Train/val/test split in PyTorch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of PyTorch dataset split code

What will be the output of the following code snippet that splits a dataset into train, validation, and test sets using PyTorch's random_split?

PyTorch

from torch.utils.data import random_split
from torch.utils.data import Dataset

class DummyDataset(Dataset):
    def __init__(self, length):
        self.data = list(range(length))
    def __len__(self):
        return len(self.data)
    def __getitem__(self, idx):
        return self.data[idx]

full_dataset = DummyDataset(100)
train_size = 70
val_size = 20
test_size = 10
train_set, val_set, test_set = random_split(full_dataset, [train_size, val_size, test_size])

print(len(train_set), len(val_set), len(test_set))

A 33 33 34

B 100 0 0

C 70 20 10

D 50 25 25

💡 Hint

Check how the sizes are passed to random_split and what lengths are printed.

The random_split function divides the dataset into subsets with the exact sizes provided. Here, 70, 20, and 10 sum to 100, so the lengths printed are 70, 20, and 10 respectively.

🧠 Conceptual

intermediate

Purpose of validation set in train/val/test split

What is the main purpose of the validation set in a train/val/test split during machine learning model training?

A To tune hyperparameters and prevent overfitting

B To train the model with more data

C To evaluate the model's performance on unseen data after training

D To test the model's final accuracy before deployment

💡 Hint

Think about which set helps decide model settings without biasing final evaluation.

The validation set is used during training to tune hyperparameters and monitor if the model is overfitting. It is separate from the test set, which is used only for final evaluation.

❓ Hyperparameter

advanced

Choosing split ratios for train/val/test

Which of the following split ratios is most appropriate for a dataset with 10,000 samples to ensure reliable training, validation, and testing?

A 60% train, 20% validation, 20% test

B 50% train, 25% validation, 25% test

C 90% train, 5% validation, 5% test

D 80% train, 10% validation, 10% test

💡 Hint

Consider enough data for training and meaningful validation/testing.

80/10/10 is a common split that balances enough training data with sufficient validation and test samples for reliable evaluation.

🔧 Debug

advanced

Identify error in PyTorch dataset splitting code

What error will the following code raise when trying to split a dataset into train, validation, and test sets?

PyTorch

from torch.utils.data import random_split
full_dataset = list(range(50))
train_set, val_set, test_set = random_split(full_dataset, [30, 15, 10])

A TypeError: 'list' object has no attribute '__len__'

B ValueError: Sum of input lengths does not equal the length of the input dataset

C TypeError: random_split expects a Dataset object, not a list

D No error, splits successfully

💡 Hint

Check the sum of split sizes compared to dataset length.

The sum of split sizes is 30+15+10=55, which is greater than the dataset length 50, causing a ValueError.

❓ Model Choice

expert

Best approach to split highly imbalanced dataset

You have a highly imbalanced classification dataset with 1% positive and 99% negative samples. Which approach is best to split the dataset into train, validation, and test sets to maintain class distribution?

A Use stratified splitting to keep class proportions in each subset

B Manually shuffle and split the dataset randomly

C Use random_split from PyTorch directly without stratification

D Split only into train and test, skip validation

💡 Hint

Think about preserving class balance in each subset for fair evaluation.

Stratified splitting ensures each subset has the same class distribution as the full dataset, which is crucial for imbalanced data.

Practice

1. What is the main purpose of splitting data into train, validation, and test sets in PyTorch?

easy

A. To train the model, tune hyperparameters, and evaluate performance separately

B. To increase the size of the dataset artificially

C. To reduce the number of features in the dataset

D. To convert data into tensors automatically

Solution

1. Step 1: Understand the role of each data split

   The training set is used to teach the model, the validation set helps tune settings, and the test set checks how well the model works on new data.

2. Step 2: Match purpose to options

   Only To train the model, tune hyperparameters, and evaluate performance separately correctly describes these roles. Other options describe unrelated tasks.

3. Final Answer:

   To train the model, tune hyperparameters, and evaluate performance separately -> Option A

4. Quick Check:

   Train/val/test split = separate training, tuning, testing [OK]

Hint: Remember: train teaches, val tunes, test checks [OK]

Common Mistakes:

• Thinking splitting increases dataset size
• Confusing splitting with feature selection
• Assuming splitting converts data types

2. Which of the following is the correct way to split a PyTorch dataset into train and test sets using random_split?

easy

A. train_set, test_set = random_split(dataset, [80%, 20%])

B. train_set, test_set = random_split(dataset, [80, 20])

C. train_set, test_set = random_split(dataset, [0.8, 0.2])

D. train_set, test_set = random_split(dataset, [80])

Solution

1. Step 1: Check the expected input for random_split

   random_split expects the dataset and a list of lengths (integers) for each split, not fractions or percentages.

2. Step 2: Evaluate each option

   train_set, test_set = random_split(dataset, [80, 20]) uses integer lengths [80, 20], which is correct. train_set, test_set = random_split(dataset, [0.8, 0.2]) uses floats, train_set, test_set = random_split(dataset, [80%, 20%]) uses invalid syntax with %, and train_set, test_set = random_split(dataset, [80]) provides only one length.

3. Final Answer:

   train_set, test_set = random_split(dataset, [80, 20]) -> Option B

4. Quick Check:

   random_split needs integer lengths [OK]

Hint: Use integer lengths, not fractions, in random_split [OK]

Common Mistakes:

• Passing fractions or percentages instead of integers
• Providing only one length instead of two
• Using invalid syntax like % in list

3. Given the code below, what will be the length of val_set?

from torch.utils.data import random_split

full_dataset = list(range(100))
train_size = 70
val_size = 20
test_size = 10
train_set, val_set, test_set = random_split(full_dataset, [train_size, val_size, test_size])
print(len(val_set))

medium

A. 10

B. 70

C. 20

D. 100

Solution

1. Step 1: Understand random_split lengths

   The list [70, 20, 10] tells random_split to split the dataset into parts of those exact lengths.

2. Step 2: Identify val_set length

   val_set corresponds to the second split with length 20, so len(val_set) will be 20.

3. Final Answer:

   20 -> Option C

4. Quick Check:

   val_size = 20 means val_set length = 20 [OK]

Hint: Lengths in random_split match output sizes exactly [OK]

Common Mistakes:

• Confusing sizes with percentages
• Assuming val_set length is sum of others
• Forgetting order of splits in output

4. You wrote this code to split your dataset but get a ValueError: Sum of input lengths does not equal the length of the input dataset!. What is the error?

from torch.utils.data import random_split

dataset = list(range(50))
train_set, test_set = random_split(dataset, [30, 25])

medium

A. The sum of lengths 30 + 25 is greater than dataset length 50

B. random_split requires fractions, not lengths

C. dataset must be a PyTorch tensor, not a list

D. random_split cannot split into two parts

Solution

1. Step 1: Check dataset length and split lengths

   Dataset length is 50. The sum of split lengths is 30 + 25 = 55, which is more than 50.

2. Step 2: Understand random_split requirements

   random_split requires the sum of split lengths to exactly match dataset length.

3. Final Answer:

   The sum of lengths 30 + 25 is greater than dataset length 50 -> Option A

4. Quick Check:

   Sum of split lengths must equal dataset length [OK]

Hint: Sum of split lengths must equal dataset size exactly [OK]

Common Mistakes:

• Passing split lengths that don't sum to dataset size
• Thinking random_split accepts fractions
• Assuming dataset must be tensor, not list

5. You want to split a PyTorch dataset of 1000 samples into 70% train, 15% validation, and 15% test sets. Which code correctly performs this split using random_split?

hard

A. train_set, val_set, test_set = random_split(dataset, [0.7, 0.15, 0.15])

B. train_set, val_set, test_set = random_split(dataset, [700, 150, 100])

C. train_set, val_set, test_set = random_split(dataset, [700, 150])

D. train_set, val_set, test_set = random_split(dataset, [700, 150, 150])

Solution

1. Step 1: Calculate integer split sizes

   70% of 1000 is 700, 15% is 150, so splits are [700, 150, 150].

2. Step 2: Check options for correctness

   train_set, val_set, test_set = random_split(dataset, [700, 150, 150]) uses correct integer lengths summing to 1000. train_set, val_set, test_set = random_split(dataset, [0.7, 0.15, 0.15]) uses floats (invalid), train_set, val_set, test_set = random_split(dataset, [700, 150]) misses one split, train_set, val_set, test_set = random_split(dataset, [700, 150, 100]) sums to 950 (incorrect).

3. Final Answer:

   train_set, val_set, test_set = random_split(dataset, [700, 150, 150]) -> Option D

4. Quick Check:

   Use integer lengths summing to dataset size [OK]

Hint: Convert percentages to integers summing to dataset size [OK]

Common Mistakes:

• Passing fractions instead of integers
• Not summing split sizes to dataset length
• Missing one of the splits