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enose_2025
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ifiguero 2025-03-16 22:33:58 -03:00
parent 9d509ab8c7
commit c8439445f0
1 changed files with 6 additions and 5 deletions
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11
TrainerClass.py
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@ -237,7 +237,7 @@ class eNoseTrainer:
'dropout': tune.choice([0.05, 0.15, 0.3]),
'lr': tune.choice([0.01, 0.005, 0.001]),
'batch_size': tune.choice([16, 32, 64]),
'epochs': epochssample_space
'epochs': epochs
}
total_space = (3*3*3*2*3*3*3*3)
@ -492,6 +492,7 @@ class eNoseTrainer:
self.saveCheckPoint()
sample_size = 50000
epochs = 50
self.loader.smooth = None
self.loader.reset()
for window in windows:
@ -530,10 +531,10 @@ class eNoseTrainer:
self.logger.debug(f"Y_train_sample: {Y_train_sample.shape}")
self.logger.debug(f"Y_test_sample: {Y_test_sample.shape}")
optimized_model, model_params = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample)
optimized_model, model_params = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample, epochs=epochs//3)
self.logger.info(f"Training Model {model_id} with {model_params}")
optimized_model.fit(X_train, Y_train, epochs=model_params['epochs'], batch_size=model_params['batch_size'], verbose=1)
optimized_model.fit(X_train, Y_train, epochs=epochs, batch_size=model_params['batch_size'], verbose=1)
Y_train_pred = optimized_model.predict(X_train)
Y_test_pred = optimized_model.predict(X_test)
@ -606,10 +607,10 @@ class eNoseTrainer:
self.logger.debug(f"Y_train_sample: {Y_train_sample.shape}")
self.logger.debug(f"Y_test_sample: {Y_test_sample.shape}")
optimized_model, model_params = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample)
optimized_model, model_params = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample, epochs=epochs//3)
self.logger.info(f"Training Model {model_id} with {model_params}")
optimized_model.fit(X_train, Y_train, epochs=model_params['epochs'], batch_size=model_params['batch_size'], verbose=1)
optimized_model.fit(X_train, Y_train, epochs=epochs, batch_size=model_params['batch_size'], verbose=1)
Y_train_pred = optimized_model.predict(X_train)
Y_test_pred = optimized_model.predict(X_test)