pytorch实现自注意力 pytorch self-attention代码 - 51CTO博客

pytorch实现自注意力 pytorch self-attention代码,注意力机制系列可以参考前面的一文:注意力机制及其理解TransformerBlockBERT中的点积注意力模型公式:代码:classAttention(nn.Module):"""ScaledDotProductAttention"""defforward(self,query,key,value,mask=None,dropout=

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transformer版本pytorch_mob6454cc7c698b的技术博客_51CTO博客

transformer版本pytorch,原文链接: 本来今年(2020)七月份就想写这篇博客,因为毕业、工作入职等一系列事情一直拖到了现在(主要是因为懒)。transformer的实现不止一个版本的源码,本文主要讲解哈佛大学利用torch实现的版本。相对更“高级”的源码,这个用框架实现的版本显得不是那么底层,但 ...

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注意力(Attention)机制详解(附代码)_attention机制-CSDN博客

文章浏览阅读76次,点赞3次,收藏2次。在自动文本摘要任务中,模型需要从长文本中提取关键信息,并生成一个简短的摘要。注意力机制可以帮助模型识别哪些句子或短语对于理解全文内容最为重要,从而在生成摘要时保留这些关键信息。# 假设参数input_dim = 1000 # 源语言词汇表大小output_dim = 1000 ...

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Python----循环神经网络(LSTM:长短期记忆网络) - CSDN博客

文章浏览阅读559次,点赞9次,收藏13次。rnn在处理长序列时存在长期依赖问题,即随着序列增长,模型难以记住较早时刻的信息,导致梯度消失或爆炸。为解决这一问题,lstm(长短期记忆网络)被提出,它通过引入门控机制(输入门、遗忘门、输出门)来选择性保留或丢弃信息,从而更好地捕捉 ...

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OverLoCK:先概览,再聚焦。CVPR2025全新主干网络 - CSDN博客

自上而下的注意力在人类视觉系统中至关重要,大脑先概览场景找线索,再细察详情。但现代卷积神经网络(ConvNets)采用金字塔结构扩大感受野,忽略了这一仿生原理。本文提出了 OverLoCK,这是首个明确融入自上而下注意力机制的纯卷积神经网络骨干架构。与金字塔骨干网络不同,本文的设计采用 ...

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LSNet:以小见大,CVPR2025全新轻量级主干网络 - CSDN博客

本文提出了一种新型的轻量级视觉网络架构——LSNet(Large-Small Network),旨在通过高效的感知和聚合策略,在有限的计算成本下实现高性能的视觉信息处理。LSNet的设计灵感来源于人类视觉系统的“看大,聚焦小”策略,通过结合大核感知(Large-Kernel Perception, LKP)和小核聚合(Small-Kernel Aggregation, SKA ...

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一文说尽深度学习发展历程!(非常详细)从零基础入门到精通,收藏这篇就够了-CSDN博客

本文我们聊一聊深度学习模型的发展历史及原理。:在20世纪40年代,心理学家Warren McCulloch和数学家Walter Pitts提出了M-P模型。这是最早的神经网络模型,基于生物神经元的结构和功能进行建模。M-P模型通过逻辑运算模拟了神经元的激活过程,为后续的神经网络研究奠定了基础。

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AIGC领域与AIGC写作:开启内容创作的新时代 - 51CTO博客

PyTorch/TensorFlow:主流深度学习框架,支持AIGC模型的高效训练; Hugging Face Transformers:包含3万+预训练模型,支持快速模型部署; Stable Diffusion Toolkit:开源图像生成工具链,支持自定义模型训练; 7.3 相关论文著作推荐 7.3.1 经典论文

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