Loading MNIST dataset using TensorFlow/Keras
This code snippet load mnist dataset keras example using Keras, retrieves the training images and labels, and then plots four images in a row with their corresponding labels. Each image is displayed in grayscale.
from tensorflow.keras.datasets import mnist
import matplotlib.pyplot as plt
import numpy as np
# Load the MNIST dataset
(X_train, y_train), (_, _) = mnist.load_data()
# Print 4 images in a row
plt.figure(figsize=(10, 5))
for i in range(4):
plt.subplot(1, 4, i+1)
plt.imshow(X_train[i], cmap='gray')
plt.title(f"Label: {y_train[i]}")
plt.axis('off')
plt.tight_layout()
plt.show()
Output:
MNIST Dataset : Practical Applications Using Keras and PyTorch
The MNIST dataset is a popular dataset used for training and testing in the field of machine learning for handwritten digit recognition. The article aims to explore the MNIST dataset, its characteristics and its significance in machine learning.
Table of Content
- What is MNIST Dataset?
- Structure of MNIST dataset
- Origin of the MNIST Dataset
- Methods to load MNIST dataset in Python
- Loading MNIST dataset using TensorFlow/Keras
- Loading MNIST dataset Using PyTorch
- Significance of MNIST in Machine Learning
- Applications of MNIST