nn.Sequential

`nn.Sequential` 是 PyTorch 中的一个模型容器，它允许用户通过添加一系列的层来构建神经网络模型。`nn.Sequential` 可以接受一个有序的模块列表作为参数，每个模块都是一个 PyTorch 中的层，这些层将按照传入的顺序被依次添加到模型中。

使用 `nn.Sequential` 可以方便地创建神经网络模型，同时也可以轻松地对模型进行修改和调整。例如，我们可以像下面这样使用 `nn.Sequential` 来创建一个简单的前馈神经网络模型：

import torch.nn as nn

model = nn.Sequential(
    nn.Linear(784, 256),
    nn.ReLU(),
    nn.Linear(256, 10),
    nn.Softmax(dim=1)
)

这个模型包含两个线性层和两个非线性激活函数层，其中第一个线性层的输入大小为 784，输出大小为 256，第二个线性层的输入大小为 256，输出大小为 10。这个模型还包含一个 `Softmax` 层，用于对输出进行归一化。

相关问题

nn.sequential

nn.Sequential is a module in PyTorch that allows users to create a neural network by sequentially adding layers to it. It simplifies the process of creating a neural network by providing a way to define the architecture of the network in a single line of code.

The nn.Sequential module takes a list of layers as input and creates a neural network by stacking these layers one after the other. Each layer in the list is called in the order that it appears in the list.

Here is an example of how to use nn.Sequential to create a simple neural network:

import torch.nn as nn

model = nn.Sequential(
    nn.Linear(784, 256),
    nn.ReLU(),
    nn.Linear(256, 10),
    nn.LogSoftmax(dim=1)
)

In this example, we create a neural network with two linear layers and two activation functions. The first linear layer has 784 input features and 256 output features, while the second linear layer has 256 input features and 10 output features. We apply a ReLU activation function after the first linear layer and a LogSoftmax activation function after the second linear layer.

Overall, nn.Sequential is a useful tool for building simple neural networks quickly and efficiently. However, for more complex architectures, it may be necessary to use other PyTorch modules such as nn.Module or nn.Functional.