pytorch 数据分析 示例.

PyTorch 是一个开源的深度学习框架，用于构建和训练神经网络。数据分析示例通常涉及到数据预处理、模型训练、评估以及结果可视化。这里有一个简单的 PyTorch 数据分析的概述：

1. **数据加载**：

   import torch
   from torch.utils.data import DataLoader, Dataset

   class CustomDataset(Dataset):
       def __init__(self, data, labels):
           self.data = data
           self.labels = labels

   dataset = CustomDataset(data_tensor, label_tensor)
   dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

2. **数据预处理**：

   def preprocess_data(sample):
       # 图像归一化、标准化或转换成Tensor等操作
       return sample / 255.0  # 假设是图片数据

   for data, _ in dataloader:
       data = preprocess_data(data)

3. **模型定义**：

   model = torch.nn.Sequential(
       torch.nn.Linear(input_dim, hidden_dim),
       torch.nn.ReLU(),
       torch.nn.Linear(hidden_dim, output_dim)
   )

4. **训练过程**：

   criterion = torch.nn.CrossEntropyLoss()
   optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

   for epoch in range(num_epochs):
       for inputs, targets in dataloader:
           outputs = model(inputs)
           loss = criterion(outputs, targets)
           optimizer.zero_grad()
           loss.backward()
           optimizer.step()

5. **模型评估与预测**：

   model.eval()
   with torch.no_grad():
       predictions = model(test_dataloader.dataset)
   correct_count = (predictions.argmax(dim=1) == test_labels).sum().item()
   accuracy = correct_count / len(test_labels)

6. **结果分析与可视化**：

   import matplotlib.pyplot as plt
   confusion_matrix = torch.confusion_matrix(test_labels, predictions)
   plt.imshow(confusion_matrix, cmap='Blues')
   plt.xlabel('Predicted')
   plt.ylabel('Actual')
   plt.show()