDSolver(args.solver)
solver.net.save(args.model)
# Initialize objects
net = BaristaNet(args.architecture,
args.model,
args.driver,
reset_log=True)
replay_dataset = ReplayDataset(args.dataset,
net.state[0].shape,
dset_size=args.dset_size,
overwrite=args.overwrite)
net.add_dataset(replay_dataset)
game = SnakeGame()
preprocessor = generate_preprocessor(net.state.shape[2:], gray_scale)
exp_gain = ExpGain(net, ['w', 'a', 's', 'd'], preprocessor, game.cpu_play,
replay_dataset, game.encode_state())
if (args.overwrite):
for _ in xrange(min(args.initial_replay, args.dset_size)):
exp_gain.generate_experience(0)
# Start server loop
serversocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
serversocket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
serversocket.bind(('127.0.0.1', args.port))
serversocket.listen(5)
print
print "*" * 80
print "* Starting BARISTA server: listening on port %d." % args.port
print "*" * 80
# Signal Spark Executor that Barista is ready to receive connections
issue_ready_signal(args.port)
while True:
(clientsocket, address) = serversocket.accept()
if args.debug:
handler = debug_process_connection
else:
handler = process_connection
client_thread = threading.Thread(target=handler,
args=(clientsocket, net, exp_gain))
client_thread.run()