1. 필요한 라이브러리 가져오기:

   import torch
   import torch.nn as nn
   import torch.optim as optim
   import torch.utils.data as data
   import torchvision.transforms as transforms
   from torchvision.datasets import MNIST
   import keras
   from keras.models import Sequential
   from keras.layers import Dense
   

2. 케라스 모델 정의:

   model = Sequential()
   model.add(Dense(64, activation='relu', input_dim=784))
   model.add(Dense(64, activation='relu'))
   model.add(Dense(10, activation='softmax'))
   

3. 파이토치(Torch) 모델로 변환:

   model_torch = torch.nn.Sequential(*list(model.layers))
   

4. 손실 함수와 최적화 알고리즘 설정:

   criterion = nn.CrossEntropyLoss()
   optimizer = optim.SGD(model_torch.parameters(), lr=0.001, momentum=0.9)
   

5. 데이터셋 준비:

   transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
   train_dataset = MNIST('./data', train=True, download=True, transform=transform)
   test_dataset = MNIST('./data', train=False, download=True, transform=transform)
   train_loader = data.DataLoader(train_dataset, batch_size=64, shuffle=True)
   test_loader = data.DataLoader(test_dataset, batch_size=64, shuffle=False)
   

6. 훈련과 평가:

   for epoch in range(10):
       for images, labels in train_loader:
           images = images.view(-1, 784)
           optimizer.zero_grad()
           outputs = model_torch(images)
           loss = criterion(outputs, labels)
           loss.backward()
           optimizer.step()
          
   # 테스트 데이터셋으로 평가
   correct = 0
   total = 0
   with torch.no_grad():
       for images, labels in test_loader:
           images = images.view(-1, 784)
           outputs = model_torch(images)
           _, predicted = torch.max(outputs.data, 1)
           total += labels.size(0)
           correct += (predicted == labels).sum().item()
      
   accuracy = 100 * correct / total
   print('Accuracy: {}%'.format(accuracy))
   

위의 예시를 통해 파이썬 PyTorch에서 케라스 모델을 사용하는 방법을 알 수 있습니다.

참고 문서:

• PyTorch 공식 문서: https://pytorch.org/
• Keras 공식 문서: https://keras.io/