K-fold cross-validation in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the KFold class from scikit-learn.

TensorFlow

from sklearn.model_selection import [1]

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

AGridSearchCV

Btrain_test_split

Ccross_val_score

DKFold

Attempts:

3 left

💡 Hint

Common Mistakes

Using train_test_split instead of KFold

Importing cross_val_score instead of KFold

The KFold class is used to split data into K folds for cross-validation.

2fill in blank

medium

Complete the code to create a KFold object with 5 splits and shuffling enabled.

TensorFlow

kf = KFold(n_splits=[1], shuffle=True, random_state=42)

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

A5

B10

C1

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 split which disables cross-validation

Using too few splits like 3 which is less common

Setting n_splits=5 creates 5 folds for cross-validation.

3fill in blank

hard

Fix the error in the loop to correctly split data indices using KFold.

TensorFlow

for train_index, [1] in kf.split(X):

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

Aval_index

Btest_index

Csplit_index

Dindex

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'val_index' which is not returned by kf.split

Using a single variable instead of two

The kf.split() method returns train and test indices for each fold.

4fill in blank

hard

Fill both blanks to create training and testing sets from data arrays X and y.

TensorFlow

X_train, y_train = X[[1]], y[[1]]
X_test, y_test = X[[2]], y[[2]]

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

Atrain_index

Btest_index

Cval_index

Dsplit_index

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same index for both training and testing

Using undefined variables like val_index

Use train_index to select training data and test_index for testing data.

5fill in blank

hard

Fill all three blanks to compile accuracy scores for each fold using a TensorFlow model.

TensorFlow

accuracies = []
for train_idx, test_idx in kf.split(X):
    X_train, y_train = X[[1]], y[[1]]
    X_test, y_test = X[[2]], y[[2]]
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(10, activation='relu', input_shape=(X.shape[1],)),
        tf.keras.layers.Dense(1, activation='sigmoid')
    ])
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    model.fit(X_train, y_train, epochs=5, verbose=0)
    loss, acc = model.evaluate(X_test, y_test, verbose=0)
    accuracies.append(acc)
print(f"Average accuracy: {sum(accuracies)/len(accuracies):.2f}")

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

Atrain_idx

Btest_idx

Cval_idx

Dsplit_idx

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing train and test indices

Appending loss instead of accuracy

Use train_idx for training data and test_idx for testing data. The accuracy is appended from the test evaluation.

Practice

1. What is the main purpose of using K-fold cross-validation in TensorFlow model training?

easy

A. To increase the size of the training dataset

B. To evaluate model performance on different parts of the data

C. To speed up the training process

D. To reduce the number of model parameters

Solution

1. Step 1: Understand K-fold cross-validation concept

   K-fold cross-validation splits the dataset into K parts and trains the model K times, each time using a different part as test data.

2. Step 2: Identify the purpose of this process

   This method helps evaluate how well the model performs on unseen data by testing it on different subsets.

3. Final Answer:

   To evaluate model performance on different parts of the data -> Option B

4. Quick Check:

   Purpose = Evaluate model on different data parts [OK]

Hint: K-fold tests model on all data parts for better evaluation [OK]

Common Mistakes:

• Thinking it increases training data size
• Believing it speeds up training
• Confusing it with parameter tuning

2. Which of the following is the correct way to split data into 5 folds using TensorFlow's tf.data API for K-fold cross-validation?

easy

A. Use tf.data.Dataset.shard(num_shards=5, index=i) inside a loop over i from 0 to 4

B. Use tf.data.Dataset.batch(5) to create 5 folds

C. Use tf.data.Dataset.repeat(5) to repeat data 5 times

D. Use tf.data.Dataset.shuffle(5) to shuffle into 5 folds

Solution

1. Step 1: Recall TensorFlow data splitting method

   TensorFlow's shard method splits a dataset into equal parts by specifying total shards and the index of the shard.

2. Step 2: Match method to K-fold splitting

   For 5 folds, use num_shards=5 and loop over index from 0 to 4 to get each fold.

3. Final Answer:

   Use tf.data.Dataset.shard(num_shards=5, index=i) inside a loop over i from 0 to 4 -> Option A

4. Quick Check:

   Sharding splits data into folds correctly [OK]

Hint: Use shard with num_shards = K for K-fold splits [OK]

Common Mistakes:

• Using batch instead of shard for splitting
• Repeating data instead of splitting
• Shuffling does not create folds

3. Consider this code snippet for 3-fold cross-validation in TensorFlow:

for i in range(3):
    test_data = dataset.shard(num_shards=3, index=i)
    train_data = dataset.shard(num_shards=3, index=(i+1) % 3)
    # model training code here
    print(f'Fold {i} train size:', len(list(train_data)))
    print(f'Fold {i} test size:', len(list(test_data)))

What will be the output sizes for train and test data in each fold if the original dataset has 9 samples?

medium

A. Train size: 9, Test size: 0 for all folds

B. Train size: 6, Test size: 3 for all folds

C. Train size: 3, Test size: 3 for all folds

D. Train size: 3, Test size: 6 for all folds

Solution

1. Step 1: Understand shard usage in code

   Each fold uses dataset.shard(num_shards=3, index=i) for test data and dataset.shard(num_shards=3, index=(i+1) % 3) for train data. This means train and test data are both single shards, not complementary sets.

2. Step 2: Calculate sizes based on shards

   Each shard has 3 samples (9/3). So test_data has 3 samples. But train_data is only one shard (3 samples), not the other two shards combined (6 samples). So train and test overlap and total is 6 samples, missing 3 samples.

3. Step 3: Check code behavior

   Since train_data and test_data are both shards, the rest of the data is ignored. The code prints sizes of 3 for train and 3 for test, but the rest 3 samples are missing from both.

4. Final Answer:

   Train size: 3, Test size: 3 for all folds -> Option C

5. Quick Check:

   Shard selects one part only, so train = 3, test = 3 [OK]

Hint: Shard picks one part; train must combine other shards [OK]

Common Mistakes:

• Assuming train is all except test shard
• Counting total dataset size for train
• Ignoring shard overlap

4. You wrote this code for 4-fold cross-validation but get an error:

for i in range(4):
    test_data = dataset.shard(num_shards=4, index=i)
    train_data = dataset.shard(num_shards=4, index!=i)
    # train model here

What is the error and how to fix it?

medium

A. Syntax error in index!=i; use a loop to combine other shards

B. Wrong number of shards; should be 5 instead of 4

C. Missing shuffle before sharding causes error

D. Using shard twice causes conflict; use split instead

Solution

1. Step 1: Identify syntax error in shard index

   The expression index!=i is invalid syntax for the index parameter, which expects an integer, not a boolean expression.

2. Step 2: Correct approach to get train data

   To get train data (all shards except test shard), you must combine shards with indices other than i using a loop or filtering, not a single shard call with index!=i.

3. Final Answer:

   Syntax error in index!=i; use a loop to combine other shards -> Option A

4. Quick Check:

   Shard index must be integer, not condition [OK]

Hint: Shard index needs integer; combine others in loop [OK]

Common Mistakes:

• Using boolean condition as shard index
• Assuming shard auto-excludes test fold
• Not combining shards for train data

5. You want to implement 5-fold cross-validation in TensorFlow to train a neural network on a dataset with 1000 samples. Which approach correctly creates train and test datasets for each fold to avoid data leakage?

hard

A. For each fold i, use test_data = dataset.shard(5, i) and train_data = dataset.filter(lambda x: x not in test_data)

B. For each fold i, use test_data = dataset.shard(5, i) and train_data = dataset.shard(5, (i+1) % 5)

C. For each fold i, use dataset.shuffle(1000) and split 80% train, 20% test randomly

D. For each fold i, use test_data = dataset.shard(5, i) and combine all other shards except i for train_data

Solution

1. Step 1: Understand correct train-test split in K-fold

   Test data is one shard. Train data must be all other shards combined to avoid overlap and data leakage.

2. Step 2: Evaluate options

   For each fold i, use test_data = dataset.shard(5, i) and train_data = dataset.filter(lambda x: x not in test_data) tries to filter dataset by membership, which is not supported directly in TensorFlow datasets. For each fold i, use test_data = dataset.shard(5, i) and train_data = dataset.shard(5, (i+1) % 5) uses only one shard for train, missing other data. For each fold i, use dataset.shuffle(1000) and split 80% train, 20% test randomly shuffles and splits randomly, not K-fold. For each fold i, use test_data = dataset.shard(5, i) and combine all other shards except i for train_data correctly combines all shards except the test shard for training.

3. Final Answer:

   For each fold i, use test_data = dataset.shard(5, i) and combine all other shards except i for train_data -> Option D

4. Quick Check:

   Train = all shards except test shard [OK]

Hint: Train = all shards except test shard combined [OK]

Common Mistakes:

• Using only one shard for train data
• Filtering datasets by membership incorrectly
• Random split instead of K-fold