TensorFlow and Keras Datasets

TensorFlow and Keras Datasets

If you’re looking to get started with deep learning, then you’ll need to have access to quality datasets. In this blog post, we’ll show you how to use TensorFlow and Keras to work with datasets.

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Introduction

TensorFlow and Keras are two of the most popular open source tools for deep learning. They are both powerful frameworks that allow you to build and train complex models.

TensorFlow is a framework for building machine learning models. It allows you to define the structure of your model and then train it using a variety of different algorithms. Keras is a high-level API that allows you to easily build and train complex models using TensorFlow.

Datasets are an important part of machine learning. They provide the data that is used to train and test models. TensorFlow and Keras both have a number of built-in datasets that you can use for training and testing your models. In this article, we will take a look at some of the most popular TensorFlow and Keras datasets.

What is TensorFlow?

TensorFlow is an open source software library for machine learning, used by developers and researchers to create sophisticated systems. Keras is a high-level API for TensorFlow, providing a simpler interface for specifying models. Both TensorFlow and Keras are widely used in the field of deep learning.

TensorFlow provides a plethora of datasets for training and evaluation purposes. The most popular dataset is the MNIST handwritten digit dataset, which consists of 60,000 training examples and 10,000 testing examples. Other popular datasets include the CIFAR-10 dataset (60,000 images of 10 classes), the ImageNet dataset (1.2 million images with 1,000 classes), and the Yelp Challenge dataset (5 million reviews with 1-5 star ratings).

Keras also provides a number of datasets for training and evaluation purposes. The most popular dataset is the MNIST handwritten digit dataset, which consists of 60,000 training examples and 10,000 testing examples. Other popular datasets include the CIFAR-10 dataset (60,000 images of 10 classes), the ImageNet dataset (1.2 million images with 1,000 classes), and the Yelp Challenge dataset (5 million reviews with 1-5 star ratings).

What is Keras?

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Keras allows you to quickly prototype your ideas and evaluate them without having to write code in a low-level language like C++.

TensorFlow and Keras Datasets

There are a number of ways to load data into TensorFlow and Keras. In this section, we will cover three of the most common methods: using built-in datasets, the tf.keras.datasets module, and the tf.data module.

Built-in Datasets
TensorFlow and Keras come with a number of built-in datasets. These can be accessed through the tf.keras.datasets module. The most commonly used dataset is the MNIST dataset, which consists of 70,000 images of handwritten digits from 0-9. The dataset is split into 60,000 images for training and 10,000 images for testing.

To load the MNIST dataset using the built-in datasets function:
“`python
from tensorflow.keras.datasets import mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
“`

The MNIST Dataset

The MNIST dataset is a dataset of handwritten digits, consisting of a training set of 60,000 examples and a test set of 10,000 examples. The MNIST dataset is widely used in computer vision and machine learning for training and testing models.

The MNIST dataset consists of images of handwritten digits, each with 28×28 pixels. Each image is labeled with a label corresponding to the digit it represents. The training set contains 60,000 images, and the test set contains 10,000 images.

TensorFlow and Keras provide built-in support for the MNIST dataset. The MNIST dataset can be loaded using the following code:

“`python
from tensorflow.keras.datasets import mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
“`

The CIFAR-10 Dataset

The CIFAR-10 dataset is a collection of images that are commonly used to train machine learning models. The dataset is split into 10 different classes, each containing 6,000 images. The classes are mutually exclusive and there is no overlap between them.

The Fashion-MNIST Dataset

Fashion-MNIST is a dataset of Zalando’s article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28×28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

The original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the hello world dataset for deep learning practitioners. “If you can’t train your algorithm on MNIST, you are not doing it right.” goes the saying.

However, MNIST is too easy. Convolutional Neural Networks can achieve 99.7%+ on MNIST without breaking a sweat.[1] Humans get around 95% classification accuracy on MNIST[2], soMNIST doesn’t even present a challenging problem for us humans (and we are pattern recognition experts).

In comes Fashion-MNIST – a dataset that’s similar to MNIST but much harder – designed to be a drop-in replacement for MNIST.[3] It contains images from 10 fashion categories instead of handwritten digits:

0 T-shirt/top
1 Trouser
2 Pullover
3 Dress
4 Coat5 Sandal6 Shirt7 Sneaker8 Bag9 Ankle boot

The IMDB Dataset

The IMDB dataset is a collection of 50,000 movie reviews from the Internet Movie Database. The dataset is split into 25,000 reviews for training and 25,000 reviews for testing. The reviews are each labeled as positive or negative, and the objective is to predict the label of a review based on its text.

The TensorFlow and Keras libraries include several built-in datasets that can be used for training and testing models. The IMDB dataset is one of these datasets. To use the IMDB dataset with Keras, you will need to first download it from the TensorFlow website. After downloading the dataset, you can load it into your program using the following code:

“`python
from keras.datasets import imdb

(x_train, y_train), (x_test, y_test) = imdb.load_data()
“`

The Reuters Dataset

The Reuters dataset contains text articles with topics ranging from world news to sports to economics. The articles are labeled with one or more of 130 possible topic labels. The dataset is available for download from the Keras website.

Conclusion

We’ve seen how to load and preprocess data using TensorFlow and Keras. We’ve also seen how to build and train models on this data. In this final section, we’ll take a look at some of the ways that we can evaluate our models.

Keyword: TensorFlow and Keras Datasets

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