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神经网络-搭建小实战和Sequential的使用
版本1——未用Sequential
版本2——用Sequential 神经网络-搭建小实战和Sequential的使用
torch.nn.Sequential的官方文档地址#xff0c;模块将按照它们在构造函数中传递的顺序添加。代码实现的是下图#xff1a;
版本1—…目录
神经网络-搭建小实战和Sequential的使用
版本1——未用Sequential
版本2——用Sequential 神经网络-搭建小实战和Sequential的使用
torch.nn.Sequential的官方文档地址模块将按照它们在构造函数中传递的顺序添加。代码实现的是下图
版本1——未用Sequential
import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linearclass Model(nn.Module):def __init__(self):super(Model, self).__init__()# 3,32,32 --- 32,32,32self.conv1 Conv2d(in_channels3, out_channels32, kernel_size5, stride1, padding2)# 32,32,32 --- 32,16,16self.maxpool1 MaxPool2d(kernel_size2, stride2)# 32,16,16 --- 32,16,16self.conv2 Conv2d(in_channels32, out_channels32, kernel_size5, stride1, padding2)# 32,16,16 --- 32,8,8self.maxpool2 MaxPool2d(kernel_size2, stride2)# 32,8,8 --- 64,8,8self.conv3 Conv2d(in_channels32, out_channels64, kernel_size5, stride1, padding2)# 64,8,8 --- 64,4,4self.maxpool3 MaxPool2d(kernel_size2, stride2)# 64,4,4 --- 1024self.flatten Flatten() # 因为start_dim默认为1所以可不再另外设置# 1024 --- 64self.linear1 Linear(1024, 64)# 64 --- 10self.linear2 Linear(64, 10)def forward(self, x):x self.conv1(x)x self.maxpool1(x)x self.conv2(x)x self.maxpool2(x)x self.conv3(x)x self.maxpool3(x)x self.flatten(x)x self.linear1(x)x self.linear2(x)return xmodel Model()
print(model)input torch.ones((64, 3, 32, 32))
out model(input)
print(out.shape) # torch.Size([64, 10])版本2——用Sequential
代码更简洁而且会给每层自动从0开始编序。
import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequentialclass Model(nn.Module):def __init__(self):super(Model, self).__init__()self.model Sequential(Conv2d(in_channels3, out_channels32, kernel_size5, stride1, padding2),MaxPool2d(kernel_size2, stride2),Conv2d(in_channels32, out_channels32, kernel_size5, stride1, padding2),MaxPool2d(kernel_size2, stride2),Conv2d(in_channels32, out_channels64, kernel_size5, stride1, padding2),MaxPool2d(kernel_size2, stride2),Flatten(),Linear(1024, 64),Linear(64, 10))def forward(self, x):return self.model(x)model Model()
print(model)input torch.ones((64, 3, 32, 32))
out model(input)
print(out.shape) # torch.Size([64, 10])在代码最末尾加上writer.add_gragh(model, input)就可看到模型计算图可放大查看。
writer SummaryWriter(./logs/Seq)
writer.add_graph(model, input)
writer.close()
