我想用 [skopt] ( https://scikit-optimize.github.io/stable/auto_examples/bayesian-optimization.html ) 计算贝叶斯搜索。我的数据集是一个时间序列，t 是我的时间步长。
但我有一个错误：recursionerror：比较中超出了最大递归深度
这是我的代码：
def Grid_search_class(X_train=X_train[:,0:t+1,:]
,y_train=y_train
,X_test=X_test[:,0:t+1,:],
y_test=y_test
,n_calls=20,
print_score=False,t=t):
""" INPUTS : Train Test data
n_calls Number of calls to func"""
import tensorflow as tf
Adam = tf.keras.optimizers.Adam(learning_rate=0.007)
Adagrad = tf.keras.optimizers.Adagrad(learning_rate=0.007)
dim_num_input_text = Categorical([16,32,64,128,256,512,1024,2048], name='num_dense_layers_text')
dim_num_dense_text = Integer(low=0, high=5, name='num_HLD_nodes_text')
dim_drop_text = Categorical([0.01,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4], name='drop_text')
dim_num_input_temp = Categorical([16,32,64,128,256,512,1024,2048], name='num_dense_layers_temp')
dim_num_dense_temp = Integer(low=0, high=5, name='num_HLD_nodes_temp')
dim_drop_temp = Categorical([0.01,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4], name='drop_temp')
dim_num_input_fixe = Categorical([16,32,64,128,256,512,1024,2048], name='num_dense_layers_fixe')
dim_num_dense_fixe = Integer(low=0, high=5, name='num_HLD_nodes_fixe')
dim_drop_fixe = Categorical([0.01,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4], name='drop_fixe')
dim_num_input_merge = Categorical([16,32,64,128,256,512,1024,2048], name='num_dense_layers_merge')
dim_num_dense_merge = Integer(low=0, high=5, name='num_HLD_nodes_merge')
dim_drop_merge = Categorical([0.01,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4], name='drop_merge')
dim_optim=Categorical([Adam,Adagrad], name='optim')
dimensions = [dim_num_input_text,
dim_num_dense_text,
dim_drop_text,
dim_num_input_temp,
dim_num_dense_temp,
dim_drop_temp,
dim_num_input_fixe,
dim_num_dense_fixe,
dim_drop_fixe,
dim_num_input_merge,
dim_num_dense_merge,
dim_drop_merge,
dim_optim
]
default_parameters = [512,0,0.1,512,0,0.1,512,0,0.1,512,0,0.1,Adam]
def create_model(num_dense_layers_text,num_HLD_nodes_text,drop_text,
num_dense_layers_temp,num_HLD_nodes_temp,drop_temp,
num_dense_layers_fixe,num_HLD_nodes_fixe,drop_fixe,
num_dense_layers_merge,num_HLD_nodes_merge,drop_merge,optim,t=t):
x_text = model_text.layers[ind_list[-1]-1].output
if num_dense_layers_text>0:
for i in range(num_dense_layers_text):
x_text =tf.keras.layers.Dense(num_HLD_nodes_text,activation='relu')(x_text)
x_text=tf.keras.layers.Dropout(drop_text)(x_text)
x_temp = model_temp[t].layers[ind_list[t]].output
if num_dense_layers_temp>0:
for i in range(num_dense_layers_temp):
x_temp =tf.keras.layers.Dense(num_HLD_nodes_temp,activation='relu')(x_temp)
x_temp=tf.keras.layers.Dropout(drop_temp)(x_temp)
x_fixe= model_fixe.layers[1].output
if num_dense_layers_fixe>0:
for i in range(num_dense_layers_fixe):
x_fixe =tf.keras.layers.Dense(num_HLD_nodes_fixe,activation='relu')(x_fixe)
x_fixe=tf.keras.layers.Dropout(drop_fixe)(x_fixe)
merge = tf.keras.layers.concatenate([x_text,x_temp,x_fixe])
if num_dense_layers_merge>0:
for i in range(num_dense_layers_merge):
merge =tf.keras.layers.Dense(num_HLD_nodes_merge,activation='relu')(merge)
merge=tf.keras.layers.Dropout(drop_merge)(merge)
#add our classification layer.
predictions = tf.keras.layers.Dense(3,activation='softmax')(merge)
model = tf.keras.Model(inputs = [model_text.input,model_temp[t].input,model_fixe.input], outputs = predictions)
#setup our optimizer and compile
model.compile(optimizer=optim, loss=ncce,
metrics=[ tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.Recall(name='recall'),F1Score(num_classes=3,name='F1',average='macro')])
return model
score='val_F1'
@use_named_args(dimensions=dimensions)
def fitness(num_dense_layers_text,num_HLD_nodes_text,drop_text,
num_dense_layers_temp,num_HLD_nodes_temp,drop_temp,
num_dense_layers_fixe,num_HLD_nodes_fixe,drop_fixe,
num_dense_layers_merge,num_HLD_nodes_merge,drop_merge,optim):
print(num_dense_layers_text,num_HLD_nodes_text,drop_text,
num_dense_layers_temp,num_HLD_nodes_temp,drop_temp,
num_dense_layers_fixe,num_HLD_nodes_fixe,drop_fixe,
num_dense_layers_merge,num_HLD_nodes_merge,drop_merge,optim)
model = create_model(num_dense_layers_text=num_dense_layers_text,
num_HLD_nodes_text=num_HLD_nodes_text,drop_text=drop_text,
num_dense_layers_temp=num_dense_layers_temp,
num_HLD_nodes_temp=num_HLD_nodes_temp,drop_temp=drop_temp,
num_dense_layers_fixe=num_dense_layers_fixe,
num_HLD_nodes_fixe=num_HLD_nodes_fixe,drop_fixe=drop_fixe,
num_dense_layers_merge=num_dense_layers_merge,drop_merge=drop_merge,
num_HLD_nodes_merge=num_HLD_nodes_merge,optim=optim,t=t)
callback=tf.keras.callbacks.EarlyStopping(
monitor=score, min_delta=0.01, patience=1, verbose=0, mode='auto',
baseline=0, restore_best_weights=False
)
#named blackbox becuase it represents the structure
blackbox = model.fit(x=X_train,
y=y_train,verbose=1,
epochs=2,
batch_size=32,
validation_data=(X_test,y_test)
)
#return the validation accuracy for the last epoch.
val_loss = blackbox.history[score][-1]
if score=='val_F1':
val_loss=-val_loss
# Print the classification accuracy.
if print_score :
print()
print("val_score: {}".format(val_loss))
print()
# Delete the Keras model with these hyper-parameters from memory.
del model
# Clear the Keras session, otherwise it will keep adding new
# models to the same TensorFlow graph each time we create
# a model with a different set of hyper-parameters.
tf.keras.backend.clear_session()
tf.compat.v1.reset_default_graph()
# the optimizer aims for the lowest score, so we return our negative accuracy
return -val_loss
gp_result = gp_minimize(fitness,
dimensions=dimensions,
n_calls=n_calls,n_random_starts=7,
noise= 0.01,
x0=default_parameters)
a=pd.concat([pd.DataFrame(gp_result.x_iters, columns = ["dense layers text","HLD nodes text","drop text",
"dense layers temp","HLD nodes temp","drop temp",
"dense layers fixe","HLD nodes fixe","drop fixe",
"dense layers merge","HLD nodes merge","drop merge",
"optim","batch size"]),
(pd.Series(gp_result.func_vals*-1, name="val_loss"))], axis=1)
a.sort_values(by=['val_loss'], inplace=True,ascending=False)
print(a.iloc[:10])
return a
这一步是寻找最佳参数a step t
def Run_Grid_search_temp(j=0,n_calls=25):
while j<X_train.shape[1] :
temp=Grid_search_class(t=j,n_calls=n_calls)
print(temp)
j+=1
return
而这一步是一个循环。