Importing Libraries and Dataset
- Pandas – This library helps to load the data frame in a 2D array format and has multiple functions to perform analysis tasks in one go.
- Numpy – Numpy arrays are very fast and can perform large computations in a very short time.
- Matplotlib – This library is used to draw visualizations.
- Sklearn – This module contains multiple libraries having pre-implemented functions to perform tasks from data preprocessing to model development and evaluation.
- OpenCV – This is an open-source library mainly focused on image processing and handling.
- Tensorflow – This is an open-source library that is used for Machine Learning and Artificial intelligence and provides a range of functions to achieve complex functionalities with single lines of code.
Python3
import numpy as np import pandas as pd import seaborn as sb import matplotlib.pyplot as plt import cv2 from glob import glob import tensorflow as tf from tensorflow import keras from keras import layers from tqdm.notebook import tqdm, trange from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder, StandardScaler import warnings warnings.filterwarnings('ignore') |
Now let’s create a data frame of the image path and the classes from which they belong. Creating a data frame helps us to analyze the distribution of the data across various classes.
Python3
images = glob('images/train/*/*.jpg') len(images) |
Output:
28821
Python3
df = pd.DataFrame({'image_path': images}) df.head() |
Output:
Python3
df['label'] = df['image_path'].str.split('/', expand=True)[2] df.head() |
Output:
Python3
df.groupby('label').count().plot.bar() plt.show() |
Output:
Bar Chart to visualize number of images in each class
Hidden Layer Perceptron in TensorFlow
In this article, we will learn about hidden layer perceptron. A hidden layer perceptron is nothing but a hi-fi terminology for a neural network with one or more hidden layers. The purpose which is being served by these hidden layers is that they help to learn complex and non-linear functions for a task.
Hidden Layer Perceptron in TensorFlow
The above image is the simplest representation of the hidden layer perceptron with a single hidden layer. Here we can see that the input for the final layer is the neurons of the hidden layers. So, in a hidden layer perceptron network input for the current layer is the output of the previous layer.
We will try to understand how one can implement a Hidden layer perceptron network using TensorFlow. Also, the data used for this purpose is the famous Facial Recognition dataset.