Sudden drop in loss when lr scheduler is applied

username: Sunyun_Lee
cooked: <p>I am training a conditional variational autoencoder with ELBO loss function and I am seeing a sudden drop in loss when I apply lr scheduler at some epoch.<br>
Does anyone one have any idea why the learning rate decay drops my loss value suddenly instead of giving me a loss continuous from the loss I get when training with the learning rate before the decay?</p>
<p>Here is the code snippet</p>
<pre><code class="lang-auto">def loss_fn(mu_z, std_z, z_sample, mu_x, std_x, x):
    S = x.shape[0]
    
    # log posterior q(z|x)
    q_z_dist = torch.distributions.Normal(mu_z, torch.exp(std_z))
    log_q_z = q_z_dist.log_prob(z_sample)
    
    # log likelihood p(x|z)
    p_x_dist = torch.distributions.Normal(mu_x, torch.exp(std_x))
    log_p_x = p_x_dist.log_prob(x)
    
    # log prior 
    p_z_dist = torch.distributions.Normal(0, 1)
    log_p_z = p_z_dist.log_prob(z_sample)
    
    loss = (1 / S) * (
        torch.sum(log_q_z) - torch.sum(log_p_x) - torch.sum(log_p_z)
    )
    
    return torch.sum(log_q_z), torch.sum(log_p_x), torch.sum(log_p_z), loss


optimizer = torch.optim.Adam(list(enc.parameters()) + list(dec.parameters()), lr=lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=500, gamma=0.1)

train_dataset = TensorDataset(X_train, C_train, K_train)
test_dataset = TensorDataset(X_test, C_test, K_test)

train_iter = DataLoader(train_dataset, batch_size=BATCH_SIZE)
test_iter = DataLoader(test_dataset, batch_size=BATCH_SIZE)

train_loss_avg = []
test_loss_avg = []

for i in range(N_EPOCHS):
    train_loss_avg.append(0)
    num_batches = 0
    
    for x, c, k in train_iter: 
        # zero grad 
        optimizer.zero_grad()
        
        # forward pass 
        mu_z, std_z = enc(x.to(device), torch.cat([c, k], axis=1).to(device))
        eps = torch.randn_like(std_z)
        z_samples = mu_z + eps * torch.exp(std_z)
        mu_x, std_x = dec(z_samples.to(device), torch.cat([c, k], axis=1).to(device))
        
        # loss 
        _, _, _, loss = loss_fn(mu_z, std_z, z_samples, mu_x, std_x, x)
        
        # backward pass 
        loss.backward()
        
        # update 
        optimizer.step()
        train_loss_avg[-1] += loss.item()
        num_batches += 1
    
    if i &lt; 501: 
        scheduler.step()

    train_loss_avg[-1] /= num_batches
    
    with torch.no_grad():
        test_loss_avg.append(0)
        num_batches = 0 
        
        for x_test, c_test, k_test in test_iter: 
            # forward
            mu_z_test, std_z_test = enc(x_test.to(device), torch.cat([c_test, k_test], axis=1).to(device))
            eps_test = torch.randn_like(std_z_test)
            z_samples_test = mu_z_test + eps_test * torch.exp(std_z_test)
            mu_x_test, std_x_test = dec(z_samples_test.to(device), torch.cat([c_test, k_test], axis=1).to(device))
            
            # loss
            _, _, _, test_loss = loss_fn(mu_z_test, std_z_test, z_samples_test, mu_x_test, std_x_test, x_test)
            
            test_loss_avg[-1] += test_loss.item()
            num_batches += 1
        
        test_loss_avg[-1] /= num_batches
    
    print("Epoch [%d / %d] train loss: %f, test loss: %f" % (i+1, N_EPOCHS, train_loss_avg[-1], test_loss_avg[-1]))
</code></pre>

username: ptrblck
cooked: <p>This effect is often observed when decreasing the learning rate, as your loss might be “stuck” due to too large gradient steps. E.g. the <a href="https://arxiv.org/abs/1512.03385">ResNet paper</a> shows the same behavior in the loss curves (this paper of course doesn’t discuss this effect).</p>