# 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,
