有谁能帮我纠正这个错误吗?
RuntimeError: invalid argument 2: size '[-1 x 400]' is invalid for input of with 1597248 elements at /Users/soumith/miniconda2/conda-bld/pytorch_1503975723910/work/torch/lib/TH/THStorage.c:37
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(2): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs
inputs, labels = data
# wrap them in Variable
inputs, labels = Variable(inputs), Variable(labels)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.data[0]
if i % 5 == 4: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 5))
running_loss = 0.0
print('Finished Training')
你的问题是图像的大小不是32*32。 要解决这个问题,需要根据网络的输入大小计算
self.fc1
的输入大小。计算输入大小应该可以在不缩放图像的情况下解决它。 公式是 输出大小=(图像大小+2*填充-过滤器)/跨距+1)
当我缩放图像大小时,错误停止发生。
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