{"id":44113,"date":"2025-06-17T01:43:27","date_gmt":"2025-06-16T17:43:27","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/44113.html"},"modified":"2025-06-17T01:43:27","modified_gmt":"2025-06-16T17:43:27","slug":"python%e6%89%93%e5%8d%a1%e7%ac%ac52%e5%a4%a9","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/44113.html","title":{"rendered":"python\u6253\u5361\u7b2c52\u5929"},"content":{"rendered":"

\u77e5\u8bc6\u70b9\u56de\u987e：<\/span><\/p>\n

  • \u968f\u673a\u79cd\u5b50<\/span><\/li>\n
  • \u5185\u53c2\u7684\u521d\u59cb\u5316<\/span><\/li>\n
  • \u795e\u7ecf\u7f51\u7edc\u8c03\u53c2\u6307\u5357<\/span>\n
  • \u53c2\u6570\u7684\u5206\u7c7b<\/span><\/li>\n
  • \u8c03\u53c2\u7684\u987a\u5e8f<\/span><\/li>\n
  • \u5404\u90e8\u5206\u53c2\u6570\u7684\u8c03\u6574\u5fc3\u5f97<\/span><\/li>\n<\/li>\n

    \u00a0## \u968f\u673a\u79cd\u5b50<\/p>\n

    import torch
    \nimport torch.nn as nn<\/p>\n

    # \u5b9a\u4e49\u7b80\u5355\u7684\u7ebf\u6027\u6a21\u578b（\u65e0\u9690\u85cf\u5c42）
    \n# \u8f93\u51652\u4e2a\u7eac\u5ea6\u7684\u6570\u636e，\u5f97\u52301\u4e2a\u7eac\u5ea6\u7684\u8f93\u51fa
    \nclass SimpleNet(nn.Module):
    \n def __init__(self):
    \n super(SimpleNet, self).__init__()
    \n # \u7ebf\u6027\u5c42：2\u4e2a\u8f93\u5165\u7279\u5f81，1\u4e2a\u8f93\u51fa\u7279\u5f81
    \n self.linear = nn.Linear(2, 1)<\/p>\n

    def forward(self, x):
    \n # \u524d\u5411\u4f20\u64ad：y = w1*x1 + w2*x2 + b
    \n return self.linear(x)<\/p>\n

    # \u521b\u5efa\u6a21\u578b\u5b9e\u4f8b
    \nmodel = SimpleNet()<\/p>\n

    # \u67e5\u770b\u6a21\u578b\u53c2\u6570
    \nprint("\u6a21\u578b\u53c2\u6570:")
    \nfor name, param in model.named_parameters():
    \n print(f"{name}: {param.data}") <\/p>\n

    ### \u968f\u673a\u79cd\u5b50<\/p>\n

    \u4e4b\u524d\u6211\u4eec\u8bf4\u8fc7，torch\u4e2d\u5f88\u591a\u573a\u666f\u90fd\u4f1a\u5b58\u5728\u968f\u673a\u6570<\/p>\n

    1. \u6743\u91cd\u3001\u504f\u7f6e\u7684\u968f\u673a\u521d\u59cb\u5316<\/p>\n

    2. \u6570\u636e\u52a0\u8f7d（shuffling\u6253\u4e71）\u4e0e\u6279\u6b21\u52a0\u8f7d（\u968f\u673a\u6279\u6b21\u52a0\u8f7d）\u7684\u968f\u673a\u5316<\/p>\n

    3. \u6570\u636e\u589e\u5f3a\u7684\u968f\u673a\u5316（\u968f\u673a\u65cb\u8f6c\u3001\u7f29\u653e\u3001\u5e73\u79fb\u3001\u88c1\u526a\u7b49）<\/p>\n

    4. \u968f\u673a\u6b63\u5219\u5316dropout<\/p>\n

    5. \u4f18\u5316\u5668\u4e2d\u7684\u968f\u673a\u6027<\/p>\n

    import torch
    \nimport numpy as np
    \nimport os
    \nimport random<\/p>\n

    # \u5168\u5c40\u968f\u673a\u51fd\u6570
    \ndef set_seed(seed=42, deterministic=True):
    \n """
    \n \u8bbe\u7f6e\u5168\u5c40\u968f\u673a\u79cd\u5b50，\u786e\u4fdd\u5b9e\u9a8c\u53ef\u91cd\u590d\u6027<\/p>\n

    \u53c2\u6570:
    \n seed: \u968f\u673a\u79cd\u5b50\u503c，\u9ed8\u8ba4\u4e3a42
    \n deterministic: \u662f\u5426\u542f\u7528\u786e\u5b9a\u6027\u6a21\u5f0f，\u9ed8\u8ba4\u4e3aTrue
    \n """
    \n # \u8bbe\u7f6ePython\u7684\u968f\u673a\u79cd\u5b50
    \n random.seed(seed)
    \n os.environ['PYTHONHASHSEED'] = str(seed) # \u786e\u4fddPython\u54c8\u5e0c\u51fd\u6570\u7684\u968f\u673a\u6027\u4e00\u81f4，\u6bd4\u5982\u5b57\u5178\u3001\u96c6\u5408\u7b49\u65e0\u5e8f<\/p>\n

    # \u8bbe\u7f6eNumPy\u7684\u968f\u673a\u79cd\u5b50
    \n np.random.seed(seed)<\/p>\n

    # \u8bbe\u7f6ePyTorch\u7684\u968f\u673a\u79cd\u5b50
    \n torch.manual_seed(seed) # \u8bbe\u7f6eCPU\u4e0a\u7684\u968f\u673a\u79cd\u5b50
    \n torch.cuda.manual_seed(seed) # \u8bbe\u7f6eGPU\u4e0a\u7684\u968f\u673a\u79cd\u5b50
    \n torch.cuda.manual_seed_all(seed) # \u5982\u679c\u4f7f\u7528\u591aGPU<\/p>\n

    # \u914d\u7f6ecuDNN\u4ee5\u786e\u4fdd\u7ed3\u679c\u53ef\u91cd\u590d
    \n if deterministic:
    \n torch.backends.cudnn.deterministic = True
    \n torch.backends.cudnn.benchmark = False<\/p>\n

    # \u8bbe\u7f6e\u968f\u673a\u79cd\u5b50
    \nset_seed(42) <\/p>\n

    \u00a0<\/p>\n

    \u4ecb\u7ecd\u4e00\u4e0b\u8fd9\u4e2a\u968f\u673a\u51fd\u6570\u7684\u51e0\u4e2a\u90e8\u5206<\/p>\n

    1. python\u7684\u968f\u673a\u79cd\u5b50，\u9700\u8981\u786e\u4fddrandom\u6a21\u5757\u3001\u4ee5\u53ca\u4e00\u4e9b\u65e0\u5e8f\u6570\u636e\u7ed3\u6784\u7684\u4e00\u81f4\u6027<\/p>\n

    2. numpy\u7684\u968f\u673a\u79cd\u5b50，\u63a7\u5236\u6570\u7ec4\u7684\u968f\u673a\u6027<\/p>\n

    3. torch\u7684\u968f\u673a\u79cd\u5b50，\u63a7\u5236\u5f20\u91cf\u7684\u968f\u673a\u6027，\u5728cpu\u548cgpu\u4e0a\u5747\u9002\u7528<\/p>\n

    4. cuDNN（CUDA Deep Neural Network library ，CUDA \u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u5e93）\u7684\u968f\u673a\u6027，\u9488\u5bf9cuda\u7684\u4f18\u5316\u7b97\u6cd5\u7684\u968f\u673a\u6027<\/p>\n<\/p>\n

    \u4e0a\u8ff0\u79cd\u5b50\u53ef\u4ee5\u5904\u7406\u5927\u90e8\u5206\u573a\u666f，\u5b9e\u9645\u4e0a\u8fd8\u6709\u5c11\u90e8\u5206\u573a\u666f（\u5177\u4f53\u7684\u51fd\u6570）\u53ef\u80fd\u9700\u8981\u81ea\u884c\u8bbe\u7f6e\u5176\u5bf9\u5e94\u7684\u968f\u673a\u79cd\u5b50\u3002<\/p>\n

    \u65e5\u5e38\u4f7f\u7528\u4e2d，\u5728\u6700\u5f00\u59cb\u8c03\u7528\u8fd9\u90e8\u5206\u5df2\u7ecf\u8db3\u591f<\/p>\n

    \u6211\u4eec\u90fd\u77e5\u9053，\u795e\u7ecf\u7f51\u7edc\u7684\u6743\u91cd\u9700\u8981\u901a\u8fc7\u53cd\u5411\u4f20\u64ad\u6765\u5b9e\u73b0\u66f4\u65b0，\u90a3\u4e48\u6700\u5f00\u59cb\u80af\u5b9a\u9700\u8981\u4e00\u4e2a\u503c\u624d\u53ef\u4ee5\u66f4\u65b0\u53c2\u6570<\/p>\n<\/p>\n

    \u8fd9\u4e2a\u6700\u5f00\u59cb\u7684\u503c\u662f\u4ec0\u4e48\u6837\u5b50\u7684\u5462？\u5982\u679c\u6070\u597d\u4ed6\u4eec\u5c31\u662f\u90a3\u4e00\u7ec4\u6700\u4f73\u7684\u53c2\u6570\u9644\u8fd1\u7684\u6570，\u90a3\u4e48\u53ef\u80fd\u6211\u8bad\u7ec3\u7684\u901f\u5ea6\u4f1a\u5feb\u5f88\u591a<\/p>\n<\/p>\n

    \u4e3a\u4e86\u641e\u61c2\u8fd9\u4e2a\u95ee\u9898，\u5e2e\u52a9\u6211\u4eec\u771f\u6b63\u7406\u89e3\u795e\u7ecf\u7f51\u7edc\u53c2\u6570\u7684\u672c\u8d28，\u6211\u4eec\u9700\u8981\u6df1\u5165\u5256\u6790\u4e00\u4e0b，\u5173\u6ce8\u4ee5\u4e0b\u51e0\u4e2a\u95ee\u9898：<\/p>\n

    1. \u521d\u59cb\u503c\u7684\u533a\u95f4<\/p>\n

    2. \u521d\u59cb\u503c\u7684\u5206\u5e03<\/p>\n

    3. \u521d\u59cb\u503c\u662f\u591a\u5c11<\/p>\n

    \u5148\u4ecb\u7ecd\u4e00\u4e0b\u795e\u7ecf\u7f51\u7edc\u7684\u5bf9\u79f0\u6027—-\u4e3a\u4ec0\u4e48\u795e\u7ecf\u5143\u7684\u521d\u59cb\u503c\u9700\u8981\u5404\u4e0d\u76f8\u540c？<\/p>\n<\/p>\n

    \u672c\u8d28\u795e\u7ecf\u7f51\u7edc\u7684\u6bcf\u4e00\u4e2a\u795e\u7ecf\u5143\u90fd\u662f\u5728\u505a\u4e00\u4ef6\u4e8b，\u8f93\u5165x–\u8f93\u51fay\u7684\u6620\u5c04，\u8fd9\u91cc\u6211\u4eec\u5047\u8bbe\u6fc0\u6d3b\u51fd\u6570\u662fsigmoid<\/p>\n<\/p>\n

    y=sigmoid（wx+b），\u5176\u4e2dw\u662f\u8fde\u63a5\u5230\u8be5\u795e\u7ecf\u5143\u7684\u6743\u91cd\u77e9\u9635，b\u662f\u8be5\u795e\u7ecf\u5143\u7684\u504f\u7f6e<\/p>\n<\/p>\n

    \u5982\u679c\u6240\u6709\u795e\u7ecf\u5143\u7684\u6743\u91cd\u548c\u504f\u7f6e\u90fd\u4e00\u6837，<\/p>\n

    1. \u5982\u679c\u90fd\u4e3a0，\u90a3\u4e48\u6240\u6709\u795e\u7ecf\u5143\u7684\u8f93\u51fa\u90fd\u4e00\u81f4，\u65e0\u6cd5\u533a\u5206\u4e0d\u540c\u7279\u5f81；\u6b64\u65f6\u53cd\u5411\u4f20\u64ad\u7684\u65f6\u5019\u68af\u5ea6\u90fd\u4e00\u6837，\u65e0\u6cd5\u5b66\u4e60\u5230\u7279\u5f81，\u66f4\u65b0\u540e\u7684\u6743\u91cd\u4e5f\u5b8c\u5168\u4e00\u81f4\u3002<\/p>\n

    2. \u5982\u679c\u4e0d\u4e3a0，\u540c\u4e0a<\/p>\n<\/p>\n

    \u6240\u4ee5，\u65e0\u8bba\u521d\u59cb\u503c\u662f\u5426\u4e3a 0，\u76f8\u540c\u7684\u6743\u91cd\u548c\u504f\u7f6e\u4f1a\u5bfc\u81f4\u795e\u7ecf\u5143\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u59cb\u7ec8\u4fdd\u6301\u540c\u6b65\u3002（\u56e0\u4e3a\u795e\u7ecf\u7f51\u7edc\u7684\u524d\u5411\u4f20\u64ad\u662f\u5bfc\u81f4\u6743\u91cd\u7684\u6570\u5b66\u542b\u4e49\u662f\u5b8c\u5168\u5bf9\u79f0\u7684）\u5177\u4f53\u8868\u73b0\u4e3a：<\/p>\n<\/p>\n

    \u540c\u4e00\u5c42\u7684\u795e\u7ecf\u5143\u76f8\u5f53\u4e8e\u5728\u505a\u5b8c\u5168\u76f8\u540c\u7684\u8ba1\u7b97，\u65e0\u8bba\u8f93\u5165\u5982\u4f55\u53d8\u5316，\u5b83\u4eec\u7684\u8f93\u51fa\u6a21\u5f0f\u59cb\u7ec8\u4e00\u81f4\u3002\u4f8b\u5982：\u8f93\u5165\u56fe\u50cf\u4e2d\u4e0d\u540c\u4f4d\u7f6e\u7684\u8fb9\u7f18\u7279\u5f81，\u4f1a\u88ab\u8fd9\u4e9b\u795e\u7ecf\u5143\u4ee5\u76f8\u540c\u65b9\u5f0f\u5904\u7406，\u65e0\u6cd5\u5b66\u4e60\u5230\u7a7a\u95f4\u5206\u5e03\u7684\u5dee\u5f02\u3002<\/p>\n<\/p>\n

    \u6240\u4ee5\u9700\u8981\u968f\u673a\u521d\u59cb\u5316，\u8ba9\u521d\u59cb\u7684\u795e\u7ecf\u5143\u5404\u4e0d\u76f8\u540c\u3002\u5373\u4f7f\u521d\u59cb\u5dee\u5f02\u5f88\u5c0f，\u4f46\u6fc0\u6d3b\u51fd\u6570\u7684\u975e\u7ebf\u6027（\u68af\u5ea6\u4e0d\u540c）\u4f1a\u653e\u5927\u8fd9\u79cd\u5dee\u5f02\u3002\u968f\u7740\u8bad\u7ec3\u8fdb\u884c，\u8fd9\u79cd\u5206\u6b67\u4f1a\u9010\u6e10\u6269\u5927，\u6700\u7ec8\u5f62\u6210\u529f\u80fd\u5404\u5f02\u7684\u795e\u7ecf\u5143\u3002<\/p>\n

    \u6240\u4ee5，\u660e\u767d\u4e86\u4e0a\u8ff0\u601d\u60f3，\u4f60\u5c31\u77e5\u9053\u521d\u59cb\u503c\u4e4b\u524d\u7684\u5dee\u5f02\u5e76\u4e0d\u9700\u8981\u5de8\u5927\u3002<\/p>\n

    \u4e8b\u5b9e\u4e0a，\u795e\u7ecf\u7f51\u7edc\u7684\u521d\u59cb\u6743\u91cd\u901a\u5e38\u8bbe\u7f6e\u5728\u63a5\u8fd1 0 \u7684\u5c0f\u8303\u56f4\u5185（\u5982 [-0.1, 0.1] \u6216 [-0.01, 0.01]），\u6216\u901a\u8fc7\u7279\u5b9a\u5206\u5e03（\u5982\u6b63\u6001\u5206\u5e03\u3001\u5747\u5300\u5206\u5e03）\u751f\u6210\u5c0f\u503c，\u6709\u5f88\u591a\u597d\u5904 ![image.png](attachment:image.png)<\/p>\n

    \u907f\u514d\u68af\u5ea6\u6d88\u5931 \/ \u7206\u70b8： \u4ee5 sigmoid \u6fc0\u6d3b\u51fd\u6570\u4e3a\u4f8b，\u5176\u5bfc\u6570\u5728\u8f93\u5165\u7edd\u5bf9\u503c\u8f83\u5927\u65f6\u8d8b\u8fd1\u4e8e 0（\u5982 | x|>5 \u65f6，\u5bfc\u6570\u22480）\u3002\u82e5\u521d\u59cb\u6743\u91cd\u8fc7\u5927，\u8f93\u5165 x=w\u30fbinput+b \u53ef\u80fd\u5bfc\u81f4\u6fc0\u6d3b\u51fd\u6570\u8fdb\u5165 \u201c\u9971\u548c\u533a\u201d，\u53cd\u5411\u4f20\u64ad\u65f6\u68af\u5ea6\u63a5\u8fd1 0，\u6743\u91cd\u66f4\u65b0\u7f13\u6162（\u68af\u5ea6\u6d88\u5931）\u3002 \u7c7b\u6bd4：\u82e5\u521d\u59cb\u6743\u91cd\u662f \u201c\u5927\u503c\u201d，\u76f8\u5f53\u4e8e\u8ba9\u795e\u7ecf\u5143\u4e00\u5f00\u59cb\u5c31\u8fdb\u5165 \u201c\u6781\u7aef\u72b6\u6001\u201d，\u5931\u53bb\u5bf9\u8f93\u5165\u53d8\u5316\u7684\u654f\u611f\u5ea6\u3002<\/p>\n

    \u5982\u679c\u68af\u5ea6\u76f8\u5bf9\u8f83\u5927，\u5c31\u53ef\u4ee5\u8ba9\u53d8\u5316\u5904\u4e8esigmoid\u51fd\u6570\u7684\u975e\u9971\u548c\u533a<\/p>\n

    \u6240\u4ee5\u5176\u5b9e\u5bf9\u4e8e\u4e0d\u540c\u7684\u6fc0\u6d3b\u51fd\u6570 ，\u90fd\u6709\u5bf9\u5e94\u7684\u9971\u548c\u533a\u548c\u975e\u9971\u548c\u533a，\u6df1\u5c42\u7f51\u7edc\u4e2d，\u9971\u548c\u533a\u4f1a\u4f7f\u68af\u5ea6\u5728\u53cd\u5411\u4f20\u64ad\u65f6\u9010\u5c42\u8870\u51cf，\u5e95\u5c42\u53c2\u6570\u51e0\u4e4e\u65e0\u6cd5\u66f4\u65b0；<\/p>\n

    \u6ce8\u610f\u4e0b，\u8fd9\u91cc\u662fwx\u540e\u624d\u4f1a\u7ecf\u8fc7\u6fc0\u6d3b\u51fd\u6570，\u662f\u591a\u4e2a\u6743\u91cd\u5370\u8c61\u7684\u7ed3\u679c，\u4e0d\u662f\u6536\u5230\u5355\u4e2a\u6743\u91cd\u51b3\u5b9a\u7684，\u6240\u4ee5\u5355\u4e2a\u6743\u91cd\u53ef\u4ee5\u53d6\u8d1f\u6570，\u4f46\u662f\u5982\u679c\u6c42\u548c\u540e\u4ecd\u7136\u5c0f\u4e8e0，\u90a3\u4e48\u8f93\u51fa\u4f1a\u4e3a0 \u00a0<\/p>\n

    import torch
    \nimport torch.nn as nn
    \nimport matplotlib.pyplot as plt
    \nimport numpy as np<\/p>\n

    # \u8bbe\u7f6e\u8bbe\u5907
    \ndevice = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")<\/p>\n

    # \u5b9a\u4e49\u6781\u7b80CNN\u6a21\u578b（\u4ec51\u4e2a\u5377\u79ef\u5c42+1\u4e2a\u5168\u8fde\u63a5\u5c42）
    \nclass SimpleCNN(nn.Module):
    \n def __init__(self):
    \n super(SimpleCNN, self).__init__()<\/p>\n

    # \u5377\u79ef\u5c42：\u8f93\u51653\u901a\u9053，\u8f93\u51fa16\u901a\u9053，\u5377\u79ef\u68383×3
    \n self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)<\/p>\n

    # \u6c60\u5316\u5c42：2×2\u7a97\u53e3，\u5c3a\u5bf8\u51cf\u534a
    \n self.pool = nn.MaxPool2d(kernel_size=2)<\/p>\n

    # \u5168\u8fde\u63a5\u5c42：\u5c55\u5e73\u540e\u8fde\u63a5\u523010\u4e2a\u8f93\u51fa（\u5bf9\u5e9410\u4e2a\u7c7b\u522b）
    \n # \u8f93\u5165\u5c3a\u5bf8：16\u901a\u9053 \u00d7 16×16\u7279\u5f81\u56fe = 16\u00d716\u00d716=4096
    \n self.fc = nn.Linear(16 * 16 * 16, 10)<\/p>\n

    def forward(self, x):
    \n # \u5377\u79ef+\u6c60\u5316
    \n x = self.pool(self.conv1(x)) # \u8f93\u51fa\u5c3a\u5bf8: [batch, 16, 16, 16]<\/p>\n

    # \u5c55\u5e73
    \n x = x.view(-1, 16 * 16 * 16) # \u5c55\u5e73\u4e3a: [batch, 4096]<\/p>\n

    # \u5168\u8fde\u63a5
    \n x = self.fc(x) # \u8f93\u51fa\u5c3a\u5bf8: [batch, 10]<\/p>\n

    return x<\/p>\n

    # \u521d\u59cb\u5316\u6a21\u578b
    \nmodel = SimpleCNN()
    \nmodel = model.to(device)<\/p>\n

    # \u67e5\u770b\u6a21\u578b\u7ed3\u6784
    \nprint(model)<\/p>\n

    # \u67e5\u770b\u521d\u59cb\u6743\u91cd\u7edf\u8ba1\u4fe1\u606f
    \ndef print_weight_stats(model):
    \n # \u5377\u79ef\u5c42
    \n conv_weights = model.conv1.weight.data
    \n print("\\\\n\u5377\u79ef\u5c42 \u6743\u91cd\u7edf\u8ba1:")
    \n print(f" \u5747\u503c: {conv_weights.mean().item():.6f}")
    \n print(f" \u6807\u51c6\u5dee: {conv_weights.std().item():.6f}")
    \n print(f" \u7406\u8bba\u6807\u51c6\u5dee (Kaiming): {np.sqrt(2\/3):.6f}") # \u8f93\u5165\u901a\u9053\u6570\u4e3a3<\/p>\n

    # \u5168\u8fde\u63a5\u5c42
    \n fc_weights = model.fc.weight.data
    \n print("\\\\n\u5168\u8fde\u63a5\u5c42 \u6743\u91cd\u7edf\u8ba1:")
    \n print(f" \u5747\u503c: {fc_weights.mean().item():.6f}")
    \n print(f" \u6807\u51c6\u5dee: {fc_weights.std().item():.6f}")
    \n print(f" \u7406\u8bba\u6807\u51c6\u5dee (Kaiming): {np.sqrt(2\/(16*16*16)):.6f}")<\/p>\n

    # \u6539\u8fdb\u7684\u53ef\u89c6\u5316\u6743\u91cd\u5206\u5e03\u51fd\u6570
    \ndef visualize_weights(model, layer_name, weights, save_path=None):
    \n plt.figure(figsize=(12, 5))<\/p>\n

    # \u6743\u91cd\u76f4\u65b9\u56fe
    \n plt.subplot(1, 2, 1)
    \n plt.hist(weights.cpu().numpy().flatten(), bins=50)
    \n plt.title(f'{layer_name} \u6743\u91cd\u5206\u5e03')
    \n plt.xlabel('\u6743\u91cd\u503c')
    \n plt.ylabel('\u9891\u6b21')<\/p>\n

    # \u6743\u91cd\u70ed\u56fe
    \n plt.subplot(1, 2, 2)
    \n if len(weights.shape) == 4: # \u5377\u79ef\u5c42\u6743\u91cd [out_channels, in_channels, kernel_size, kernel_size]
    \n # \u53ea\u663e\u793a\u7b2c\u4e00\u4e2a\u8f93\u5165\u901a\u9053\u7684\u524d10\u4e2a\u6ee4\u6ce2\u5668
    \n w = weights[:10, 0].cpu().numpy()
    \n plt.imshow(w.reshape(-1, weights.shape[2]), cmap='viridis')
    \n else: # \u5168\u8fde\u63a5\u5c42\u6743\u91cd [out_features, in_features]
    \n # \u53ea\u663e\u793a\u524d10\u4e2a\u795e\u7ecf\u5143\u7684\u6743\u91cd，\u91cd\u5851\u4e3a\u66f4\u5408\u7406\u7684\u77e9\u5f62
    \n w = weights[:10].cpu().numpy()<\/p>\n

    # \u8ba1\u7b97\u66f4\u5408\u7406\u7684\u4e8c\u7ef4\u5f62\u72b6（\u5c1d\u8bd5\u63a5\u8fd1\u6b63\u65b9\u5f62）
    \n n_features = w.shape[1]
    \n side_length = int(np.sqrt(n_features))<\/p>\n

    # \u5982\u679c\u4e0d\u80fd\u5b8c\u7f8e\u6574\u9664，\u6dfb\u52a0\u96f6\u586b\u5145\u4f7f\u80fd\u91cd\u5851
    \n if n_features % side_length != 0:
    \n new_size = (side_length + 1) * side_length
    \n w_padded = np.zeros((w.shape[0], new_size))
    \n w_padded[:, :n_features] = w
    \n w = w_padded<\/p>\n

    # \u91cd\u5851\u5e76\u663e\u793a
    \n plt.imshow(w.reshape(w.shape[0] * side_length, -1), cmap='viridis')<\/p>\n

    plt.colorbar()
    \n plt.title(f'{layer_name} \u6743\u91cd\u70ed\u56fe')<\/p>\n

    plt.tight_layout()
    \n if save_path:
    \n plt.savefig(f'{save_path}_{layer_name}.png')
    \n plt.show()<\/p>\n

    # \u6253\u5370\u6743\u91cd\u7edf\u8ba1
    \nprint_weight_stats(model)<\/p>\n

    # \u53ef\u89c6\u5316\u5404\u5c42\u6743\u91cd
    \nvisualize_weights(model, "Conv1", model.conv1.weight.data, "initial_weights")
    \nvisualize_weights(model, "FC", model.fc.weight.data, "initial_weights")<\/p>\n

    # \u53ef\u89c6\u5316\u504f\u7f6e
    \nplt.figure(figsize=(12, 5))<\/p>\n

    # \u5377\u79ef\u5c42\u504f\u7f6e
    \nconv_bias = model.conv1.bias.data
    \nplt.subplot(1, 2, 1)
    \nplt.bar(range(len(conv_bias)), conv_bias.cpu().numpy())
    \nplt.title('\u5377\u79ef\u5c42 \u504f\u7f6e')<\/p>\n

    # \u5168\u8fde\u63a5\u5c42\u504f\u7f6e
    \nfc_bias = model.fc.bias.data
    \nplt.subplot(1, 2, 2)
    \nplt.bar(range(len(fc_bias)), fc_bias.cpu().numpy())
    \nplt.title('\u5168\u8fde\u63a5\u5c42 \u504f\u7f6e')<\/p>\n

    plt.tight_layout()
    \nplt.savefig('biases_initial.png')
    \nplt.show()<\/p>\n

    print("\\\\n\u504f\u7f6e\u7edf\u8ba1:")
    \nprint(f"\u5377\u79ef\u5c42\u504f\u7f6e \u5747\u503c: {conv_bias.mean().item():.6f}")
    \nprint(f"\u5377\u79ef\u5c42\u504f\u7f6e \u6807\u51c6\u5dee: {conv_bias.std().item():.6f}")
    \nprint(f"\u5168\u8fde\u63a5\u5c42\u504f\u7f6e \u5747\u503c: {fc_bias.mean().item():.6f}")
    \nprint(f"\u5168\u8fde\u63a5\u5c42\u504f\u7f6e \u6807\u51c6\u5dee: {fc_bias.std().item():.6f}")<\/p>\n

    \u90a3\u6211\u4eec\u76d1\u63a7\u6743\u91cd\u56fe\u7684\u76ee\u7684\u662f\u4ec0\u4e48\u5462？<\/p>\n<\/p>\n

    \u8bad\u7ec3\u65f6，\u6743\u91cd\u4f1a\u968f\u53cd\u5411\u4f20\u64ad\u8fed\u4ee3\u66f4\u65b0\u3002\u901a\u8fc7\u6743\u91cd\u5206\u5e03\u56fe，\u80fd\u76f4\u89c2\u770b\u5230\u5176\u4ece\u521d\u59cb\u5316（\u5982\u968f\u673a\u5206\u5e03）\u5230\u9010\u6e10\u6536\u655b\u3001\u5f62\u6210\u89c4\u5f8b\u6a21\u5f0f\u7684\u52a8\u6001\u53d8\u5316，\u7406\u89e3\u6a21\u578b\u5982\u4f55\u4e00\u6b65\u6b65 \u201c\u5b66\u4e60\u201d \u7279\u5f81 \u3002\u6bd4\u5982，\u5377\u79ef\u5c42\u6743\u91cd\u521d\u671f\u6742\u4e71，\u8bad\u7ec3\u540e\u53ef\u80fd\u805a\u7126\u4e8e\u8fb9\u7f18\u3001\u7eb9\u7406\u7b49\u7279\u5b9a\u6a21\u5f0f\u3002<\/p>\n<\/p>\n

    \u8bc6\u522b\u68af\u5ea6\u5f02\u5e38：<\/p>\n

    1. \u68af\u5ea6\u6d88\u5931：\u82e5\u6743\u91cd\u5206\u5e03\u8d8a\u6765\u8d8a\u96c6\u4e2d\u5728 0 \u9644\u8fd1，\u4e14\u66f4\u65b0\u5e45\u5ea6\u6781\u5c0f，\u53ef\u80fd\u662f\u68af\u5ea6\u6d88\u5931，\u6a21\u578b\u96be\u5b66\u5230\u6709\u6548\u7279\u5f81（\u6bd4\u5982\u6df1\u5c42\u7f51\u7edc\u7528 Sigmoid \u6fc0\u6d3b\u6613\u51fa\u73b0 ）\u3002<\/p>\n

    2. \u68af\u5ea6\u7206\u70b8：\u6743\u91cd\u503c\u7a81\u7136\u5927\u5e45\u9707\u8361\u3001\u8d85\u51fa\u5408\u7406\u8303\u56f4（\u6bd4\u5982\u4ece [-0.1, 0.1] \u8df3\u5230 [-10, 10] ），\u8981\u8b66\u60d5\u68af\u5ea6\u7206\u70b8，\u53ef\u80fd\u8ba9\u8bad\u7ec3\u5d29\u6e83\u3002<\/p>\n<\/p>\n

    \u501f\u52a9tensorboard\u53ef\u4ee5\u770b\u5230\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u6743\u91cd\u56fe\u7684\u53d8\u5316<\/p>\n

    ## \u795e\u7ecf\u7f51\u7edc\u8c03\u53c2\u6307\u5357<\/p>\n<\/p>\n

    \u5927\u90e8\u5206\u65f6\u5019，\u7531\u4e8e\u5149\u662f\u56fa\u5b9a\u8d85\u53c2\u6570\u7684\u60c5\u51b5\u4e0b，\u8bad\u7ec3\u5b8c\u6a21\u578b\u5c31\u5df2\u7ecf\u5f88\u8017\u65f6\u4e86，\u6240\u4ee5\u6b63\u5e38\u800c\u8a00，\u57fa\u672c\u4e0d\u4f1a\u91c7\u7528\u4f20\u7edf\u673a\u5668\u5b66\u4e60\u7684\u90a3\u4e9b\u8d85\u53c2\u6570\u65b9\u6cd5，\u7f51\u683c\u3001\u8d1d\u53f6\u65af\u3001optuna\u4e4b\u7c7b\u7684，\u770b\u5230\u4e00\u4e9b\u535a\u4e3b\u7528\u8fd9\u4e9b\u5199\u6587\u6848\u5565\u7684，\u611f\u89c9\u8fd9\u4e9b\u4eba\u90fd\u662f\u8111\u5b50\u6709\u95ee\u9898\u7684，\u4f30\u8ba1\u4e5f\u6ca1\u5b66\u8fc7\u673a\u5668\u5b66\u4e60\u76f4\u63a5\u5c31\u5b66\u6df1\u5ea6\u5b66\u4e60\u4e86，\u641e\u6df7\u4e86\u4e8c\u8005\u7684\u5173\u7cfb\u3002<\/p>\n<\/p>\n

    \u5de5\u4e1a\u754c\u5361\u7279\u522b\u591a\u7684\u60c5\u51b5\u4e0b，\u53ef\u80fd\u53ef\u4ee5\u8003\u8651，\u5c24\u5176\u662f\u5728\u63a2\u7a76\u4e00\u4e2a\u65b0\u67b6\u6784\u7684\u65f6\u5019，\u6211\u4eec\u76f4\u63a5\u5ffd\u89c6\u8fd9\u4e9b\u5373\u53ef，\u53ea\u6709\u624b\u52a8\u8c03\u53c2\u8fd9\u4e00\u6761\u8def\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

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