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enose_2025
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ifiguero 2025-03-16 05:02:01 -03:00
parent a3a4bd9238
commit 88f9b142ff
1 changed files with 10 additions and 2 deletions
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12
TrainerClass.py
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@ -154,6 +154,11 @@ class eNoseTrainer:
def train_and_score_conv1D_v1(self, X_train, X_test, y_train, y_test, epochs=30, num_samples=25):
ray.init(ignore_reinit_error=True)
X_train_ref = ray.put(X_train)
Y_train_ref = ray.put(Y_train)
X_test_ref = ray.put(X_test)
Y_test_ref = ray.put(Y_test)
def build_model(config, input_shape, output_dim):
model = keras.Sequential([
layers.Conv1D(filters=config['filters'], kernel_size=config['kernel_size'], activation='relu', input_shape=input_shape),
@ -199,8 +204,11 @@ class eNoseTrainer:
}
scheduler = ASHAScheduler(metric='mse', mode='min', max_t=epochs, grace_period=5, reduction_factor=2)
analysis = tune.run(train_model, config=config_space, num_samples=num_samples, scheduler=scheduler)
# analysis = tune.run(train_model, config=config_space, num_samples=num_samples, scheduler=scheduler)
analysis = tune.run(
tune.with_parameters(train_model, X_train=ray.get(X_train_ref), Y_train=ray.get(Y_train_ref), X_test=ray.get(X_test_ref), Y_test=ray.get(Y_test_ref)),
config=config_space, num_samples=num_samples, scheduler=scheduler
)
best_config = analysis.get_best_config(metric='mse', mode='min')
best_model = build_model(best_config, X_train.shape[1:], Y_train.shape[1])
best_model.fit(X_train, Y_train, epochs=epochs, batch_size=best_config['batch_size'], verbose=0)