OK, I tried interlacing this model with the MNIST example. It seems the model is not correctly implemented.
The code is:

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(784,512)
self.hidden2 = nn.Linear(512,512)
self.hidden3 = nn.Linear(512,10)
self.out = nn.Linear(10,1)



def forward(self, x):
    x = F.tanh(self.hidden(x))
    #x = F.dropout(self.hidden(x),0.2)
    x = F.sigmoid(self.hidden2(x))
    #x = F.dropout(self.hidden(x),0.2)
    x = F.softmax(self.hidden3(x))
    x = self.out(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(), target.cuda()
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
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(), target.cuda()
data, target = Variable(data, volatile=True), Variable(target)
output = model(data)
test_loss += 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()

the output is:

Net (
(hidden): Linear (784 -> 512)
(hidden2): Linear (512 -> 512)
(hidden3): Linear (512 -> 10)
(out): Linear (10 -> 1)
)
Traceback (most recent call last):
File “”, line 1, in 
File “mymodel.py”, line 145, in 
train(epoch)
File “mymodel.py”, line 116, in train
output = model(data)
File “/home/slava/dev/miniconda2/lib/python2.7/site-packages/torch/nn/modules/module.py”, line 206, in call
result = self.forward(*input, **kwargs)
File “mymodel.py”, line 85, in forward
x = F.tanh(self.hidden(x))
File “/home/slava/dev/miniconda2/lib/python2.7/site-packages/torch/nn/modules/module.py”, line 206, in call
result = self.forward(*input, **kwargs)
File “/home/slava/dev/miniconda2/lib/python2.7/site-packages/torch/nn/modules/linear.py”, line 54, in forward
return self._backend.Linear()(input, self.weight, self.bias)
File "/home/slava/dev/miniconda2/lib/python2.7/site-packages/torch/nn/functions/linear.py", line 10, in forward
output.addmm(0, 1, input, weight.t())
RuntimeError: matrices expected, got 4D, 2D tensors at /b/wheel/pytorch-src/torch/lib/TH/generic/THTensorMath.c:1232