pytorch中的named_parameters()和parameters()

　　pytorch named_parameters()和parameters()

　　nn.Module

　　nn.Module里面关于参数有两个很重要的属性named_parameters()和parameters()，前者给出网络层的名字和参数的迭代器，而后者仅仅是参数的迭代器。

　　import torchvision.models as models

　　model = models.resnet18()

　　for param in model.named_parameters():

　　print(param[0])

　　'''

　　conv1.weight

　　bn1.weight

　　bn1.bias

　　layer1.0.conv1.weight

　　layer1.0.bn1.weight

　　layer1.0.bn1.bias

　　layer1.0.conv2.weight

　　layer1.0.bn2.weight

　　layer1.0.bn2.bias

　　layer1.1.conv1.weight

　　layer1.1.bn1.weight

　　layer1.1.bn1.bias

　　layer1.1.conv2.weight

　　layer1.1.bn2.weight

　　layer1.1.bn2.bias

　　layer2.0.conv1.weight

　　layer2.0.bn1.weight

　　layer2.0.bn1.bias

　　layer2.0.conv2.weight

　　layer2.0.bn2.weight

　　layer2.0.bn2.bias

　　layer2.0.downsample.0.weight

　　layer2.0.downsample.1.weight

　　layer2.0.downsample.1.bias

　　layer2.1.conv1.weight

　　layer2.1.bn1.weight

　　layer2.1.bn1.bias

　　layer2.1.conv2.weight

　　layer2.1.bn2.weight

　　layer2.1.bn2.bias

　　layer3.0.conv1.weight

　　layer3.0.bn1.weight

　　layer3.0.bn1.bias

　　layer3.0.conv2.weight

　　layer3.0.bn2.weight

　　layer3.0.bn2.bias

　　layer3.0.downsample.0.weight

　　layer3.0.downsample.1.weight

　　layer3.0.downsample.1.bias

　　layer3.1.conv1.weight

　　layer3.1.bn1.weight

　　layer3.1.bn1.bias

　　layer3.1.conv2.weight

　　layer3.1.bn2.weight

　　layer3.1.bn2.bias

　　layer4.0.conv1.weight

　　layer4.0.bn1.weight

　　layer4.0.bn1.bias

　　layer4.0.conv2.weight

　　layer4.0.bn2.weight

　　layer4.0.bn2.bias

　　layer4.0.downsample.0.weight

　　layer4.0.downsample.1.weight

　　layer4.0.downsample.1.bias

　　layer4.1.conv1.weight

　　layer4.1.bn1.weight

　　layer4.1.bn1.bias

　　layer4.1.conv2.weight

　　layer4.1.bn2.weight

　　layer4.1.bn2.bias

　　fc.weight

　　fc.bias

　　'''

　　模型参数：named_parameters()、parameters()、state_dict()区别

　　torch中存在3个功能极其类似的方法，它们分别是model.parameters()、model.named_parameters()、model.state_dict()，

　　下面就具体来说说这三个函数的差异：

　　一、model.parameters()和model.named_parameters()差别

　　1、model.named_parameters()里的网络层名字

　　import torchvision.models as models

　　model = models.resnet18()

　　for param_tuple in model.named_parameters():

　　name, param = param_tuple

　　print("name = ", name)

　　print("-" * 100)

　　打印结果：

　　name = conv1.weight

　　----------------------------------------------------------------------------------------------------

　　name = bn1.weight

　　----------------------------------------------------------------------------------------------------

　　name = bn1.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer1.0.conv1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer1.0.bn1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer1.0.bn1.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer1.0.conv2.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer1.0.bn2.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer1.0.bn2.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer1.1.conv1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer1.1.bn1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer1.1.bn1.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer1.1.conv2.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer1.1.bn2.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer1.1.bn2.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer2.0.conv1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer2.0.bn1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer2.0.bn1.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer2.0.conv2.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer2.0.bn2.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer2.0.bn2.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer2.0.downsample.0.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer2.0.downsample.1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer2.0.downsample.1.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer2.1.conv1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer2.1.bn1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer2.1.bn1.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer2.1.conv2.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer2.1.bn2.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer2.1.bn2.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer3.0.conv1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer3.0.bn1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer3.0.bn1.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer3.0.conv2.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer3.0.bn2.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer3.0.bn2.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer3.0.downsample.0.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer3.0.downsample.1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer3.0.downsample.1.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer3.1.conv1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer3.1.bn1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer3.1.bn1.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer3.1.conv2.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer3.1.bn2.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer3.1.bn2.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer4.0.conv1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer4.0.bn1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer4.0.bn1.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer4.0.conv2.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer4.0.bn2.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer4.0.bn2.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer4.0.downsample.0.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer4.0.downsample.1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer4.0.downsample.1.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer4.1.conv1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer4.1.bn1.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer4.1.bn1.bias

　　----------------------------------------------------------------------------------------------------

　　name = layer4.1.conv2.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer4.1.bn2.weight

　　----------------------------------------------------------------------------------------------------

　　name = layer4.1.bn2.bias

　　----------------------------------------------------------------------------------------------------

　　name = fc.weight

　　----------------------------------------------------------------------------------------------------

　　name = fc.bias

　　----------------------------------------------------------------------------------------------------

　　Process finished with exit code 0

　　2、model.named_parameters()里的网络层名字、参数

　　import torchvision.models as models

　　model = models.resnet18()

　　for param_tuple in model.named_parameters():

　　name, param = param_tuple

　　print("name = ", name)

　　print("-" * 100)

　　print("param_tuple = ", param_tuple)

　　print("*" * 200)

　　打印结果：

　　C:Program_Files_AIAnaconda3531python.exe C:/Users/Admin/OneDrive/WorkSpace_AI/0-基于知识库的智能问答系统-华控智加/01-意图识别/test.py

　　name = conv1.weight

　　----------------------------------------------------------------------------------------------------

　　param_tuple = ('conv1.weight', Parameter containing:

　　tensor([[[[-1.4115e-05, 2.9187e-02, 2.9325e-03, ..., -4.2247e-02,

　　1.7490e-02, -4.5253e-02],

　　[-2.4594e-02, -3.0836e-02, 3.8604e-02, ..., 3.5473e-02,

　　-4.7046e-03, -2.9440e-02],

　　[ 2.4811e-02, 1.2679e-02, 1.0070e-02, ..., -8.3476e-03,

　　1.7960e-02, -1.7406e-02],

　　...,

　　[-1.3021e-02, 2.9023e-02, -6.1800e-02, ..., -5.2802e-02,

　　-4.7817e-02, -2.2377e-02],

　　[-3.8513e-03, -1.0603e-02, -3.9712e-02, ..., 5.1941e-03,

　　8.2868e-03, -8.3469e-03],

　　[ 3.8993e-03, 3.2017e-02, -3.6292e-02, ..., -2.0210e-02,

　　-4.0358e-02, 1.7709e-02]],

　　[[-1.0894e-03, 1.5720e-02, 7.0129e-03, ..., -1.2024e-02,

　　1.8644e-02, 1.7892e-02],

　　[-2.3866e-02, 9.1136e-03, 3.5243e-02, ..., -1.6756e-02,

　　1.4441e-03, 4.7943e-02],

　　[-2.0514e-03, 4.3022e-02, 2.6358e-02, ..., -2.3662e-02,

　　-7.8241e-04, 1.0167e-02],

　　...

　　[[-4.6689e-02, -1.1407e-03, 1.8674e-02, ..., 1.2649e-03,

　　-2.9532e-02, 6.4535e-04],

　　[ 1.4171e-03, -1.9274e-02, -8.6811e-03, ..., 2.4428e-02,

　　6.9516e-03, 4.3715e-02],

　　[ 1.9982e-02, 1.3124e-02, 9.1508e-03, ..., 2.5405e-02,

　　-1.3132e-02, 4.0835e-02],

　　...,

　　[-3.4174e-03, 1.8623e-02, -1.4386e-02, ..., 1.0627e-03,

　　-5.1297e-04, 2.2055e-02],

　　[ 2.7333e-02, 2.4858e-02, -5.4305e-02, ..., -1.2139e-02,

　　1.7735e-03, -3.4184e-03],

　　[ 1.1412e-03, 1.5794e-02, -2.0699e-02, ..., -1.7846e-02,

　　3.7425e-02, -1.6059e-02]]],

　　...,

　　[[[-2.7389e-02, -3.8327e-02, -2.9043e-02, ..., -7.6396e-03,

　　-1.6519e-02, 3.9659e-02],

　　[ 2.8740e-03, -1.0621e-02, -9.2430e-03, ..., 2.2581e-02,

　　5.1526e-03, -2.0006e-02],

　　[ 1.3575e-02, 1.5290e-02, -1.7260e-02, ..., 6.3830e-03,

　　-1.9759e-02, 1.5501e-02],

　　...,

　　[ 1.6091e-02, 2.4038e-02, 2.4507e-02, ..., -4.5613e-02,

　　-3.6233e-02, 2.1632e-02],

　　[-1.1573e-02, -3.6514e-02, 4.1576e-02, ..., 1.8090e-02,

　　-2.3350e-02, -8.7074e-03],

　　[-1.5837e-02, -3.1353e-02, 1.8726e-02, ..., 9.3698e-03,

　　3.0781e-02, 1.0976e-02]],

　　[[-2.7063e-02, 8.7158e-03, 2.7193e-03, ..., -1.6670e-03,

　　-4.3033e-03, 7.2011e-04],

　　[ 2.7870e-03, 1.4264e-02, -5.0581e-02, ..., 2.5463e-02,

　　7.6864e-03, -4.9655e-02],

　　[ 2.6030e-03, 2.5918e-02, 2.9615e-02, ..., 3.0676e-02,

　　-2.7723e-02, -7.3628e-03],

　　...,

　　[ 2.5969e-02, -1.4247e-02, 1.2516e-02, ..., 5.9602e-03,

　　-3.2843e-02, 3.5822e-02],

　　[ 1.2845e-02, -2.0035e-02, 9.9398e-04, ..., -3.1800e-02,

　　5.7984e-03, 2.8756e-02],

　　[ 2.3458e-02, 3.8193e-02, -2.3754e-03, ..., -1.3867e-02,

　　8.0831e-03, -3.2438e-02]],

　　...

　　[[-9.9291e-03, -5.6023e-03, -1.7064e-02, ..., 8.8544e-03,

　　-5.8145e-03, 2.3248e-02],

　　[ 1.2148e-02, -1.0730e-02, -1.2682e-02, ..., 9.4389e-03,

　　1.2149e-02, 3.8613e-03],

　　[ 3.5913e-02, -5.2048e-04, -8.7133e-02, ..., -2.0969e-03,

　　-5.4117e-03, 5.4637e-05],

　　...,

　　[ 4.0351e-03, -1.3189e-02, 3.1229e-02, ..., 3.2340e-02,

　　-2.8351e-02, 1.0634e-02],

　　[ 2.6041e-02, -3.0633e-04, -1.2732e-02, ..., 2.9417e-02,

　　-7.3859e-03, 1.7207e-02],

　　[ 6.9960e-04, 3.8486e-03, 1.0397e-02, ..., 1.4535e-03,

　　-3.6449e-02, 3.4848e-02]]]], requires_grad=True))

　　********************************************************************************************************************************************************************************************************

　　name = bn1.weight

　　----------------------------------------------------------------------------------------------------

　　param_tuple = ('bn1.weight', Parameter containing:

　　tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,

　　1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,

　　1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], requires_grad=True))

　　name = bn1.bias

　　----------------------------------------------------------------------------------------------------

　　param_tuple = ('bn1.bias', Parameter containing:

　　tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,

　　0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,

　　0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],

　　requires_grad=True))

　　name = layer1.0.conv2.weight

　　----------------------------------------------------------------------------------------------------

　　param_tuple = ('layer1.0.conv2.weight', Parameter containing:

　　tensor([[[[-8.6159e-02, 1.8507e-04, 5.4006e-03],

　　[-6.3063e-03, 3.9225e-03, -6.3141e-02],

　　[-7.0145e-02, -3.9266e-02, 1.9724e-03]],

　　[[ 4.6454e-02, 2.1519e-02, 5.3696e-02],

　　[ 1.1086e-02, 1.6269e-01, -7.0579e-02],

　　[-1.1220e-01, -4.9811e-02, -7.5515e-02]],

　　[[ 5.5275e-02, -8.2407e-02, -8.9807e-02],

　　[ 5.8418e-02, 4.4029e-02, 3.0584e-03],

　　[ 5.2371e-02, -1.5983e-02, 5.1494e-02]],

　　...,

　　[[ 7.3441e-02, 4.5401e-02, -1.9175e-02],

　　[-6.2500e-02, -8.5905e-03, -7.4856e-02],

　　[-1.6170e-02, 3.7529e-02, -5.1231e-02]],

　　[[ 7.7501e-04, -5.7506e-02, 1.8422e-01],

　　[ 2.4594e-02, 1.7378e-02, 4.0000e-02],

　　[-8.6796e-02, -6.0548e-02, 2.6795e-02]],

　　[[ 5.3264e-02, -8.6190e-02, 4.2443e-02],

　　[-6.8029e-03, -1.6581e-02, 7.8568e-02],

　　[ 3.2037e-02, -7.3002e-02, 4.9353e-02]]]], requires_grad=True))

　　...，

　　name = layer4.1.bn2.bias

　　----------------------------------------------------------------------------------------------------

　　param_tuple = ('layer4.1.bn2.bias', Parameter containing:

　　tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,

　　0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,

　　...,

　　0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,

　　0., 0., 0., 0., 0., 0., 0., 0.], requires_grad=True))

　　name = fc.weight

　　----------------------------------------------------------------------------------------------------

　　param_tuple = ('fc.weight', Parameter containing:

　　tensor([[-0.0125, 0.0437, -0.0014, ..., -0.0230, 0.0280, 0.0249],

　　[-0.0105, 0.0242, 0.0291, ..., 0.0153, 0.0366, -0.0236],

　　[-0.0315, 0.0306, -0.0216, ..., 0.0387, 0.0403, 0.0056],

　　...,

　　[-0.0068, -0.0222, -0.0027, ..., -0.0243, 0.0260, 0.0065],

　　[ 0.0213, 0.0167, -0.0379, ..., -0.0140, 0.0037, -0.0372],

　　[ 0.0180, 0.0101, -0.0341, ..., -0.0295, -0.0146, 0.0416]],

　　requires_grad=True))

　　name = fc.bias

　　----------------------------------------------------------------------------------------------------

　　param_tuple = ('fc.bias', Parameter containing:

　　tensor([ 3.5711e-02, 3.2682e-02, 7.5932e-03, -3.1623e-02, -9.6316e-03,

　　-2.4051e-02, -1.0393e-02, 2.3210e-02, -3.6044e-02, 2.3099e-02,

　　-3.5723e-02, -3.9482e-02, 4.8526e-03, -3.2688e-02, 3.7720e-03,

　　-2.2014e-02, -4.0935e-02, 4.0533e-02, -4.1172e-02, 3.9513e-02,

　　-3.0332e-02, 3.2777e-02, 1.3342e-02, 2.3394e-02, 8.2328e-03,

　　1.3757e-02, -1.7578e-02, -2.7165e-02, 3.8495e-03, -3.2116e-02,

　　7.9903e-03, 9.9640e-04, -8.3106e-03, 2.5033e-02, -3.0446e-02,

　　-1.8282e-02, -3.8420e-03, -8.6129e-03, -4.2712e-03, 1.7169e-02,

　　...,

　　-5.3570e-05, -3.7353e-02, -9.8633e-03, -9.1069e-03, 3.2688e-02,

　　2.2457e-02, 7.6379e-03, -3.6287e-02, -1.0444e-02, 2.1669e-02,

　　2.5270e-02, -4.3881e-02, 2.1960e-02, 2.6293e-02, -3.5049e-02,

　　-2.0074e-02, -9.7686e-03, -2.3766e-02, -5.0265e-03, -2.1095e-02,

　　2.0981e-02, -3.5132e-02, 8.6407e-03, 1.8453e-02, 2.4282e-02,

　　3.8392e-02, -1.7470e-02, 3.6958e-02, -3.7590e-02, -4.1951e-02,

　　-1.8246e-02, 9.0818e-03, 3.8774e-02, 7.3408e-03, 1.7728e-02,

　　3.5547e-02, -7.2857e-03, -2.7015e-02, -8.6983e-03, -2.3785e-02],

　　requires_grad=True))

　　Process finished with exit code 0

　　二、model.named_parameters()和model.state_dict()差别

　　它们的差异主要体现在3方面：

　　named_parameters()state_dict()将layer_name : layer_param的键值信息打包成一个元祖然后再存到list当中将layer_name : layer_param的键值信息存储为dict形式只保存可学习、可被更新的参数，model.buffer()中的参数不包含在model.named_parameters()中存储的是该model中包含的所有layer中的所有参数require_grad属性都是True存储的模型参数tensor的require_grad属性都是False

　　为何model.parameters()迭代出来的所有参数的require_grad属性都是True，因为它们在被创建时，默认的require_grad就是True。

　　这也符合逻辑，即，使用nn.Parameter()创建的变量是模型参数，本就是要参与学习和更新的

　　总结

　　以上为个人经验，希望能给大家一个参考，也希望大家多多支持脚本之家。

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