Dataset from files in TensorFlow - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of loading text files with tf.data

What is the output of this code snippet that loads text lines from two files using TensorFlow's tf.data API?

TensorFlow

import tensorflow as tf

# Create two small text files
with open('file1.txt', 'w') as f:
    f.write('apple\nbanana')
with open('file2.txt', 'w') as f:
    f.write('cherry\ndate')

# Load dataset from files
files = ['file1.txt', 'file2.txt']
dataset = tf.data.TextLineDataset(files)

# Collect all lines into a list
lines = list(dataset.as_numpy_iterator())
print(lines)

A [b'file1.txt', b'file2.txt']

B SyntaxError

C [b'apple banana', b'cherry date']

D [b'apple', b'banana', b'cherry', b'date']

💡 Hint

tf.data.TextLineDataset reads each line from all files in order.

The TextLineDataset reads each line from the files in the order they are listed. So it outputs each line as a byte string in sequence.

❓ data_output

intermediate

Number of elements in a dataset from multiple CSV files

Given three CSV files each with 2 rows, what is the number of elements in the dataset created by tf.data.experimental.CsvDataset loading all files?

TensorFlow

import tensorflow as tf

# Assume files: data1.csv, data2.csv, data3.csv each with 2 rows
files = ['data1.csv', 'data2.csv', 'data3.csv']
dataset = tf.data.experimental.CsvDataset(files, [tf.float32, tf.int32])

count = 0
for _ in dataset:
    count += 1
print(count)

A 6

B 3

C 2

D 9

💡 Hint

Each file has 2 rows, and dataset reads all rows from all files.

CsvDataset reads all rows from all files. 3 files * 2 rows each = 6 elements.

🔧 Debug

advanced

Error raised when loading non-existent files

What error will this code raise when trying to create a TextLineDataset from a file that does not exist?

TensorFlow

import tensorflow as tf

files = ['missing_file.txt']
dataset = tf.data.TextLineDataset(files)

for line in dataset:
    print(line.numpy())

A No error, prints nothing

B tf.errors.NotFoundError

C ValueError

D FileNotFoundError

💡 Hint

TensorFlow raises its own error type for missing files.

TensorFlow raises tf.errors.NotFoundError when a file is missing during dataset creation or iteration.

🚀 Application

advanced

Creating a dataset from image files with labels

You have a folder with images and a CSV file mapping image filenames to labels. Which code snippet correctly creates a tf.data.Dataset yielding (image_tensor, label) pairs?

A Use tf.data.TextLineDataset on CSV, parse lines, then map to load images with tf.io.read_file and tf.image.decode_jpeg

B Use tf.data.Dataset.list_files on images, then map to load images and assign labels manually

C Use tf.data.experimental.CsvDataset on CSV, then map to load images and parse labels

D Use tf.data.Dataset.from_tensor_slices with image paths and labels loaded into memory

💡 Hint

CsvDataset is designed to read CSV files with typed columns.

tf.data.experimental.CsvDataset reads CSV files with typed columns, allowing you to get filenames and labels. Then mapping loads images and pairs them with labels.

🧠 Conceptual

expert

Effect of interleave on dataset from multiple files

What is the main difference between tf.data.TextLineDataset(files) and tf.data.Dataset.from_tensor_slices(files).interleave(tf.data.TextLineDataset, cycle_length=2) when reading multiple text files?

A TextLineDataset reads files sequentially; interleave reads lines from files in parallel, mixing lines

B TextLineDataset reads files in parallel; interleave reads files sequentially

C Both produce the same output order

D TextLineDataset reads only first file; interleave reads all files

💡 Hint

Interleave cycles through datasets to mix their elements.

TextLineDataset reads all lines from the first file, then second, etc. Interleave cycles through files, reading lines from each in turn, mixing lines from files.

Practice

1. What is the main purpose of using tf.data.Dataset.from_tensor_slices() with file paths in TensorFlow?

easy

A. To convert tensors into image files

B. To directly read image data from files into memory

C. To save datasets to disk as files

D. To create a dataset that holds file paths which can be read later

Solution

1. Step 1: Understand the function purpose

   tf.data.Dataset.from_tensor_slices() creates a dataset from a tensor, often a list of file paths, not the file contents themselves.

2. Step 2: Clarify dataset content

   The dataset holds file paths as strings, which can be mapped later to read actual file data.

3. Final Answer:

   To create a dataset that holds file paths which can be read later -> Option D

4. Quick Check:

   from_tensor_slices(file_paths) = dataset of paths [OK]

Hint: Remember: from_tensor_slices holds paths, not file data [OK]

Common Mistakes:

• Thinking it reads file contents immediately
• Confusing dataset creation with saving files
• Assuming it converts tensors to images

2. Which of the following is the correct way to create a dataset from a list of image file paths in TensorFlow?

easy

A. dataset = tf.data.Dataset.from_tensor_slices(image_paths)

B. dataset = tf.data.Dataset.read_files(image_paths)

C. dataset = tf.data.Dataset.load(image_paths)

D. dataset = tf.data.Dataset.create(image_paths)

Solution

1. Step 1: Recall correct TensorFlow method

   The method to create a dataset from a list of tensors (like file paths) is from_tensor_slices().

2. Step 2: Verify options

   Methods like tf.data.Dataset.load(), tf.data.Dataset.read_files(), and tf.data.Dataset.create() are not valid TensorFlow dataset creation methods.

3. Final Answer:

   dataset = tf.data.Dataset.from_tensor_slices(image_paths) -> Option A

4. Quick Check:

   Correct method is from_tensor_slices [OK]

Hint: Use from_tensor_slices for lists of file paths [OK]

Common Mistakes:

• Using non-existent methods like read_files or load
• Confusing dataset creation with file reading
• Misspelling method names

3. Given the code below, what will be the output when iterating over the dataset?

import tensorflow as tf
image_paths = ["img1.jpg", "img2.jpg"]
dataset = tf.data.Dataset.from_tensor_slices(image_paths)
for item in dataset:
    print(item.numpy().decode())

medium

A. Error: decode() not found

B. [b'img1.jpg', b'img2.jpg']

C. img1.jpg\nimg2.jpg

D. Tensor objects printed

Solution

1. Step 1: Understand dataset content

   The dataset contains string tensors of file paths: b'img1.jpg', b'img2.jpg'.

2. Step 2: Decode bytes to string

   Calling item.numpy() returns bytes, and decode() converts bytes to normal strings.

3. Final Answer:

   img1.jpg\nimg2.jpg -> Option C

4. Quick Check:

   Decoded bytes = file names [OK]

Hint: Use .numpy().decode() to get string from tensor [OK]

Common Mistakes:

• Printing tensor directly without decoding
• Expecting list output instead of individual prints
• Confusing bytes and strings

4. Identify the error in the following code snippet that tries to read image files from paths:

import tensorflow as tf
image_paths = ["img1.jpg", "img2.jpg"]
dataset = tf.data.Dataset.from_tensor_slices(image_paths)
dataset = dataset.map(tf.io.read_file)
for img in dataset:
    print(img.numpy().shape)

medium

A. Cannot print shape of a scalar string tensor

B. tf.io.read_file is not a valid function

C. from_tensor_slices requires a tensor, not list

D. map() cannot be used on datasets

Solution

1. Step 1: Analyze dataset after map

   After mapping tf.io.read_file, each element is a scalar string tensor containing raw file bytes.

2. Step 2: Understand tensor shape

   img.numpy() returns Python bytes (raw file content), which has no .shape attribute. Printing img.numpy().shape raises AttributeError.

3. Final Answer:

   Cannot print shape of a scalar string tensor -> Option A

4. Quick Check:

   img.numpy() is bytes; no .shape [OK]

Hint: Raw file bytes are scalars; no shape attribute [OK]

Common Mistakes:

• Assuming read_file returns image tensor
• Thinking from_tensor_slices rejects lists
• Believing map() is invalid on datasets

5. You want to create a TensorFlow dataset from a folder of images, resize each image to 128x128, and batch them in groups of 16. Which code snippet correctly achieves this?

hard

A. dataset = tf.keras.utils.image_dataset_from_directory('images', image_size=(128,128), batch_size=16)

B. dataset = tf.data.Dataset.list_files('images/*').map(lambda x: tf.image.resize(tf.io.decode_image(tf.io.read_file(x)), (128,128))).batch(16)

C. dataset = tf.data.Dataset.from_tensor_slices('images').map(tf.io.read_file).batch(16)

D. dataset = tf.keras.preprocessing.image_dataset_from_directory('images', batch_size=128, image_size=(16,16))

Solution

1. Step 1: Understand dataset creation from folder

   dataset = tf.data.Dataset.list_files('images/*').map(lambda x: tf.image.resize(tf.io.decode_image(tf.io.read_file(x)), (128,128))).batch(16) uses list_files to get file paths, then maps reading, decoding, and resizing images correctly.

2. Step 2: Check batch and resize parameters

   Images are resized to (128,128) and batched in groups of 16 as required.

3. Final Answer:

   dataset = tf.data.Dataset.list_files('images/*').map(lambda x: tf.image.resize(tf.io.decode_image(tf.io.read_file(x)), (128,128))).batch(16) -> Option B

4. Quick Check:

   list_files + map + resize + batch = correct pipeline [OK]

Hint: Use list_files + map with decode and resize, then batch [OK]

Common Mistakes:

• Using wrong batch size or image size parameters
• Confusing keras and tf.data APIs
• Not decoding images before resizing