from run_rnn import GruSettings, run_gru settings = GruSettings() settings.max_verbosity = 2 settings.epochs = 200 settings.epochs_retrain = 60 settings.online = True nodes = 200 lr = 0.003#231262326395 settings.nodes = nodes settings.batch_size = 128 settings.lr = lr settings.loss = 'mae' settings.stateful=False settings.lookback = 50 settings.lookback_as_features=False settings.feature_count = 3 settings.predictionStep = 5 settings.season = 48 settings.adam_eps = 0.001 settings.retrain_interval = 1000 settings.reset_on_retrain = False settings.refeed_on_retrain = True settings.cutoff_normalize = True settings.use_dropout = True settings.nTrain = 5000 settings.ignore_for_error = [5500,10000] settings.normalization_type = 'default' settings.implementation = 'keras' settings.rnn_type = 'lstm'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' #Suppress build warnings from run_rnn import GruSettings, run_gru settings = GruSettings() settings.max_verbosity = 2 settings.epochs = 50 settings.epochs_retrain = 25 settings.online = True nodes = 40 #317 lr = 0.003 #0.0031802801373# 0.0077 settings.nodes = nodes settings.batch_size = 1024 settings.lr = lr settings.loss = 'mae' settings.stateful = False settings.lookback = 60 #None to not use lookbacks settings.lookback_as_features = True #Considers each lookback value a separate feature if True, ignored if lookback is None settings.feature_count = 3 #Uses the first `feature_count` selected columns as features, ignored if `lookback_as_features` settings.predictionStep = 5 settings.season = 48 settings.adam_eps = 0.001 settings.retrain_interval = 1000 settings.reset_on_retrain = False settings.onego_refeed_on_retrain = True settings.cutoff_normalize = True settings.use_dropout = False settings.nTrain = 5000 settings.ignore_for_error = [5500, 10000] settings.normalization_type = 'default' settings.implementation = 'keras' settings.rnn_type = 'lstm'
sys.path.append(".") #..welp
from run_rnn import GruSettings, run_gru
settings = GruSettings()
settings.max_verbosity = 2
settings.epochs = 100
settings.online = True
nodes = 20
lr = 0.1
settings.nodes = nodes
settings.batch_size = 1
settings.lr = lr
settings.loss = 'mse'
settings.stateful = False
settings.lookback = None
settings.lookback_as_features = False
settings.feature_count = 3
settings.predictionStep = 5
settings.season = 48
settings.adam_eps = 0.001
settings.retrain_interval = 336
settings.reset_on_retrain = True
settings.refeed_on_retrain = False
settings.cutoff_normalize = False
settings.use_dropout = False
settings.nTrain = 5000
settings.ignore_for_error = [5500, 10000]
settings.normalization_type = 'default'
settings.implementation = 'keras'
settings.rnn_type = 'lstm'
5: lr
6: lookback
7: epochs
8: online
9: batch
10: lb_as_features
11: feature_count
12: implementation
13: adam epsilon
"""
settings = GruSettings()
settings.max_verbosity = 0
settings.epochs = int(argv[7])
settings.online = True if argv[8] == "True" else False
try:
settings.lookback = int(argv[6])
except:
settings.lookback = None
settings.nodes = int(argv[3])
try:
settings.batch_size = int(argv[9])
except:
settings.batch_size = None
settings.retrain_interval = int(argv[4])
settings.lookback_as_features = True if argv[10] == "True" else False
settings.feature_count = int(argv[11])
settings.lr = float(argv[5])
settings.predictionStep = 5
settings.season = 48
settings.ignore_for_error = [5500]
settings.nTrain = 5000