我在使用 skopt 库时遇到问题。我尝试优化神经网络的大小，即神经元和层大小，但是我得到的结果与预期相反。优化不喜欢这些参数，它会产生奇怪的结果，比如 NN 有 4 层，每层有 1 个神经元和 RMSE 8*1e-4。我知道使用高斯过程和皮尔逊指数 0.999 的 RMSE 的最佳值为 4*1e-4，但我无法得到相同的结果。有任何想法吗？
from skopt import gbrt_minimize
#--------- Hyperparameters -------------------
space = [
Real(1e-6, 1e-1, name='alpha' ),
Real(1e-6,1e-2 , name='learning_rate_init', prior ='log-uniform', ),
Real(0.9, 0.9999 , name='beta_1'),
Real(0.95, 1 , name='beta_2'),
Integer(1,4, name='n_hidden_layer'),
Integer(1,700, name='n_neurons_per_layer') ]
#------------------------------------------------------
#---------Initialization --------------------
alpha_0 = 0.0001
learning_rate_init_0 = 0.001
beta_1_0 = 0.9001
beta_2_0 = 0.999
n_hidden_layer_0 = 3
n_neurons_per_layer_0 = 400
default_parameters = [ alpha_0,
learning_rate_init_0,
beta_1_0,
beta_2_0,
n_hidden_layer_0,
n_neurons_per_layer_0, ]
#------------------------------------------------------
#---------MLPRegressor model --------------------
EPOCHS = 400
mlp = MLPRegressor(hidden_layer_sizes=(400,400,400),
activation='relu',
solver='adam',
alpha=alpha_0, #L2 penalty (regularization term) paramete
batch_size=16,
learning_rate='invscaling', #Only used when solver=’sgd’.
learning_rate_init=learning_rate_init_0,
power_t=0.5, #Only used when solver=’sgd’.
max_iter= EPOCHS, #EPOCHS
shuffle=True,
random_state=42,
tol=1e-12,
verbose=0,
warm_start=False,
momentum=0.9, #only used when sgd
nesterovs_momentum=True, #only used when sgd
early_stopping=False, #will automatically set aside 10% of training data as validation# 10% too much
validation_fraction=0.1,
beta_1=beta_1_0,
beta_2=beta_2_0,
epsilon=1e-08,
n_iter_no_change=20,
max_fun=15000 ) #Only used when solver=’lbfgs’.
#------------------------------------------------------
@use_named_args(space)
def objective(**params):
n_neurons=params['n_neurons_per_layer']
n_layers=params['n_hidden_layer']
# create the hidden layers as a tuple with length n_layers and n_neurons per layer
params['hidden_layer_sizes']=(n_neurons,)*n_layers
# the parameters are deleted to avoid an error from the MLPRegressor
params.pop('n_neurons_per_layer')
params.pop('n_hidden_layer')
mlp.set_params(**params)
y_pred = cross_val_predict(mlp,
x_main,
y_main,
cv=5, # 5-Kfold cross validation
n_jobs=-1,)
rmse = np.sqrt(mean_squared_error(y_main_o,y_pred))
pearson = float(correlation.result(y_pred, y_main_o))
obj_pear = 1.0-pearson
print(f"Cross-validated Pearson's: {pearson} ")
print(f"Cross-validated score (RMSE): {rmse}")
print('-'*40)
return rmse+obj_pear #+obj_pear #RMSE as objective function
#------------------------------------------------------
n_calls = 110
#verbose = VerboseCallback(n_total=n_calls)
delta = DeltaXStopper(1e-6)
mlp_gbrt = gbrt_minimize(objective,
space,
n_calls=n_calls,
n_random_starts=100,
acq_func='EI',
acq_optimizer='auto',
x0=default_parameters,
y0=None,
random_state=42,
verbose=1,
callback= [delta],
n_points=10000,
xi=1e-1,
kappa=1.96,
n_jobs=-1,
model_queue_size=None)