我是 pytorch 的新手，只是嘗試編寫一個網(wǎng)絡(luò)。是data.shape(204,6170)，最后 5 列是一些標(biāo)簽。數(shù)據(jù)中的數(shù)字是浮點數(shù)，如 0.030822。#%%from sklearn.feature_selection import RFEimport numpy as npimport pandas as pdimport torchimport torch.nn as nnfrom sklearn.model_selection import train_test_splitimport torch.functional as F#%%data = pd.read_table("table.log")data_x = data.iloc[:, 0:(data.shape[1]-5)]data_y = data.loc[:, 'target']X_train, X_test, y_train, y_test = train_test_split(data_x,data_y,test_size=0.2,random_state=0)#%%from sklearn.linear_model import LinearRegressionlr = LinearRegression(normalize=True)lr.fit(X_train,y_train)rfe1 = RFE(estimator=lr,n_features_to_select=2000)rfe1 = rfe1.fit(X_train,y_train)#%%x_train_rfe1 = X_train[X_train.columns[rfe1.support_]]print(x_train_rfe1.head())class testmodel(nn.Module):    def __init__(self):        super(testmodel,self).__init__()        self.conv = nn.Sequential(            nn.Conv1d(1500, 500, 1500, 0, 0),            nn.ReLU(),            nn.Conv1d(500, 100, 500, 0),            nn.ReLU(),            nn.Conv1d(100, 20, 100, 0),            nn.Sigmoid()        )    def forward(self,x):        x = self.conv        return x#%%x_train_rfe1 = torch.Tensor(x_train_rfe1.values)y_train = torch.Tensor(y_train.values.astype(np.int64))model = testmodel()y = model(x_train_rfe1)criterion = nn.MSELoss()loss = criterion(y, y_train)print(loss)錯誤在哪里？網(wǎng)上一般都是這樣寫的嗎？我該如何改進它？