Tensorflow中卷积神经网络的内存估计 [英] Memory Estimation for Convolution Neural Network in Tensorflow

问题描述

我正在使用张量流和卷积神经网络处理图像分类问题. 我的模型有以下几层.

I am working on a Image classification problem using tensorflow and Convolution Neural Network. My model is having following layers.

• 输入的图片大小为2456x2058
• 3个卷积层{Con1-shape(10,10,1,32); Con2-shape(5,5,32,64); Con3-shape(5,5,64,64)}
• 3个最大池2x2层
• 1个完全连接的层.
  

我曾尝试使用NVIDIA-SMI工具，但是它显示了模型运行时的GPU内存消耗.
我想知道在GPU上运行模型之前是否有任何方法或方法可以找到内存的估算值.这样我就可以在考虑可用内存的情况下设计模型.
我尝试使用此估算方法，但是我计算出的内存和观察到的内存利用率彼此之间无处可寻.

I have tried using the NVIDIA-SMI tool but it shows me the GPU memory consumption as the model runs.
I would like to know if there is any method or a way to find the estimate of memory before running the model on GPU. So that I can design models with the consideration of available memory.
I have tried using this method for estimation but my calculated memory and observed memory utilisation are no where near to each other.

推荐答案

据我了解，当您使用tensorflow-gpu打开会话时，它将在GPUS中分配所有可用的内存.因此，当您查看nvidia-smi输出时，即使实际上只使用了一部分内存，也始终会看到相同数量的已用内存.打开会话时有一些选项可强制Tensorflow仅分配一部分可用内存(请参阅

As far as I understand, when you open a session with tensorflow-gpu, it allocates all the memory in the GPUS that are available. So, when you look at the nvidia-smi output, you will always see the same amount of used memory, even if it actually uses only a part of it. There are options when opening a session to force tensorflow to allocate only a part of the available memory (see How to prevent tensorflow from allocating the totality of a GPU memory? for instance)

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