Saving entire model in PyTorch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this PyTorch model saving code?

Consider the following PyTorch code that defines and saves a simple model. What will be the output when loading and printing the saved model's state_dict keys?

PyTorch

import torch
import torch.nn as nn

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

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

model = SimpleModel()
torch.save(model, 'model.pth')
loaded_model = torch.load('model.pth')
print(list(loaded_model.state_dict().keys()))

A ['model.linear.weight', 'model.linear.bias']

B ['linear.weight', 'linear.bias']

C ['weight', 'bias']

D []

💡 Hint

Think about how PyTorch names parameters inside nn.Module layers.

When saving the entire model, the state_dict keys correspond to the layer names and their parameters. Here, the linear layer's weights and biases are named 'linear.weight' and 'linear.bias'.

❓ Model Choice

intermediate

Which method saves the entire PyTorch model including architecture and weights?

You want to save a PyTorch model so that you can load it later without redefining the model class. Which method should you use?

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

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

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

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

💡 Hint

Saving the entire model means saving both architecture and weights.

torch.save(model, 'model.pth') saves the entire model object including architecture and weights. Saving state_dict only saves weights, not architecture.

❓ Hyperparameter

advanced

What is a key consideration when saving entire PyTorch models for future use?

When saving entire PyTorch models, which of the following is an important consideration to ensure the model can be loaded correctly later?

A The model's optimizer state must be saved with the model

B The model must be saved only after training for at least 10 epochs

C The model class definition must be available in the loading environment

D The model must be saved using torch.save(model.state_dict())

💡 Hint

Think about what happens when loading a saved model object.

When saving the entire model object, PyTorch saves the class name and parameters but not the class code. The class definition must be present when loading.

🔧 Debug

advanced

Why does loading a saved entire PyTorch model raise an error?

You saved a PyTorch model using torch.save(model, 'model.pth'). Later, when you try to load it with torch.load('model.pth'), you get an error: ModuleNotFoundError: No module named 'mymodel'. What is the cause?

A The model class 'mymodel' is not defined or imported in the loading script

B The saved file 'model.pth' is corrupted

C torch.load requires the model to be saved with state_dict, not entire model

D The PyTorch version used to save and load the model must be the same

💡 Hint

Check if the model class is available when loading.

Saving entire models requires the class definition to be present when loading. If the class is missing, Python cannot find it and raises ModuleNotFoundError.

❓ Metrics

expert

After loading an entire PyTorch model, how to verify it matches the original model?

You saved a PyTorch model using torch.save(model, 'model.pth') and later loaded it with torch.load('model.pth'). Which method correctly verifies that the loaded model's parameters match the original model's parameters?

PyTorch

import torch
import torch.nn as nn

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

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

original_model = SimpleModel()
torch.save(original_model, 'model.pth')
loaded_model = torch.load('model.pth')

# Which code below correctly checks parameter equality?

A original_model.parameters() == loaded_model.parameters()

B original_model.state_dict() == loaded_model.state_dict()

C torch.allclose(original_model.state_dict(), loaded_model.state_dict())

D all(torch.equal(p1, p2) for p1, p2 in zip(original_model.parameters(), loaded_model.parameters()))

💡 Hint

Compare tensors element-wise for equality.

state_dict() returns dicts, so comparing dicts directly with == compares keys and values but not tensor content properly. Using torch.equal on each parameter tensor pair is the correct way.