问题描述
在高级深度学习库Keras中，存在多种类型的循环层；其中包括LSTM(长期短期记忆)和CuDNNLSTM.根据 Keras文档，CuDNNLSTM是:
In Keras, the high-level deep learning library, there are multiple types of recurrent layers; these include LSTM (Long short term memory) and CuDNNLSTM. According to the Keras documentation, a CuDNNLSTM is a:
我相信Keras会尽可能自动使用GPU.根据 TensorFlow构建说明，要使TensorFlow GPU后端正常工作，您将需要CuDNN:
It is my belief that Keras automatically uses the GPU wherever possible. According to the TensorFlow build instructions, to have a working TensorFlow GPU backend, you will need CuDNN:
- NVIDIA的Cuda工具包(> = 7.0).我们建议使用9.0版.有关详细信息，请参阅NVIDIA文档.确保按照NVIDIA文档中的说明将相关的Cuda路径名附加到LD_LIBRARY_PATH环境变量中.
- 与NVIDIA Cuda Toolkit相关的NVIDIA驱动程序.
- cuDNN (> = v3).我们建议使用6.0版.有关详细信息，请参阅NVIDIA文档，尤其是将适当的路径名附加到LD_LIBRARY_PATH环境变量的说明.
- NVIDIA's Cuda Toolkit (>= 7.0). We recommend version 9.0. For details, see NVIDIA's documentation. Ensure that you append the relevant Cuda pathnames to the LD_LIBRARY_PATH environment variable as described in the NVIDIA documentation.
- The NVIDIA drivers associated with NVIDIA's Cuda Toolkit.
- cuDNN (>= v3). We recommend version 6.0. For details, see NVIDIA's documentation, particularly the description of appending the appropriate pathname to your LD_LIBRARY_PATH environment variable.
因此，使用TensorFlow GPU后端，CuDNNLSTM与普通LSTM有何不同?找到可用的TensorFlow GPU后端后，会自动选择CuDNNLSTM并替换普通的LSTM吗?
Therefore, how would a CuDNNLSTM differ in any way from a normal LSTM using a TensorFlow GPU backend? Will CuDNNLSTM be automatically selected and replace the normal LSTM when an available TensorFlow GPU backend is found?
推荐答案
您为什么不亲自尝试一下，看看呢?以我为例，用LSTM训练模型需要10分钟30秒.只需将通话从LSTM()切换到CuDNNLSTM()，就花费了不到一分钟的时间.
Why don't you try it out for yourself and see?In my case, training a model with LSTM took 10mins 30seconds.Simply switching the call from LSTM() to CuDNNLSTM() took less than a minute.
我还注意到，切换到CuDNNLSTM()也会大大提高model.evaluate()和model.predict()的速度.
I also noticed that switching to CuDNNLSTM() speeds up model.evaluate() and model.predict() substantially as well.
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