Created
September 13, 2022 13:53
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variational_autoencoder3
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# Define train function | |
def train(num_epochs, model, optimizer, loss_fn): | |
# Start training | |
for epoch in range(num_epochs): | |
loop = tqdm(enumerate(train_loader)) | |
for i, (x, y) in loop: | |
# Forward pass | |
x = x.to(device).view(-1, INPUT_DIM) | |
x_reconst, mu, sigma = model(x) | |
# loss, formulas from https://www.youtube.com/watch?v=igP03FXZqgo&t=2182s | |
reconst_loss = loss_fn(x_reconst, x) | |
kl_div = - torch.sum(1 + torch.log(sigma.pow(2)) - mu.pow(2) - sigma.pow(2)) | |
# Backprop and optimize | |
loss = reconst_loss + kl_div | |
optimizer.zero_grad() | |
loss.backward() | |
optimizer.step() | |
loop.set_postfix(loss=loss.item()) | |
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