self.std_batch_z = self.get_noise_batches(batch_size=10 *
10)
self.std_test_data = True
loss_G, loss_D, D1, D2, batch_fx = self.sess.run(
[self.loss_G, self.loss_D, self.D1, self.D2, self.G],
feed_dict={
self.x: self.std_batch_x,
self.y: self.std_batch_y,
self.z: self.std_batch_z,
self.keep_prob: 1.0
})
self.plot_losses(epoch=i, loss_G=loss_G, loss_D=loss_D)
self.plot_fake_data(epoch=i, batch_fx=batch_fx)
time_diff = time.time() - start_time
start_time = time.time()
logger.info(
"Epoch: {:3d} - L_G: {:0.3f} - L_D: {:0.3f} - D1: {:0.3f} - D2: {:0.3f} - Time: {:0.1f}"
.format(i, loss_G, loss_D, D1[0][0], D2[0][0], time_diff))
if __name__ == "__main__":
setup_logger(log_directory=get_project_directory("mnist_cnn", "logs"),
file_handler_type=HandlerType.TIME_ROTATING_FILE_HANDLER,
allow_console_logging=True,
allow_file_logging=True,
max_file_size_bytes=10000,
change_log_level=None)
mnist_gan_mlp = GAN_CNN(z_dim=10, batch_size=100)
mnist_gan_mlp.run(epochs=1000, batch_size=100, summary_epochs=1)
"""Entry point for the backend application."""
from pylogging import HandlerType, setup_logger
from flask_app import server
if __name__ == '__main__':
setup_logger(log_directory='./logs',
file_handler_type=HandlerType.TIME_ROTATING_FILE_HANDLER,
allow_console_logging=True,
allow_file_logging=False,
backup_count=100,
max_file_size_bytes=100000,
when_to_rotate='D',
change_log_level=None)
server.main()
# -*- coding: utf-8 -*-
import logging
import pickle
import time
from datetime import date, datetime
from datetime import timedelta
import feedparser
from pylogging import HandlerType, setup_logger
import json
from pymongo import MongoClient
logger = logging.getLogger(__name__)
setup_logger(log_directory='./logs',
file_handler_type=HandlerType.ROTATING_FILE_HANDLER,
allow_console_logging=True,
console_log_level=logging.DEBUG,
max_file_size_bytes=1000000)
from os import listdir
from os.path import isfile, join
client = MongoClient("localhost", 27017)
mydb = client["amivmat"]
mydb['links'].drop()
def insert(dictObject, db):
""" Inserts a given menu Object into the menus database.<br>
If an object with the current id, date, mensaName and lang allready exists, it will be updated """
""" res = db["menus"].update_one(
{"id": dictObject["id"],
"""Entry point for the backend application."""
from pylogging import HandlerType, setup_logger
from flask_app import server
if __name__ == '__main__':
setup_logger(log_directory='./logs',
file_handler_type=HandlerType.TIME_ROTATING_FILE_HANDLER,
allow_console_logging=True,
allow_file_logging=False,
backup_count=100,
max_file_size_bytes=100000,
when_to_rotate='D',
change_log_level=None)
server.main()
if it.heartbeat:
logger.info(it.itr_message())
logger.info('\tVisitationError:' +
str(it.pop_mean('VisitationInfNormError')))
return reward_fn, q_rew
if __name__ == "__main__":
# test IRL
# from q_iteration import q_iteration
from simple_env import random_env
# np.set_logger.infooptions(suppress=True)
setup_logger(log_directory='./logs',
file_handler_type=HandlerType.ROTATING_FILE_HANDLER,
allow_console_logging=True,
console_log_level="INFO")
# Environment parameters
env = random_env(16, 4, seed=1, terminate=False, t_sparsity=0.8)
dS = env.spec.observation_space.flat_dim
dU = env.spec.action_space.flat_dim
dO = 8
ent_wt = 1.0
discount = 0.9
obs_matrix = np.random.randn(dS, dO)
# Compute optimal policy for double checking
true_q = q_iteration(env, K=150, ent_wt=ent_wt, gamma=discount)
true_sa_visits = compute_visitation(env,
true_q,
from graypy import GELFUDPHandler
from pylogging import HandlerType, setup_logger
logger = logging.getLogger(__name__)
# If want to add extra fields.
# logger = logging.LoggerAdapter(logger, {"app_name": "test-service"})
if __name__ == '__main__':
gelf_handler = GELFUDPHandler(host="elk.recogizer.net",
port=12201,
level_names=True,
debugging_fields=False)
setup_logger(
log_directory='./logs',
file_handler_type=HandlerType.TIME_ROTATING_FILE_HANDLER,
allow_console_logging=True,
allow_file_logging=True,
backup_count=100,
max_file_size_bytes=100000,
when_to_rotate='D',
change_log_level=None,
gelf_handler=gelf_handler,
log_tags={"app_name": "Test-App"},
)
logger.error("Error logs")
logger.debug("Debug logs")
logger.info("Info logs")
from rllab.misc import logger as rllog
# from rllab.algos.ppo import PPO
from sandbox.rocky.tf.envs.base import TfEnv
from sandbox.rocky.tf.policies.gaussian_mlp_policy import GaussianMLPPolicy
log = logging.getLogger(__name__)
rllog.print = log.debug
rlc.print = log.debug
register_custom_envs()
setup_logger(log_directory='./logs',
file_handler_type=HandlerType.ROTATING_FILE_HANDLER,
allow_console_logging=True,
console_log_level="DEBUG",
change_log_level={
'tensorflow': 'error',
'matplotlib': 'error',
'GP': 'error',
'gpirl': 'info',
'gpirl.utils2': 'error',
__name__: 'info',
'gym': 'error'
})
data_path = 'data/lunarlander_demo/'
env_name = 'LunarLanderContinuous-v3'
state_var_names = 'state_0,state_1,state_2,state_3,state_4,state_5,state_6,state_7'
nstate_var_names = state_var_names.replace('state', 'next_state')
action_names = 'action_0,action_1'
log.debug("Column names : {},{},{}".format(state_var_names, action_names, nstate_var_names))
trajectories = []
experts = []
