Dataset from files in TensorFlow

Introduction

We use datasets from files to easily load and work with data stored on your computer. This helps us train machine learning models with real data.

You have images saved in folders and want to train a model to recognize them.

You want to read text data from files to analyze or build a language model.

You have CSV files with tabular data for prediction tasks.

You want to load large datasets without loading everything into memory at once.

You want to preprocess data as you load it for efficient training.

Syntax

TensorFlow

tf.data.Dataset.from_tensor_slices(filenames)

# or for images
image_dataset = tf.keras.utils.image_dataset_from_directory(directory_path, batch_size=32, image_size=(256, 256))

from_tensor_slices creates a dataset from a list of file paths.

image_dataset_from_directory loads images from folders and labels them automatically.

Examples

This creates a dataset from a list of text file names and prints each file name.

TensorFlow

import tensorflow as tf

filenames = ["file1.txt", "file2.txt"]
dataset = tf.data.Dataset.from_tensor_slices(filenames)
for file in dataset:
    print(file.numpy())

This loads images from a folder called 'images', resizes them to 128x128, and prints the shape of one batch and its labels.

TensorFlow

import tensorflow as tf

image_dataset = tf.keras.utils.image_dataset_from_directory(
    "./images",
    batch_size=16,
    image_size=(128, 128)
)

for images, labels in image_dataset.take(1):
    print(images.shape, labels.numpy())

Sample Model

This program creates two text files, loads their paths into a TensorFlow dataset, reads the file contents, and prints them.

TensorFlow

import tensorflow as tf
import os

# Create example text files
os.makedirs('data', exist_ok=True)
with open('data/file1.txt', 'w') as f:
    f.write('Hello TensorFlow')
with open('data/file2.txt', 'w') as f:
    f.write('Dataset from files')

# List of file paths
filenames = ["data/file1.txt", "data/file2.txt"]

# Create dataset from file names
file_dataset = tf.data.Dataset.from_tensor_slices(filenames)

# Function to read file content
@tf.function
def read_file(filename):
    text = tf.io.read_file(filename)
    return text

# Map function to dataset
text_dataset = file_dataset.map(read_file)

# Print contents
for text in text_dataset:
    print(text.numpy().decode('utf-8'))

Important Notes

Use tf.io.read_file to read file contents inside the dataset pipeline.

Mapping functions lets you preprocess data as it loads.

Datasets help handle large data efficiently without loading all at once.

Summary

Datasets from files let you load data stored on disk easily.

You can create datasets from file paths or directly from image folders.

Use mapping to read and preprocess file contents during loading.

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