Train Half a VAE (latent -> decoder)

I have a variational autoencoder reconstructing signals. Now, I want to map the latent variables to the output only. In other words, I want to train the decoder from the latent layer to the output.

  • Is there an easier way than building a whole new model?
  • Would I still need the KL divergence? If not, what loss should I use?

The model:

import torch
import torch.nn as nn
import torch.nn.functional as F


class Network(nn.Module):
    def __init__(self, input_dim, output_dim, latent_dim, layer_dim):
        """
        Parameter:
        input_dim (int): number of inputs
        output_dim (int): number of outputs
        latent_dim (int): number of latent neurons
        Layer_dim (int): number of neurons in hidden layers
        """
        super(Network, self).__init__()
        self.latent_dim = latent_dim

        self.enc1 = nn.Linear(input_dim, layer_dim)

        self.enc2 = nn.Linear(layer_dim, layer_dim)

        self.latent = nn.Linear(layer_dim, latent_dim*2)

        self.dec1 = nn.Linear(latent_dim, layer_dim)
        self.dec2 = nn.Linear(layer_dim, layer_dim)

        self.out = nn.Linear(layer_dim, output_dim)

        #self.dropout = nn.Dropout(p=0.75)
    def encoder(self, x):
        z = F.elu(self.enc1(x))
        #z = F.elu(self.dropout(z))
        z = F.elu(self.enc2(z))
        z = self.latent(z)
        self.mu = z[:,0:self.latent_dim]
        self.log_sigma = z[:,self.latent_dim:]
        self.sigma = torch.exp(self.log_sigma)

        eps = torch.randn(x.size(0), self.latent_dim)
        z_sample = self.mu + self.sigma * eps

        self.kl_loss = kl_divergence(self.mu, self.log_sigma, dim=self.latent_dim)

        return z_sample

    def decoder(self, z):
        x = F.elu(self.dec1(z))
        x = F.elu(self.dec2(x))
        return self.out(x)

    def forward(self, batch):
        self.latent_rep = self.encoder(batch)
        dec_input = self.latent_rep
        return self.decoder(dec_input)


def kl_divergence(means, log_sigma, dim, target_sigma=0.1):
    """
    Computes KullbackÔÇôLeibler divergence for arrays of mean and log(sigma)
    """
    target_sigma = torch.Tensor([target_sigma])
    inner = 1 / target_sigma**2 * means**2 + torch.exp(2 * log_sigma) / target_sigma**2 - 2 * log_sigma + 2 * torch.log(target_sigma)
    inner = torch.mean(inner, dim = 0)
    out = 1 / 2. * torch.mean(inner - dim)
    out = out
    return out

If you want to freeze the encoder you can set the .requires_grad attribute of all its parameters to false.
Based on your model definition something like this should work:

for param in model.env1.parameters():
    param.requires_grad = False
for param in model.env2.parameters():
    param.requires_grad = False

and train the latent + decoder only.

Thanks! Allow me to explain some more about what I am trying to achieve:

I want to load the model from a different training, freeze the encoder, and just decode from the already trained latent layer to a different output (meaning different decoder, transfer learning).
Now, the model I already saved, I cannot load due to size mismatch (the new model has less output neurons than the saved model).

I did:

model = Network(7200, 2400, 4, 1200)
model_dict = model.state_dict()
pretrained_dict = torch.load("trained/model_h2h_e50_lr0.000010_noise0.028000x.dat")
pretrained_dict = {k: v for k, v in pretrained_dict.items() if not k.startswith('dec') and not k.startswith('out')}

model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)