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Deep CNN in Python using TensorFlow & Keras for Face Mask Detection

March 31, 2022

We implement deep CNN in Python using TensorFlow and Keras libraries. TensorFlow is an open-source platform that JetBrains created for machine learning.

It has properties such as flexible libraries, community resources, and an ecosystem of tools. Due to these properties, researchers can develop state-of-the-art machine learning.

Keras, a programming interface, is a Python program capable of running on TensorFlow and a machine learning platform. It is used for training neural networks. The development of the program was made to improve experiment speed.

This tutorial will discuss how to use TensorFlow (TF) and Keras (K) in Python to implement deep CNN. We will also look at where you can get the dataset for your project.

This tutorial will discuss how to implement deep CNN in Python to classify images. This example will classify faces into two, i.e. ‘withmask’ and ‘withoutmask’. The goal here is to detect face masks in offline mode.


To follow along with this tutorial, you will need:

  • To be familiar with the Python programming language.
  • To have Python installed on your computer. You can download it from here.
  • To install anaconda.
  • To install Spyder IDE. We can execute pip install spyder in the terminal to install it.

Proposed scheme for deep CNN

Proposed scheme

Deep CNN scheme

The above image is the generalized scheme of the deep CNN. Here, the input image is first convolved on various filters, and the output goes to the Rectified Linear Unit(ReLu).

The ReLu is where the negative values are omitted. Next, the positive output values are passed through max-pooling to get the maximum value. This process is repeated for a similar layer combination. It is known as feature detection.

The fully connected layer is used for classification. In the end, we have the softmax layer to get the confidence category-wise as the output.

How to implement deep CNN in Python

Deep CNN is implemented in Python using TensorFlow and Keras libraries. Tensorflow can run on CPU’s, GPU’s, and TPU systems.

Keras provides the necessary libraries for developing machine learning. It also helps engineers take advantage of the scaling ability of TensorFlow.

For Python code development, spyder IDE is used under anaconda package manager. TensorFlow does not come with an anaconda. Therefore, we are required to install it ourselves.

To install TensorFlow (TF 2.3), open the anaconda prompt and execute the command below:

pip install tensorflow==2.3

Follow the instructions and wait for the installation to complete. This command also downloads all the dependencies. The version of TensorFlow we installed works on python 3.8.

Image dataset

The image dataset we are using here is available in kaggle.

User interface

Kaggle website interface

We download the dataset, but we only use the train folder. We then move this train folder to the current directory for use.

The train folder includes two sub-folders, withmask and withoutmask. These images are coloured images of the .png format. They are also of different sizes. The total images are 10,000 therefore each folder has 5,000 images.

Python code for deep CNN

We first import some libraries and packages as shown below:

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import pathlib
from tensorflow import keras
import tensorflow.keras import layers
from tensorflow.keras.models import Sequential
import tkinter as tk
from tkinter import filedialog

Matplotlib.pyplot is a library that enables plotting. This means it contains the plot() function.

The numpy package is for numerical computations. Tensorflow and keras are used to implement the deep CNN.

Tkinter and filedialog are used to open the system dialogue. They enable you to select a single image from different folders to test.

We need to define our database. This is done by defining its directory as shown below:

data_dir = "/Documents/MATLAB/MATLAB/Articles/database/Train"
data_dir = pathlib.Path(data_dir)

With the help of the path() function, which is a property of pathlib, we can read the data directory. Since our dataset contains images of different sizes, we resize them to a uniform size. To do this, we specify the image height and width.

For our case, we use a size of 64x64 pixels as shown below:

batch_size = 16
img_height = 64
img_width = 64

Next, we read the images in the database and resize them. We also split the images into two, for example, training and validation.

# Reading Training images from the directory
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir, validation_split = 0.2, subset = "training", seed = 123,
    image_size = (img_height, img_width), batch_size = batch_size)

In the program above, we are reading the image_dataset_from_directory() function. This function is a property of keras. The validation_split is used to show the splitting ratio.

This means the image dataset is split into two. The ratio for training is 0.8, and that of testing and validation is 0.2. If you perform this calculation, a total of 8000 images is used for training, and the other 2000 is for testing validation.

All the images will be under training, and the seed is 123. Seed is the reshuffling format. This is because the images are picked randomly. The image size remains to be the resized dimensions.

In this similar way, we read the validation images from the directory using the image_dataset_from_directory() function. All the images will be under the name validation.

We do this using the code below:

# Reading validation images from the directory
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir, validation_split = 0.2, subset = "validation", seed = 123,
    image_size = (img_height, img_width), batch_size = batch_size)

Define the classes since there are two folders, withmask and withoutmask. The command above finds the label for the folder names.

The command below prints the folder names as class_labels:

class_names = train_ds.class_names

This is the automatic way of finding the name of the labels. Let’s add a code for memory optimization and speed up execution:

#Memory optimization and speed up execution
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size = AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size = AUTOTUNE)

The program above is used because the dataset is extensive. Here, we are using two functions i.e cache() and prefetch(). Cache() keeps the image in the RAM after reading from the disk.

This helps with a speedy execution. The prefetch() helps systems execute the model and fetches data from the library. This means it helps in parallel execution.

Define the number of classes which is two, i.e. withmask and withoutmask.

num_class = 2

Now, let’s define the CNN and the number of epochs:

#Definning CNN
model = Sequential([
    layers.experimental.preprocessing.Rescaling(1./255, input_shape = (img_height, img_width, 3)),
    layers.Conv2D(16, 3, padding = 'same', activation = 'relu'),
    layers.Conv2D(32, 3, padding='same', activation='relu'),
    layers.Conv2D(64, 3, padding='same', activation='relu'),
    layers.Dense(64, activation = 'relu'),

noepochs = 7

Here, we define our CNN model, which is sequential. In this case, we use the sequential() function. This function rescales the image and defines the CNN layers.

The images are rescaled in the range 0-1. This is because our datasets are RGB images and they have a pixel value of the range 0-255. Therefore it is not settable for the deep CNN model. This is the reason for rescaling.

Rescaling is done by dividing all the pixel values by 255. The convolution layer defined is conv2D. It has 16 filters of the size 3x3, the padding used is same, and the activation is relu.

The next layer we have defined is maxPooling. This layer only considers the maximum values and ignores others. Then we continue defining conv2D but with more filters.

To avoid overfitting, we use the dropout() function. We then define the fully connected layer (layer.Dense) and the number of epochs. The higher the number of epochs, the better the results.

Define the training parameters using the code below:

model.compile(optimizer = 'adam', loss = tf.keras.SparseCategoricalCrossentropy(from_logits = True),
                  metrics = ['accuracy'])
mymodel =, validation_data = val_ds, epochs = noepochs) #training the model

The training parameter that we defined is the optimizer that our model should use. The fit() function, which is the model property, trains our model. The function takes the training dataset train_ds, validation data , and the number of epochs as the arguments.

After the training, we should get some data such as model accuracy, validation accuracy, training loss, and validation loss using the code below:

acc = mymodel.history['accuracy']
val_acc = mymodel.history['val_accuracy']
loss = mymodel.history['loss']
val_loss = mymodel.history['val_loss']
epochs_range = range(noepochs)

These values are used to plot the accuracy and loss curves. This gives us an idea for the training procedures.

Let us plot these values for visualization using the code below:

plt.figure(figsize=(15, 15)) #creates figure for the plot
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label = 'Training Accuracy')
plt.plot(epochs_range, val_acc, label = 'Validation Accuracy')
plt.legend(loc = 'lower right')
plt.title('Training and validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label = 'Training loss')
plt.plot(epochs_range, val_loss, label = 'Validation loss')
plt.legend(loc = 'Upper right')
plt.title('Training and validation loss')

The first plot is for the training accuracy, and the second plot is for training losses. Therefore, if we run this training process, we get the training process and plots as shown below:

Training process

Training process

Plots of the training

Plots for the output

The last step is to create a function for testing single images.

The function is:

# Function to test single image
def recogout():
    root = tk.Tk()
    img_path = filedialog.askopenfilename()
    img = keras.preprocessing.image.load_img(img_path, target_size = (img_height, img_width))
    img_array = keras.preprocessing.image.img_to_array(img)
    img_array = tf.expand_dims(img_array, 0)
    predictions = model.predict(img_array)
    score = tf.nn.softmax(predictions[0])

    print("This image most likely belongs to {} with a {:.2f} percent confidence."
          .format(class_names[np.argmax(score)], 100*np.max(score)))

The tkinter library tk.Tk and root.withdraw() opens a dialog. It allows you to select your input images from any folder. The path of this file is read using filedialog(). The image is then loaded with a predefined dimension, i.e., the image is resized using the load_image() function.

The image is further preprocessed using the Keras property keras to make it compatible. We then use the predict() function and the final image as the arguments to get the predictions.

The softmax layer is applied to this prediction using the softmax() function and the confidence score stored in the score variable. We then print the output using the print() function. It helps to tell whether the input image belongs to the first or second category.

We execute the recogout() function in the syder terminal for testing single images.

Let us execute our function to see how it works:

  • Open the spyder terminal and run recogout() in the command window. The system dialogue opens up when you run this command and select your input image.
  • After the selection, the class and the confidence score is displayed in the terminal as shown below:

Image selected

Image selected


Prediction and the confidence score

Let’s now try giving an image without a mask as the input for the test.

Image selected

Image selected


Prediction and the confidence score


Implementation of CNN in Python is done using TensorFlow and Keras. These are packages that contain all the functions for deep learning.

While using CNN, you have to keep track of all the process that is going on. It helps improve the output.

Also, preprocessing of the dataset is required. It is for compatibility and taking control of the memory.

Happy coding!

Peer Review Contributions by: Dawe Daniel