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Mixture usage of CPU and GPU in Keras

I am building a neural network on Keras, including multiple layers of LSTM, Permute and Dense.

It seems LSTM is GPU-unfriendly. So I did research and use 

With tf.device('/cpu:0'):
   out = LSTM(cells)(inp)

But based on my understanding about with, with is try...finally block to ensure that clean-up code is executed. I don’t know whether the following CPU/GPU mixture usage code works or not? Will they accelerate speed of training?

With tf.device('/cpu:0'):
  out = LSTM(cells)(inp)
With tf.device('/gpu:0'):
  out = Permute(some_shape)(out)
With tf.device('/cpu:0'):
  out = LSTM(cells)(out)
With tf.device('/gpu:0'):
  out = Dense(output_size)(out)

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Answer

As you may read here – tf.device is a context manager which switches a default device to this passed as its argument in a context (block) created by it. So this code should run all '/cpu:0' device at CPU and rest on GPU.

The question will it speed up your training is really hard to answer because it depends on the machine you use – but I don’t expect computations to be faster as each change of a device makes data to be copied between GPU RAM and machine RAM. This could even slow down your computations.

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