Pytorch implementation ofImproved Training of Wasserstein GANsby Gulrajani et al. Examples MNIST Parameters used werelr=1e-4,betas=(.9, .99),dim=16,latent_dim=100. Note that the images were resized from (28, 28) to (32, 32).
super(DiscriminatorWGANGP, self).__init__()defconv_ln_lrelu(in_dim, out_dim):returnnn.Sequential( nn.Conv2d(in_dim, out_dim,5, 2, 2),#Since there is no effective implementation of LayerNorm,#we use InstanceNorm2d instead of LayerNorm here.nn.InstanceNorm2d(out_dim, affine=True),...
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: ...
Pytorch Dataset to produce sines.y=A*sin(B*x):param frequency_range:rangeofA:param amplitude_range:rangeofB:param n_series:numberofsinesinyour dataset:param datapoints:lengthofeach sample:param seed:random seed""" self.n_series=n_series self.datapoints=datapoints self.seed=seed self.frequency_...
我们首先编写一个Pytorch数据集来产生不同的正弦函数。Pytorch数据集是方便的实用程序,可以使数据加载更容易,并提高代码的可读性。看看这里。 获取完整代码,见文末 fromtypingimportSequencefromtorch.utils.dataimportDatasetimportnumpyasnpclassSines(Dataset):def__init__(self,frequency_range:Sequence[float],amplitude...
问越来越大的正WGAN-GP损失EN一般来说，我们在进行机器学习任务时，使用的每一个算法都有一个目标函数，算法便是对这个目标函数进行优化，特别是在分类或者回归任务中，便是使用损失函数（Loss Function）作为其目标函数，又称为代价函数(Cost Function)。 损失函数是用来评价模型的预测值Y^=f(X)与...
3 AI Use Cases (That Are Not a Chatbot) Machine Learning Feature engineering, structuring unstructured data, and lead scoring Shaw Talebi August 21, 2024 7 min read Solving a Constrained Project Scheduling Problem with Quantum Annealing Data Science ...
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 ofImproved Training of Wasserstein GANs, arxiv:1704.00028 Results Generated samples after training 1 epoch on LSUN Bedroom dataset $ git clone https://github.com/kuc2477/pytorch-wgan-gp && cd pytorch-wgan-gp $ pip install -r requirements.txt ...
但是这种灵活的算法也伴随着优化的不稳定性，导致模式崩溃（mode collapse）。将自动编码器(auto-encoder)与GAN相结合，能够使模型更好的表示所有被训练的数据，以阻止模式崩溃。来自Google DeepMind的研究者Mihaela Rosca等人利用生成模型的层级结构，提出了将自动编码器与生成对抗网络相结合的原则，结 选自...