# TODO: figure out best way to do this # TODO: Until then, this must be run from the root directory of the project # # statement below appears to re-run from inner directory # # from risk_definitions import ROOT_DIR MAX_ARMIES = 12 # ##### Working from root directory ##### # import repackage # repackage.up(1) # from model_tree import model_tree # ##### End Working from root directory ##### ##### Working from root directory ##### import repackage repackage.up(2) from q_funcs.model_tree import model_tree import utils ##### End Working from root directory ##### class TwoLayerAttackNet(): """ Class to hold a linear neural network Will be used to learn Attacks in RISK """ def __init__(self, nS, act_list, model_instance='0', checkpoint_index=-1,
#!/usr/bin/python import logging import logging.handlers import os from datetime import * import repackage import spotipy from dateutil import parser from spotipy.oauth2 import SpotifyOAuth from package import settings from dotenv import load_dotenv load_dotenv() repackage.up() logging.basicConfig( handlers=[logging.FileHandler(settings.logging['filepath'], 'a', 'utf-8')], format= '[%(asctime)s] [%(process)d][%(threadName)s - %(thread)d]- %(message)s', level=logging.INFO) log = logging.getLogger(__name__) class SpotifyUpdater(): sorted_out_playlist = settings.playlists['sorted_out'] current_playlist = settings.playlists['current'] whitelist_playlist = settings.playlists['whitelist'] counter = 0
""" import numpy as np import tensorflow as tf import matplotlib.pyplot as plt import os, sys # Define Root directory # TODO: figure out best way to do this # TODO: Until then, this must be run from the root directory of the project # # statement below appears to re-run from inner directory # # from risk_definitions import ROOT_DIR ##### Working from root directory ##### import repackage repackage.up(1) from model_tree import model_tree ##### End Working from root directory ##### class LeakyRelu3Layer(): """ Class to hold a linear neural network Will be used to learn Attacks in RISK """ def __init__(self, nS, act_list, model_instance='0', checkpoint_index=-1, learning_rate=0.001,