to tune hyperparamters of deep studying fashions (Keras Sequential model), compared with a conventional method — Grid Search.
Bayesian Optimization
Bayesian Optimization is a sequential design technique for international optimization of black-box capabilities.
It’s significantly well-suited for capabilities which might be costly to judge, lack an analytical type, or have unknown derivatives.
Within the context of hyperparameter optimization, the unknown perform could be:
- an goal perform,
- accuracy worth for a coaching or validation set,
- loss worth for a coaching or validation set,
- entropy gained or misplaced,
- AUC for ROC curves,
- A/B check outcomes,
- computation value per epoch,
- mannequin measurement,
- reward quantity for reinforcement studying, and extra.
In contrast to conventional optimization strategies that depend on direct perform evaluations, Bayesian Optimization builds and refines a probabilistic mannequin of the target perform, utilizing this mannequin to intelligently choose the subsequent analysis level.
The core thought revolves round two key elements:
1. Surrogate Mannequin (Probabilistic Mannequin)
The mannequin approximates the unknown goal perform (f(x)) to a surrogate mannequin resembling Gaussian Course of (GP).
A GP is a non-parametric Bayesian mannequin that defines a distribution over capabilities. It present:
- a prediction of the perform worth at a given level μ(x) and
- a measure of uncertainty round that prediction σ(x), usually represented as a confidence interval.
Mathematically, for a Gaussian Course of, the predictions at an unobserved level (x∗), given noticed information (X, y), are usually distributed:
the place
- μ(x∗): the imply prediction and
- σ²(x∗): the predictive variance.
2. Acquisition Perform
The acquisition perform determines a subsequent level (x_t+1) to judge by quantifying how “promising” a candidate level is for bettering the target perform, by balancing:
- Exploration (Excessive Variance): Sampling in areas with excessive uncertainty to find new promising areas and
- Exploitation (Excessive Imply): Sampling in areas the place the surrogate mannequin predicts excessive goal values.
Frequent acquisition capabilities embody:
Chance of Enchancment (PI)
PI selects the purpose that has the best chance of bettering upon the present finest noticed worth (f(x+)):
the place
- Φ: the cumulative distribution perform (CDF) of the usual regular distribution, and
- ξ≥0 is a trade-off parameter (exploration vs. exploitation).
ξ controls a trade-off between exploration and exploitation, and a bigger ξ encourages extra exploration.
Anticipated Enchancment (EI)
Quantifies the anticipated quantity of enchancment over the present finest noticed worth:
Assuming a Gaussian Course of surrogate, the analytical type of EI is outlined:
the place ϕ is the chance density perform (PDF) of the usual regular distribution.
EI is without doubt one of the most generally used acquisition capabilities. EI additionally considers the magnitude of the development in contrast to PI.
Higher Confidence Certain (UCB)
UCB balances exploitation (excessive imply) and exploration (excessive variance), specializing in factors which have each a excessive predicted imply and excessive uncertainty:
κ≥0 is a tuning parameter that controls the stability between exploration and exploitation.
A bigger κ places extra emphasis on exploring unsure areas.
Bayesian Optimization Technique (Iterative Course of)
Bayesian Optimization iteratively updates the surrogate mannequin and optimizes the acquisition perform.
It guides the search in direction of optimum areas whereas minimizing the variety of costly goal perform evaluations.
Now, allow us to see the method with code snippets utilizing KerasTuner for a fraud detection activity (binary classification the place y=1 (fraud) prices us essentially the most.)
Step 1. Initialization
Initializes the method by sampling the hyperparameter house randomly or low-discrepancy sequencing (ususally selecting up 5 to 10 factors) to get an thought of the target perform.
These preliminary observations are used to construct the primary model of the surrogate mannequin.
As we construct Keras Sequential mannequin, we first outline and compile the mannequin, then outline theBayesianOptimization tuner with the variety of preliminary factors to evaluate.
import keras_tuner as kt
import tensorflow as tf
from tensorflow import keras
from keras.fashions import Sequential
from keras.layers import Dense, Dropout, Enter
# initialize a Keras Sequential mannequin
mannequin = Sequential([
Input(shape=(self.input_shape,)),
Dense(
units=hp.Int(
'neurons1', min_value=20, max_value=60, step=10),
activation='relu'
),
Dropout(
hp.Float(
'dropout_rate1', min_value=0.0, max_value=0.5, step=0.1
)),
Dense(
units=hp.Int(
'neurons2', min_value=20, max_value=60, step=10),
activation='relu'
),
Dropout(
hp.Float(
'dropout_rate2', min_value=0.0, max_value=0.5, step=0.1
)),
Dense(
1, activation='sigmoid',
bias_initializer=keras.initializers.Constant(
self.initial_bias_value
)
)
])
# compile the mannequin
mannequin.compile(
optimizer=optimizer,
loss='binary_crossentropy',
metrics=[
'accuracy',
keras.metrics.Precision(name='precision'),
keras.metrics.Recall(name='recall'),
keras.metrics.AUC(name='auc')
]
)
# outline a tuner with the intial factors
tuner = kt.BayesianOptimization(
hypermodel=custom_hypermodel,
goal=kt.Goal("val_recall", route="max"),
max_trials=max_trials,
executions_per_trial=executions_per_trial,
listing=listing,
project_name=project_name,
num_initial_points=num_initial_points,
overwrite=True,
)num_initial_points defines what number of preliminary, randomly chosen hyperparameter configurations needs to be evaluated earlier than the algorithm begins to information the search.
If not given, KerasTuner takes a default worth: 3 * dimensions of the hyperparameter house.
Step 2. Surrogate Mannequin Coaching
Construct and prepare the probabilistic mannequin (surrogate mannequin, usually a Gaussian Course of or a Tree-structured Parzen Estimator for Bayesian Optimization) utilizing all out there noticed datas factors (enter values and their corresponding output values) to approximate the true perform.
The surrogate mannequin gives the imply prediction (μ(x)) (almost definitely from the Gaussian course of) and uncertainty (σ(x)) for any unobserved level.
KerasTuner makes use of an inner surrogate mannequin to mannequin the connection between hyperparameters and the target perform’s efficiency.
After every goal perform analysis through prepare run, the noticed information factors (hyperparameters and validation metrics) are used to replace the inner surrogate mannequin.
Step 3. Acquisition Perform Optimization
Use an optimization algorithm (usually an inexpensive, native optimizer like L-BFGS and even random search) to search out the subsequent level (x_t+1) that maximizes the chosen acquisition perform.
This step is essential as a result of it identifies essentially the most promising subsequent candidate for analysis by balancing exploration (making an attempt new, unsure areas of the hyperparameter house) and exploitation (refining promising areas).
KerasTuner makes use of an optimization technique resembling Anticipated Enchancment or Higher Confidence Certain to search out the subsequent set of hyperparameters.
Step 4. Goal Perform Analysis
Consider the true, costly goal perform (f(x)) on the new candidate level (x_t+1).
The Keras mannequin is educated utilizing the offered coaching datasets and evaluated on the validation information. We set val_recall as the results of this analysis.
def match(self, hp, mannequin=None, *args, **kwargs):
mannequin = self.construct(hp=hp) if not mannequin else mannequin
batch_size = hp.Alternative('batch_size', values=[16, 32, 64])
epochs = hp.Int('epochs', min_value=50, max_value=200, step=50)
return mannequin.match(
batch_size=batch_size,
epochs=epochs,
class_weight=self.class_weights_dict,
*args,
**kwargs
)Step 5. Knowledge Replace
Add the newly noticed information level (x_(t+1), f(x_(t+1))) to the set of observations.
Step 6. Iteration
Repeat Step 2 — 5 till a stopping criterion is met.
Technically, the tuner.search() technique orchestrates the whole Bayesian optimization course of from Step 2 to five:
tuner.search(
X_train, y_train,
validation_data=(X_val, y_val),
callbacks=[early_stopping_callback]
)
best_hp = tuner.get_best_hyperparameters(num_trials=1)[0]
best_keras_model_from_tuner = tuner.get_best_models(num_models=1)[0]The tactic repeatedly performs these steps till the max_trials restrict is reached or different inner stopping standards resembling early_stopping_callback are met.
Right here, we set recall as our key metrics to penalize the misclassification as False Constructive prices us essentially the most within the fraud detection case.
Study Extra: KerasTuner Supply Code
Outcomes
The Bayesian Optimization course of aimed to reinforce the mannequin’s efficiency, primarily by maximizing recall.
The tuning efforts yielded a trade-off throughout key metrics, leading to a mannequin with considerably improved recall on the expense of some precision and total accuracy in comparison with the preliminary state:
- Recall: 0.9055 (0.6595 -> 0.6450) — 0.8400
- Precision: 0.6831 (0.8338 -> 0.8113) — 0.6747
- Accuracy: 0.7427 (0.7640 -> 0.7475) — 0.7175
(From improvement (coaching / validation mixed) to check section)
Finest performing hyperparameter set:
- neurons1: 40
- dropout_rate1: 0.0
- neurons2: 20,
- dropout_rate2: 0.4
- optimizer_name: lion,
- learning_rate: 0.004019639999963362
- batch_size: 64
- epochs: 200
- beta_1_lion: 0.9
- beta_2_lion: 0.99
Optimum Neural Community Abstract:
Key Efficiency Metrics:
- Recall: The mannequin demonstrated a big enchancment in recall, growing from an preliminary worth of roughly 0.66 (or 0.645) to 0.8400. This means the optimized mannequin is notably higher at figuring out optimistic instances.
- Precision: Concurrently, precision skilled a lower. Ranging from round 0.83 (or 0.81), it settled at 0.6747 post-optimization. This implies that whereas extra optimistic instances are being recognized, the next proportion of these identifications may be false positives.
- Accuracy: The general accuracy of the mannequin additionally noticed a decline, transferring from an preliminary 0.7640 (or 0.7475) all the way down to 0.7175. That is in step with the noticed trade-off between recall and precision, the place optimizing for one usually impacts the others.
Evaluating with Grid Search
We tuned a Keras Sequential mannequin with Grid Search on Adam optimizer for comparability:
import tensorflow as tf
from tensorflow import keras
from keras.fashions import Sequential
from keras.layers import Dense, Dropout, Enter
from sklearn.model_selection import GridSearchCV
from scikeras.wrappers import KerasClassifier
param_grid = {
'model__learning_rate': [0.001, 0.0005, 0.0001],
'model__neurons1': [20, 30, 40],
'model__neurons2': [20, 30, 40],
'model__dropout_rate1': [0.1, 0.15, 0.2],
'model__dropout_rate2': [0.1, 0.15, 0.2],
'batch_size': [16, 32, 64],
'epochs': [50, 100],
}
input_shape = X_train.form[1]
initial_bias = np.log([np.sum(y_train == 1) / np.sum(y_train == 0)])
class_weights = class_weight.compute_class_weight(
class_weight='balanced',
lessons=np.distinctive(y_train),
y=y_train
)
class_weights_dict = dict(zip(np.distinctive(y_train), class_weights))
keras_classifier = KerasClassifier(
mannequin=create_model,
model__input_shape=input_shape,
model__initial_bias_value=initial_bias,
loss='binary_crossentropy',
metrics=[
'accuracy',
keras.metrics.Precision(name='precision'),
keras.metrics.Recall(name='recall'),
keras.metrics.AUC(name='auc')
]
)
grid_search = GridSearchCV(
estimator=keras_classifier,
param_grid=param_grid,
scoring='recall',
cv=3,
n_jobs=-1,
error_score='increase'
)
grid_result = grid_search.match(
X_train, y_train,
validation_data=(X_val, y_val),
callbacks=[early_stopping_callback],
class_weight=class_weights_dict
)
optimal_params = grid_result.best_params_
best_keras_classifier = grid_result.best_estimator_Outcomes
Grid Search tuning resulted in a mannequin with sturdy precision and good total accuracy, although with a decrease recall in comparison with the Bayesian Optimization method:
- Recall: 0.8214(0.7735 -> 0.7150)— 0.7100
- Precision: 0.7884 (0.8331 -> 0.8034) — 0.8304
- Accuracy:0.8005 (0.8092 -> 0.7700) — 0.7825
Finest performing hyperparameter set:
- neurons1: 40
- dropout_rate1: 0.15
- neurons2: 40
- dropout_rate2: 0.1
- learning_rate: 0.001
- batch_size: 16
- epochs: 100
Optimum Neural Community Abstract:
Grid Search Efficiency:
- Recall: Achieved a recall of 0.7100, a slight lower from its preliminary vary (0.7735–0.7150).
- Precision: Confirmed strong efficiency at 0.8304, an enchancment over its preliminary vary (0.8331–0.8034).
- Accuracy: Settled at 0.7825, sustaining a stable total predictive functionality, barely decrease than its preliminary vary (0.8092–0.7700).
Comparability with Bayesian Optimization:
- Recall: Bayesian Optimization (0.8400) considerably outperformed Grid Search (0.7100) in figuring out optimistic instances.
- Precision: Grid Search (0.8304) achieved a lot increased precision than Bayesian Optimization (0.6747), indicating fewer false positives.
- Accuracy: Grid Search’s accuracy (0.7825) was notably increased than Bayesian Optimization’s (0.7175).
Normal Comparability with Grid Search
1. Approaching the Search Area
Bayesian Optimization
- Clever/Adaptive: Bayesian Optimization builds a probabilistic mannequin (usually a Gaussian Course of) of the target perform (e.g., mannequin efficiency as a perform of hyperparameters). It makes use of this mannequin to foretell which hyperparameter mixtures are almost definitely to yield higher outcomes.
- Knowledgeable: It learns from earlier evaluations. After every trial, the probabilistic mannequin is up to date, guiding the search in direction of extra promising areas of the hyperparameter house. This enables it to make “clever” selections about the place to pattern subsequent, balancing exploration (making an attempt new, unknown areas) and exploitation (specializing in areas which have proven good outcomes).
- Sequential: It usually operates sequentially, evaluating one level at a time and updating its mannequin earlier than choosing the subsequent.
Grid Search:
- Exhaustive/Brute-force: Grid Search systematically tries each attainable mixture of hyperparameter values from a pre-defined set of values for every hyperparameter. You specify a “grid” of values, and it evaluates each level on that grid.
- Uninformed: It doesn’t use the outcomes of earlier evaluations to tell the collection of the subsequent set of hyperparameters to strive. Every mixture is evaluated independently.
- Deterministic: Given the identical grid, it is going to all the time discover the identical mixtures in the identical order.
2. Computational Value
Bayesian Optimization
- Extra Environment friendly: Designed to search out optimum hyperparameters with considerably fewer evaluations in comparison with Grid Search. This makes it significantly efficient when evaluating the target perform (e.g., coaching a Machine Studying mannequin) is computationally costly or time-consuming.
- Scalability: Usually scales higher to higher-dimensional hyperparameter areas than Grid Search, although it may nonetheless be computationally intensive for very excessive dimensions as a result of overhead of sustaining and updating the probabilistic mannequin.
Grid Search
- Computationally Costly: Because the variety of hyperparameters and the vary of values for every hyperparameter improve, the variety of mixtures grows exponentially. This results in very long term instances and excessive computational value, making it impractical for big search areas. That is sometimes called the “curse of dimensionality.”
- Scalability: Doesn’t scale effectively with high-dimensional hyperparameter areas.
3. Ensures and Exploration
Bayesian Optimization
- Probabilistic assure: It goals to search out the worldwide optimum effectively, however it does not provide a tough assure like Grid Seek for discovering the very best inside a discrete set. As an alternative, it converges probabilistically in direction of the optimum.
- Smarter exploration: Its stability of exploration and exploitation helps it keep away from getting caught in native optima and uncover optimum values extra successfully.
Grid Search
- Assured to search out finest in grid: If the optimum hyperparameters are inside the outlined grid, Grid Search is assured to search out them as a result of it tries each mixture.
- Restricted exploration: It might miss optimum values in the event that they fall between the discrete factors outlined within the grid.
4. When to Use Which
Bayesian Optimization:
- Massive, high-dimensional hyperparameter areas: When evaluating fashions is dear and you’ve got many hyperparameters to tune.
- When effectivity is paramount: To seek out good hyperparameters shortly, particularly in conditions with restricted computational sources or time.
- Black-box optimization issues: When the target perform is complicated, non-linear, and doesn’t have a identified analytical type.
Grid Search
- Small, low-dimensional hyperparameter areas: When you may have only some hyperparameters and a restricted variety of values for every, Grid Search generally is a easy and efficient alternative.
- When exhaustiveness is essential: Should you completely have to discover each single outlined mixture.
Conclusion
The experiment successfully demonstrated the distinct strengths of Bayesian Optimization and Grid Search in hyperparameter tuning.
Bayesian Optimization, by design, proved extremely efficient at intelligently navigating the search house and prioritizing a selected goal, on this case, maximizing recall.
It efficiently achieved the next recall fee (0.8400) in comparison with Grid Search, indicating its means to search out extra optimistic cases.
This functionality comes with an inherent trade-off, resulting in diminished precision and total accuracy.
Such an final result is very beneficial in purposes the place minimizing false negatives is essential (e.g., medical prognosis, fraud detection).
Its effectivity, stemming from probabilistic modeling that guides the search in direction of promising areas, makes it a most popular technique for optimizing expensive experiments or simulations the place every analysis is dear.
In distinction, Grid Search, whereas exhaustive, yielded a extra balanced mannequin with superior precision (0.8304) and total accuracy (0.7825).
This implies Grid Search was extra conservative in its predictions, leading to fewer false positives.
In abstract, whereas Grid Search gives a simple and exhaustive method, Bayesian Optimization stands out as a extra refined and environment friendly technique able to find superior outcomes with fewer evaluations, significantly when optimizing for a selected, usually complicated, goal like maximizing recall in a high-dimensional house.
The optimum alternative of tuning technique in the end is dependent upon the particular efficiency priorities and useful resource constraints of the appliance.
Creator: Kuriko IWAI
Portfolio / LinkedIn / Github
Might 26, 2025
All photos, until in any other case famous, are by the creator.
The article makes use of artificial information, licensed beneath Apache 2.0 for business use.
