我有两个神经网络，使用pytorch操作以某种花哨的方式组合它们的参数。我将结果存储在第三个神经网络中，该神经网络的参数被设置为不可训练。然后我通过这个新的神经网络传递数据。这个新的神经网络只是一个占位符：
placeholder_net.W = Op( not_trainable_net.W, trainable_net.W )
然后我传递数据：
output = placeholder_net(input)
我担心由于占位符网络的参数被设置为不可训练，它实际上不会训练应该训练的变量。这种情况会发生吗？或者当您将可训练参数与不可训练参数组合在一起时（然后将其设置为参数不可训练），结果是什么？
当前解决方案：
del net3.conv0.weight
net3.conv0.weight = net.conv0.weight + net2.conv0.weight
import torch
from torch import nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from collections import OrderedDict
import copy
def dont_train(net):
'''
set training parameters to false.
'''
for param in net.parameters():
param.requires_grad = False
return net
def get_cifar10():
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,shuffle=True, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat','deer', 'dog', 'frog', 'horse', 'ship', 'truck')
return trainloader,classes
def combine_nets(net_train, net_no_train, net_place_holder):
'''
Combine nets in a way train net is trainable
'''
params_train = net_train.named_parameters()
dict_params_place_holder = dict(net_place_holder.named_parameters())
dict_params_no_train = dict(net_no_train.named_parameters())
for name, param_train in params_train:
if name in dict_params_place_holder:
layer_name, param_name = name.split('.')
param_no_train = dict_params_no_train[name]
## get place holder layer
layer_place_holder = getattr(net_place_holder, layer_name)
delattr(layer_place_holder, param_name)
## get new param
W_new = param_train + param_no_train # notice addition is just chosen for the sake of an example
## store param in placehoder net
setattr(layer_place_holder, param_name, W_new)
return net_place_holder
def combining_nets_lead_to_error():
'''
Intention is to only train the net with trainable params.
Placeholder rnet is a dummy net, it doesn't actually do anything except hold the combination of params and its the
net that does the forward pass on the data.
'''
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
''' create three musketeers '''
net_train = nn.Sequential(OrderedDict([
('conv1', nn.Conv2d(1,20,5)),
('relu1', nn.ReLU()),
('conv2', nn.Conv2d(20,64,5)),
('relu2', nn.ReLU())
])).to(device)
net_no_train = copy.deepcopy(net_train).to(device)
net_place_holder = copy.deepcopy(net_train).to(device)
''' prepare train, hyperparams '''
trainloader,classes = get_cifar10()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net_train.parameters(), lr=0.001, momentum=0.9)
''' train '''
net_train.train()
net_no_train.eval()
net_place_holder.eval()
for epoch in range(2): # loop over the dataset multiple times
running_loss = 0.0
for i, (inputs, labels) in enumerate(trainloader, 0):
optimizer.zero_grad() # zero the parameter gradients
inputs, labels = inputs.to(device), labels.to(device)
# combine nets
net_place_holder = combine_nets(net_train,net_no_train,net_place_holder)
#
outputs = net_place_holder(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
''' DONE '''
print('Done \a')
if __name__ == '__main__':
combining_nets_lead_to_error()
