Generative Adversarial Networks (GAN) – implementation in Keras
Implemented Generative Adversarial Networks (GAN) using Keras.
Github link: https://github.com/AarohiSingla/Generative-Adversarial-Network-for-an-MNIST-Handwritten-Digits-From-Scratch-in-Keras/
1- Implementation of Generator
2- Implementation of Discriminator
3- Loss function for Discriminator and Generator.
For any queries, You can contact on aarohisingla1987@gmail.com
Link for Previous video: https://www.youtube.com/watch?v=tIExopLw29U&t=1247s
GANs are a framework for teaching a DL model to capture the training data’s distribution so we can generate new data from that same distribution. GANs were invented by Ian Goodfellow in 2014 and first described in the paper Generative Adversarial Nets. They are made of two distinct models, a generator and a discriminator. The job of the generator is to spawn ‘fake’ images that look like the training images. The job of the discriminator is to look at an image and output whether or not it is a real training image or a fake image from the generator. During training, the generator is constantly trying to outsmart the discriminator by generating better and better fakes, while the discriminator is working to become a better detective and correctly classify the real and fake images. The equilibrium of this game is when the generator is generating perfect fakes that look as if they came directly from the training data, and the discriminator is left to always guess at 50% confidence that the generator output is real or fake.
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https://www.youtube.com/channel/UCgHDngFV50KmbqF_6-K8XhA/join
#GANs #GenerativeAdversarialNetworks #GenerativeModels #AI #DeepLearning #ML #MachineLearning #ArtificialIntelligence #ComputerVision #PifordTechnologies #Aarohi #Akminder
Implemented Generative Adversarial Networks (GAN) using Keras.
Github link: https://github.com/AarohiSingla/Generative-Adversarial-Network-for-an-MNIST-Handwritten-Digits-From-Scratch-in-Keras/
1- Implementation of Generator
2- Implementation of Discriminator
3- Loss function for Discriminator and Generator.
For any queries, You can contact on aarohisingla1987@gmail.com
Link for Previous video: https://www.youtube.com/watch?v=tIExopLw29U&t=1247s
GANs are a framework for teaching a DL model to capture the training data’s distribution so we can generate new data from that same distribution. GANs were invented by Ian Goodfellow in 2014 and first described in the paper Generative Adversarial Nets. They are made of two distinct models, a generator and a discriminator. The job of the generator is to spawn ‘fake’ images that look like the training images. The job of the discriminator is to look at an image and output whether or not it is a real training image or a fake image from the generator. During training, the generator is constantly trying to outsmart the discriminator by generating better and better fakes, while the discriminator is working to become a better detective and correctly classify the real and fake images. The equilibrium of this game is when the generator is generating perfect fakes that look as if they came directly from the training data, and the discriminator is left to always guess at 50% confidence that the generator output is real or fake.
Join this channel to get access to perks:
https://www.youtube.com/channel/UCgHDngFV50KmbqF_6-K8XhA/join
#GANs #GenerativeAdversarialNetworks #GenerativeModels #AI #DeepLearning #ML #MachineLearning #ArtificialIntelligence #ComputerVision #PifordTechnologies #Aarohi #Akminder
