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...
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...
Projects Security Insights Additional navigation options master 11Branches0Tags Code This branch is1 commit ahead offacebookresearch/SparseConvNet:main. This is the PyTorch library for training Submanifold Sparse Convolutional Networks. Spatial sparsity ...
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...
We believe that TAMT module is the key to the success of HMD-NeMo in handling missing/partial observation, so we compare it with an alternative commonly used in the Vision Transform- ers [10, 12], which uses a learned set of parameters (i.e., nn.Pa...