Python激活函数

    import torch
    import torchvision.models as models

    # 加载预训练的AlexNet
    model = models.alexnet(weights=models.AlexNet_Weights.DEFAULT)
    model.eval()

    def analyze_alexnet(model):
        total_params = 0
        print(f"{'Layer':<15} {'Type':<15} {'Params':<10} {'Shape':<25}")
        print("-" * 65)
        
        # 卷积层
        conv_params = 0
        for i, layer in enumerate(model.features):
            if isinstance(layer, torch.nn.Conv2d):
                params = sum(p.numel() for p in layer.parameters())
                conv_params += params
                total_params += params
                print(f"{f'conv{(i//3)+1}':<15} {'Conv2d':<15} {params:<10,} {str(tuple(layer.weight.shape)):<25}")
        
        # 全连接层  
        fc_params = 0
        fc_layers = [layer for layer in model.classifier if isinstance(layer, torch.nn.Linear)]
        names = ['fc1', 'fc2', 'fc3']
        
        for name, layer in zip(names, fc_layers):
            params = sum(p.numel() for p in layer.parameters())
            fc_params += params
            total_params += params
            print(f"{name:<15} {'Linear':<15} {params:<10,} {str(tuple(layer.weight.shape)):<25}")
        
        print("-" * 65)
        print(f"{'Total':<15} {'-':<15} {total_params:<10,} {'-':<25}")
        
        return total_params, conv_params, fc_params

    # 执行分析
    total, conv, fc = analyze_alexnet(model)

    print(f"\n参数分布:")
    print(f"卷积层: {conv:,} ({conv/total*100:.1f}%)")
    print(f"全连接层: {fc:,} ({fc/total*100:.1f}%)")
    print(f"总参数: {total:,} ({total/1e6:.1f}M)")

    # 测试输入输出
    test_input = torch.randn(1, 3, 224, 224)
    output = model(test_input)
    print(f"\n输入: {test_input.shape} -> 输出: {output.shape}")