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scan_round.py
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scan_round.py
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def scan_round(self):
'''The main operational function that manages the experiment
on the level of execution of each round.'''
import time
import gc
# print round params
if self.print_params is True:
print(self.round_params)
# set start time
round_start = time.strftime('%D-%H%M%S')
start = time.time()
# fit the model
from ..model.ingest_model import ingest_model
self.model_history, self.round_model = ingest_model(self)
self.round_history.append(self.model_history.history)
# handle logging of results
from ..logging.logging_run import logging_run
self = logging_run(self, round_start, start, self.model_history)
# apply reductions
from ..reducers.reduce_run import reduce_run
self = reduce_run(self)
try:
# save model and weights
self.saved_models.append(self.round_model.to_json())
if self.save_weights:
self.saved_weights.append(self.round_model.get_weights())
else:
self.saved_weights.append(None)
except AttributeError as e:
# make sure that the error message is from torch
if str(e) == "'Model' object has no attribute 'to_json'":
if self.save_weights:
self.saved_models.append(self.round_model.state_dict())
else:
self.saved_weights.append(None)
# clear tensorflow sessions
if self.clear_session is True:
del self.round_model
gc.collect()
# try TF specific and pass for everyone else
try:
from keras import backend as K
K.clear_session()
except ImportError:
pass
return self