I write a code but it has a error, I can not fix it
my environment is : cuda9 + cudnn 7.1  python=3.6.6 pytorch =1.0.1
my code:
import torch
from torch import nn, optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision import datasets

# 定义超参数
batch_size = 100
learning_rate = 1e-3
num_epoches = 20

# 下载训练集 MNIST 手写数字训练集
train_dataset = datasets.MNIST(
    root='D:/data', train=True, transform=transforms.ToTensor(), download=True)

test_dataset = datasets.MNIST(
    root='D:/data', train=False, transform=transforms.ToTensor())

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 定义 Recurrent Network 模型


class Rnn(nn.Module):
    def __init__(self, in_dim, hidden_dim, n_layer, n_class):
        super(Rnn, self).__init__()
        self.n_layer = n_layer
        self.hidden_dim = hidden_dim
        self.lstm = nn.LSTM(in_dim, hidden_dim, n_layer, batch_first=True)
        self.classifiers = nn.Linear(hidden_dim, n_class)

    def forward(self, x):
        out, _ = self.lstm(x)
        out = out[:, -1, :]
        out = self.classifiers(out)
        return out


model = Rnn(28, 128, 2, 10)
model = model.cuda()

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

for epoch in range(num_epoches):
    running_loss = 0.
    running_acc = 0.
    for i, data in enumerate(train_loader, 1):
        img, label = data
        b, c, h, w = img.size()
        assert c == 1, 'Channel must be 1'
        img = img.squeeze(1)

        img = Variable(img).cuda()
        label = Variable(label).cuda()

        # 向前传播
        out = model(img)
        loss = criterion(out, label)
        running_loss += loss.data.item() * label.size(0)
        _, pred = torch.max(out, 1)
        num_correct = (pred == label).sum()
        running_acc += num_correct.data.item()
        # 向后传播
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if i % 300 == 0:
            print("[{} / {}] Loss: {:.6f}, Acc: {:.6f}".format(
                epoch + 1, num_epoches, running_loss / (batch_size * i),
                running_acc / (batch_size * i)
            ))
    print("Finish {} epoch, Loss: {:.6f}, Acc: {:.6f}".format(
        epoch + 1, running_loss /
        (len(train_dataset)), running_acc / (len(train_dataset))
    ))

    model.eval()
    eval_loss = 0. 
    eval_acc = 0. 
    for data in test_loader:
        img, label = data
        b, c, h, w = img.size()
        assert c == 1, "channel must be 1"
        img = img.squeeze(1)

        with torch.no_grad():
            img = Variable(img).cuda()
            label = Variable(label).cuda()
        out = model(img)
        loss = criterion(out, label)
        eval_loss += loss.data.item() * label.size(0)
        _, pred = torch.max(out, 1)
        num_correct = (pred == label).sum()
        eval_acc += num_correct.data.item()
    print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(
        test_dataset)), eval_acc / (len(test_dataset))))

# 保存模型
torch.save(model.state_dict(), './rnn.pth')


the error:
RuntimeError
cudnn RNN backward can only be called in training mode
  File "D:\documents\th_torch\recurrent_network001.py", line 68, in <module>
    loss.backward()


error.jpg1383×588 92.5 KB