Training and validation loss tracking in PyTorch

Introduction

We track training and validation loss to see how well our model learns and if it works well on new data.

When training a model to check if it is improving over time.

To detect if the model is overfitting by comparing training and validation loss.

When tuning model settings to find the best performance.

To decide when to stop training if the model stops improving.

To visualize learning progress with graphs.

Syntax

PyTorch

for epoch in range(num_epochs):
    model.train()
    train_loss = 0
    for inputs, targets in train_loader:
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = loss_function(outputs, targets)
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
    train_loss /= len(train_loader)

    model.eval()
    val_loss = 0
    with torch.no_grad():
        for inputs, targets in val_loader:
            outputs = model(inputs)
            loss = loss_function(outputs, targets)
            val_loss += loss.item()
    val_loss /= len(val_loader)

    print(f"Epoch {epoch+1}, Training Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}")

Use model.train() before training and model.eval() before validation to set correct modes.

Use torch.no_grad() during validation to save memory and computation.

Examples

This calculates average training loss for one epoch.

PyTorch

train_loss = 0
for inputs, targets in train_loader:
    optimizer.zero_grad()
    outputs = model(inputs)
    loss = loss_function(outputs, targets)
    loss.backward()
    optimizer.step()
    train_loss += loss.item()
train_loss /= len(train_loader)

This calculates average validation loss without updating model weights.

PyTorch

val_loss = 0
with torch.no_grad():
    for inputs, targets in val_loader:
        outputs = model(inputs)
        loss = loss_function(outputs, targets)
        val_loss += loss.item()
val_loss /= len(val_loader)

Sample Model

This program trains a simple linear model to fit y = 2x + 1 with some noise. It prints training and validation loss for 5 epochs.

PyTorch

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset

# Simple dataset: y = 2x + 1
x_train = torch.linspace(0, 1, 100).unsqueeze(1)
y_train = 2 * x_train + 1 + 0.1 * torch.randn_like(x_train)

x_val = torch.linspace(0, 1, 20).unsqueeze(1)
y_val = 2 * x_val + 1 + 0.1 * torch.randn_like(x_val)

train_dataset = TensorDataset(x_train, y_train)
val_dataset = TensorDataset(x_val, y_val)

train_loader = DataLoader(train_dataset, batch_size=10)
val_loader = DataLoader(val_dataset, batch_size=5)

# Simple linear model
model = nn.Linear(1, 1)

loss_function = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.1)

num_epochs = 5

for epoch in range(num_epochs):
    model.train()
    train_loss = 0
    for inputs, targets in train_loader:
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = loss_function(outputs, targets)
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
    train_loss /= len(train_loader)

    model.eval()
    val_loss = 0
    with torch.no_grad():
        for inputs, targets in val_loader:
            outputs = model(inputs)
            loss = loss_function(outputs, targets)
            val_loss += loss.item()
    val_loss /= len(val_loader)

    print(f"Epoch {epoch+1}, Training Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}")

Important Notes

Training loss usually decreases every epoch.

If validation loss starts increasing while training loss decreases, the model may be overfitting.

Use small batches to get smoother loss estimates.

Summary

Track training and validation loss to monitor model learning and generalization.

Use model.train() and model.eval() to switch modes correctly.

Calculate average loss over batches for clear progress tracking.

Practice

1. Why do we track both training and validation loss during model training in PyTorch?

easy

A. To speed up the training process

B. To increase the batch size automatically

C. To monitor if the model is learning and generalizing well to new data

D. To reduce the number of model parameters

Solution

1. Step 1: Understand the purpose of training loss

   Training loss shows how well the model fits the training data during learning.

2. Step 2: Understand the purpose of validation loss

   Validation loss shows how well the model performs on unseen data, indicating generalization.

3. Final Answer:

   To monitor if the model is learning and generalizing well to new data -> Option C

4. Quick Check:

   Training and validation loss track learning and generalization [OK]

Hint: Training loss = fit, validation loss = generalization [OK]

Common Mistakes:

• Thinking validation loss speeds up training
• Confusing loss tracking with batch size changes
• Assuming loss tracking reduces parameters

2. Which PyTorch method should you call before starting the validation phase to ensure correct behavior?

easy

A. model.train()

B. model.eval()

C. model.validate()

D. model.freeze()

Solution

1. Step 1: Recall PyTorch modes

   model.train() sets the model to training mode, enabling dropout and batch norm updates.

2. Step 2: Identify validation mode

   model.eval() sets the model to evaluation mode, disabling dropout and batch norm updates for stable validation.

3. Final Answer:

   model.eval() -> Option B

4. Quick Check:

   Use model.eval() before validation [OK]

Hint: Use model.eval() to disable training-only layers [OK]

Common Mistakes:

• Using model.train() during validation
• Assuming model.validate() exists
• Confusing freeze() with eval()

3. Given the code below, what will be the printed average training loss after one epoch?

train_losses = []
for inputs, targets in train_loader:
    optimizer.zero_grad()
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    loss.backward()
    optimizer.step()
    train_losses.append(loss.item())
avg_train_loss = sum(train_losses) / len(train_losses)
print(f"Average training loss: {avg_train_loss:.4f}")

Assuming losses per batch are [0.8, 0.6, 0.4], what is the output?

medium

A. Average training loss: 0.4000

B. Average training loss: 1.8000

C. Average training loss: 0.0000

D. Average training loss: 0.6000

Solution

1. Step 1: Sum the batch losses

   Sum = 0.8 + 0.6 + 0.4 = 1.8

2. Step 2: Calculate average loss

   Average = 1.8 / 3 = 0.6

3. Final Answer:

   Average training loss: 0.6000 -> Option D

4. Quick Check:

   Sum 1.8 / 3 batches = 0.6 [OK]

Hint: Average = sum of losses ÷ number of batches [OK]

Common Mistakes:

• Printing sum instead of average
• Dividing by wrong number of batches
• Using loss tensor instead of loss.item()

4. What is the bug in this code snippet for calculating average validation loss?

model.eval()
val_losses = []
for inputs, targets in val_loader:
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    val_losses.append(loss)
avg_val_loss = sum(val_losses) / len(val_losses)
print(f"Validation loss: {avg_val_loss:.4f}")

medium

A. Appending loss tensor instead of loss.item()

B. Not calling model.train() before validation

C. Using model.eval() disables loss calculation

D. Forgetting to zero gradients before validation

Solution

1. Step 1: Check what is appended to val_losses

   The code appends loss tensors, not scalar values.

2. Step 2: Understand sum behavior on tensors list

   Summing loss tensors retains computation graphs from each forward pass, preventing memory release and causing high memory usage over multiple epochs.

3. Final Answer:

   Appending loss tensor instead of loss.item() -> Option A

4. Quick Check:

   Use loss.item() to get scalar loss [OK]

Hint: Append loss.item(), not loss tensor, for correct averaging [OK]

Common Mistakes:

• Appending loss tensor directly
• Confusing model.train() and model.eval() roles
• Expecting loss calculation to stop in eval mode

5. You want to track training and validation loss per epoch and plot them to check overfitting. Which approach correctly implements this in PyTorch?

hard

A. Use separate lists for training and validation losses, append average loss each epoch, then plot both lists

B. Append batch losses directly to one list for both training and validation, then plot

C. Only track training loss since validation loss is not useful

D. Call model.train() during validation to get accurate loss

Solution

1. Step 1: Understand loss tracking per epoch

   We need to store average losses per epoch separately for training and validation.

2. Step 2: Use separate lists and append averages

   Appending average loss per epoch to separate lists allows clear plotting and comparison.

3. Final Answer:

   Use separate lists for training and validation losses, append average loss each epoch, then plot both lists -> Option A

4. Quick Check:

   Track average losses per epoch separately [OK]

Hint: Track average losses per epoch separately for clear plots [OK]

Common Mistakes:

• Mixing batch losses in one list
• Ignoring validation loss tracking
• Using model.train() during validation