Best model saving pattern in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to save the model's state dictionary.

PyTorch

torch.save(model.[1](), 'model.pth')

Drag options to blanks, or click blank then click option'

Astate_dict

Bweights

Cparameters

Dsave_state

Attempts:

3 left

💡 Hint

Common Mistakes

Using model.parameters() instead of model.state_dict()

Trying to save the entire model object directly

In PyTorch, the recommended way to save a model is to save its state_dict(), which contains all the learned parameters.

2fill in blank

medium

Complete the code to load the saved model state dictionary.

PyTorch

model = MyModel()
model.[1](torch.load('model.pth'))

Drag options to blanks, or click blank then click option'

Aload_parameters

Bload_weights

Cload_model

Dload_state_dict

Attempts:

3 left

💡 Hint

Common Mistakes

Using torch.load directly on the model

Calling a non-existent method like load_parameters

To load saved parameters into a PyTorch model, use load_state_dict() with the loaded state dictionary.

3fill in blank

hard

Fix the error in saving the best model during training.

PyTorch

if val_loss < best_loss:
    best_loss = val_loss
    torch.save(model.[1](), 'best_model.pth')

Drag options to blanks, or click blank then click option'

Asave_state

Bparameters

Cstate_dict

Dweights

Attempts:

3 left

💡 Hint

Common Mistakes

Saving model.parameters() which is a generator, not a dictionary

Saving the whole model object causing large files and incompatibility

Saving the state_dict() ensures only the model parameters are saved, which is the correct way to save the best model.

4fill in blank

hard

Fill both blanks to implement a training loop that saves the best model based on validation accuracy.

PyTorch

best_acc = 0.0
for epoch in range(num_epochs):
    train()
    val_acc = validate()
    if val_acc [1] best_acc:
        best_acc = val_acc
        torch.save(model.[2](), 'best_model.pth')

Drag options to blanks, or click blank then click option'

A>

B<

Cstate_dict

Dparameters

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for accuracy comparison

Saving model.parameters() instead of state_dict()

We save the model when validation accuracy improves, so the comparison is val_acc > best_acc. The model's state_dict() is saved.

5fill in blank

hard

Fill all three blanks to load the best saved model and evaluate it.

PyTorch

model = MyModel()
model.[1](torch.load('best_model.pth'))
model.[2]()
accuracy = evaluate(model, test_loader)
print(f'Best model test accuracy: [3]')

Drag options to blanks, or click blank then click option'

Aload_state_dict

Beval

Caccuracy

Dtrain

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to call eval() before evaluation

Trying to print a variable not defined or misspelled

Load the saved parameters with load_state_dict(), set the model to evaluation mode with eval(), then print the accuracy variable.

Practice

1. What is the best practice for saving a PyTorch model during training?

easy

A. Save the model only at the start of training.

B. Save the model only when it improves on validation data.

C. Save the model after every training batch.

D. Save the model only if the training loss increases.

Solution

1. Step 1: Understand model saving timing

   Saving the model only when validation improves ensures you keep the best version, avoiding unnecessary saves.

2. Step 2: Compare other options

   Saving every batch wastes space; saving at start or on loss increase is not useful for best model.

3. Final Answer:

   Save the model only when it improves on validation data. -> Option B

4. Quick Check:

   Save best validation model = C [OK]

Hint: Save model only on validation improvement to keep best [OK]

Common Mistakes:

• Saving model too frequently wastes storage
• Saving only at start misses improvements
• Saving on training loss increase is wrong

2. Which of the following is the correct PyTorch code to save only the model weights?

easy

A. torch.save(model.state_dict(), 'model.pth')

B. torch.save(model, 'model.pth')

C. model.save('model.pth')

D. model.state_dict().save('model.pth')

Solution

1. Step 1: Identify correct saving method

   PyTorch saves weights using torch.save(model.state_dict(), filename).

2. Step 2: Check other options

   Saving the whole model (torch.save(model, 'model.pth')) is possible but less flexible; options C and D are invalid syntax.

3. Final Answer:

   torch.save(model.state_dict(), 'model.pth') -> Option A

4. Quick Check:

   Save weights with state_dict() = A [OK]

Hint: Use torch.save(model.state_dict(), filename) to save weights [OK]

Common Mistakes:

• Trying to save model directly without state_dict
• Using non-existent save methods on model
• Confusing saving weights vs full model

3. Given this code snippet, what will be printed?

import torch
import torch.nn as nn

model = nn.Linear(2, 1)
torch.save(model.state_dict(), 'best.pth')
new_model = nn.Linear(2, 1)
new_model.load_state_dict(torch.load('best.pth'))
print(new_model.weight.shape)

medium

A. torch.Size([1, 2])

B. torch.Size([2, 1])

C. torch.Size([1, 1])

D. Error: shape mismatch

Solution

1. Step 1: Understand model architecture

   nn.Linear(2,1) creates weights of shape [1, 2] (output features, input features).

2. Step 2: Loading weights into new model

   Loading saved weights into identical model keeps weight shape same.

3. Final Answer:

   torch.Size([1, 2]) -> Option A

4. Quick Check:

   Linear(2,1) weight shape = [1, 2] [OK]

Hint: Linear layer weights shape = (out_features, in_features) [OK]

Common Mistakes:

• Confusing input/output dimensions order
• Expecting error when loading identical model
• Misreading weight shape as (2,1)

4. What is wrong with this code snippet for saving the best model?

if val_loss < best_loss:
    best_loss = val_loss
    torch.save(model, 'best_model.pth')

medium

A. There is no condition to check validation loss.

B. It should save model.state_dict() instead of model.

C. It does not update best_loss correctly.

D. It saves the entire model, which is less flexible than saving state_dict.

Solution

1. Step 1: Analyze saving method

   Saving entire model works but is less flexible and may cause issues when loading on different devices or PyTorch versions.

2. Step 2: Compare with best practice

   Best practice is saving model.state_dict() for portability and smaller files.

3. Final Answer:

   It saves the entire model, which is less flexible than saving state_dict. -> Option D

4. Quick Check:

   Save state_dict() preferred over full model [OK]

Hint: Save state_dict() for flexibility, not full model [OK]

Common Mistakes:

• Saving full model without state_dict
• Ignoring portability issues
• Assuming full model save is always best

5. You want to save the best model during training based on validation accuracy. Which code snippet correctly implements this pattern?

best_acc = 0.0
for epoch in range(epochs):
    train()
    val_acc = validate()
    # Save best model here
    ???

hard

A. if val_acc < best_acc:
best_acc = val_acc
torch.save(model.state_dict(), 'best_model.pth')

B. if val_acc == best_acc:
torch.save(model.state_dict(), 'best_model.pth')

C. if val_acc > best_acc:
best_acc = val_acc
torch.save(model.state_dict(), 'best_model.pth')

D. torch.save(model.state_dict(), 'best_model.pth') # save every epoch

Solution

1. Step 1: Identify saving condition

   We save model only if validation accuracy improves (val_acc > best_acc).

2. Step 2: Update best accuracy and save weights

   Update best_acc and save model.state_dict() to keep best weights.

3. Final Answer:

   if val_acc > best_acc: best_acc = val_acc torch.save(model.state_dict(), 'best_model.pth') -> Option C

4. Quick Check:

   Save on val_acc improvement = B [OK]

Hint: Save model only if validation accuracy improves [OK]

Common Mistakes:

• Saving when accuracy decreases
• Saving every epoch wastes space
• Not updating best accuracy variable