Benchmark datasets in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the popular MNIST dataset using TensorFlow.

Prompt Engineering / GenAI

import tensorflow as tf
mnist = tf.keras.datasets.[1].load_data()

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

Afashion_mnist

Bcifar10

Cimagenet

Dmnist

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'cifar10' instead of 'mnist' will load a different dataset.

Using 'imagenet' requires different loading methods.

The MNIST dataset is loaded using tf.keras.datasets.mnist.load_data().

2fill in blank

medium

Complete the code to split the dataset into training and testing sets using scikit-learn.

Prompt Engineering / GenAI

from sklearn.model_selection import [1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

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Atrain_test_split

Bcross_val_score

CGridSearchCV

DKFold

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'cross_val_score' does not split data but evaluates models.

Using 'GridSearchCV' is for hyperparameter tuning.

The function train_test_split splits data into training and testing sets.

3fill in blank

hard

Fix the error in the code to load the CIFAR-10 dataset correctly using PyTorch.

Prompt Engineering / GenAI

import torchvision.datasets as datasets
cifar10 = datasets.CIFAR10(root='./data', train=True, download=True, transform=[1])

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ANone

Btransforms.ToTensor()

Ctorch.Tensor

Dtransform.ToTensor()

Attempts:

3 left

💡 Hint

Common Mistakes

Passing None disables transformations and causes errors later.

Using 'transform.ToTensor()' is incorrect syntax.

The transform argument requires a callable like transforms.ToTensor() to convert images to tensors.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps dataset names to their sample counts.

Prompt Engineering / GenAI

dataset_sizes = {name: len([1]) for name, [2] in datasets.items()}

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

Adata

Bdataset

Cvalue

Dsample

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'dataset' or 'sample' as variable names that don't match the iteration.

In the dictionary datasets, each value is a dataset object or list named data or value. Using len(data) or len(value) gets sample counts.

5fill in blank

hard

Fill all three blanks to filter datasets with more than 10,000 samples and create a new dictionary.

Prompt Engineering / GenAI

large_datasets = {name: [1] for name, [2] in datasets.items() if len([3]) > 10000}

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

Avalue

Bdata

Ddataset

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing variable names inconsistently causes errors.

Using the wrong variable in the length check.

The comprehension uses datasets.items() with variables name and value. The dictionary stores value as values. The filter checks len(value) > 10000.

Practice

1. What is the main purpose of benchmark datasets in machine learning?

easy

A. To speed up model training by using smaller data

B. To provide a standard way to test and compare models

C. To store user data for training

D. To create new machine learning algorithms

Solution

1. Step 1: Understand the role of benchmark datasets

   Benchmark datasets are used to test machine learning models on the same data so results can be compared fairly.

2. Step 2: Identify the correct purpose

   They are not for creating algorithms or storing user data, but for evaluation and comparison.

3. Final Answer:

   To provide a standard way to test and compare models -> Option B

4. Quick Check:

   Benchmark datasets = standard test data [OK]

Hint: Benchmark datasets test models fairly with known data [OK]

Common Mistakes:

• Thinking benchmark datasets create algorithms
• Confusing benchmark datasets with training data
• Assuming benchmark datasets speed up training

2. Which of the following is the correct way to load the popular MNIST benchmark dataset in Python using TensorFlow?

easy

A. from tensorflow.keras.datasets import mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

B. import mnist
train_images, train_labels = mnist.load()

C. from sklearn.datasets import mnist
mnist.load()

D. load_mnist()

Solution

1. Step 1: Recall the TensorFlow MNIST loading syntax

   TensorFlow provides MNIST via keras.datasets with the load_data() method.

2. Step 2: Match the correct code snippet

   from tensorflow.keras.datasets import mnist (train_images, train_labels), (test_images, test_labels) = mnist.load_data() matches the correct import and loading syntax exactly.

3. Final Answer:

   from tensorflow.keras.datasets import mnist\n(train_images, train_labels), (test_images, test_labels) = mnist.load_data() -> Option A

4. Quick Check:

   TensorFlow MNIST load = keras.datasets.mnist.load_data() [OK]

Hint: TensorFlow MNIST loads with keras.datasets.mnist.load_data() [OK]

Common Mistakes:

• Using sklearn.datasets for MNIST (wrong library)
• Calling load() instead of load_data()
• Missing proper import statement

3. Given the following code snippet using the Iris dataset, what will be the output of print(data.target_names)?

from sklearn.datasets import load_iris
data = load_iris()
print(data.target_names)

medium

A. ['red', 'green', 'blue']

B. [0 1 2]

C. ['iris-setosa', 'iris-versicolor', 'iris-virginica']

D. ['setosa' 'versicolor' 'virginica']

Solution

1. Step 1: Understand the Iris dataset target names

   The Iris dataset target_names attribute contains the species names as numpy array strings without commas.

2. Step 2: Match the output format

   ['setosa' 'versicolor' 'virginica'] shows the correct array format with species names as strings without commas, matching sklearn output.

3. Final Answer:

   ['setosa' 'versicolor' 'virginica'] -> Option D

4. Quick Check:

   Iris target_names = species names array [OK]

Hint: Iris target_names shows species as array of strings [OK]

Common Mistakes:

• Confusing target_names with numeric labels
• Expecting commas inside numpy array print
• Using wrong species names

4. You try to load the CIFAR-10 dataset using this code but get an error:

from tensorflow.keras.datasets import cifar10
(train_images, train_labels), (test_images, test_labels) = cifar10.load()

What is the error and how to fix it?

medium

A. Error: SyntaxError due to missing parentheses, fix by adding () after load

B. Error: ImportError because cifar10 is not in keras.datasets, fix by installing extra package

C. Error: AttributeError because method is load_data(), fix by using cifar10.load_data()

D. No error, code runs fine

Solution

1. Step 1: Identify the method name for loading CIFAR-10

   The correct method to load CIFAR-10 in keras.datasets is load_data(), not load().

2. Step 2: Understand the error and fix

   Using cifar10.load() causes AttributeError. Changing to cifar10.load_data() fixes it.

3. Final Answer:

   Error: AttributeError because method is load_data(), fix by using cifar10.load_data() -> Option C

4. Quick Check:

   CIFAR-10 load method = load_data() [OK]

Hint: Use load_data() method to load datasets in keras.datasets [OK]

Common Mistakes:

• Using load() instead of load_data()
• Assuming cifar10 is not in keras.datasets
• Ignoring error message details

5. You want to compare two image classification models fairly. Which benchmark dataset should you choose and why?

hard

A. CIFAR-10 standard labeled image dataset for fair comparison

B. Unlabeled dataset for unsupervised learning

C. Small random dataset without standard labels

D. Single-class dataset to simplify training

Solution

1. Step 1: Understand the need for fair comparison

   Fair comparison requires a standard benchmark dataset with known labels and wide acceptance.

2. Step 2: Evaluate options for benchmark suitability

   CIFAR-10 is a popular benchmark with labeled images, suitable for comparing image classifiers fairly.

3. Final Answer:

   CIFAR-10 standard labeled image dataset for fair comparison -> Option A

4. Quick Check:

   Standard labeled dataset = fair model comparison [OK]

Hint: Choose standard labeled datasets for fair model comparison [OK]

Common Mistakes:

• Using unlabeled or small random datasets for comparison
• Choosing datasets with only one class
• Ignoring the need for standard benchmarks