Pytorch中 list(net.parameters())[0]
Pytorch中 list(net.parameters())[0]
先看一下Lenet的代码
class LeNet(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(1, 6, 5), # in_channels, out_channels, kernel_size
nn.Sigmoid(),
nn.MaxPool2d(2, 2), # kernel_size, stride
nn.Conv2d(6, 16, 5),
nn.Sigmoid(),
nn.MaxPool2d(2, 2)
)
self.fc = nn.Sequential(
nn.Linear(16*4*4, 120),
nn.Sigmoid(),
nn.Linear(120, 84),
nn.Sigmoid(),
nn.Linear(84, 10)
)
def forward(self, img):
feature = self.conv(img)
output = self.fc(feature.view(img.shape[0], -1)) # img.shape[0]是batch_size
print(img.size())
return output
net = LeNet()
加上FC层共有5个参数层（每层有权值和bias），运行下面的代码
print(list(net.parameters()))
[Parameter containing:
tensor([[[[ 0.0991, 0.1349, -0.0070, -0.1155, -0.0294],
[-0.0658, 0.0957, 0.1981, 0.1192, -0.0146],
[-0.1672, 0.1914, 0.1532, -0.1125, 0.0129],
[ 0.1950, -0.0386, 0.1198, -0.0127, -0.0673],
[ 0.1181, 0.1342, 0.1384, -0.0352, 0.1887]]],
[[[ 0.0795, -0.1427, 0.1126, -0.0740, 0.1322],
[ 0.0405, -0.0449, 0.1535, -0.1308, -0.0610],
[-0.1037, -0.1754, -0.1544, 0.1937, 0.0915],
[ 0.1464, -0.1385, -0.0502, 0.0471, -0.0091],
[ 0.0878, -0.1831, 0.0909, -0.1313, -0.1462]]],
[[[-0.0178, 0.0972, -0.1304, 0.0813, -0.0893],
[ 0.1059, 0.0222, -0.0230, -0.1466, -0.0590],
[-0.0327, -0.0503, -0.1373, 0.1634, -0.0452],
[ 0.1720, 0.1130, -0.1299, -0.0550, 0.1723],
[ 0.0730, 0.1294, -0.0941, 0.0520, -0.0408]]],
[[[-0.1357, -0.1327, -0.0143, 0.1415, 0.0842],
[ 0.0013, 0.0838, -0.1388, 0.0106, 0.0555],
[-0.0590, -0.1105, -0.0641, -0.1874, -0.0973],
[ 0.1863, 0.0678, 0.1571, 0.1057, 0.0562],
[ 0.1442, -0.1517, -0.0078, -0.1562, -0.0867]]],
[[[ 0.0950, -0.0273, -0.1122, 0.0195, 0.1027],
[-0.1541, 0.0725, -0.1750, -0.1083, -0.0328],
[-0.1178, 0.0273, -0.0396, -0.1906, -0.0434],
[-0.1573, 0.1007, -0.0713, -0.0218, -0.1169],
[-0.1945, 0.1164, -0.1634, 0.1479, 0.1212]]],
[[[ 0.1842, 0.0659, -0.0614, 0.1092, -0.1675],
[-0.1057, 0.0857, -0.0968, 0.1906, 0.1513],
[-0.1912, 0.1350, -0.0484, -0.0251, 0.1643],
[ 0.1141, 0.0235, -0.1294, -0.0091, -0.0069],
[ 0.0336, -0.0086, -0.0673, 0.1272, -0.0220]]]], requires_grad=True), Parameter containing:
tensor([-0.0706, -0.0415, -0.1431, -0.1451, -0.1992, -0.1000],
requires_grad=True),
Parameter containing:..........
这是第一个卷积层的参数，包含权值和10个bias
运行下面代码
print(len(list(net.parameters())))
print(type(list(net.parameters())[0]))
10
<class 'torch.nn.parameter.Parameter'>
这是意味着这个list共有10个元素，每个元素是Parameter，因为这里有5层，每层包含权值与bias，所以list有10个Parameter元素。
注意：
BN层也有两个参数（gamma, beta）。