Hi PyTorch users,
I’m training an autoencoder, but I’m interested in the output of the encoder (so the small linear layer in the middle) for my test values.
Is there an efficient way of testing the encoder, once trained, separately rather than testing on the full network and pulling out the encoders output values?
If not… how does one pull out the encoders output values?
My autoencoder looks as follows:
class autoencoder(nn.Module):
    def __init__(self):
        super(autoencoder, self).__init__()
        self.encoder = nn.Sequential(
            nn.Linear(4, 32),
            nn.ReLU(True), 
            nn.Linear(32, 12), 
            nn.ReLU(True), 
            nn.Linear(12, 5))
        self.decoder = nn.Sequential(
            nn.Linear(5, 12),
            nn.ReLU(True),
            nn.Linear(12, 32),
            nn.ReLU(True), 
            nn.Linear(32, 4), 
            )

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

It may help to know that I save my models as follows:
torch.save(model, save_path + 'autoencode_epoch_'+str(epoch))

I think this is probably a simple problem but it would be nice to see how people think this is best done… thanks in advance!
EDIT: Please ignore the fact I scale a 4 parameter input up and then encode it into 5 variables…this is just a test case.