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give me a code to implement baysian search with cross validation in python on DNN

Evil Echidna answered on October 27, 2023 Popularity 1/10 Helpfulness 1/10

answer give me a code to implement baysian search with cross validation in python on DNN

give me a code to implement baysian search with cross validation in python on DNN

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from sklearn.datasets import load_boston
from sklearn.model_selection import cross_val_score, KFold
from sklearn.metrics import mean_squared_error
from bayes_opt import BayesianOptimization
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from keras.wrappers.scikit_learn import KerasRegressor
import numpy as np

# Load a sample dataset (you can replace this with your own dataset)
data = load_boston()
X, y = data.data, data.target

# Define the DNN model with hyperparameters as input
def create_model(neurons, learning_rate, epochs, batch_size):
    model = Sequential()
    model.add(Dense(neurons, input_dim=X.shape[1], activation='relu'))
    model.add(Dense(neurons, activation='relu'))
    model.add(Dense(1, activation='linear'))

    optimizer = Adam(learning_rate=learning_rate)
    model.compile(loss='mean_squared_error', optimizer=optimizer)
    return model

# Define the objective function for Bayesian optimization
def objective(neurons, learning_rate, epochs, batch_size):
    model = KerasRegressor(build_fn=create_model, epochs=int(epochs), batch_size=int(batch_size), verbose=0)
    kfold = KFold(n_splits=5, shuffle=True, random_state=42)
    mse_scores = -cross_val_score(model, X, y, cv=kfold, scoring='neg_mean_squared_error')
    return np.mean(mse_scores)

# Define the hyperparameter search space
pbounds = {
    'neurons': (8, 128),
    'learning_rate': (0.001, 0.1),
    'epochs': (10, 100),
    'batch_size': (8, 64)
}

# Create the Bayesian optimization object
optimizer = BayesianOptimization(f=objective, pbounds=pbounds, verbose=2, random_state=42)

# Perform Bayesian optimization
optimizer.maximize(init_points=5, n_iter=10)

# Get the best hyperparameters
best_hyperparameters = optimizer.max

print("Best Hyperparameters:")
print(best_hyperparameters)

Popularity 1/10 Helpfulness 1/10 Language python

Source: chat.openai.com

Tags: cross-validation python search

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Contributed on Oct 27 2023

Evil Echidna

0 Answers Avg Quality 2/10

give me a code to implement baysian search with cross validation in python on DNN

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