from datetime import datetime, timedelta
sys.path.insert(0, '..')
from noveltyDetectionConfig import CONFIG
import numpy as np
import itertools
import multiprocessing
import pprint
from SAENoveltyDetectionAnalysis import SAENoveltyDetectionAnalysis
num_processes = multiprocessing.cpu_count()
from Functions.telegrambot import Bot
my_bot = Bot("lisa_thebot")
# Enviroment variables
data_path = CONFIG['OUTPUTDATAPATH']
results_path = CONFIG['PACKAGE_NAME']
training_params = {
"Technique": "StackedAutoEncoder",
"TechniqueParameters": {
"allow_change_weights": False #False
},
"DevelopmentMode": False,
"DevelopmentEvents": 400,
"NoveltyDetection": True,
"InputDataConfig": {
"database": "4classes",
gpu_id = args.gpu_id
tf.get_logger().setLevel(logging.ERROR)
tf.debugging.set_log_device_placement(False)
gpus = tf.config.experimental.list_physical_devices('GPU')
print("Num GPUs Available: ", len(gpus))
if gpus:
tf.config.experimental.set_visible_devices(gpus[gpu_id], f'GPU')
num_processes = multiprocessing.cpu_count()
my_bot = Bot("lisa_thebot")
# Enviroment variables
data_path = CONFIG['OUTPUTDATAPATH']
results_path = CONFIG['PACKAGE_NAME']
def main(training_params: dict):
analysis = VAENoveltyDetectionAnalysis(parameters=training_params,
load_hash=False,
load_data=True,
verbose=True)
all_data, all_trgt, trgt_sparse = analysis.getData()
analysis.emulate_novelties()
analysis.build_vae_models()
import os
import time
import multiprocessing
import pprint
from datetime import datetime, timedelta
import numpy as np
import matplotlib.pyplot as plt
from Packages.NoveltyDetection.setup.noveltyDetectionConfig import CONFIG
from Packages.NoveltyDetection.StackedAutoEncoders.SAENoveltyDetectionAnalysis import SAENoveltyDetectionAnalysis
from Functions.telegrambot import Bot
num_processes = multiprocessing.cpu_count()
my_bot = Bot("lisa_thebot")
# Enviroment variables
data_path = CONFIG['OUTPUTDATAPATH']
results_path = CONFIG['PACKAGE_NAME']
training_params = {"Technique": "StackedAutoEncoder"}
analysis = SAENoveltyDetectionAnalysis(
parameters=training_params,
# model_hash='3aa7b6b2a784922c348292561edca3d5201d6d6567f727e6ce7e403d7f175b10',
# model_hash="048afa2017b8b40203db1ef3f94e806a4d9772fe14b032a479f96f9551853574",
# model_hash='5fee10c0e061666bbd9b5ad4544503a562deb56f25bd5f80b9f2c8ca3bf76b81', # PF linear no decoder
model_hash=
'f84a0f006d8a19e5c3dbe25d4907659013495f79bb8b377e88673a899d9e2a2d', # PF tanh no decoder
load_hash=True,
load_data=True,