Saving entire model in PyTorch - ML Experiment: Train & Evaluate

Experiment - Saving entire model

Problem:You have trained a PyTorch model but only saved the model's state dictionary. When loading, you face issues because the model architecture code must be available and consistent.

Current Metrics:Model trains with 90% accuracy on training data and 85% on validation data. Model saving uses torch.save(model.state_dict(), 'model.pth').

Issue:Saving only the state dictionary requires re-defining the model architecture when loading, which can cause errors or inconsistencies if the code changes.

Your Task

Save the entire PyTorch model including architecture and weights so it can be loaded directly.

Use PyTorch's recommended methods for saving and loading entire models.

Do not change the model architecture or training code.

Hint 1

Hint 2

Hint 3

Solution

PyTorch

import torch
import torch.nn as nn
import torch.optim as optim

# Define a simple model
class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.fc = nn.Linear(10, 2)
    def forward(self, x):
        return self.fc(x)

# Create model instance
model = SimpleNet()

# Dummy training loop (skipped for brevity)
# Assume model is trained here

# Save entire model
torch.save(model.cpu(), 'model_full.pth')

# Later or in another script: Load entire model
loaded_model = torch.load('model_full.pth')
loaded_model.eval()

# Test loaded model with dummy input
input_tensor = torch.randn(1, 10)
output = loaded_model(input_tensor)
print('Model output:', output)

Changed saving method from saving only state_dict to saving entire model with torch.save(model, path).

Used torch.load to load the entire model.

Moved model to CPU before saving to ensure portability.

Results Interpretation

Before: Saved only state_dict. Needed model class code to load. Risk of mismatch or errors.

After: Saved entire model. Can load directly. Easier and safer for deployment.

Saving the entire model in PyTorch allows you to reload it easily, reducing errors and simplifying deployment.

Bonus Experiment

Try saving and loading the model's state_dict only, then re-define the model class and load the weights. Compare this approach with saving the entire model.

💡 Hint

Use model.state_dict() to save and model.load_state_dict() to load weights. Ensure the model architecture matches exactly.

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