Dataset from files in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a dataset from a CSV file using TensorFlow.

TensorFlow

dataset = tf.data.experimental.make_csv_dataset('data.csv', batch_size=[1])

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

A1

B10

C100

D32

Attempts:

3 left

💡 Hint

Common Mistakes

Using batch size 1 can be very slow.

Using too large batch size can cause memory errors.

The batch size controls how many samples are loaded at once. 32 is a common batch size.

2fill in blank

medium

Complete the code to shuffle the dataset with a buffer size of 1000.

TensorFlow

dataset = dataset.shuffle(buffer_size=[1])

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

A1000

B100

C10

D5000

Attempts:

3 left

💡 Hint

Common Mistakes

Using too small buffer size results in poor shuffling.

Using too large buffer size may use too much memory.

Shuffling with a buffer size of 1000 ensures good mixing of data.

3fill in blank

hard

Fix the error in the code to map a function that normalizes features.

TensorFlow

def normalize(features, label):
    features = tf.cast(features, tf.float32) / [1]
    return features, label

dataset = dataset.map(normalize)

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

A255.0

B1.0

C100.0

D0.255

Attempts:

3 left

💡 Hint

Common Mistakes

Dividing by 1.0 does not change values.

Dividing by 0.255 is incorrect scaling.

Dividing by 255.0 normalizes pixel values from 0-255 to 0-1.

4fill in blank

hard

Fill both blanks to batch the dataset and repeat it indefinitely.

TensorFlow

dataset = dataset.[1](batch_size=32).[2]()

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Abatch

Bshuffle

Crepeat

Dmap

Attempts:

3 left

💡 Hint

Common Mistakes

Calling repeat before batch changes the dataset shape.

Using shuffle instead of batch changes data order but not batching.

First batch the dataset, then repeat it indefinitely for training.

5fill in blank

hard

Fill all three blanks to create a dataset from text files, shuffle, and batch it.

TensorFlow

files = tf.data.Dataset.list_files([1])
dataset = files.interleave(tf.data.TextLineDataset, cycle_length=4)
dataset = dataset.[2](buffer_size=1000).[3](batch_size=64)

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

A'data/*.txt'

Bshuffle

Cbatch

D'data.csv'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a single file name instead of a pattern.

Batching before shuffling reduces randomness.

Use list_files with a pattern to find text files, then shuffle and batch the dataset.

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