Saving entire model in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to save the entire PyTorch model to a file.

PyTorch

import torch

# Assume model is a PyTorch model
torch.save(model, [1])

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

Amodel.state_dict()

B"model.pth"

C"weights.pth"

Dtorch.nn.Module()

Attempts:

3 left

💡 Hint

Common Mistakes

Using model.state_dict() instead of the whole model.

Not providing a filename as a string.

Passing a model class instead of an instance.

To save the entire model in PyTorch, use torch.save(model, 'filename') where the filename is a string.

2fill in blank

medium

Complete the code to load the entire PyTorch model from a file.

PyTorch

import torch

model = torch.load([1])

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

A"model.pth"

Bmodel.state_dict()

Ctorch.nn.Module()

D"weights.pth"

Attempts:

3 left

💡 Hint

Common Mistakes

Passing model.state_dict() instead of a filename.

Using the wrong filename string.

Trying to load weights only instead of the whole model.

To load a saved entire model, use torch.load('filename') with the filename as a string.

3fill in blank

hard

Fix the error in the code to save the entire model correctly.

PyTorch

import torch

# Incorrect saving code
torch.save([1], "model.pth")

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

Atorch.nn.Module()

Bmodel.state_dict()

Cmodel

D"model.pth"

Attempts:

3 left

💡 Hint

Common Mistakes

Saving model.state_dict() when intending to save the whole model.

Passing a string instead of the model object.

Passing a new model instance instead of the trained model.

To save the entire model, pass the model instance itself, not its state dictionary.

4fill in blank

hard

Fill both blanks to save and then load the entire PyTorch model.

PyTorch

import torch

# Save model
torch.save([1], [2])

# Load model
loaded_model = torch.load("model.pth")

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

Amodel

Bmodel.state_dict()

C"model.pth"

D"weights.pth"

Attempts:

3 left

💡 Hint

Common Mistakes

Saving model.state_dict() instead of the whole model.

Using wrong filename strings.

Mixing up save and load functions.

Use torch.save(model, 'model.pth') to save and torch.load('model.pth') to load the entire model.

5fill in blank

hard

Fill all three blanks to save the entire model, load it, and print its type.

PyTorch

import torch

# Save the model
torch.save([1], [2])

# Load the model
loaded_model = torch.load([3])

print(type(loaded_model))

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

Amodel

B"model.pth"

Dmodel.state_dict()

Attempts:

3 left

💡 Hint

Common Mistakes

Using different filenames for saving and loading.

Saving model.state_dict() instead of the whole model.

Passing wrong arguments to torch.load.

Save the model with torch.save(model, 'model.pth'), load it with torch.load('model.pth'), then print its type.

Practice

1. What does torch.save(model, PATH) do in PyTorch?

easy

A. Saves the entire model including its architecture and weights

B. Saves only the model's weights

C. Saves only the model's architecture

D. Saves the training data used for the model

Solution

1. Step 1: Understand torch.save usage

   torch.save(model, PATH) saves the whole model object, which includes both architecture and weights.

2. Step 2: Differentiate from saving weights only

   Saving only weights uses model.state_dict(), but here the entire model is saved.

3. Final Answer:

   Saves the entire model including its architecture and weights -> Option A

4. Quick Check:

   torch.save(model, PATH) saves full model [OK]

Hint: Remember torch.save(model, PATH) saves full model [OK]

Common Mistakes:

• Confusing saving weights only with saving entire model
• Thinking it saves training data
• Assuming it saves only architecture

2. Which of the following is the correct syntax to save an entire PyTorch model to a file named model.pth?

easy

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

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

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

D. model.save_state('model.pth')

Solution

1. Step 1: Identify correct torch.save usage

   To save the entire model, use torch.save(model, 'model.pth').

2. Step 2: Differentiate from saving weights only

   model.state_dict() saves only weights, so torch.save(model.state_dict(), 'model.pth') is incorrect for entire model.

3. Final Answer:

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

4. Quick Check:

   torch.save(model, PATH) saves full model [OK]

Hint: Use torch.save(model, PATH) to save entire model [OK]

Common Mistakes:

• Using model.state_dict() when saving entire model
• Calling non-existent model.save() method
• Confusing syntax with other frameworks

3. Consider this code snippet:

import torch
import torch.nn as nn

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

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

model = SimpleNet()
torch.save(model, 'model.pth')
loaded_model = torch.load('model.pth')
loaded_model.eval()

input_tensor = torch.tensor([[1.0, 2.0]])
output = loaded_model(input_tensor).item()
print(round(output, 2))

What will be printed?

medium

A. A number close to 0.0 (random weights)

B. An error because model.eval() is missing

C. A tensor object instead of a number

D. An error because torch.load cannot load entire model

Solution

1. Step 1: Understand model saving and loading

   The entire model is saved and loaded correctly with torch.save and torch.load. Calling eval() sets model to evaluation mode.

2. Step 2: Predict output value type

   Since weights are random (not trained), output will be a float number close to 0.0. The print rounds it to 2 decimals.

3. Final Answer:

   A number close to 0.0 (random weights) -> Option A

4. Quick Check:

   Loaded model outputs float with random weights [OK]

Hint: Loaded model outputs float with random weights [OK]

Common Mistakes:

• Expecting trained output without training
• Thinking eval() is mandatory to avoid error
• Confusing tensor output with float

4. You saved your entire model using torch.save(model, 'model.pth'). When loading with loaded_model = torch.load('model.pth'), you get an error: AttributeError: Can't get attribute 'SimpleNet'. What is the likely cause?

medium

A. The file 'model.pth' is corrupted

B. The model class SimpleNet is not defined or imported before loading

C. You must use model.load_state_dict() instead of torch.load

D. The model was saved incorrectly with torch.save(model.state_dict())

Solution

1. Step 1: Understand how torch.load works with entire models

   Loading entire models requires the model class definition to be available in the current scope.

2. Step 2: Identify cause of AttributeError

   The error means Python cannot find the class SimpleNet, so it must be defined or imported before loading.

3. Final Answer:

   The model class SimpleNet is not defined or imported before loading -> Option B

4. Quick Check:

   Model class must be defined before torch.load [OK]

Hint: Define model class before loading entire model [OK]

Common Mistakes:

• Assuming torch.load works without class definition
• Confusing state_dict loading with entire model loading
• Thinking file corruption causes this error

5. You want to save a PyTorch model so that it can be loaded later without needing the original model class code. Which approach is best?

hard

A. Save the model architecture as JSON and weights separately

B. Save only the model weights with torch.save(model.state_dict(), PATH) and recreate the model class before loading

C. Save the entire model using torch.save(model, PATH) and load with torch.load(PATH)

D. Export the model to ONNX format for framework-independent loading

Solution

1. Step 1: Understand limitations of saving entire model

   Saving entire model requires the original class code to load, so it is not independent.

2. Step 2: Identify framework-independent saving method

   Exporting to ONNX format allows loading the model in other frameworks without original class code.

3. Final Answer:

   Export the model to ONNX format for framework-independent loading -> Option D

4. Quick Check:

   ONNX export enables class-free model loading [OK]

Hint: Use ONNX export for class-free model loading [OK]

Common Mistakes:

• Thinking torch.save saves model independent of class code
• Assuming JSON saves PyTorch model architecture
• Confusing state_dict saving with full model saving