이것은 내 코드이며 pycharm을 사용하고 있습니다!

수입품

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.utils.data as DataLoader
import torchvision.datasets as Datasets
import torchvision.transforms as transforms

완전히 연결된 네트워크 생성

class NN(nn.Module):
    def __init__(self, input_size, num_classes): #(28x28 = 784)
        super(NN, self).__init__()
        self.fc1 = nn.Linear(input_size, 50)
        self.fc2 = nn.Linear(50, input_size) #hidden layer

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x
  

# 장치 설정

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

하이퍼 파라미터

input_size = 784
num_classes = 10
learning_rate = 0.001
batch_size = 2
num_epochs = 1
  

데이터로드

train_dataset = Datasets.MNIST(root='dataset/', train=True, transform=transforms.ToTensor(), download=True)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_dataset = Datasets.MNIST(root='dataset/', train=False, transform=transforms.ToTensor(), download=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
  

네트워크 초기화

model = NN(input_size=input_size, num_classes=num_classes).to(device)
  

손실 및 최적화

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
  

네트워크 훈련

for epoch in range(num_epochs):
    for batch_idx, (data, targets) in enumerate(train_loader):
        data = data.to(device=device)
        targets = targets.to(device=device)

        print(data.shape)

이 줄에 오류가 발생합니다.

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

오류는

in <module> train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) TypeError: 'module' object is not callable