Saving model state_dict in PyTorch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this PyTorch code snippet?

Consider the following PyTorch code that saves a model's state_dict. What will be printed after loading the saved state_dict?

PyTorch

import torch
import torch.nn as nn

class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.linear = nn.Linear(2, 1)

    def forward(self, x):
        return self.linear(x)

model = SimpleModel()

# Save the state_dict
torch.save(model.state_dict(), 'model.pth')

# Create a new model instance
new_model = SimpleModel()

# Load the saved state_dict
new_model.load_state_dict(torch.load('model.pth'))

# Check if parameters are equal
params_equal = all(torch.equal(p1, p2) for p1, p2 in zip(model.parameters(), new_model.parameters()))
print(params_equal)

A True

B False

C Raises RuntimeError due to missing keys

D Raises FileNotFoundError

💡 Hint

Think about what happens when you save and load the state_dict of the same model architecture.

Saving and loading the state_dict preserves the model's parameters exactly. Since both models have the same architecture, their parameters match after loading.

❓ Model Choice

intermediate

Which model saving method saves only the model's learned parameters in PyTorch?

You want to save only the learned parameters of your PyTorch model to reduce file size and allow flexible model loading. Which method should you use?

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

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

C torch.save(model.parameters(), 'model.pth')

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

💡 Hint

Think about what state_dict contains versus the whole model object.

The state_dict contains only the model's parameters and buffers, making it the recommended way to save learned weights without saving the entire model architecture.

❓ Hyperparameter

advanced

Which hyperparameter affects the size of the saved model state_dict file?

You notice that your saved model state_dict file is very large. Which hyperparameter adjustment can help reduce the file size without changing the model architecture?

A Increasing the learning rate

B Reducing the batch size during training

C Using 16-bit floating point precision (mixed precision training)

D Adding dropout layers

💡 Hint

Consider how data type precision affects memory and storage size.

Using 16-bit precision reduces the size of each parameter stored in the state_dict, thus reducing the file size without changing the model's architecture.

🔧 Debug

advanced

Why does loading a saved state_dict sometimes raise a RuntimeError?

You saved a model's state_dict and later tried to load it into a model instance but got a RuntimeError about missing keys. What is the most likely cause?

A The saved state_dict file is corrupted

B The model was saved using torch.save(model) instead of state_dict

C The file path to the state_dict is incorrect

D The model architecture of the new instance differs from the saved model

💡 Hint

Think about what happens if the model layers do not match between saving and loading.

If the new model instance has different layers or parameters than the saved state_dict, loading will fail due to missing or unexpected keys.

❓ Metrics

expert

How to verify that a loaded model state_dict produces identical predictions?

After loading a saved state_dict into a new model instance, which method best verifies that the loaded model produces the same predictions as the original?

A Compare the model parameters using torch.equal for each parameter tensor

B Run the same input through both models and compare their output tensors with torch.allclose

C Check the file size of the saved state_dict matches the original model size

D Compare the training loss values before and after loading

💡 Hint

Think about verifying actual model behavior, not just parameters or file sizes.

Comparing outputs on the same input using torch.allclose confirms that the loaded model behaves identically, accounting for floating point precision.