Convolution Autoencoder Image Dimension Error
I have the following Convolutional Autoencoder setup:

class autoencoder(nn.Module):
def  __init__(self):
    super(autoencoder, self).__init__()
    self.encoder = nn.Sequential(
        nn.Conv2d(1, 16, 3, stride=3, padding=1),  # b, 16, 10, 10
        nn.ReLU(True),
        nn.MaxPool2d(2, stride=2),  # b, 16, 5, 5
        nn.Conv2d(16, 8, 3, stride=2, padding=1),  # b, 8, 3, 3
        nn.ReLU(True),
        nn.MaxPool2d(2, stride=1)  # b, 8, 2, 2                     
                                )

    self.decoder = nn.Sequential(
        nn.ConvTranspose2d(8, 16, 3, stride=2),  # b, 16, 5, 5
        nn.ReLU(True),
        nn.ConvTranspose2d(16, 8, 5, stride=3, padding=1),  # b, 8, 15, 15
        nn.ReLU(True),
        nn.ConvTranspose2d(8, 1, 2, stride=2, padding=1),  # b, 1, 28, 28
        nn.Tanh()          
                                )


This is the main Loop:

for epoch in range(epochs):
running_loss = 0
for data in (train_loader):
    image,_=data
    inputs = image.view(image.size(0),-1)
    optimizer.zero_grad()
    #image = np.expand_dims(img, axis=0)

    outputs = net(inputs)
    loss = criterion(outputs,inputs)
    loss.backward()
    optimizer.step()
    running_loss += loss.data[0]
print('At Iteration : %d   ;  Mean-Squared Error : %f'%(epoch + 1,running_loss/(train_set.train_data.size(0)/batch_size)))


This is the Error:

RuntimeError: Expected 4-dimensional input for 4-dimensional weight [16, 1, 3, 3], but got input of size [1000, 784] instead


This has something to do with the flattening of the image but Im not exactly sure how to deflatten it. 

Why are you "flattening" your input image (2nd line of main loop):

inputs = image.view(image.size(0),-1)


This line turns your 4 dimensional image (batch - channels - height - width) to a two dimensional "flat" vector (batch - c * h * w).
You autoencoder expects its inputs to be 4D and not "flat". just remove this line and you should be okay.
