def main():
parser = argparse.ArgumentParser()
parser.add_argument('--bind-address', default='127.0.0.1')
parser.add_argument('--port', '-p', type=int, default=5000)
parser.add_argument('--pg-agent')
parser.add_argument('--predict-agent')
parser.add_argument('--q-agent')
parser.add_argument('--ac-agent')
args = parser.parse_args()
bots = {'mcts': mcts.MCTSAgent(800, temperature=0.7)}
if args.pg_agent:
bots['pg'] = agent.load_policy_agent(h5py.File(args.pg_agent))
if args.predict_agent:
bots['predict'] = agent.load_prediction_agent(
h5py.File(args.predict_agent))
if args.q_agent:
q_bot = rl.load_q_agent(h5py.File(args.q_agent))
q_bot.set_temperature(0.01)
bots['q'] = q_bot
if args.ac_agent:
ac_bot = rl.load_ac_agent(h5py.File(args.ac_agent))
ac_bot.set_temperature(0.05)
bots['ac'] = ac_bot
web_app = httpfrontend.get_web_app(bots)
web_app.run(host=args.bind_address, port=args.port, threaded=False)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--bind-address', default='127.0.0.1')
parser.add_argument('--port', '-p', type=int, default=5000)
args = parser.parse_args()
zero_agent = {
'zero': zero.load_zero_agent(h5py.File("bots/zeroagent_v1.hdf5", 'r'))
}
web_app = httpfrontend.get_web_app(zero_agent)
web_app.run(host=args.bind_address, port=args.port, threaded=False)
def main():
samp = 1000
epo = 1
# tag::e2e_processor[]
timestr = time.strftime("%Y%m%d-%H%M%S")
model_h5filename = "./agents/deep_bot_" + timestr + "_s" + str(
samp) + "e" + str(epo) + ".h5"
go_board_rows, go_board_cols = 19, 19
nb_classes = go_board_rows * go_board_cols
encoder = XPlaneEncoder((go_board_rows, go_board_cols))
data_dir = "data/" + str(encoder.num_planes) + "-planes"
processor = GoDataProcessor(encoder=encoder.name(),
data_directory=data_dir)
X, y = processor.load_go_data(num_samples=samp)
# end::e2e_processor[]
# tag::e2e_model[]
input_shape = (encoder.num_planes, go_board_rows, go_board_cols)
model = Sequential()
network_layers = large.layers(input_shape)
for layer in network_layers:
model.add(layer)
try:
with tf.device('/device:GPU:0'):
model.add(Dense(nb_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
model.fit(X, y, batch_size=128, epochs=epo, verbose=1)
# end::e2e_model[]
# tag::e2e_agent[]
deep_learning_bot = DeepLearningAgent(model, encoder)
deep_learning_bot.serialize(h5py.File(model_h5filename, "w"))
# end::e2e_agent[]
# tag::e2e_load_agent[]
model_file = h5py.File(model_h5filename, "r")
bot_from_file = load_prediction_agent(model_file)
web_app = get_web_app({'predict': bot_from_file})
web_app.run()
# end::e2e_load_agent[]
except RuntimeError as e:
print(e)
def main():
workdir = '//home/nail//Code_Go//checkpoints//'
os.chdir(workdir)
bind_address = '127.0.0.1'
port = 5000
predict_agent, pg_agent, q_agent, ac_agent = '', '', '', ''
agent_type = input('Агент(pg/predict/q/ac = ').lower()
if agent_type == 'pg':
pg_agent = input(
'Введите имя файла для игры с ботом политика градиентов =')
pg_agent = workdir + pg_agent + '.h5'
if agent_type == 'predict':
predict_agent = input(
'Введите имя файла для игры с ботом предсказания хода =')
predict_agent = workdir + predict_agent + '.h5'
if agent_type == 'q':
q_agent = input(
'Введите имя файла для игры с ботом ценность действия =')
q_agent = workdir + q_agent + '.h5'
if agent_type == 'ac':
ac_agent = input('Введите имя файла для игры с ботом актор-критик =')
ac_agent = workdir + ac_agent + '.h5'
bots = {'mcts': mcts.MCTSAgent(800, temperature=0.7)}
if agent_type == 'pg':
bots['pg'] = agent.load_policy_agent(h5py.File(pg_agent, 'r'))
if agent_type == 'predict':
bots['predict'] = agent.load_prediction_agent(
h5py.File(predict_agent, 'r'))
if agent_type == 'q':
q_bot = rl.load_q_agent(h5py.File(q_agent, 'r'))
q_bot.set_temperature(0.01)
bots['q'] = q_bot
if agent_type == 'ac':
ac_bot = rl.load_ac_agent(h5py.File(ac_agent, 'r'))
ac_bot.set_temperature(0.05)
bots['ac'] = ac_bot
web_app = httpfrontend.get_web_app(bots)
web_app.run(host=bind_address, port=port, threaded=False)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# Include the path to the local version of dlgo.
dir_path = os.path.dirname(os.path.realpath(__file__))
sys.path.insert(0, dir_path)
from dlgo.agent.predict import DeepLearningAgent, load_prediction_agent
from dlgo.httpfrontend import get_web_app
# load model
model_file = h5py.File("betago/agents/predict_net.hdf5", "r")
bot_from_file = load_prediction_agent(model_file)
model_file.close()
# build app
app = get_web_app({'predict': bot_from_file})
# add favicon route
@app.route("/favicon.ico")
def favicon():
return send_from_directory(os.path.join(dir_path, "static"),
"favicon.ico",
mimetype="image/vnd.microsoft.icon")
# add home page route
@app.route('/')
def index():
return redirect(url_for("static", filename="play_predict_19.html"))
def main():
bot = mcts.MCTSAgent(700, temperature=1.4)
web_app = httpfrontend.get_web_app({'mcts': bot})
web_app.run()
def main():
bot = mcts.MCTSAgent(700, temperature=1.4)
web_app = httpfrontend.get_web_app(bot, BOARD_SIZE)
web_app.run()
import sys
sys.path.append('../')
from dlgo.agent import load_prediction_agent, load_policy_agent, AlphaGoMCTS
from dlgo.rl import load_value_agent
import h5py
from dlgo import httpfrontend
# 13.4 end
fast_policy = load_prediction_agent(
h5py.File('alphago_sl_policy_e20_2k.h5', 'r'))
strong_policy = load_policy_agent(h5py.File('alphago_rl_policy_e20_2k.h5',
'r'))
value = load_value_agent(h5py.File('alphago_value_e20_2k.h5', 'r'))
alphago = AlphaGoMCTS(strong_policy, fast_policy, value)
# HTTP FRONTEND
bots = {}
bots['predict'] = alphago
web_app = httpfrontend.get_web_app(bots)
web_app.run(threaded=False)
#
# TODO: implement GTP frontend