def__init__中的Optuna试用版中的其他参数

如何解决def__init__中的Optuna试用版中的其他参数

class ConvolutionalNetwork(nn.Module):
    def __init__(self,in_features):
        super().__init__()
        self.in_features = in_features
        # this computes num features outputted from the two conv layers
        c1 = int(((self.in_features - 2)) / 64)  # this is to account for the loss due to conversion to int type
        c2 = int((c1-2)/64)
        self.n_conv = int(c2*16)
        #self.n_conv = int((( ( (self.in_features - 2)/4 ) - 2 )/4 ) * 16) 
        self.conv1 = nn.Conv1d(1,16,3,1)
        self.conv1_bn = nn.Batchnorm1d(16)
        self.conv2 = nn.Conv1d(16,1)
        self.conv2_bn = nn.Batchnorm1d(16)
        self.dp = nn.Dropout(trial.suggest_uniform('dropout_rate',1.0))
        self.fc3 = nn.Linear(self.n_conv,2)

如您所见，def __init__已经具有self和in_features作为变量。我正在考虑添加另一个变量trial（这是Optuna软件包的一部分）以容纳

    self.dp = nn.Dropout(trial.suggest_uniform('dropout_rate',1.0))

上述代码中的

。请告知如何，大多数资源仅包含def __init__ (self,trial)，这非常简单，但就我而言，我有3个要在目标中传递的变量。

解决方法

你可以这样做：

class ConvolutionalNetwork(nn.Module):
    def __init__(self,trial,in_features):
        # code for initialization
        ..........

def objective(trial):
    # code to create in_features
    in_features = ......
    #generate the model
    model = ConvolutionalNetwork(trial,in_features)
    ..................
    # code to run the CNN and calculate the accuracy.
    return accuracy

# Create study and optimise the objective function.
study = optuna.create_study()
study.optimize(objective,n_trails=100,timeout=600)