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Better detection of previous files

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David Rotermund 2022-04-30 17:46:17 +02:00 committed by GitHub
parent f3385b34ac
commit 9cfd4eb740
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1 changed files with 35 additions and 17 deletions
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52
learn_it.py
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@ -41,6 +41,7 @@ import time
import dataconf
import logging
from datetime import datetime
import glob
from Dataset import (
DatasetMaster,
@ -220,26 +221,43 @@ for id in range(0, len(network)):
+ ".npy"
)
# Are there weights that overwrite the initial weights?
file_to_load: str = (
cfg.learning_parameters.overload_path + "/Weight_L" + str(id) + ".npy"
)
if os.path.exists(file_to_load) is True:
network[id].weights = torch.tensor(
np.load(file_to_load),
dtype=torch.float64,
)
wf[id] = np.load(file_to_load)
logging.info(f"File used: {file_to_load}")
for id in range(0, len(network)):
file_to_load = cfg.learning_parameters.overload_path + "/EpsXY_L" + str(id) + ".npy"
if os.path.exists(file_to_load) is True:
network[id].epsilon_xy = torch.tensor(
np.load(file_to_load),
# Are there weights that overwrite the initial weights?
file_to_load = glob.glob(
cfg.learning_parameters.overload_path + "/Weight_L" + str(id) + "*.npy"
)
if len(file_to_load) > 1:
raise Exception(
f"Too many previous weights files {cfg.learning_parameters.overload_path}/Weight_L{id}*.npy"
)
if len(file_to_load) == 1:
network[id].weights = torch.tensor(
np.load(file_to_load[0]),
dtype=torch.float64,
)
eps_xy[id] = np.load(file_to_load)
logging.info(f"File used: {file_to_load}")
wf[id] = np.load(file_to_load[0])
logging.info(f"File used: {file_to_load[0]}")
# Are there epsinlon xy files that overwrite the initial epsilon xy?
file_to_load = glob.glob(
cfg.learning_parameters.overload_path + "/EpsXY_L" + str(id) + "*.npy"
)
if len(file_to_load) > 1:
raise Exception(
f"Too many previous epsilon xy files {cfg.learning_parameters.overload_path}/EpsXY_L{id}*.npy"
)
if len(file_to_load) == 1:
network[id].epsilon_xy = torch.tensor(
np.load(file_to_load[0]),
dtype=torch.float64,
)
eps_xy[id] = np.load(file_to_load[0])
logging.info(f"File used: {file_to_load[0]}")
#######################################################################
# Optimizer and LR Scheduler #