pytorch搭建网络模型的4种方法
importtorch
importtorch.nn.functional as F
fromcollectionsimportOrderedDict
# Method 1 -----------------------------------------
classNet1(torch.nn.Module):
def__init__(self):
super(Net1,self).__init__()
self.conv1=torch.nn.Conv2d(3,32,3,1,1)
self.dense1=torch.nn.Linear(32*3*3,128)
self.dense2=torch.nn.Linear(128,10)
defforward(self, x):
x=F.max_pool2d(F.relu(self.conv(x)),2)
x=x.view(x.size(0),-1)
x=F.relu(self.dense1(x))
x=self.dense2()
returnx
print("Method 1:")
model1=Net1()
print(model1)
# Method 2 ------------------------------------------
classNet2(torch.nn.Module):
def__init__(self):
super(Net2,self).__init__()
self.conv=torch.nn.Sequential(
torch.nn.Conv2d(3,32,3,1,1),
torch.nn.ReLU(),
torch.nn.MaxPool2d(2))
self.dense=torch.nn.Sequential(
torch.nn.Linear(32*3*3,128),
torch.nn.ReLU(),
torch.nn.Linear(128,10)
)
defforward(self, x):
conv_out=self.conv1(x)
res=conv_out.view(conv_out.size(0),-1)
out=self.dense(res)
returnout
print("Method 2:")
model2=Net2()
print(model2)
# Method 3 -------------------------------
classNet3(torch.nn.Module):
def__init__(self):
super(Net3,self).__init__()
self.conv=torch.nn.Sequential()
self.conv.add_module("conv1",torch.nn.Conv2d(3,32,3,1,1))
self.conv.add_module("relu1",torch.nn.ReLU())
self.conv.add_module("pool1",torch.nn.MaxPool2d(2))
self.dense=torch.nn.Sequential()
self.dense.add_module("dense1",torch.nn.Linear(32*3*3,128))
self.dense.add_module("relu2",torch.nn.ReLU())
self.dense.add_module("dense2",torch.nn.Linear(128,10))
defforward(self, x):
conv_out=self.conv1(x)
res=conv_out.view(conv_out.size(0),-1)
out=self.dense(res)
returnout
print("Method 3:")
model3=Net3()
print(model3)
# Method 4 ------------------------------------------
classNet4(torch.nn.Module):
def__init__(self):
super(Net4,self).__init__()
self.conv=torch.nn.Sequential(
OrderedDict(
[
("conv1", torch.nn.Conv2d(3,32,3,1,1)),
("relu1", torch.nn.ReLU()),
("pool", torch.nn.MaxPool2d(2))
]
))
self.dense=torch.nn.Sequential(
OrderedDict([
("dense1", torch.nn.Linear(32*3*3,128)),
("relu2", torch.nn.ReLU()),
("dense2", torch.nn.Linear(128,10))
])
)
defforward(self, x):
conv_out=self.conv1(x)
res=conv_out.view(conv_out.size(0),-1)
out=self.dense(res)
returnout
model4=Net4()
print("Method 4:")
print(model4)
