CODEWITHSUNDEEP
pythonmemorytensorflowconv-neural-network
Motiss
Motiss

Reputation: 82

Why is tensorflow consuming this much memory?

And TensorFlow is consuming more that the available memory (causing the program to crash, obviously).

My question is : why does TensorFlow requires this much memory to run my network ? I don't understand what is taking this much space (maybe caching the data several time to optimize convolution computation ? Saving all the hidden outputs for backpropagation purpose ?). And is there a way to prevent TensorFlow from consuming this much memory ?

Side notes :

Upvotes: 2

Views: 2139

Answers (1)

Akash Goyal
Akash Goyal

Reputation: 1303

As you have mentioned:

All my convolutions are 5x5 windows, 1x1 stride, with (from 1st one to last one) 32, 64, 128 and 256 features. I am using leaky ReLUs and 2x2 max pooling. FC layers are composed of 64 and 3 neurones.

So, the memory consumption of your network goes like :

Input: 640x640x3 = 1200 (in KB)

C1: 636x636x32 = 12.5 MB (stride=1 worked)

P1: 635x635x32 = 12.3 MB (stride=1 worked)

C2: 631x631x64 = 24.3 MB

P2: 630x630x64 = 24.2 MB

C3: 626x626x128 = 47.83 MB

P3: 625x625x128 = 47.68 MB

C4: 621x621x256 = 94.15 MB

P4: 620x620x256 = 93.84 MB

FC1: 64 = 0.0625 KB (negligible)

FC2: 3 = 0.003 KB (negligible)

Total for one image = ~ 358 MB

For batch of 56 image = 56 x 358 ~19.6 GB

That's why your network does not run on 6 GB. Try with some higher stride or lower sized image to adjust it into 6 GB space. And it should work.

You can refer this to better understand memory consumption calculation.

Upvotes: 6

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