How to Load CIFAR10 Datasets in Keras?
To load the CIFAR-10 dataset using Keras, you can use the CIFAR10
module from tensorflow.keras.datasets
.
Syntax:
from tensorflow.keras.datasets import cifar10
# Load the CIFAR-10 dataset
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
Example:
The code to do so is as follows:
import matplotlib.pyplot as plt
from tensorflow.keras.datasets import cifar10
# Load the CIFAR-10 dataset
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
# Display some images from the dataset
fig, axes = plt.subplots(2, 5, figsize=(10, 5))
for i, ax in enumerate(axes.flatten()):
ax.imshow(x_train[i])
ax.set_title(f'Label: {y_train[i][0]}')
ax.axis('off')
plt.tight_layout()
plt.show()
Output:
This code will load the CIFAR-10 dataset and display the first 10 images along with their labels in a grid of 2 rows and 5 columns. Make sure you have matplotlib and tensorflow installed in your environment to run this script.
CIFAR10 DataSet in Keras (Tensorflow) for Object Recognition
The CIFAR-10 dataset is readily accessible in Python through the Keras library, which is part of TensorFlow, making it a convenient choice for developers and researchers working on machine learning projects, especially in image classification. In this article, we will explore CIFAR10 (classification of 10 image labels) from Keras/tensorflow.
Table of Content
- What is the CIFAR10 Keras/Tensorflow Datasets?
- Characteristics of CIFAR10 Dataset
- How to Load CIFAR10 (classification of 10 image labels) keras Datasets?
- Significance of CIFAR10 in Machine Learning
- Applications of the CIFAR10 Dataset:
- FAQ – CIFAR10 – Keras/Tensorflow Datasets
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