【torch.nn.Sequential】序列容器的介绍和使用

文章目录

• torch.nn.Sequential
• • 简单介绍
  • 构建实例
  • • 参数列表
    • 字典
  • 基本操作
  • 参考

torch.nn.Sequential

简单介绍

nn.Sequential是一个有序的容器，该类将按照传入构造器的顺序，依次创建相应的函数，并记录在Sequential类对象的数据结构中，同时以神经网络模块为元素的有序字典也可以作为传入参数。

因此，Sequential可以看成是有多个函数运算对象，串联成的神经网络，其返回的是Module类型的神经网络对象。

构建实例

参数列表

• 以参数列表的方式来实例化

print("利用系统提供的神经网络模型类：Sequential,以参数列表的方式来实例化神经网络模型对象")# A sequential container. Modules will be added to it in the order they are passed in the constructor. # Example of using Sequentialmodel_c = nn.Sequential(nn.Linear(28*28, 32), nn.ReLU(), nn.Linear(32, 10), nn.Softmax(dim=1))print(model_c) print("\n显示网络模型参数")print(model_c.parameters) print("\n定义神经网络样本输入")x_input = torch.randn(2, 28, 28, 1)print(x_input.shape) print("\n使用神经网络进行预测")y_pred = model.forward(x_input.view(x_input.size()[0],-1))print(y_pred)

利用系统提供的神经网络模型类：Sequential,以参数列表的方式来实例化神经网络模型对象Sequential((0): Linear(in_features=784, out_features=32, bias=True)(1): ReLU()(2): Linear(in_features=32, out_features=10, bias=True)(3): Softmax(dim=1))显示网络模型参数<bound method Module.parameters of Sequential((0): Linear(in_features=784, out_features=32, bias=True)(1): ReLU()(2): Linear(in_features=32, out_features=10, bias=True)(3): Softmax(dim=1))>定义神经网络样本输入torch.Size([2, 28, 28, 1])使用神经网络进行预测tensor([[-0.1526,0.0437, -0.1685,0.0034, -0.0675,0.0423,0.2807,0.0527, -0.1710,0.0668],[-0.1820,0.0860,0.0174,0.0883,0.2046, -0.1609,0.0165, -0.2392, -0.2348,0.1697]], grad_fn=<AddmmBackward>)

字典

• 以字典的方式实例化

# Example of using Sequential with OrderedDictprint("利用系统提供的神经网络模型类：Sequential,以字典的方式来实例化神经网络模型对象")model = nn.Sequential(OrderedDict([('h1', nn.Linear(28*28, 32)), ('relu1', nn.ReLU()), ('out', nn.Linear(32, 10)), ('softmax', nn.Softmax(dim=1))])) print(model) print("\n显示网络模型参数")print(model.parameters) print("\n定义神经网络样本输入")x_input = torch.randn(2, 28, 28, 1)print(x_input.shape) print("\n使用神经网络进行预测")y_pred = model.forward(x_input.view(x_input.size()[0],-1))print(y_pred)

利用系统提供的神经网络模型类：Sequential,以字典的方式来实例化神经网络模型对象Sequential((h1): Linear(in_features=784, out_features=32, bias=True)(relu1): ReLU()(out): Linear(in_features=32, out_features=10, bias=True)(softmax): Softmax(dim=1))显示网络模型参数<bound method Module.parameters of Sequential((h1): Linear(in_features=784, out_features=32, bias=True)(relu1): ReLU()(out): Linear(in_features=32, out_features=10, bias=True)(softmax): Softmax(dim=1))>定义神经网络样本输入torch.Size([2, 28, 28, 1])使用神经网络进行预测tensor([[0.1249, 0.1414, 0.0708, 0.1031, 0.1080, 0.1351, 0.0859, 0.0947, 0.0753, 0.0607],[0.0982, 0.1102, 0.0929, 0.0855, 0.0848, 0.1076, 0.1077, 0.0949, 0.1153, 0.1029]], grad_fn=<SoftmaxBackward>)

基本操作

• 查看结构

通过打印 Sequential对象来查看它的结构

print(net)# Sequential(# (0): Linear(in_features=20, out_features=10, bias=True)# (1): ReLU()# (2): Linear(in_features=10, out_features=5, bias=True)# )

• 索引

我们可以使用索引来查看其子模块

print(net[0])# Linear(in_features=20, out_features=10, bias=True)print(net[1])# ReLU()

• 长度

print(len(net))# 3

• 修改子模块

net[1] = nn.Sigmoid()print(net)# Sequential(# (0): Linear(in_features=20, out_features=10, bias=True)# (1): Sigmoid()# (2): Linear(in_features=10, out_features=5, bias=True)# )

• 删除子模块

del net[2]print(net)# Sequential(# (0): Linear(in_features=20, out_features=10, bias=True)# (1): Sigmoid()# )

• 添加子模块

net.append(nn.Linear(10, 2))# 均会添加到末尾print(net)# Sequential(# (0): Linear(in_features=20, out_features=10, bias=True)# (1): Sigmoid()# (2): Linear(in_features=10, out_features=2, bias=True)# )

• 遍历

net = nn.Sequential(nn.Linear(20, 10),nn.ReLU(),nn.Linear(10, 5))for sub_module in net:print(sub_module)# Linear(in_features=20, out_features=10, bias=True)# ReLU()# Linear(in_features=10, out_features=5, bias=True)

• 嵌套

'''在一个 Sequential 中嵌套两个 Sequential'''seq_1 = nn.Sequential(nn.Linear(15, 10), nn.ReLU(), nn.Linear(10, 5))seq_2 = nn.Sequential(nn.Linear(25, 15), nn.Sigmoid(), nn.Linear(15, 10))seq_3 = nn.Sequential(seq_1, seq_2)print(seq_3)# Sequential(# (0): Sequential(# (0): Linear(in_features=15, out_features=10, bias=True)# (1): ReLU()# (2): Linear(in_features=10, out_features=5, bias=True)# )# (1): Sequential(# (0): Linear(in_features=25, out_features=15, bias=True)# (1): Sigmoid()# (2): Linear(in_features=15, out_features=10, bias=True)# )# )''''使用多级索引进行访问'''print(seq_3[1])# Sequential(# (0): Linear(in_features=25, out_features=15, bias=True)# (1): Sigmoid()# (2): Linear(in_features=15, out_features=10, bias=True)# )print(seq_3[0][1])# ReLU()'''使用双重循环进行遍历'''for seq in seq_3:for module in seq:print(module)# Linear(in_features=15, out_features=10, bias=True)# ReLU()# Linear(in_features=10, out_features=5, bias=True)# Linear(in_features=25, out_features=15, bias=True)# Sigmoid()# Linear(in_features=15, out_features=10, bias=True)

参考

PyTorch学习笔记（六）–Sequential类、参数管理与GPU_Lareges的博客-CSDN博客_sequential类

[Pytorch系列-30]：神经网络基础 – torch.nn库五大基本功能：nn.Parameter、nn.Linear、nn.functioinal、nn.Module、nn.Sequentia_文火冰糖的硅基工坊的博客-CSDN博客_torch库nn