以下是使用PyTorch实现稀疏自编码器的一个简单示例。我们将使用Keras API,它是PyTorch的高级API,允许我们以类似于构建传统神经网络的方式来构建模型。 首先,确保已经安装了PyTorch和Keras: !pip install torch torchvision !pip install keras 然后,导入所需的库并定义稀疏自编码器的模型: import keras from keras impor...
由此,我们可以结合PyTorch实现KL散度的计算: # 计算p和q之间的KL散度# p为期望激活值扩充为hidden_size的tensor# q为隐藏层结点激活后的输出值defKL_divergence(p,q):"""Calculate the KL-divergence of (p,q):param p::param q::return:"""q=torch.nn.functional.softmax(q,dim=0)# 首先要用softmax...
def calculate_loss(autoencoder: SparseAutoEncoder, model_activations_BD: torch.Tensor, l1_coeffient: float) -> torch.Tensor: reconstructed_model_activations_BD, encoded_representation_BF = autoencoder.forward_pass(model_activations_BD) reconstruction_error_BD = (reconstructed_model_activations_BD - m...
本文为AutoEncoder系列文章第三篇，旨在介绍稀疏自动编码器(Sparse AutoEncoder)的概念、原理，并通过MNIST数据集进行实践。所有相关代码已同步至GitHub。稀疏自动编码器是基于普通自动编码器的基础上，引入了稀疏性约束。这一约束使得神经网络在隐藏层神经元数量较多的情况下，仍能提取样本特征与结构。稀疏性惩...
10:59 [动手写神经网络] pytorch 高维张量 Tensor 维度操作与处理,einops 23:03 [动手写 Transformer] 手动实现 Transformer Decoder(交叉注意力,encoder-decoder cross attentio) 14:43 [动手写神经网络] kSparse AutoEncoder 稀疏性激活的显示实现(SAE on LLM) 16:22 [...
We support distributed training via PyTorch's torchrun command. By default we use the Distributed Data Parallel method, which means that the weights of each SAE are replicated on every GPU. torchrun --nproc_per_node gpu -m sae meta-llama/Meta-Llama-3-8B --batch_size 1 --layers 16 24...
This repository contains the official PyTorch implementation* of Fused Sparse Autoencoder and Graph Net (FuSAGNet), introduced in "Learning Sparse Latent Graph Representations for Anomaly Detection in Multivariate Time Series" (KDD '22).*Partly based on the implementation of GDN, introduced in "Graph...
Lee, K., Carlberg, K.: Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders. arXiv preprint arXiv:1812.08373 (2018) Lévy, B.: A numerical algorithm for L2 semi-discrete optimal transport in 3D. ESAIM Math. Model. Numer. Anal. 49(6), 1693–17...
The experiments were conducted using the PyTorch framework [38] and trained on an RTX 4090D GPU with 60 GB of memory, utilizing the Adam optimizer [39]. The initial learning rate was set to 1 × 10−4, and it was halved if the performance on the validation set did not improve over...
All models were implemented in PyTorch. Optimization was carried out using Adam with an initial learning rate of 0.10.1, training until the learning rate decayed to 0.0010.001 or the number of training rounds reached 100,000. The backbone network used was the standard Conv4 architecture. 4.2....