如何在pytorch神经网络中的层中循环创建变量名

一尘不染

如何在pytorch神经网络中的层中循环创建变量名

python

我在PyTorch中实现了一个简单的前馈神经传递函数。但是我想知道是否有更好的方法向网络添加灵活的层数？也许是在一个循环中命名它们，但是我听说那不可能吗？

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

阅读 505

2021-01-20

共1个答案

一尘不染

您可以将图层放入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

对于这些层使用pytorch容器非常重要，而不仅仅是简单的python列表。

2021-01-20