Forward pass, loss, backward, step in PyTorch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of the forward pass in a neural network?

The forward pass calculates the output of the network by passing input data through its layers. It shows how the input transforms into predictions.

beginner

What does the loss function measure in training a model?

The loss function measures how far the model's predictions are from the true answers. It gives a number showing the error to minimize.

intermediate

What happens during the backward pass (backpropagation)?

During the backward pass, the model calculates gradients that show how to change each weight to reduce the loss. It moves backward through the network.

beginner

What does the optimizer's step() function do?

The step() function updates the model's weights using the gradients calculated in the backward pass. It moves weights to reduce the loss.

intermediate

Why do we zero the gradients before the backward pass?

We zero the gradients to clear old gradient values. Otherwise, gradients would accumulate and give wrong updates.

What is the first step in training a neural network?

A Performing the forward pass

B Calling optimizer.step()

C Calculating gradients

D Zeroing the gradients

Training starts by passing input data through the network to get predictions, called the forward pass.

Which function calculates how wrong the model's predictions are?

A optimizer.step()

B loss function

C backward()

D zero_grad()

The loss function measures the difference between predictions and true values.

What does backward() do in PyTorch?

A Calculates gradients

B Clears gradients

C Updates weights

D Computes loss

backward() computes gradients of the loss with respect to model parameters.

Why do we call optimizer.zero_grad() before backward()?

A To update weights

B To reset weights

C To calculate loss

D To clear old gradients

zero_grad() clears previous gradients to avoid accumulation.

What does optimizer.step() do?

A Calculates loss

B Computes gradients

C Updates model weights

D Clears gradients

optimizer.step() updates the model's weights using the gradients.

Explain the sequence of steps in one training iteration of a PyTorch model including forward pass, loss, backward, and step.

Think about what happens from input to weight update.

Why is it important to zero gradients before calling backward in PyTorch training?

Consider what happens if you don't clear gradients.

Practice

1. What is the main purpose of the forward pass in a PyTorch model?

easy

A. To compute gradients for backpropagation

B. To update the model weights after training

C. To run input data through the model and get predictions

D. To calculate the loss between predictions and targets

Solution

1. Step 1: Understand the forward pass role

   The forward pass takes input data and processes it through the model layers to produce predictions.

2. Step 2: Differentiate from other steps

   Updating weights is done by the optimizer step, loss calculation measures error, and backward computes gradients.

3. Final Answer:

   To run input data through the model and get predictions -> Option C

4. Quick Check:

   Forward pass = predictions [OK]

Hint: Forward pass means getting predictions from inputs [OK]

Common Mistakes:

• Confusing forward pass with weight update
• Mixing loss calculation with forward pass
• Thinking backward computes predictions

2. Which of the following is the correct PyTorch code to perform a backward pass on a loss tensor named loss?

easy

A. loss.step()

B. loss.backward()

C. loss.forward()

D. loss.update()

Solution

1. Step 1: Identify the backward method

   In PyTorch, calling loss.backward() computes gradients for all tensors with requires_grad=True.

2. Step 2: Check other options

   step() is for optimizers, forward() is for models, and update() is not a PyTorch method.

3. Final Answer:

   loss.backward() -> Option B

4. Quick Check:

   Backward pass uses loss.backward() [OK]

Hint: Use loss.backward() to compute gradients [OK]

Common Mistakes:

• Using step() on loss instead of optimizer
• Calling forward() on loss tensor
• Trying to update loss directly

3. Given the following PyTorch code snippet, what will be printed?

import torch
import torch.nn as nn

model = nn.Linear(2, 1)
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)

inputs = torch.tensor([[1.0, 2.0]])
targets = torch.tensor([[1.0]])

outputs = model(inputs)
loss_fn = nn.MSELoss()
loss = loss_fn(outputs, targets)

loss.backward()
optimizer.step()

print(loss.item())

medium

A. A positive float value representing the loss before weight update

B. Zero, because optimizer.step() resets the loss

C. A tensor object, not a float

D. An error because backward() must be called after step()

Solution

1. Step 1: Understand loss calculation and printing

   The loss is computed before backward and step, so loss.item() returns the scalar loss value as a float.

2. Step 2: Confirm optimizer.step() effect

   Calling optimizer.step() updates weights but does not change the already computed loss tensor value.

3. Final Answer:

   A positive float value representing the loss before weight update -> Option A

4. Quick Check:

   loss.item() shows loss before update [OK]

Hint: Loss printed is before optimizer updates weights [OK]

Common Mistakes:

• Thinking loss becomes zero after optimizer.step()
• Expecting loss to be a tensor when printed
• Calling backward() after step() causes error

4. Identify the error in this PyTorch training snippet:

optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
outputs = model(inputs)
loss = loss_fn(outputs, targets)
optimizer.step()
loss.backward()

medium

A. Missing optimizer.zero_grad() before backward

B. Model parameters not passed to optimizer

C. Using wrong loss function

D. Calling optimizer.step() before loss.backward()

Solution

1. Step 1: Check order of backward and step

   Gradients must be computed by loss.backward() before calling optimizer.step() to update weights.

2. Step 2: Analyze given code order

   The code calls optimizer.step() before loss.backward(), so weights update happens without gradients.

3. Final Answer:

   Calling optimizer.step() before loss.backward() -> Option D

4. Quick Check:

   Backward before step is required [OK]

Hint: Always call loss.backward() before optimizer.step() [OK]

Common Mistakes:

• Swapping backward and step order
• Forgetting to zero gradients
• Assuming step() computes gradients

5. You want to train a PyTorch model for one step with input x and target y. Which sequence of commands correctly performs forward pass, loss calculation, backward pass, and weight update?

hard

A. optimizer.zero_grad(); outputs = model(x); loss = loss_fn(outputs, y); loss.backward(); optimizer.step()

B. outputs = model(x); loss = loss_fn(outputs, y); optimizer.step(); loss.backward(); optimizer.zero_grad()

C. loss = loss_fn(x, y); outputs = model(x); optimizer.zero_grad(); loss.backward(); optimizer.step()

D. optimizer.step(); optimizer.zero_grad(); outputs = model(x); loss = loss_fn(outputs, y); loss.backward()

Solution

1. Step 1: Understand correct training step order

   The correct order is: clear gradients, forward pass, compute loss, backward pass, then update weights.

2. Step 2: Check each option's sequence

   optimizer.zero_grad(); outputs = model(x); loss = loss_fn(outputs, y); loss.backward(); optimizer.step() follows the correct order. Others call optimizer.step() too early or compute loss before forward pass.

3. Final Answer:

   optimizer.zero_grad(); outputs = model(x); loss = loss_fn(outputs, y); loss.backward(); optimizer.step() -> Option A

4. Quick Check:

   Zero grad -> forward -> loss -> backward -> step [OK]

Hint: Clear grads, forward, loss, backward, then step [OK]

Common Mistakes:

• Calling optimizer.step() before backward
• Not zeroing gradients before backward
• Computing loss before forward pass