DeepLearning

Mastering Backpropagation: A Comprehensive Guide for Neural Network

Install relevant libraries

!pip -q install numpy tensorflow keras

Step 1: Loading and Preprocessing the MNIST Dataset

Loading the Dataset

from keras.utils import to_categorical

from keras.datasets import mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

Exploratory Analysis

This section focuses on the visualization of some handwritten digits and the distribution of each label (from 0 to 9) in the training dataset

import matplotlib.pyplot as plt
import random

print("Training data")
print(f"- X = {train_images.shape}, y = {train_labels.shape}")
print(f"- Hols {train_images.shape[0]/70000* 100}% of the overall data")

print("\n")

print("Testing data")
print(f"- X = {test_images.shape}, y = {test_labels.shape}")
print(f"- Hols {test_images.shape[0]/70000* 100}% of the overall data")

Random Digits

Now let's plot 9 random images from the training dataset.

def plot_images(nb_images_to_plot, train_data):
    # Generate a list of random indices from the training data
    random_indices = random.sample(range(len(train_data)), nb_images_to_plot)

    # Plot each image using the random indices
    for i, idx in enumerate(random_indices):
        plt.subplot(330 + 1 + i)
        plt.imshow(train_data[idx], cmap=plt.get_cmap('gray'))

    plt.show()

nb_images_to_plot = 9

plot_images(nb_images_to_plot, train_images)

Digits Distribution

import numpy as np

def plot_labels_distribution(data_labels):
    
    counts = np.bincount(data_labels)

    plt.style.use('seaborn-dark-palette')

    fig, ax = plt.subplots(figsize=(10,5))
    ax.bar(range(10), counts, width=0.8, align='center')
    ax.set(xticks=range(10), xlim=[-1, 10], title='Training data distribution')

    plt.show()
    

plot_labels_distribution(train_labels)

Note: we notice that all the ten digits are almost evenly distributed in the training dataset.

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