人工智能_项目实践_生成对抗网络_在 PyTorch 和 Tensorflow 中实现的多生成对抗网络 (GAN)

# gans: Generative Adversarial Networks Multiple Generative Adversarial Networks (GANs) implemented in PyTorch and Tensorflow. [Check out this blog post](https://medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f) for an introduction to Generative Networks. <img src=".images/dcgan_mnist.gif" width="275"> <img src=".images/dcgan_cifar.gif" width="275"> ## Vanilla GANs Vanilla GANs found in this project were developed based on the original paper [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661) by Goodfellow et al. These are trained on the [MNIST dataset](http://yann.lecun.com/exdb/mnist/), and learn to create hand-written digit images using a 1-Dimensional vector representation for 2D input images. - [PyTorch Notebook](https://github.com/diegoalejogm/gans/blob/master/1.%20Vanilla%20GAN%20PyTorch.ipynb) - [TensorFlow Notebook](https://github.com/diegoalejogm/gans/blob/master/1.%20Vanilla%20GAN%20TensorFlow.ipynb) <img src=".images/vanilla_mnist_pt_raw.png" width="300"> <img src=".images/vanilla_mnist_pt.png" width="300"> __MNIST-like generated images before & after training.__ ## DCGANs Deep Convolutional Generative Adversarial Networks (DCGANs) in this repository were developed based on the original paper [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434) by Radford et al. These are trained on the [CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) and the [MNIST](http://yann.lecun.com/exdb/mnist/) datasets. They use 3 dimensional representations for images (length x height x colors) directly for training. - [TensorFlow CIFAR10 Notebook](https://github.com/diegoalejogm/gans/blob/master/2.%20DC-GAN%20TensorFlow.ipynb) - [PyTorch CIFAR10 Notebook](https://github.com/diegoalejogm/gans/blob/master/2.%20DC-GAN%20PyTorch.ipynb) - [PyTorch MNIST Notebook](https://github.com/diegoalejogm/gans/blob/master/2.%20DC-GAN%20PyTorch-MNIST.ipynb) <img src=".images/dcgan_cifar_pt_raw.png" width="300"> <img src=".images/dcgan_cifar_pt.png" width="300"> __CIFAR-like generated images before & after training.__