如何通过Keras Tuner函数传递多个参数

Question

我很难弄清楚如何通过keras tuner函数传递多个参数。我找遍了所有的available documentation以及与此相关的问题，但我找不到任何与此相关的问题

我只希望能够通过此函数传递其他参数：

def build_model(hp, some_val_1, some_val_2)

总体代码（简化）：

import kerastuner as kt

def build_model(hp, some_val_1, some_val_2):
    print(some_val_1)
    print(some_val_2)
    
    conv1d_val_1 = hp.Int("1-input_units", min_value=32, max_value=1028, step=64)
    conv1d_filt_1 = hp.Int("1b-filter_units", min_value=2, max_value=10, step=1)
    model.add(Conv1D(conv1d_val_1, conv1d_filt_1, activation='relu', input_shape=input_shape, padding='SAME'))
    model.add(Dense(1))
    model.compile(loss='mae', optimizer='adam')
    return model

model = kt.Hyperband(build_model, objective="val_loss", max_epochs = 10, factor = 3, directory=os.path.normpath(path_save_dir))
model.search(x=x_train, y=y_train, epochs=10, batch_size=500, validation_data=(x_test, y_test), shuffle=True)

尝试#1（我尝试了许多变体）-不起作用：

model = kt.Hyperband(build_model(kt.HyperParameters(), some_val_1, some_val_2), objective="val_loss", max_epochs = 10, factor = 3, directory=os.path.normpath(path_save_dir))

尝试#2（我尝试了许多变体）-不起作用：

model = kt.Hyperband(build_model, some_val_1='1', some_val_2='2',objective="val_loss", max_epochs = 10, factor = 3, directory=os.path.normpath(path_save_dir))

尝试#3（我尝试了许多变体）-不起作用：

model = kt.Hyperband(build_model, args=(some_val_1, some_val_2,),objective="val_loss", max_epochs = 10, factor = 3, directory=os.path.normpath(path_save_dir))

请派人来帮忙

Answer 1

您可以创建自己的超模型子类来实现这一点，请选中此link

示例实现，它将执行您尝试执行的操作：-

import kerastuner as kt

class MyHyperModel(kt.HyperModel):

    def __init__(self, some_val_1, some_val_2):
        self.some_val_1 = some_val_1
        self.some_val_2 = some_val_2

    def build(self, hp):
        ## You can use self.some_val_1 and self.some_val_2 here
        conv1d_val_1 = hp.Int("1-input_units", min_value=32, max_value=1028, step=64)
        conv1d_filt_1 = hp.Int("1b-filter_units", min_value=2, max_value=10, step=1)
        model.add(Conv1D(conv1d_val_1, conv1d_filt_1, activation='relu', input_shape=input_shape, padding='SAME'))
        model.add(Dense(1))
        model.compile(loss='mae', optimizer='adam')
        
        return model

some_val_1 = 10
some_val_2 = 20
my_hyper_model = MyHyperModel(some_val_1 = some_val_1, some_val_2 = some_val_2)
model = kt.Hyperband(my_hyper_model, objective="val_loss", max_epochs = 10, 
                     factor = 3, directory=os.path.normpath(path_save_dir))