如何在循环中为pytorch神经网络中的层创建变量名

Posted 2023-03-29

【中文标题】如何在循环中为pytorch神经网络中的层创建变量名

【英文标题】：How to create variable names in loop for layers in pytorch neural network

【发布时间】：2020-01-25 14:50:20

【问题描述】：

我正在 PyTorch 中实现一个简单的前馈神经 newtork。但是我想知道是否有更好的方法来向网络添加灵活数量的层？也许通过在循环中命名它们，但我听说那是不可能的？

目前我正在这样做

import torch
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):

    def __init__(self, input_dim, output_dim, hidden_dim):
        super(Net, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.hidden_dim = hidden_dim
        self.layer_dim = len(hidden_dim)
        self.fc1 = nn.Linear(self.input_dim, self.hidden_dim[0])
        i = 1
        if self.layer_dim > i:
            self.fc2 = nn.Linear(self.hidden_dim[i-1], self.hidden_dim[i])
            i += 1
        if self.layer_dim > i:
            self.fc3 = nn.Linear(self.hidden_dim[i-1], self.hidden_dim[i])
            i += 1
        if self.layer_dim > i:
            self.fc4 = nn.Linear(self.hidden_dim[i-1], self.hidden_dim[i])
            i += 1
        if self.layer_dim > i:
            self.fc5 = nn.Linear(self.hidden_dim[i-1], self.hidden_dim[i])
            i += 1
        if self.layer_dim > i:
            self.fc6 = nn.Linear(self.hidden_dim[i-1], self.hidden_dim[i])
            i += 1
        if self.layer_dim > i:
            self.fc7 = nn.Linear(self.hidden_dim[i-1], self.hidden_dim[i])
            i += 1
        if self.layer_dim > i:
            self.fc8 = nn.Linear(self.hidden_dim[i-1], self.hidden_dim[i])
            i += 1
        self.fcn = nn.Linear(self.hidden_dim[-1], self.output_dim)

    def forward(self, x):
        # Max pooling over a (2, 2) window
        x = F.relu(self.fc1(x))
        i = 1
        if self.layer_dim > i:
            x = F.relu(self.fc2(x))
            i += 1
        if self.layer_dim > i:
            x = F.relu(self.fc3(x))
            i += 1
        if self.layer_dim > i:
            x = F.relu(self.fc4(x))
            i += 1
        if self.layer_dim > i:
            x = F.relu(self.fc5(x))
            i += 1
        if self.layer_dim > i:
            x = F.relu(self.fc6(x))
            i += 1
        if self.layer_dim > i:
            x = F.relu(self.fc7(x))
            i += 1
        if self.layer_dim > i:
            x = F.relu(self.fc8(x))
            i += 1
        x = F.softmax(self.fcn(x))
        return x

【问题讨论】：

为什么不使用列表？如果您开始在变量名中添加数字，您就做错了。

【参考方案1】：

您可以将您的图层放入ModuleList 容器中：

import torch
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):

    def __init__(self, input_dim, output_dim, hidden_dim):
        super(Net, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.hidden_dim = hidden_dim
        current_dim = input_dim
        self.layers = nn.ModuleList()
        for hdim in hidden_dim:
            self.layers.append(nn.Linear(current_dim, hdim))
            current_dim = hdim
        self.layers.append(nn.Linear(current_dim, output_dim))

    def forward(self, x):
        for layer in self.layers[:-1]:
            x = F.relu(layer(x))
        out = F.softmax(self.layers[-1](x))
        return out    

将pytorch Containers 用于图层非常重要，而不仅仅是简单的python 列表。请参阅this answer 了解原因。