nn.Module vs nn.functional
前者會保存權重等信息，後者只是作運算python
parameters()
返回可訓練參數網絡
nn.ModuleList vs. nn.ParameterList vs. nn.Sequential
layer_list = [nn.Conv2d(5,5,3), nn.BatchNorm2d(5), nn.Linear(5,2)]
class myNet(nn.Module):
def __init__(self):
super().__init__()
self.layers = layer_list
def forward(x):
for layer in self.layers:
x = layer(x)
net = myNet()
print(list(net.parameters())) # Parameters of modules in the layer_list don't show up.
nn.ModuleList的做用就是wrap pthon list，這樣其中的參數會被註冊，所以能夠返回可訓練參數(ParameterList)。ide
nn.Sequential的做用以下：ui
class myNet(nn.Module):
def __init__(self):
super().__init__()
self.layers = nn.Sequential(
nn.Relu(inplace=True),
nn.Linear(10, 10)
)
def forward(x):
x = layer(x)
x = torch.rand(10)
net = myNet()
print(net(x).shape)
能夠看到Sequential的做用就是按照指定的順序構建網絡結構，獲得一個完整的模塊，而ModuleList則只是像list那樣把元素集合起來而已。url
nn.modules vs. nn.children
class myNet(nn.Module):
def __init__(self):
super().__init__()
self.convBN = nn.Sequential(nn.Conv2d(10,10,3), nn.BatchNorm2d(10))
self.linear = nn.Linear(10,2)
def forward(self, x):
pass
Net = myNet()
print("Printing children\n------------------------------")
print(list(Net.children()))
print("\n\nPrinting Modules\n------------------------------")
print(list(Net.modules()))
輸出信息以下：spa
Printing children
------------------------------
[Sequential(
(0): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1))
(1): BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
), Linear(in_features=10, out_features=2, bias=True)]
Printing Modules
------------------------------
[myNet(
(convBN1): Sequential(
(0): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1))
(1): BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(linear): Linear(in_features=10, out_features=2, bias=True)
), Sequential(
(0): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1))
(1): BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
), Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1)), BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), Linear(in_features=10, out_features=2, bias=True)]
能夠看到children只會返回子元素，子元素多是單個操做，如Linear，也多是Sequential。 而modules()返回的信息更加詳細，不只會返回children同樣的信息，同時還會遞歸地返回，例如modules()會迭代地返回Sequential中包含的若干個子元素。.net
named_*
- named_parameters: 返回一個
iterator,每次它會提供包含參數名的元組。
In [27]: x = torch.nn.Linear(2,3)
In [28]: x_name_params = x.named_parameters()
In [29]: next(x_name_params)
Out[29]:
('weight', Parameter containing:
tensor([[-0.5262, 0.3480],
[-0.6416, -0.1956],
[ 0.5042, 0.6732]], requires_grad=True))
In [30]: next(x_name_params)
Out[30]:
('bias', Parameter containing:
tensor([ 0.0595, -0.0386, 0.0975], requires_grad=True))
- named_modules
這個其實就是把上面提到的nn.modules以iterator的形式返回，每次讀取和上面同樣也是用next()，示例以下：
In [46]: class myNet(nn.Module):
...: def __init__(self):
...: super().__init__()
...: self.convBN1 = nn.Sequential(nn.Conv2d(10,10,3), nn.BatchNorm2d(10))
...: self.linear = nn.Linear(10,2)
...:
...: def forward(self, x):
...: pass
...:
In [47]: net = myNet()
In [48]: net_named_modules = net.named_modules()
In [49]: next(net_named_modules)
Out[49]:
('', myNet(
(convBN1): Sequential(
(0): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1))
(1): BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(linear): Linear(in_features=10, out_features=2, bias=True)
))
In [50]: next(net_named_modules)
Out[50]:
('convBN1', Sequential(
(0): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1))
(1): BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
))
In [51]: next(net_named_modules)
Out[51]: ('convBN1.0', Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1)))
In [52]: next(net_named_modules)
Out[52]:
('convBN1.1',
BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))
In [53]: next(net_named_modules)
Out[53]: ('linear', Linear(in_features=10, out_features=2, bias=True))
In [54]: next(net_named_modules)
---------------------------------------------------------------------------
StopIteration Traceback (most recent call last)
<ipython-input-54-05e848b071b8> in <module>
----> 1 next(net_named_modules)
StopIteration:
- named_children
同named_modulescode