如何设计网站域名,个人网站做淘宝客容易封吗,定制型网站 成功案例,网站图标 代码# 可以使用以下3种方式构建模型#xff1a;
#
# 1#xff0c;继承nn.Module基类构建自定义模型。
#
# 2#xff0c;使用nn.Sequential按层顺序构建模型。
#
# 3#xff0c;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… # 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。 # 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_channels3,out_channels32,kernel_size 3)self.pool1 nn.MaxPool2d(kernel_size 2,stride 2)self.conv2 nn.Conv2d(in_channels32,out_channels64,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_channels3,out_channels32,kernel_size 3))
net.add_module(pool1,nn.MaxPool2d(kernel_size 2,stride 2))
net.add_module(conv2,nn.Conv2d(in_channels32,out_channels64,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)
# 利用变长参数
net nn.Sequential(nn.Conv2d(in_channels3,out_channels32,kernel_size 3),nn.MaxPool2d(kernel_size 2,stride 2),nn.Conv2d(in_channels32,out_channels64,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)
# 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_channels3,out_channels32,kernel_size 3),nn.MaxPool2d(kernel_size 2,stride 2),nn.Conv2d(in_channels32,out_channels64,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)
# 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_channels3,out_channels32,kernel_size 3),nn.MaxPool2d(kernel_size 2,stride 2),nn.Conv2d(in_channels32,out_channels64,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)
# 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_channels3,out_channels32,kernel_size 3),pool: nn.MaxPool2d(kernel_size 2,stride 2),conv2:nn.Conv2d(in_channels32,out_channels64,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)
