DEV Community

Cover image for Beginners guide to TensorFlow text classification using Python

Beginners guide to TensorFlow text classification using Python

kalebu profile image Jordan Kalebu ・6 min read

The original article can be found on

Hi guys,

In this article, you're going to learn about text classification using a popular Python framework for machine learning, Tensorflow in just a couple of lines of code.

what is text classification?

Text classification is a subpart of natural language processing that focuses on grouping a paragraph into predefined groups based on its content, for instance classifying categories of news whether its sports, business, music and etc

what will you learn?

In this tutorial, we learn in brief how to perform text classification using Tensorflow, you're going to learn text processing concepts such as word embedding and how to build a neural network with an embedding layer.

You will be learning all those concepts while by building a simple model to properly classify text as negative and positive reviews based on data we used to train it.

what you need to have?

For you to successfully follow through with this tutorial, you're supposed to have the following libraries python libraries installed on your machine.


There are two approaches that you can follow when it comes to installing the setup environment for doing machine learning together with data science-based projects.

  • Installing Anaconda
  • Installing independently using pip

Installing Anaconda

If it's your first time hearing about Anaconda, it is the toolkit that equips you to work with thousands of open-source packages and libraries. It saves the time for installing each library independently together with handling dependencies issues.

What you need to do is go to their official website at and then follow the guide to download and install it on your machine depending on the Operating system you're using.

Once you install it, it will install thousands of other packages for doing machine learning and data science tasks such as numpypandas, matplotlib, scikit-learnjupyter notebook, and many others

Almost here

Now once dependencies have been installed together with Anaconda its time to install the TensorFlow library, Anaconda comes with its package manager known as conda.

Now Let's use conda to install TensorFlow

conda create -n tf tensorflow

conda activate tf
Enter fullscreen mode Exit fullscreen mode

Installing independently using pip

If you love handling every piece of details of yourself, then you can also install all the required python libraries just by using pip just as shown below;

pip install tensorflow

pip install numpy

pip install matplotlib

pip install jupyter notebook
Enter fullscreen mode Exit fullscreen mode

Now once everything is installed let's start building our classification model


The TensorFlow that has been using while preparing this tutorial is TensorFlow 2.0 which comes with keras already integrated into it, therefore I recommend using it or a more updated version to avoid bugs.

Let's get started

For convenience we usually use a jupyter notebook in training our machine learning models therefore I would you to use it too since in this article I will be showing you individual chunks of code equivalent to a single cell in a jupyter notebook

Starting a jupyter notebook

To start a jupyter notebook it just simple and straight forward it's just you have to type jupyter notebook on your terminal and then it gonna automatically open a notebook on your default browser.

Importing all required libraries

import numpy as np

import tensorflow as tf

import matplotlib.pyplot as plt
Enter fullscreen mode Exit fullscreen mode
Create array of random Textual Data ( features ) & Labels

The array below acts as features for training our model consisting of 4 positive and 4 negative short sentences and their respective labels were by 1 for positive and 0 for negative

data_x = [

 'good',  'well done', 'nice', 'Excellent',

 'Bad', 'OOps I hate it deadly', 'embrassing', 'A piece of shit'


label_x = np.array([1,1,1,1, 0,0,0,0])
Enter fullscreen mode Exit fullscreen mode

Use one-hot encoding to convert textual feature to numerical

One hot encoding is a process by which categorical variables are converted into a form that could be provided to ML algorithms to do a better job in prediction.

Follow the below code to encode the above textual features into numerical values .

one_hot_x = [tf.keras.preprocessing.text.one_hot(d, 50) for d in data_x]


[[21], [9, 34], [24], [20], [28], [41, 26, 9, 17, 26], [36], [9, 41]]

Enter fullscreen mode Exit fullscreen mode

As we can see after using one-hot encoding to our textual data, it has resulted in an array of different sizes.

The array of textual data require the same length to be well fitted on Machine Learning Model. Therefore we have to process it again to form an array of Identical lengths.

Apply padding to features array & restrict its length to 4

you can edit or change individual array length by changing the maxlen parameter, the choice of value for maxlen depends on where most of the paragraph in your training data lies

padded_x = tf.keras.preprocessing.sequence.pad_sequences(one_hot_x, maxlen=4, padding = 'post')

Enter fullscreen mode Exit fullscreen mode

Output :

array([[21,  0,  0,  0],

 [ 9, 34,  0,  0], [24,  0,  0,  0], [20,  0,  0,  0],

 [28,  0,  0,  0], [26,  9, 17, 26], [36,  0,  0,  0],

 [ 9, 41,  0,  0]], dtype=int32)
Enter fullscreen mode Exit fullscreen mode

After we have already processed the training data now let's create our Sequential Model to fit our data.

Let's build a Sequential model for our classification

model = tf.keras.models.Sequential()
Enter fullscreen mode Exit fullscreen mode

Now Let's add an Embedding Layer to receive the processed textual feature

model.add(tf.keras.layers.Embedding(50, 8, input_length=4))
Enter fullscreen mode Exit fullscreen mode

Add Flatten layer to flatten the features array

Enter fullscreen mode Exit fullscreen mode

Finally, Let's add a dense layer with a sigmoid activation function to effectively learn the textual relationship

model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
Enter fullscreen mode Exit fullscreen mode

Compile the Model and Check it's summary Structure

model.compile(optimizer='adam', loss='binary_crossentropy', 

Enter fullscreen mode Exit fullscreen mode


Model: "sequential"


Layer (type)                 Output Shape              Param #


embedding (Embedding)        (None, 4, 8)              400


flatten (Flatten)            (None, 32)                0


dense (Dense)                (None, 1)                 33


Total params: 433

Trainable params: 433

Non-trainable params: 0

Enter fullscreen mode Exit fullscreen mode

Now Let's fit the Model with 1000 epochs & Visualizing the learning process

history =, label_x, epochs=1000, 
batch_size=2, verbose=0)


Enter fullscreen mode Exit fullscreen mode

Testing Model

Let's create a Simple function to predict new words using the model have just created, it won't be as smart since our data was really short

def predict(word):
    one_hot_word = [tf.keras.preprocessing.text.one_hot(word, 50)]
    pad_word = tf.keras.preprocessing.sequence.pad_sequences(one_hot_word, maxlen=4,  padding='post')
    result = model.predict(pad_word)
    if result[0][0]>0.1:
        print('you look positive')
        print('damn you\'re negative')
Enter fullscreen mode Exit fullscreen mode

Let's test calling predict method with different word parameters

>>>predict('this tutorial is cool')

you look positive

>>>predict('This tutorial is bad as me ')

damn you're negative
Enter fullscreen mode Exit fullscreen mode

Congratulations you have successfully trained Text classifier using TensorFlow to get the Jupyter notebook guide download here. Otherwise, in case of comment, suggestion, difficulties drop it on the comment box

I also recommend reading this


Editor guide
hb profile image
Henry Boisdequin

Thanks for the tutorial! I need to get better at ML and AI and this is a perfect starting point.

kalebu profile image
Jordan Kalebu Author

You're welcome @henry Boisdequin.

Yeah I found project-oriented learning more effective when it comes to ML/AI, so by solving pieces of problems using it thus how you get good at it