Have you ever wondered how photo editing applications are able to convert night images to day images? Also, you must have come across some photos of people who do not exist in the world. One of the powerful tools to achieve this is Generative Adversarial Networks, also known as GAN. GANs are mainly used in image-to-image translation and to generate photorealistic images that even a human fails to identify as fake or true.
In this project the Keras library is used to build the GAN model on the MNIST dataset. Finally, we learn how to use the Generator model for generating new images of digits.
The MNIST dataset is a widely-used collection of handwritten digit images, commonly used for training and evaluating machine learning models, particularly in the field of digit recognition. Below is a brief summary of the key aspects of the MNIST dataset:
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Data Type: The dataset consists of grayscale images with dimensions of 28x28 pixels. In total, there are 70,000 images.
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Classes (Labels): MNIST includes digits 0 through 9. Each image is associated with a corresponding label indicating the digit it represents.
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Training and Test Sets: Typically, the dataset is divided into 60,000 training examples and 10,000 test examples.
MNIST is often employed as a benchmark for testing the performance of algorithms, especially for evaluating the effectiveness of various machine learning and deep learning models.
The MNIST dataset is readily available through various deep learning libraries and online data repositories.
Link: https://www.tensorflow.org/datasets/catalog/mnist?hl=en