python-pytorchHow can I use Python and EfficientNet PyTorch to create a deep learning model?

You can use Python and EfficientNet PyTorch to create a deep learning model by following these steps:

1. Install the EfficientNet PyTorch library:

pip install efficientnet-pytorch

2. Import the necessary modules:

import torch
from efficientnet_pytorch import EfficientNet

3. Create the model:

model = EfficientNet.from_pretrained('efficientnet-b0')

4. Define the loss function and optimizer:

criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())

5. Train the model:

model.train()
for data, target in train_loader:
    optimizer.zero_grad()
    output = model(data)
    loss = criterion(output, target)
    loss.backward()
    optimizer.step()

6. Test the model:

model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for data, target in test_loader:
        output = model(data)
        _, predicted = torch.max(output.data, 1)
        total += target.size(0)
        correct += (predicted == target).sum().item()
    accuracy = 100 * correct / total
    print('Accuracy of the network on the test images: %d %%' % accuracy)

7. Save the model:

torch.save(model.state_dict(), 'model.pth')

Helpful links

• EfficientNet PyTorch
• PyTorch documentation

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