当尝试创建神经网络并使用 Pytorch 对其进行优化时，我得到了
ValueError：优化器得到一个空参数列表
这是代码。
import torch.nn as nn
import torch.nn.functional as F
from os.path import dirname
from os import getcwd
from os.path import realpath
from sys import argv
class NetActor(nn.Module):
def __init__(self, args, state_vector_size, action_vector_size, hidden_layer_size_list):
super(NetActor, self).__init__()
self.args = args
self.state_vector_size = state_vector_size
self.action_vector_size = action_vector_size
self.layer_sizes = hidden_layer_size_list
self.layer_sizes.append(action_vector_size)
self.nn_layers = []
self._create_net()
def _create_net(self):
prev_layer_size = self.state_vector_size
for next_layer_size in self.layer_sizes:
next_layer = nn.Linear(prev_layer_size, next_layer_size)
prev_layer_size = next_layer_size
self.nn_layers.append(next_layer)
def forward(self, torch_state):
activations = torch_state
for i,layer in enumerate(self.nn_layers):
if i != len(self.nn_layers)-1:
activations = F.relu(layer(activations))
else:
activations = layer(activations)
probs = F.softmax(activations, dim=-1)
return probs
然后打电话
self.actor_nn = NetActor(self.args, 4, 2, [128])
self.actor_optimizer = optim.Adam(self.actor_nn.parameters(), lr=args.learning_rate)
给出了非常有用的错误
ValueError：优化器得到一个空参数列表
我发现很难理解网络定义中到底是什么使网络具有参数。
我正在遵循并扩展我在中找到的示例Pytorch的教程代码 https://github.com/pytorch/examples/blob/master/reinforcement_learning/reinforce.py.
我无法真正区分我的代码和他们的代码之间的区别，这使得我的代码认为它没有需要优化的参数。
如何使我的网络具有像链接示例那样的参数？
