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"],
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 = []