Hello,
I have tried implementing an autoencoder for mnist, but the loss function does not seem to be accepting this type of network.
Code is as follows:
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable

parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                    help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                    help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=10, metavar='N',
                    help='number of epochs to train (default: 10)')
parser.add_argument('--no-cuda', action='store_true', default=False,
                    help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
                    help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                    help='how many batches to wait before logging training status')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()

torch.manual_seed(args.seed)
if args.cuda:
    torch.cuda.manual_seed(args.seed)


kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}




train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=False, transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=args.batch_size, shuffle=True, **kwargs)





class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.hidden = nn.Linear(784,1000)
        self.hidden2 = nn.Linear(1000,500)
        self.hidden3 = nn.Linear(500,250)
        self.hidden4 = nn.Linear(250,30)
        self.hidden5 = nn.Linear(30,250)
        self.hidden6 = nn.Linear(250,500)
        self.hidden7 = nn.Linear(500,1000)
        self.hidden8 = nn.Linear(1000,784)

        self.out = nn.Linear(784,784)

        

    def forward(self, x):
        x = x.view (-1, 784)
        x = F.sigmoid(self.hidden(x))
        x = F.dropout(x,0.1)
        x = F.sigmoid(self.hidden2(x))
        x = F.dropout(x,0.1)
        x = F.sigmoid(self.hidden3(x))
        x = F.dropout(x,0.1)
        x = F.sigmoid(self.hidden4(x))

        x = F.dropout(x,0.1)
        x = F.sigmoid(self.hidden5(x))

        x = F.dropout(x,0.1)
        x = F.sigmoid(self.hidden6(x))

        x = F.dropout(x,0.1)
        x = F.sigmoid(self.hidden7(x))

        x = F.dropout(x,0.1)
        x = F.sigmoid(self.hidden8(x))
        x = self.out(x)
        return x #F.log_softmax(x)

    def num_flat_features(self, x):
        size = x.size()[1:]  # all dimensions except the batch dimension
        num_features = 1
        for s in size:
            num_features *= s
        return num_features



model = Net()
print(model)

if args.cuda:
    model.cuda()

optimizer = optim.SGD(model.parameters(), lr=.01, momentum=0)

def train(epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        if args.cuda:
            data, target = data.cuda(), data.cuda()
        target = Variable(target)
        data = Variable(data)
        optimizer.zero_grad()
        output = model(data)
        loss = F.cross_entropy(output, data)#F.nll_loss(output, data)
        loss.backward()
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.data[0]))

def test():
    model.eval()
    test_loss = 0
    correct = 0
    for (data, target) in test_loader:
        if args.cuda:
            data, target = data.cuda(), data.cuda()
        target = Variable(target, volatile=True)
        data = Variable(data)
        output = model(data)
        test_loss += F.cross_entropy(output, data).data[0]# F.nll_loss(output, target).data[0] #F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss
        pred = output.data.max(1)[1] # get the index of the max log-probability
        correct += pred.eq(target.data).cpu().sum()

    test_loss /= len(test_loader.dataset)
    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))


for epoch in range(1, args.epochs + 1):
    train(epoch)
    test()

and the error I get is,

File “mymodelc.py 1”, line 139, in train
loss = F.cross_entropy(output, data)#F.nll_loss(output, data)
File “/home/slava/dev/miniconda2/lib/python2.7/site-packages/torch/nn/functional.py”, line 533, in cross_entropy
return nll_loss(log_softmax(input), target, weight, size_average)
File “/home/slava/dev/miniconda2/lib/python2.7/site-packages/torch/nn/functional.py”, line 501, in nll_loss
return f(input, target)
File “/home/slava/dev/miniconda2/lib/python2.7/site-packages/torch/nn/_functions/thnn/auto.py”, line 41, in forward
output, *self.additional_args)
TypeError: FloatClassNLLCriterion_updateOutput received an invalid combination of arguments - got (int, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, bool, NoneType, torch.FloatTensor), but expected (int state, torch.FloatTensor input, torch.LongTensor target, torch.FloatTensor output, bool sizeAverage, [torch.FloatTensor weights or None], torch.FloatTensor total_weight)

Thank you