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# 可以使用以下3种方式构建模型：
#
# 1，继承nn.Module基类构建自定义模型。
#
# 2，使用nn.Sequential按层顺序构建模型。
#
# 3，继承nn.Module基类构建模型并辅助应用模型容器进行封装(nn.Sequential,nn.ModuleList,nn.ModuleDict)。
#
# 其中 第1种方式最为常见，第2种方式最简单，第3种方式最为灵活也较为复杂。
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# 一、继承nn.Module基类构建自定义模型
from torch import nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3)
self.pool1 = nn.MaxPool2d(kernel_size = 2,stride = 2)
self.conv2 = nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5)
self.pool2 = nn.MaxPool2d(kernel_size = 2,stride = 2)
self.dropout = nn.Dropout2d(p = 0.1)
self.adaptive_pool = nn.AdaptiveMaxPool2d((1,1))
self.flatten = nn.Flatten()
self.linear1 = nn.Linear(64,32)
self.relu = nn.ReLU()
self.linear2 = nn.Linear(32,1)
def forward(self,x):
x = self.conv1(x)
x = self.pool1(x)
x = self.conv2(x)
x = self.pool2(x)
x = self.dropout(x)
x = self.adaptive_pool(x)
x = self.flatten(x)
x = self.linear1(x)
x = self.relu(x)
y = self.linear2(x)
return y
net = Net()
print(net)
#查看参数
from torchkeras import summary
summary(net,input_shape= (3,32,32));
二、使用nn.Sequential按层顺序构建模型 # 利用add_module方法
net = nn.Sequential()
net.add_module("conv1",nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3))
net.add_module("pool1",nn.MaxPool2d(kernel_size = 2,stride = 2))
net.add_module("conv2",nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5))
net.add_module("pool2",nn.MaxPool2d(kernel_size = 2,stride = 2))
net.add_module("dropout",nn.Dropout2d(p = 0.1))
net.add_module("adaptive_pool",nn.AdaptiveMaxPool2d((1,1)))
net.add_module("flatten",nn.Flatten())
net.add_module("linear1",nn.Linear(64,32))
net.add_module("relu",nn.ReLU())
net.add_module("linear2",nn.Linear(32,1))
print(net)
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# 利用变长参数
net = nn.Sequential(
nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3),
nn.MaxPool2d(kernel_size = 2,stride = 2),
nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),
nn.MaxPool2d(kernel_size = 2,stride = 2),
nn.Dropout2d(p = 0.1),
nn.AdaptiveMaxPool2d((1,1)),
nn.Flatten(),
nn.Linear(64,32),
nn.ReLU(),
nn.Linear(32,1)
)
print(net)
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# 三、继承nn.Module基类构建模型并辅助应用模型容器进行封装
# nn.Sequential作为模型容器
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3),
nn.MaxPool2d(kernel_size = 2,stride = 2),
nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),
nn.MaxPool2d(kernel_size = 2,stride = 2),
nn.Dropout2d(p = 0.1),
nn.AdaptiveMaxPool2d((1,1))
)
self.dense = nn.Sequential(
nn.Flatten(),
nn.Linear(64,32),
nn.ReLU(),
nn.Linear(32,1)
)
def forward(self,x):
x = self.conv(x)
y = self.dense(x)
return y
net = Net()
print(net)
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# nn.ModuleList作为模型容器
# 注意下面中的ModuleList不能用Python中的列表代替。（即不用省略）
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.layers = nn.ModuleList([
nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3),
nn.MaxPool2d(kernel_size = 2,stride = 2),
nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),
nn.MaxPool2d(kernel_size = 2,stride = 2),
nn.Dropout2d(p = 0.1),
nn.AdaptiveMaxPool2d((1,1)),
nn.Flatten(),
nn.Linear(64,32),
nn.ReLU(),
nn.Linear(32,1)]
)
def forward(self,x):
for layer in self.layers:
x = layer(x)
return x
net = Net()
print(net)
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# nn.ModuleDict作为模型容器
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.layers_dict = nn.ModuleDict({"conv1":nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3),
"pool": nn.MaxPool2d(kernel_size = 2,stride = 2),
"conv2":nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),
"dropout": nn.Dropout2d(p = 0.1),
"adaptive":nn.AdaptiveMaxPool2d((1,1)),
"flatten": nn.Flatten(),
"linear1": nn.Linear(64,32),
"relu":nn.ReLU(),
"linear2": nn.Linear(32,1)
})
def forward(self,x):
layers = ["conv1","pool","conv2","pool","dropout","adaptive",
"flatten","linear1","relu","linear2","sigmoid"]
for layer in layers:
x = self.layers_dict[layer](x) # 只找有的 sigmoid是没有的
return x
net = Net()
print(net)