如何在pytorch神经网络中的层中循环创建变量名
发布于 2021-01-29 15:01:41
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
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1 个回答
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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