A DCGAN built on the CIFAR10 dataset using pytorch DCGAN is one of the popular and successful network designs for GAN. It mainly composes of convolution layers without max pooling or fully connected layers. It uses convolutional stride and transposed convolution for the downsampling and the upsamp...
然后,定位到这个GitHub目录(https://github.com/myleott/mnist_png)并下载mnist_png.tar.gz。这个压缩文件包含MNIST数据集,为70000个单独的png文件。当然,我们可以使用PyTorch内置的MNIST数据集,但这样您就不能了解如何加载具体的图像数据进行训练。解压缩文件并将mnist_png目录放入数据目录中。你的项目目录应该是这样...
DCAGN通过以上的改进得到的生成器结构如下: 100 * 1 * 1 -> 3 * 64 * 64 判别器结构: 3 * 64 * 64 -> 1 * 1 * 1 实现 先弄清楚PyTorch中 nn.Conv2d与nn.ConvTranspose2d函数的用法 完整代码https://github.com/growvv/GAN-Pytorch/tree/main/DCGAN __EOF__...
dcgan combined with vae in pytorch! Contribute to seangal/dcgan_vae_pytorch development by creating an account on GitHub.
Pytorch code for GAN models This is the pytorch implementation of 3 different GAN models using same convolutional architecture. DCGAN (Deep convolutional GAN) WGAN-CP (Wasserstein GAN using weight clipping) WGAN-GP (Wasserstein GAN using gradient penalty) Dependecies The prominent packages are: numpy...
Pytorch implementation of DCGAN, WGAN-CP, WGAN-GP. Contribute to Zeleni9/pytorch-wgan development by creating an account on GitHub.
PyTorch implementations of Generative Adversarial Networks. - PyTorch-GAN/implementations/dcgan/dcgan.py at master · CharlesDDDD/PyTorch-GAN
然后应用resize()方法，尽管人脸识别工具通常会创建方形或几乎为方形的图像，所以简单地跳过这一部分，只使用pytorch的resize方法不会有太大区别。PIL的调整大小方法与下面定义的方法的区别在于，它会调整图像的大小并对其进行裁剪，以使它们适合指定尺寸的正方形图像，从而避免挤压。 但是无论如何，我已经为其他项目构建...
https://github.com/pytorch/examples 对应Python 版本示例:https://github.com/pytorch/tutorials/blob/main/beginner_source/dcgan_faces_tutorial.py 本文项目参考 dcgan 项目:https://github.com/whuanle/Maomi.Torch/tree/main/examples/dcgan 简介 本教程将通过一个示例介绍生成对抗网络(DCGAN),在教程中,我们将...
Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets - znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN