I am using the code of a variational auto-encoder from here. This is the relevant code:
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
self.fc1 = nn.Linear(784, 400)
self.fc21 = nn.Linear(400, 20)
self.fc22 = nn.Linear(400, 20)
self.fc3 = nn.Linear(20, 400)
self.fc4 = nn.Linear(400, 784)
def encode(self, x):
h1 = F.relu(self.fc1(x))
return self.fc21(h1), self.fc22(h1)
def reparametrize(self, mu, logvar):
std = logvar.mul(0.5).exp_()
if torch.cuda.is_available():
eps = torch.cuda.FloatTensor(std.size()).normal_()
else:
eps = torch.FloatTensor(std.size()).normal_()
eps = Variable(eps)
return eps.mul(std).add_(mu)
def decode(self, z):
h3 = F.relu(self.fc3(z))
return F.sigmoid(self.fc4(h3))
def forward(self, x):
mu, logvar = self.encode(x)
z = self.reparametrize(mu, logvar)
return self.decode(z), mu, logvar, z
Suppose that, during test time, I would like to transpose the decode function - that is, reverse that part of the network: feed it an input of size (400, 784) and get an output of (20, 400). Is there a way to transpose a network (without manually copying the weights)?
Thanks!