pytorch-vae/model.py/ Jump to Cannot retrieve contributors at this time 147 lines (123 sloc)4.29 KB RawBlame importtorch fromtorch.autogradimportVariable fromtorchimportnn classVAE(nn.Module): def__init__(self,label,image_size,channel_num,kernel_num,z_size): ...
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$gitclonehttps://github.com/AntixK/PyTorch-VAE$cdPyTorch-VAE$pip install -r requirements.txt Usage $cdPyTorch-VAE$python run.py -c configs/<config-file-name.yaml> Config file template model_params:name:"<name of VAE model>"in_channels:3latent_dim:.# Other parameters required by the mode...
参考代码:GitHub - AntixK/PyTorch-VAE: A Collection of Variational Autoencoders (VAE) in PyTorch. VAE的目的:构建一个解码器Decoder,通过输入从标准正态分布中采样得到的采样变量X,得到生成样本Y,使Y的分布与输入样本X的分布尽可能接近,从而完成图像生成任务。 VAE的模型结构:编码器Encoder+解码器Decoder,输入...
print('===> Test set loss: {:.4f}'.format(test_loss)) 训练 for epoch in range(1, 51): train(epoch) test() 参考: https://github.com/lyeoni/pytorch-mnist-VAE https://towardsdatascience.com/understanding-variational-autoencoders-vaes-f70510919f73...
https://github.com/SchindlerLiang/VAE-for-Anomaly-Detection 主要问题包括 使用tensorflow 1.x 实现 没有关于 PCA、kPCA、AE 等的实现 如果有任何疑问请评论,讨论一下解决问题。 6.7 重构概率计算 以下代码摘录于: https://github.com/Michedev/VAE_anomaly_detection 并且对该代码进行简单的分析: ...
论文: [Auto-Encoding Variational Bayes](https://arxiv.org/abs/1312.6114v11) 博客: [Tutorial - What is a variational autoencoder?](https://jaan.io/what-is-variational-autoencoder-vae-tutorial/) [From Autoencoder to Beta-VAE](https://lilianweng.github.io/posts/2018-08-12-vae/) 视频: ...
代码仓库:https://github.com/SingleZombie/DL-Demos/tree/master/dldemos/VQVAE 项目运行示例 如果你只是想快速地把项目运行起来,可以只阅读本节。 在本地安装好项目后,运行python dldemos/VQVAE/dataset.py来下载MNIST数据集。之后运行python dldemos/VQVAE/main.py,这个脚本会完成以下四个任务: ...
https://www.cnblogs.com/amazingter/p/14696251.html https://www.cnblogs.com/boyknight/p/16290582.html https://baileyswu.github.io/2019/11/disentangling-disentanglement-in-vae/ https://blog.csdn.net/c9Yv2cf9I06K2A9E/article/details/116246208...
VAE本质上是一个编码器-解码器模型，通过正态分布的输出和优化损失函数，成功训练解码器生成图像。代码实践可以帮助更深入理解VAE，相关实现可参考GitHub项目。总结，VAE通过正则化方法解决自编码器的过拟合问题，引入了分布输出和损失函数优化，实现高效图像生成。学习VAE不仅能掌握生成模型的核心原理，还能拓宽...