- Setting the ExecutionEnvironment option to predict and classify or the ExecutionEnvironment input to trainingOptions.
- Lowering the MiniBatchSize option to predict and classify or the MiniBatchSize input to trainingOptions. This will decrease the load on your GPU memory.
Is it possible to use both CPU and GPU to train convolution neural network (CNN)?
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Hello, I have designed a CNN with 25 layers for medical image classification. The dataset consists of 10000 images of size 227X227X3. The PC has 8GB RAM and GPU (2GB). I am facing out of memory problem for the designed network but it works well for smaller networks. Is is possible to use CPU and GPU in parallel to solve the memory issue? Kindly suggest to solve the problem. I am using MATLAB version 2017b. Thank you
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Joss Knight
2018-6-11
Yes it is. Are you averse to reading documentation? If not, try:
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sotiraw sotiroglou
2020-7-12
the question seems clear, both in title and in context. Next time please take a minute to actually read it before you try to insult with your answers . this way you will also avoid embarass yourself by not understanding what people asks
Joss Knight
2020-7-13
Well it wasn't clear to me and unless you are the original poster I suggest you not put words into their mouth. I certainly did not intend any offence, but I would happily apologise to the original poster if I was careless with my words.
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