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enose_2025
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simm

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ifiguero 2025-03-18 01:30:46 -03:00
parent 470c4453ae
commit dc8e27abe8
2 changed files with 7 additions and 5 deletions
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8
TrainerClass.py
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@ -58,6 +58,8 @@ class eNoseTrainer:
target_log = '{}/load-{}.log'.format(self.name, self.start)
fh = logging.FileHandler(target_log)
optuna.logging.enable_propagation() # Propagate logs to the root logger.
optuna.logging.disable_default_handler() # Stop showing logs in sys.stderr.
self.debug = debug
@ -302,7 +304,7 @@ class eNoseTrainer:
plt.close()
def fit(self):
windows = [64, 128, 256,]
windows = [32, 64, 128,]
# windows = [128, 256, 384]
total_train_queue = 2*int(1/self.ratio)*(len(self.get_model_train())+len(windows))
self.logger.info("{:=^60}".format(f'Begin Fit {total_train_queue} Models'))
@ -473,7 +475,7 @@ class eNoseTrainer:
self.logger.debug(f"Y_train_sample: {Y_train_sample.shape}")
self.logger.debug(f"Y_test_sample: {Y_test_sample.shape}")
best_model, study, best_batch_size = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample, epochs=10, num_trials=32)
best_model, study, best_batch_size = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample, epochs=10, num_trials=10)
# Save study results to an Excel file
trials_data = []
for trial in study.trials:
@ -560,7 +562,7 @@ class eNoseTrainer:
self.logger.debug(f"Y_train_sample: {Y_train_sample.shape}")
self.logger.debug(f"Y_test_sample: {Y_test_sample.shape}")
best_model, study, best_batch_size = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample, epochs=10, num_trials=32)
best_model, study, best_batch_size = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample, epochs=10, num_trials=10)
# Save study results to an Excel file
trials_data = []
for trial in study.trials:

4
train_sequence.py
View File

@ -12,6 +12,8 @@ eNose = eNoseTrainer(eNoseLoader, test_size=0.2, debug=False)
eNoseLoader.target_list=['H2',]
eNose.fit()
eNoseLoader.target_list=['H2', 'C2H2', 'CH4', 'C2H4', 'C2H6',]
eNose.fit()
eNoseLoader.target_list=['C2H2',]
eNose.fit()
eNoseLoader.target_list=['CH4',]
@ -20,8 +22,6 @@ eNoseLoader.target_list=['C2H4',]
eNose.fit()
eNoseLoader.target_list=['C2H6',]
eNose.fit()
eNoseLoader.target_list=['H2', 'C2H2', 'CH4', 'C2H4', 'C2H6',]
eNose.fit()
eNose.wrap_and_save()
# eNoseLoader.target_list=['H2',]