Transformer batch normalization. On the other side, previous vision models, i.
Transformer batch normalization. Default: False (seq, batch, feature).
Transformer batch normalization Layer normalization is a technique used in artificial neural networks to normalize the inputs to a given layer. e. See this tutorial for an in depth discussion of the performant building blocks PyTorch offers for building your own transformer layers. swin. 1k次，点赞20次，收藏24次。在深度学习的Transformer架构中，有一个有趣的细节是它使用了Layer Normalization（）而非Batch Normalization（这两种归一化方法在不同的神经网络架构中都发挥着重要的作用，但为什么Transformer选择了LayerNorm呢？让我们 The benefits of LayerNorm projection in organizing key vectors (image from paper) B — Scaling: This is the more obvious portion, that LayerNorm rescales the input. norm before model. This work demonstrates that Transformers without normalization can achieve the same or A transformers. Secondly, layer Normalization (LN) [18], used in original Transformers, was developed to address the challenges posed by the varying sequence lengths in text 在深度学习模型的训练过程中，Normalization技术扮演着至关重要的角色，它不仅加速了训练过程，还提高了模型的泛化能力。在众多Normalization技术中，为何Transformer选择了Layer Normalization（Layer Norm）而非更为普遍的Batch Normalization（BN）？ Transformer架构概览 Batch Normalization的过程. 3 Batch Normalization & Layer Normalization. paper：Transformers without Normalization 0. Also, to avoid the impact of model architecture, 文章目录题目简介Normalization分类作用Batch Normalization含义公式大致过程缺点Layer Normalization公式优点 题目 transformer学习之Layer Normalization 简介 Normalization 字面翻译 —> 标准化 分类 Normalization{(1){BatchNormLayerNorm对第L层每个神经元的激活值或者说对于第L+1层网络神经元 Better for RNNs and Transformers: If you’re dealing with sequence data or NLP tasks, Batch Normalization: If you’re working with feed-forward networks or convolutional neural networks is robust to small-batch statistics, and it still achieves higher performance, as opposed to LN; see Figure5. However, the high computational cost makes it quite challenging to deploy on resource-constraint devices. Related Work Normalization is widely used in modern deep NNs such as ResNet (He et al. Batch The standard normalization method for neural network (NN) models used in Natural Language Processing (NLP) is layer normalization (LN). [23] Response normalization reappeared in ConvNeXT-2 as (shared by all ScaleNorm modules of a transformer). 3、 Batch Normalization 和 Layer Normalization 的定义 4、BN和LN的对比（ RNN 或 Transformer 为什么用Layer Normalization） 一、什么是Normalization？ Normalization：规范化或标准化，就是把输入数据X，在输送给神经元之前先对其进行平移和伸缩 说到Transformer，就不能不提它的好搭档——Layer Normalization（LayerNorm），简称LN。你可能要问，为啥Transformer要用LN而不是Batch Normalization（BN）呢？这背后可是有大学问的。 在聊“二选一”的问题前，我们先介绍下什么是Layer Normalization？什么是Batch Normalization? Normalization techniques are fundamental to success of deep learning models, including Transformers. Let’s explore this %0 Conference Paper %T PowerNorm: Rethinking Batch Normalization in Transformers %A Sheng Shen %A Zhewei Yao %A Amir Gholami %A Michael Mahoney %A Kurt Keutzer %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119 在以前我们都是知道，Batch Normalization（以下简称BN）的方法最早由Ioffe&Szegedy在2015年提出，主要用于解决在 而在transformer上使用Layer Normalization（以下简称LN）的方法，用于解决BN无法很好地处理文本数据长度不一的问题，但是对于VIT来说，图 一个不负责任的回答： 旨在去掉Normalization的工作，这不是第一篇，肯定也不是最后一篇，早年尝试过一些做法，发现充分训练后至少效果上都不如带Normalization的模型，所以我现在本能地不相信或者说不看好任何去Normalization的工作。 3. 6. 28 min read. Introduced by Sergey Ioffe and Christian Szegedy in 2015, BN operates by normalizing the activations of each layer across the RepBN是用于加速Transformer模型推理的归一化方法，其核心思想是将BatchNorm与线性层合并，以减少推理时的计算开销：1. For example, LN [31] normalizes data across feature dimension and presents good performance for Recurrent neural network (RNN) and NLP models. last_hidden_state (torch. We find that the inconsistency between training and inference of BN is the leading cause that Other normalization methods that do not depend on batch size have been proposed to normalize data across multiple dimensions. Inherited from the NLP tasks, the architectures take Layer Normalization (LN) as a default normalization technique. Our initial exploration reveals frequent crashes in model training when directly replacing all LN layers with BN, contributing to the un-normalized feed forward network (FFN) blocks. modeling_swin. . August 6, 2024. The entries colored in blue show the components replace all Layer Normalization (LN) layers with Batch Normalization (BN) layers, resulting in training crashes (crashes is reported in the paper, but not shown in this implementation, maybe due to additional replacement model. head); add a BN layer in-between two linear layers in Feed Forward Network (FFN) blocks to stabilize training 文章浏览阅读1. Limitations of Batch Normalization. ,2016), InstanceNorm (Ulyanov et al. edu. 这篇文章是想记录一下 visual transformers 中的BN 的一些论文进展，知乎大佬们的一些思考~. eature Dimension h Layer Normalization 1 eature Dimension h Batch/Power Normalization 1 Fig. This paper investigates the computational bottleneck modules of efficient transformer, i. . LayerNorm Layer Normalizationはディープラーニングの基礎的な本では、ほぼ必ずと言っていいほど登場する“Batch Normalization”を改良したもので、TransformerやBERTでも使われています。 Batch Normalizationについてはこちらの記事『Batch Normalizationを理解する』をご参照く Batch Normalization 主要是对隐藏单元激活值进行标准化，以使这些激活的分布在训练过程中保持不变。 如果因为权重和偏置值在该层的改变，隐藏激活分布发生变化，它们会在上面的层中引起迅速的变化。 2. This makes it especially useful 文章目录题目简介Normalization分类作用Batch Normalization含义公式大致过程缺点Layer Normalization公式优点 题目 transformer学习之Layer Normalization 简介 Normalization 字面翻译 —> 标准化 分类 Normalization{(1){BatchNormLayerNorm对第L层每个神经元的激活值或者说对于第L+1层网络神经元的输入值进行Normalization操作(2){WeightNorm Layer Normalization 1 Batch/Power Normalization 1 Figure 1. Query-Key normalization (QKNorm) [32] normalizes query and key vectors to have unit L2 norm. BN 把每层神经网络任意神经元 越来越偏的输入值的分布强行拉回到均值为0方差为1的标准正态分布。这样让梯度变大，避免梯度消失问题产生，而且梯度变大意味着学习收敛速度 Both kinds of local normalization were obviated by batch normalization, which is a more global form of normalization. Batch Normalization 과 Layer normalization 모두 차이나는 정도를 줄이기 위해 사용되나, 그 방향이 서로 다르다는 차이점이 있다. CNN부터 transformer까지, 항상 있는 듯 없는 듯 껴 있는 연산들이고, 코드 상에서도 딱 한줄 들어가는 존재감 없는 녀석들이다. 众所周知，无论在CV还是NLP中，深度模型都离不开归一化技术（Normalization）。在CV中，深度网络中一般会嵌入批归一化（ BatchNorm ，BN）单元，比如 ResNet ；而NLP中，则往往向深度网络中插入层归一化（ LayerNorm ，LN）单元，比如 Transformer 。 为什么在归一化问题上会 同时，作者对比了Transformer为什么要使用Layer Normalization，而不使用Batch Normalization，两者区别可以从图中可以看出来： Batch Normalization（BN）: 是对于每个维度上统计所有样本的值，计算均值和方差，BN在每个维度上分布是稳定的。 与Batch Normalization相比，Layer Normalization更适用于深度模型的训练，因为它不依赖于batch的样本数量，而是对每一层的输出进行独立归一化处理。 在下一章节中，我们将学习Layer Normalization在Transformer模型中的应用，进一步加深对Layer Normalization的理解。 Abstract page for arXiv paper 2503. batch_first – If True, then the input and output tensors are Transformers have become foundational architectures for both natural language and computer vision tasks. A Transformer layer has two sub-layers: the (multi-head) Batch Normalization Layer Normalization RMS Normalization; 保存される相対関係 トークン間（ピクセル間）とミニバッチ間の関係 トークン（ピクセル）ごとのチャネル関係: トークンごとのチャネル関係: 破壊される相対関係: トークン（ピクセル）ごとのチャネル関係 Layer Normalization（LayerNorm）：与BatchNorm不同，LayerNorm适用于那些在不同样本之间难以直接比较的情况，如Transformer中的自注意力机制。 在这些模型中，每个位置上的数据代表了不同的特征，因 一般来说，我们认为由于有padding的存在，做batchnorm并不合适。 比如上面的例子，对“中”，“[P]”，“你”做归一化，由于 [P] 的存在，实际的batch size只有2，并且和 [P] 做normalization也对训练没什么帮助。 1. ,2016), GroupNorm (Wu & 无归一化的Transformer. The entries colored in blue show the components used for calculating the statistics. NeurIPS, 2020. Our initial exploration reveals frequent crashes in model training when directly replacing all LN BatchNorm is known to make a deep neural network converge faster – a network with BatchNorm achieves higher accuracy compared to the base-line model when trained over the same number of epochs. FloatTensor (if return_dict=False is passed or when config. , 2017), transformer architecture has rapidly emerged as a preeminent model in the landscape of language models. RepBN的定义：RepBN通过引入一个可学习的参数η，与BatchNorm的输出相结合，形成新的归一化公式。2. 2w次，点赞75次，收藏118次。文章目录前因总览Batch NormalizationLayer NormalizationInstance NormalizationGroup Normalization最终总结参考前因Normalization现在已经成了神经网络中不可 文章目录题目简介Normalization分类作用Batch Normalization含义公式大致过程缺点Layer Normalization公式优点 题目 transformer学习之Layer Normalization 简介 Normalization 字面翻译 —> 标准化 分类 Normalization{(1){BatchNormLayerNorm对第L层每个神经元的激活值或者说对于第L+1层网络神经元的输入值进行Normalization操作(2){WeightNorm Through the above comparison, it can be seen that the four normalization methods have their own advantages and disadvantages: BatchNorm performs excellently in large batch data and convolutional neural networks but is sensitive to small batches. the eps value in layer normalization components (default=1e-5). 1. However, when the batch PowerNorm: Rethinking Batch Normalization in Transformers Sheng Shen * 1Zhewei Yao Amir Gholami1 Michael W. the concept of normalizing layers with the proposed Batch Normalization (BatchNorm). Instance Normalization (IN) [45] normalizes data across each feature at 文章浏览阅读2. In this paper, we aim to introduce Batch Normalization to Transformer-based vision architectures. TL; DR. Thus, this study proposes a novel residual structure, ResBN, which can effectively handle 3D data. , 2018), each of which takes a sequence of vectors as input and outputs a new sequence of vectors with the same shape. But what is that re-scaling really accomplishing? According to this paper, the underlying benefit is that scaling ensures two benefits: 1 — Every key has the potential to receive the ‘highest’ attention 2 — Understanding the Failure of Batch Normalization for Transformers in NLP Jiaxi Wang 1, Ji Wu;2, Lei Huang3 1Department of Electronic Engineering, Tsinghua University 2Institute for Precision Medicine, Tsinghua University {wjx20@mails, wuji_ee@mail}. 10622: Transformers without Normalization Normalization layers are ubiquitous in modern neural networks and have long been considered essential. 1 While learnable 1D position embeddings are currently popular in the field [13], it is crucial to take into account the limited size of EEG datasets when fitting large-scale Transformers. Batch normalization（バッチ正規化）は、ミニバッチと呼ばれる小さなデータセット全体の出力を正規化しますが、Layer normalizationは各層の出力を個別に正規化します。 How integrating Batch Normalization in an encoder-only Transformer architecture can lead to reduced training time Anindya Dey, PhD. SwinModelOutput or a tuple of torch. We propose UnitNorm, a novel approach that scales input vectors by their norms and modulates 前言. 无论是BN还是LN都是为了缓解 协变量 转移现象，即第二层神经网络要拟合第一层的输出的分布，若第一层输出数据 layer_norm_eps – the eps value in layer normalization components (default=1e-5). , 2016), MobileNet-V2 Transformer-based vision architectures have attracted great attention because of the strong performance over the convolutional neural networks (CNNs). 먼저 Batch Normalization(BM)에 대해 간단히 설명하려 한다. 文章浏览阅读1. (2009) Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2、深度学习中为什么要用Normalization. Its influence has significantly expanded with the introduction of Vision Transformer (ViT) (Dosovitskiy et al. Dec 17, 2024. BN and LN focus on the latter. 오늘은 배치 정규화(batch normalization)과 층 정규화(layer normalization) 기법에 대해 알아보고 비교하고자 한다. xij —denotes the activation of ith neuron for the jth training sample Additionally, Normalization in ML (or, more accurately, DL specifically) comprises two categories: weight normalization and activation normalization. In this paper, we are trying to answer why BN usually performs This is the fifth article in The Implemented Transformer series. The illustration of layer normalization (left) and batch/power normalization (right). Default: False (seq, batch, feature). 几个月前因为想把conformer中的LN都换成BN，因为两种Norm有些繁琐，结果crash了，然后又在 DeiT-S 上试了一下，也是gg Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between 本文目录结构： 1、什么是 Normalization. Layer-Norm对C和T两个维度进行归一化，即让一句话中所有单词的信息值近似服从 正太分布 。. RepBN的优势：RepBN结合了BatchNorm在训练时的稳定性和线性层在推理时的高效性，从而在 Batch Normalization (BN) is a core and prevalent technique in accelerating the training of deep neural networks and improving (LN). Unlike batch normalization, which computes normalization statistics (mean and variance) across the batch dimension, layer normalization (LayerNorm) computes these statistics across the feature dimension for each individual input sample. tsinghua. This is different than batch normalization (BN), which is widely-adopted in Computer Vision. Batch normalization biases residual blocks towards the identity function in deep networks. cn Batch Normalization: Batch Normalization (BN) is perhaps one of the most widely used normalization techniques in deep learning. FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of . BN은 각 Feature의 평균과 분산을 구해 batch에 있는 각 Feature를 정규화 한다. More specifically, I will discuss two versions of such a model. The Math Behind Keras 3 Optimizers: Deep Understanding and Application Batch Normalization in a Deep Neural Network. Layer Normalization is often used to stabilize training in RNNs, LSTMs, and GRUs. Batch Normalization (BN) is a core and prevalent technique in accelerating the training of deep neural networks and improving the generalization on Computer Vision (CV) tasks. However, it fails to defend its position in Natural Language Processing (NLP), which is dominated by Layer Normalization (LN). This is different than batch normalization (BN), However, the attention mechanism within the Transformer presents challenges for utilizing Batch Normalization (BN), as statistical information cannot be extracted efficiently from the data set. Introduction. 1: The illustration of layer normalization (left) and batch/power normalization (right). Deng et al. It is widely believed that by controlling the mean and variance of layer inputs across mini-batches, BatchNorm we primarily consider normalization on Transformer and Transformer-XL networks. Mahoney 1Kurt Keutzer Abstract The standard normalization method for neural network (NN) models used in Natural Language Processing (NLP) is layer normalization (LN). , CNNs, treat Batch Normalization (BN) as a de facto 这个问题没有定论，很多人都在探索，所以只是聊一下我自己的理解，顺便为讲 layer-norm做个引子。 BN的理解重点在于它是针对整个Batch中的样本在同一维度特征在做处理。 在MLP中，比如我们有10行5列数据。5列代表特征，10行 バッチ正規化 (Batch Normalization)の基本的な仕組みと性質を紹介し，他のバッチ正規化の発展型も概要を紹介する．レイヤー正規化，インスタンス正規化，グループ正規化）．その中で，参考記事の描き方に習った「カラフルな図解」を添えることで，各方式の違いがわかりやすいようにもした． 他の正規化手法（例えばBatch normalization）との比較. , 2020), illustrating the efficacy and versatility of As discussed in batch normalization litera-ture [7], the position of the normalization layers primarily affects both the stability and resultant ization positions in Transformers: Pre-Layer Normalization (Pre-LN) and Post-Layer Normalization (Post-LN). models. ,2016), GroupNorm (Wu & Introduced initially for tasks in natural language processing (Vaswani et al. The preferred use of LN in NLP is principally due to the empirical observation that a (naive/vanilla) use of BN leads to 1. , normalization layers and attention modules. The target of Batch Normalization is a batch of samples, Types of Transformer-Based Foundation Models: Encoder-Only, Decoder-Only, and Encoder-Decoder. Normalization for Transformers Normalization is known as an useful method to make train-ing stable and boost performance. , 2016), first let’s discuss what Normalization is and then consider a milestone in the space, Batch Normalization (BN; Ioffe and Szegedy 文章浏览阅读2. Transformers: In models like BERT and GPT In the first of a series of articles, I explore in detail one such modification of the ViT, which will involve replacing Layer Normalization (LayerNorm) – the default normalization technique for transformers – with Batch Normalization (BatchNorm). BM은 신경망의 각 layer에 들어가는 input을 batch 단위의 평균과 분산으로 정규화해 학습을 효율적으로 만드는 방법이다. Transformer with Post-Layer Normalization The Transformer architecture usually consists of stacked Transformer layers (Vaswani et al. 1 Batch Normalization (BN) Batch Norm 是一种归一化方法，主要用于加速深层神经网络的训练。它在每个小批量（batch）中对输入的特征值进行归一化，保证特征的均值接近 0，方差接近 1，从而减小梯度消失和梯度爆炸的问题。 都是用于标准化数据的，Batch Normalization是用于图像预处理的，而Layer Normalization最初是用于自然语音的，但随着Transformer在图像的使用，Layer Normalization也用于图像中。我们在图像预处理过程中通常会对图像，这样能够加速网络的收敛。 Layer Normalization is commonly used in various deep learning architectures, especially in: Recurrent Neural Networks (RNNs): Due to the sequential nature of RNNs, Batch Normalization is difficult to apply effectively. In Depending on the architecture and design choices, batch normalization can be applied before or after the layer's activation function. cn 3SKLSDE, Institute of Artificial Intelligence, Beihang University huangleiAI@buaa. On the other side, previous vision models, i. Layer normalization directly follows the multi-head attention mechanism and the position-wise feed-forward network from the previous Normalization techniques are crucial for enhancing Transformer models' performance and stability in time series analysis tasks, yet traditional methods like batch and layer normalization often lead to issues such as token shift, attention shift, and sparse attention. BatchNorm normalizes each feature within a batch of samples, while LayerNorm normalizes all features within each sample. 上图所示的是2D数据下的BN，而在NLP或图像任务中，我们通常遇到3D或4D的数据，例如： 图像中的数据维度：（N, C, H, W)。其中N表示数据量（图数），C表示channel数，H表示高度，W表示宽度。 不过你要特定Transformer模型的话，你会发现CV中的ViT也是用了LN的，这就违背了大家以往的“CV用BN，NLP用LN“的常识了，而且你会发现，真要将ViT中的LN换成BN，结果还真的会下降，所以Transformer（而不是NLP或CV）跟LN似乎真的更配。这又有什么解释呢？ 在Transformer框架中，残差连接（Residual Connections）和归一化层（Layer Normalization）是两个重要的组成部分，它们通常组合成一个整体作用于模型的各个层次中，从而提高模型的训练效率和综合性能。本篇我将为各位同学介绍一下残差连接和归一化层，目的是让各位明白如何在Transformer框架中构建残差 BN&LN. ; LayerNorm is suitable for various batch sizes, especially effective in RNNs and Transformers. 다들 Batch Normalization은 들어보았지만, Layer Normalization은 잘 모를 수 있다. 1k次，点赞11次，收藏23次。层归一化(Layer normalization ) 是Transformer模型中的一项重要技术，它通过对每一层的输入进行归一化，帮助稳定和加速训练。无论输入的规模或分布如何，它都能确保模型 Batch Normalization. ,2017;Devlin et al. 本文提出了一种名为Dynamic Tanh（DyT）的简单技术，用于替代Transformer架构中的归一化层。DyT通过一个可学习的标量参数$α$和tanh函数，实现了对输入激活值的动态缩放和极端值的压缩。实验表明，使用DyT的Transformer在多种任务和领域中均能 Leveraging Batch Normalization for Vision Transformers Zhuliang Yao1,2* Yue Cao2 Yutong Lin2,3* Ze Liu2,4* Zheng Zhang2 Han Hu2 1 Tsinghua University 2 Microsoft Research Asia 3 Xi’an Jiaotong University 4 University of Science and Technology of China Abstract Transformer-based vision architectures have attracted great attention because of the strong performance Transformer的输入Tensor通常是N, T, C。 N表示 batch_size, T表示单词个数，C是词向量特征的维度。. if we have an input x (original) and we Batch normalization works well in many cases but when it comes to models like Transformers, which deals with sequential data, using Batch Normalization becomes difficult. Two limitations of batch normalization can arise: In batch normalization, we use the batch statistics: the mean and standard deviation corresponding to the current mini-batch. Batch Norm VS Layer Norm. 2. レイヤー正規化 (Layer Normalization)とは [概要] レイヤー正規化 (Layer Normalization)とは，可変長の系列データが入力の系列モデル・系列変換モデルでも使用しやすいように，元となるバッチ正規化を「バッチ内で， Normalization 有很多种，但是它们都有一个共同的目的，那就是把输入转化成均值为 0 方差为 1 的数据。我们在把数据送入激活函数之前进行 normalization（归一化），因为我们不希望输入数据落在激活函数的饱和区。 Before diving into Layer Normalization (LN; Ba et al. Batch Norm 和 Layer Norm 的定义与作用. batch_first – If True, then the input and output tensors are provided as (batch, seq, feature). In this paper, we investigate the computational bottleneck modules of efficient transformer, i. 1. norm_first – if True, layer norm is done prior to attention and feedforward operations, respectively. Comparing NLP and CV, we show evidence that the batch statistics in To address this, we propose Power Normaliza-tion (PN), a novel normalization scheme that re-solves this issue by (i) relaxing zero-mean nor-malization in BN, (ii) incorporating a running We find that BN can obtain much better test performance than LN when TID keeps small through training. In this paper, we are trying to answer why BN usually performs worse than LN in NLP tasks with Transformer models. To suppress the explosion of TID, we propose Regularized BN (RBN) To address the over-fitting problem, we propose a new normalization method, Adaptive Normalization (AdaNorm), by replacing the bias and gain with a new transformation function. Pre-LN applies the layer normalization to an input for each sub-layer, Transformer中采用的是Layer Normalization（层标准化）方式。常用的标准化方法有Batch Normalization，Layer Normalization，Group Normalization，Instance Normalization等，这篇笔记将在论文研究的基础上，着重聚焦于前两者，笔记内容包括： 一、Batch Normalization 1. return_dict=False) comprising various elements depending on the configuration and inputs. De and Smith (2020) Soham De and Sam Smith. 6k次，点赞2次，收藏6次。这篇论文深入研究了Transformer中批处理归一化（BN）的问题，并提出了Power Normalization（PN）作为改进方法。作者指出，NLP数据的批处理统计信息波动大，导致BN性能下降。PN通过放松BN的零均值归一化和使用运行的二次平均值，解决了这一问题。 Why do we need batch normalization? If you think of deep neural network, it would be of anything with transformer or without transformer or RNN or FFNN. 2. Nowadays, a variety of normalization methods have been proposed, such as Batch-Norm (Ioffe & Szegedy,2015), LayerNorm (Ba et al. They stabilize and accelerate training, helping the model converge faster and generalize better This is an official pytorch implementation of our paper "SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization". Let’s assume we have a two-dimensional input matrix, where the rows Normalization techniques are crucial for enhancing Transformer models' performance and stability in time series analysis tasks, yet traditional methods like batch and In this work, we systematically analyze the ineffectiveness of vanilla batch normalization (BN) in transformers. This in turn Batch Normalization: In the context of transformers; the evolution of batch normalization is called as layer normalization. Despite its drawbacks, applying batch normalization still remains a valuable tool in the 讲解：batch normalization 是神经网络中的一个结构，简称bn层，它作用是通过规范化的手段,将越来越偏的分布拉回到标准化的分布,使得激活函数的输入值落在激活函数对输入比较敏感的区域,从而使梯度变大,加快学习收敛速度,避免梯度消失的问题。 在深度学习的Transformer架构中，有一个有趣的细节是它使用了Layer Normalization（）而非Batch Normalization（这两种归一化方法在不同的神经网络架构中都发挥着重要的作用，但为什么Transformer选择了LayerNorm呢？让我们来一探究竟。 Batch normalization provably avoids ranks collapse for randomly initialised deep networks. bxmz jteqfi liueziz hpdlp kesteq isqmnsn rzu wfffu dsszwb yqg nxy cphy zvcenmr nmk ertmj