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tink2123
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May 27, 2019
| 本章我们所要介绍的深度学习模型克服了BOW表示的上述缺陷，它在考虑词顺序的基础上把文本映射到低维度的语义空间，并且以端对端（end to end）的方式进行文本表示及分类，其性能相对于传统方法有显著的提升\[[1](#参考文献)\]。 | ||
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| ## 硬件环境的要求 | ||
| 本文档支持GPU训练，如果您使用了本文配套的docker镜像，请注意：该镜像对GPU的支持仅限于CUDA 8，cuDNN 5 |
| $$ h_t=Recrurent(x_t,h_{t-1})$$ | ||
| <p align="center"> | ||
| <img src="https://github.com/JesseyXujin/book/blob/doc_1/06.understand_sentiment/image/formula_rnn_2.png?raw=true" width = "50%" align="center"/><br/> | ||
| </p> |
shanyi15
reviewed
May 27, 2019
| LSTM通过给简单的循环神经网络增加记忆及控制门的方式，增强了其处理远距离依赖问题的能力。类似原理的改进还有Gated Recurrent Unit (GRU)\[[8](#参考文献)\]，其设计更为简洁一些。**这些改进虽然各有不同，但是它们的宏观描述却与简单的循环神经网络一样（如图2所示），即隐状态依据当前输入及前一时刻的隐状态来改变，不断地循环这一过程直至输入处理完毕：** | ||
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| $$ h_t=Recrurent(x_t,h_{t-1})$$ | ||
| <p align="center"> |
tink2123
reviewed
May 27, 2019
| ## 硬件环境的要求 | ||
| 本文档支持CPU和GPU训练，如果您使用了本文配套的docker镜像，请注意：该镜像对GPU的支持仅限于CUDA 8，cuDNN 5。 | ||
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| ### 说明: ### | |
| 1. 硬件环境要求： | |
| 本文可支持在CPU、GPU下运行 | |
| 2. Docker镜像支持的CUDA/cuDNN版本： | |
| 如果使用了Docker运行Book，请注意：这里所提供的默认镜像的GPU环境为 CUDA 8/cuDNN 5，对于NVIDIA Tesla V100等要求CUDA 9的 GPU，使用该镜像可能会运行失败。 | |
| 3. 文档和脚本中代码的一致性问题： | |
| 请注意：为使本文更加易读易用，我们拆分、调整了train.py的代码并放入本文。本文中代码与train.py的运行结果一致，可直接运行[train.py](url)进行验证。 |
| $$h_t=f(x_t,h_{t-1})=\sigma(W_{xh}x_t+W_{hh}h_{t-1}+b_h)$$ | ||
| <p align="center"> | ||
| <img src="https://github.com/JesseyXujin/book/blob/doc_3/06.understand_sentiment/image/formula_rnn.png?raw=true" width = "65%" align="center"/><br/> | ||
| </p> |
Contributor
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链接最好可以用book 仓库下的图片链接，虽然无法预览，但是等pr merge之后就可以了
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