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ModelScope可視化微調(diào)的代碼主要包括以下幾個(gè)部分：

1、導(dǎo)入所需庫(kù)

2、加載預(yù)訓(xùn)練模型

3、準(zhǔn)備數(shù)據(jù)集

4、定義損失函數(shù)和優(yōu)化器

5、進(jìn)行微調(diào)

6、評(píng)估模型性能

下面是詳細(xì)的代碼實(shí)現(xiàn)：

1. 導(dǎo)入所需庫(kù)
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from modelscope import VisualizationModel
2. 加載預(yù)訓(xùn)練模型
model = VisualizationModel()
model.load_state_dict(torch.load('pretrained_model.pth'))
3. 準(zhǔn)備數(shù)據(jù)集
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
train_dataset = datasets.ImageFolder(root='train_data', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
4. 定義損失函數(shù)和優(yōu)化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
5. 進(jìn)行微調(diào)
num_epochs = 10
for epoch in range(num_epochs):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print(f'Epoch {epoch + 1}, Loss: {running_loss / (i + 1)}')
6. 評(píng)估模型性能
test_dataset = datasets.ImageFolder(root='test_data', transform=transform)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
print(f'Accuracy: {100 * correct / total}%')

這段代碼首先導(dǎo)入了所需的庫(kù)，然后加載了預(yù)訓(xùn)練模型，接著，準(zhǔn)備了訓(xùn)練和測(cè)試數(shù)據(jù)集，并定義了損失函數(shù)和優(yōu)化器，在微調(diào)過(guò)程中，進(jìn)行了多個(gè)epoch的訓(xùn)練，并在每個(gè)epoch后輸出了當(dāng)前的損失值，評(píng)估了模型在測(cè)試集上的性能。

本文名稱：ModelScope可視化微調(diào)的代碼有嗎？
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