Four Keras Techniques for Preprocessing Text for Deep Learning

Deep learning models cannot consume raw text directly, so preprocessing is required. Keras offers several built‑in functions to transform text into formats suitable for neural networks.

Keras text_to_word_sequence splits a string on spaces, lower‑cases characters, and removes a default set of punctuation symbols (!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n). Example:

from keras.preprocessing.text import text_to_word_sequence
text = 'Text to Word Sequence Function works really well'
tokens = text_to_word_sequence(text)
print(tokens)

Output:

['text', 'to', 'word', 'sequence', 'function', 'works', 'really', 'well']

Keras hashing_trick maps words to integer indices in a fixed‑size hash space. Example:

from keras.preprocessing.text import text_to_word_sequence
from keras.preprocessing.text import hashing_trick
text = 'An example for keras hashing trick function'
tokens = text_to_word_sequence(text)
length = len(tokens)
final_text = hashing_trick(text, length)
print(final_text)

Output (indices may collide): [4, 5, 2, 1, 5, 2, 4] Collisions occur because the underlying Python hash function is used; a custom hash_function can be supplied.

Keras one_hot encodes a text string as integer indices given a vocabulary size. Example:

from keras.preprocessing.text import one_hot
from keras.preprocessing.text import text_to_word_sequence
text = 'One hot encoding in Keras'
tokens = text_to_word_sequence(text)
length = len(tokens)
one_hot = one_hot(text, length)
print(one_hot)

Output: [3, 4, 1, 3, 3] The function also uses the Python hash and the same default punctuation filter as text_to_word_sequence.

Keras Tokenizer class can fit on multiple documents and provides four useful attributes after fitting: word_counts (frequency of each word), word_docs (number of documents containing each word), word_index (mapping of words to indices), and document_count (total number of documents). Example:

from keras.preprocessing.text import Tokenizer
text = ['You are learning a lot', 'That is a good thing', 'This will help you a lot']
 tokenizer = Tokenizer()
 tokenizer.fit_on_texts(text)
 print(tokenizer.word_counts)
 print(tokenizer.word_docs)
 print(tokenizer.word_index)
 print(tokenizer.document_count)
 encoded_text = tokenizer.texts_to_matrix(text)
 print(encoded_text)

Sample output shows the word frequencies, document frequencies, index mapping, document count (3), and a dense matrix representation of the three documents.

In summary, the four Keras utilities demonstrated above enable developers to convert raw textual data into token lists, hashed indices, integer encodings, or matrix representations, facilitating its use as input for deep learning models.