我正在使用scikit学习优化包来优化模型的超参数。出于性能和可读性的原因(我正在使用相同的过程培训多个模型),我希望在一个类中构建整个超参数调优:
...
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.preprocessing import sequence
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import InputLayer, Input, Dense, Embedding, BatchNormalization, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import TensorBoard, EarlyStopping
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
from sklearn.model_selection import train_test_split
import skopt
from skopt import gp_minimize
from skopt.space import Real, Categorical, Integer
from skopt.plots import plot_convergence
from skopt.plots import plot_objective, plot_evaluations
from skopt.utils import use_named_args
class hptuning:
def __init__(self, input_df):
self.inp_df = input_df
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(...)
self.param_space = self.dim_hptuning()
self.best_loss = 10000
def dim_hptuning(self):
dim_layers = Integer(low=0, high=7, name='layers')
dim_nodes = Integer(low=2, high=90, name='num_nodes')
dimensions = [dim_layers, dim_nodes]
return dimensions
def create_model(self, layers, nodes):
model = Sequential()
for layer in range(layers):
model.add(Dense(nodes))
model.add(Dense(1,activation='sigmoid'))
optimizer = Adam
model.compile(loss='mean_absolute_error',
optimizer=optimizer,
metrics=['mae', 'mse'])
return model
@use_named_args(dimensions=self.param_space)
def fitness(self,nodes, layers):
model = self.create_model(layers=layers, nodes=nodes)
history = model.fit(x=self.X_train.values,y=self.y_train.values,epochs=200,batch_size=200,verbose=0)
loss = history.history['val_loss'][-1]
if loss < self.best_loss:
model.save('model.h5')
self.best_loss = loss
del model
K.clear_session()
return loss
def find_best_model(self):
search_result = gp.minimize(func=self.fitness, dimensions=self.param_space,acq_func='EI',n_calls=10)
return search_result
hptun = hptuning(input_df=df)
search_result = hptun.find_best_model()
print(search_result.fun)
现在我遇到了一个问题,即decorator@use_named_args没有在类中工作,因为他应该是(example code of scikit-optimize).。我得到了错误消息
Traceback (most recent call last):
File "main.py", line 138, in <module>
class hptuning:
File "main.py", line 220, in hptuning
@use_named_args(dimensions=self.param_space)
NameError: name 'self' is not defined
这显然是关于在这个场景中对装饰器的滥用
可能是因为我对这些装饰器的功能缺乏了解,所以我无法运行它。有人能帮我吗
提前感谢大家的支持
未定义
self
的问题与scikit.learn无关。您不能使用self
来定义装饰器,因为它只在您正在装饰的方法中定义。但是,即使您回避了这个问题(例如,通过提供param_空间作为全局变量),我希望下一个问题是self
将被传递给use_named_args
装饰器,但它只希望优化参数最明显的解决方案是不在
fitness
方法上使用decorator,而是在find_best_model
方法内定义一个调用fitness
方法的修饰函数,如下所示:相关问题 更多 >
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