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

Experiment - Saving model state_dict

Problem:You have trained a PyTorch model but did not save its state_dict. If the program stops, you lose the trained weights.

Current Metrics:Training accuracy: 90%, Validation accuracy: 88%

Issue:No saved model state means you cannot reuse the trained model without retraining.

Your Task

Save the model's state_dict after training so you can load it later and reuse the trained weights.

Use PyTorch's recommended methods for saving and loading state_dict.

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().__init__()
        self.fc = nn.Linear(10, 2)
    def forward(self, x):
        return self.fc(x)

# Create model instance
model = SimpleNet()

# Dummy data
X = torch.randn(100, 10)
y = torch.randint(0, 2, (100,))

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1)

# Training loop
for epoch in range(5):
    optimizer.zero_grad()
    outputs = model(X)
    loss = criterion(outputs, y)
    loss.backward()
    optimizer.step()

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

# To load the model later:
# model = SimpleNet()
# model.load_state_dict(torch.load('model_state.pth'))
# model.eval()

Added torch.save(model.state_dict(), 'model_state.pth') after training to save weights.

Included example code to load the saved state_dict back into the model.

Results Interpretation

Before: No saved model weights, so trained model lost after program ends.

After: Model weights saved in 'model_state.pth' file, allowing reuse without retraining.

Saving the model's state_dict lets you keep the trained weights and load them later, saving time and resources.

Bonus Experiment

Try saving and loading the entire model instead of just the state_dict. Compare file sizes and loading flexibility.

💡 Hint

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

Practice

1. What does model.state_dict() in PyTorch contain?

easy

A. Only the optimizer settings

B. The learned parameters (weights and biases) of the model

C. The entire model architecture and code

D. The training dataset

Solution

1. Step 1: Understand what state_dict holds

   The state_dict stores all the learned parameters like weights and biases of the model layers.

2. Step 2: Differentiate from other components

   It does not include the model architecture code or optimizer settings, only the parameters.

3. Final Answer:

   The learned parameters (weights and biases) of the model -> Option B

4. Quick Check:

   state_dict = learned parameters [OK]

Hint: state_dict always means model weights only [OK]

Common Mistakes:

• Thinking state_dict saves the whole model code
• Confusing optimizer state with model state
• Assuming it saves the training data

2. Which of the following is the correct syntax to save a PyTorch model's state_dict to a file named 'model.pth'?

easy

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

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

C. model.save_state('model.pth')

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

Solution

1. Step 1: Recall the saving function

   In PyTorch, torch.save() is used to save objects to a file.

2. Step 2: Save only the state_dict

   To save the model parameters, you pass model.state_dict() to torch.save() along with the filename.

3. Final Answer:

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

4. Quick Check:

   Use torch.save with state_dict [OK]

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

Common Mistakes:

• Saving the whole model instead of state_dict
• Using non-existent save_state method
• Calling save on state_dict directly

3. Given the code below, what will be printed?

import torch
import torch.nn as nn

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

model = SimpleModel()
torch.save(model.state_dict(), 'weights.pth')
loaded_state = torch.load('weights.pth')
print(type(loaded_state))

medium

A.

B.

C.

D.

Solution

1. Step 1: Understand what torch.save stores

   Saving model.state_dict() stores an OrderedDict of parameter tensors.

2. Step 2: Loading with torch.load returns the same type

   When loaded, it returns an OrderedDict, not a Module or plain dict.

3. Final Answer:

   <class 'collections.OrderedDict'> -> Option C

4. Quick Check:

   state_dict loads as OrderedDict [OK]

Hint: state_dict loads as OrderedDict, not model or tensor [OK]

Common Mistakes:

• Expecting loaded_state to be a model instance
• Thinking it returns a plain dict
• Confusing with tensor type

4. You saved a model's state_dict with torch.save(model.state_dict(), 'model.pth'). Later, you try to load it with model.load_state_dict(torch.load('model.pth')) but get a runtime error about missing keys. What is the most likely cause?

medium

A. The model architecture does not match the saved state_dict

B. The file 'model.pth' is corrupted

C. You forgot to call model.eval() before loading

D. You used torch.save(model, 'model.pth') instead

Solution

1. Step 1: Understand load_state_dict requirements

   Loading weights requires the model architecture to match the saved parameters exactly.

2. Step 2: Identify cause of missing keys error

   If keys are missing, it usually means the model layers differ from those saved in the state_dict.

3. Final Answer:

   The model architecture does not match the saved state_dict -> Option A

4. Quick Check:

   Mismatch architecture causes missing keys error [OK]

Hint: Check model matches saved weights architecture [OK]

Common Mistakes:

• Assuming file corruption without checking
• Thinking eval mode affects loading
• Confusing saving whole model vs state_dict

5. You want to save a PyTorch model's state_dict after training and later load it to continue training on a different machine. Which sequence of steps is correct?

hard

A. Save model.state_dict() and optimizer.state_dict() together in one file, then load both on new machine

B. Save with torch.save(model, 'file.pth'), then load with model = torch.load('file.pth') without defining architecture

C. Save optimizer state only, then recreate model and optimizer on new machine

D. Save with torch.save(model.state_dict(), 'file.pth'), then on new machine create same model architecture and load with model.load_state_dict(torch.load('file.pth'))

Solution

1. Step 1: Save only model parameters

   Use torch.save(model.state_dict(), 'file.pth') to save learned weights.

2. Step 2: Recreate model architecture on new machine

   Define the same model class and create an instance before loading weights.

3. Step 3: Load saved weights into model

   Use model.load_state_dict(torch.load('file.pth')) to load parameters.

4. Final Answer:

   Save state_dict, recreate model, then load state_dict -> Option D

5. Quick Check:

   Save weights, recreate model, load weights [OK]

Hint: Always recreate model before loading state_dict [OK]

Common Mistakes:

• Trying to load weights without model definition
• Saving whole model causing compatibility issues
• Ignoring optimizer state when continuing training