我在学习使用pytorch时遇到了一个错误，导致无法继续编程。

我的代码如下：

import torch.nn as nnfrom skorch import NeuralNetClassifier #integracao com sklearnfrom sklearn.model_selection import cross_val_score,GridSearchCVfrom sklearn.preprocessing import LabelEncoder, MinMaxScalerimport torchimport torch.nn.functional as Ffrom torch import nn,optimclass classificadorFinal(nn.Module):    def __init__(self, activation=F.tanh, neurons=16, initializer=torch.nn.init.uniform_, dropout=0.3):        ##from melhores_parametros        super().__init__()        self.dense0 = nn.Linear(4, neurons)        initializer(self.dense0.weight)        self.activation0 = activation        self.dense1 = nn.Linear(neurons, neurons)        initializer(self.dense1.weight)        self.activation1 = activation        self.dense2 = nn.Linear(neurons, 3)        self.dropout = nn.Dropout(dropout)    def forward(self, X):        X = self.dense0(X)        X = self.activation0(X)        X = self.dropout(X)        X = self.dense1(X)        X = self.activation1(X)        X = self.dropout(X)        X = self.dense2(X)        return Xcriterion = nn.CrossEntropyLoss()optimizer = optim.Adam(classificador.parameters(), lr = 0.001, weight_decay = 0.0001)#treinofor epoch in range(200):##from melhores_parametros    running_loss = 0.    running_accuracy = 0.    for data in train_loader:        inputs, labels = data        optimizer.zero_grad()                outputs = classificadorFinal(inputs)        loss = criterion(outputs, labels)###erro        loss.backward()        optimizer.step()        running_loss += loss.item()        ps = F.softmax(outputs)        top_p, top_class = ps.topk(k = 1, dim = 1)        equals = top_class == labels.view(*top_class.shape)        running_accuracy += torch.mean(equals.type(torch.float))    print('Época {:3d}: perda {:3.5f} - precisão {:3.5f}'.format(epoch + 1, running_loss/len(train_loader), running_accuracy/len(train_loader)))

错误正好发生在loss = criterion(outputs, labels)这一行：

AttributeError: ‘classificadorFinal’ object has no attribute ‘log_softmax’

我发现这个错误是众所周知的，但我没有理解提出的解决方案：

disable aux_logits 在创建模型时设置aux_logits=False.

请帮帮我！

回答：

输出实际上并不是模型的输出，而是模型本身。classificadorFinal是一个类，调用它会创建该类的对象/实例，而inputs将作为__init__方法的第一个参数，即activation。

# 创建模型的实例outputs = classificadorFinal(inputs)

你首先需要创建模型（一个实例），这应该只做一次，然后用inputs调用该模型。看起来你之前已经创建了模型，因为你在优化器中使用了classificador.parameters()，因此classificador可能是模型的实例。你需要调用classificador（实例）而不是classificadorFinal（类）来创建输出。

# 调用模型的实例，而不是类 outputs = classificador(inputs)