String tensors in Tensorflow

TensorFlow is a comprehensive open-source library for data science, it offers various data types for handling complex operations. The tf.string data type is used to represent string values. Unlike numeric data types that have a fixed size, strings are variable-length and can contain sequences of characters of any length.

What is tf.string?

TensorFlow offers a wide range of functionalities for data manipulation and processing. One such essential feature is tf.string, which enables handling string data efficiently within TensorFlow operations and models. In this article, we will learn about the tf.string, exploring its operations, encoding/decoding techniques, comparison methods, real-world applications, etc.TensorFlow’s tf.string module is designed to handle string data seamlessly within TensorFlow operations and models. String tensors are crucial for various tasks in machine learning, such as natural language processing (NLP), text classification, sentiment analysis, and more.

How to create string tensors ?

Here’s an example of how to create string tensors in TensorFlow:

Python

import tensorflow as tf

# Create a scalar string tensor
scalar_string_tensor = tf.constant("Hello, TensorFlow!")
print(scalar_string_tensor)

# Create a vector of strings tensor
vector_string_tensor = tf.constant(["Hello", "TensorFlow", "World"])
print(vector_string_tensor)

# Create a 2D matrix of strings tensor
matrix_string_tensor = tf.constant([["Hello", "World"], ["TensorFlow", "Rocks!"]])
print(matrix_string_tensor)

Output:

tf.Tensor(b'Hello, TensorFlow!', shape=(), dtype=string)
tf.Tensor([b'Hello' b'TensorFlow' b'World'], shape=(3,), dtype=string)
tf.Tensor([[b'Hello' b'World']
 [b'TensorFlow' b'Rocks!']], shape=(2, 2), dtype=string)

The b prefix indicates that the strings are byte literals. If you need to work with Unicode strings, TensorFlow will encode them as UTF-8 by default. For more complex manipulations of string tensors, you can use the tf.strings module which provides various string operations.

What Operations can be performed by String Tensor?

The tf.strings module in TensorFlow provides a set of string operations that can be used on tf.string tensors. It support many operations, including concatenation, splitting, padding, and indexing. Let’s explore these operations with code examples:

Concatenation

We create two string constants using TensorFlow, join them together with a space separator, and then prints the result as a numpy array.

Python

str1 = tf.constant("Hello")
str2 = tf.constant("World")
result = tf.strings.join([str1, str2], separator=" ")
print(result.numpy()) 

Output:

b'Hello World'

Splitting

1. sentence = tf.constant("Welcome to TensorFlow"): Creates a TensorFlow constant containing the sentence “Welcome to TensorFlow”.
2. words = tf.strings.split(sentence): Splits the sentence into words. This function splits the input string(s) into substrings based on the provided delimiter (default is whitespace). It returns a RaggedTensor containing the split substrings.
3. print(words): Prints the RaggedTensor object. The RaggedTensor is a TensorFlow data structure that represents a tensor with non-uniform shape. In this case, it represents a list of words.

Python

sentence = tf.constant("Welcome to TensorFlow")
chars = tf.strings.unicode_split(sentence, "UTF-8")
print(chars[0])

Output:

tf.Tensor(b'W', shape=(), dtype=string)

Indexing

1. sentence = tf.constant("Welcome to TensorFlow"): Creates a TensorFlow constant containing the sentence “Welcome to TensorFlow”.
2. char = tf.strings.unicode_split(sentence, "UTF-8"): Splits the sentence into individual characters, treating the input as UTF-8 encoded. This function returns a RaggedTensor containing the split characters.
3. print(char[0]): Prints the first element of the char RaggedTensor, which corresponds to the first character of the sentence.

Python

char = tf.strings.unicode_split(sentence, "UTF-8")
print(char[0])

Output:

<tf.Tensor: shape=(), dtype=string, numpy=b'W'>

Encoding and Decoding of String Tensor

Encoding and decoding operations are crucial for handling string data effectively. TensorFlow provides functions for encoding and decoding string tensors using various formats like UTF-8.

Encoding

The code is using TensorFlow’s tf.strings.unicode_encode function to encode a Unicode string char into UTF-8 encoding.

Python

char = tf.ragged.constant([22, 600])
encoded_str = tf.strings.unicode_encode(char, "UTF-8")
print(encoded_str)

Output:

<tf.Tensor: shape=(), dtype=string, numpy=b'Welcome to TensorFlow'>

Decoding

The code decodes a UTF-8 encoded string encoded_str back to Unicode using TensorFlow’s tf.strings.unicode_decode function.

Python

decoded_str = tf.strings.unicode_decode(encoded_str, "UTF-8")
print(decoded_str)

Output:

tf.Tensor([ 22 600], shape=(2,), dtype=int32)

How String Tensor can be used for Comparison and Matching?

String tensors can be compared for equality, similarity, or matched using regular expressions with tf.strings functions like tf.strings.regex_match.

Comparison

The code compares two strings str1 and str2 using TensorFlow’s tf.strings.compare function to check if they are equal.

Python

str1 = tf.constant("Hello")
str2 = tf.constant("World")
print(tf.equal(str1, str2))

Output:

tf.Tensor(False, shape=(), dtype=bool)

Pattern Matching

Python

pattern = tf.constant("Ten")
sentence = tf.constant("Ten")
print(tf.strings.regex_full_match(sentence, pattern))

Output:

tf.Tensor(True, shape=(), dtype=bool)

Working with Batched String Tensors

Efficiently handling batched string tensors is essential in many machine learning tasks. TensorFlow offers operations for batching and unbatching string tensors.

Batching

• The code splits a batch of sentences into words using TensorFlow’s tf.strings.split function.

Python

batched_sentences = tf.constant(["TensorFlow is awesome", "Machine learning is fun"])
words = tf.strings.split(batched_sentences)
print(words)  

Output:

<tf.RaggedTensor [[[b'TensorFlow', b'is', b'awesome'], [b'Machine', b'learning', b'is', b'fun']]]>

Unbatching

• The code joins the words in each sentence back into sentences using TensorFlow’s tf.strings.join function.

Python

unbatched_sentences = tf.strings.join(words[0], separator="")
print(unbatched_sentences.numpy())

Output:

b'TensorFlowisawesome'

String Tensor Preprocessing in TensorFlow Models

• Preprocessing string data is crucial before feeding it into TensorFlow models.
• Utilize tf.strings functions like tf.strings.lower, tf.strings.regex_replace, etc., for preprocessing tasks.

Preprocessing

• The code converts the text to lowercase using TensorFlow’s tf.strings.lower function.

Python

text = tf.constant("Hello, TensorFlow!")
processed_text = tf.strings.lower(text)
print(processed_text)

Output:

tf.Tensor(b'hello, tensorflow!', shape=(), dtype=string)

Handling Missing Values in String Tensors

Strategies like using default values or special tokens are essential for handling missing or empty string values in TensorFlow.

• The code replaces empty strings in a tensor str_with_missing with the string “UNKNOWN” using TensorFlow’s tf.strings.replace function.

Python

str_with_missing = tf.constant("Hello Tensorflow <br /><b>contains string</b>")
str_with_default = tf.strings.regex_replace(str_with_missing, "<[^>]+>", "")
print(str_with_default)

Output:

tf.Tensor(b'Hello Tensorflow contains string', shape=(), dtype=string)

Conclusion

In conclusion, tf.string in TensorFlow is a powerful tool for handling string data, offering a wide range of operations for efficient processing and manipulation. By mastering these operations, developers can effectively work with string tensors in their TensorFlow projects, especially in NLP and text-related tasks. Experimenting with different string tensor operations has further enhanced our understanding and proficiency in TensorFlow development. In this article we learned a concise overview of the tf.String data type in TensorFlow, demonstrating its creation, manipulation, and benefits in handling textual data and so on.