I am training my model with keras. When I compare the performance on GPU vs CPU. The CPU version is much faster as the GPU version
How i can fix these errors below?
I tried to force tensorflow to the GPU, i get these errors:
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device to node 'gradients/simple_rnn_1/while/Select_1_grad/Select/f_acc':
Could not satisfy explicit device specification '/device:GPU:0' because no supported kernel for GPU devices is available.
Colocation Debug Info:
Colocation group had the following types and devices:
Tile: CPU
StackPush: GPU CPU
Relu: GPU CPU
ReluGrad: GPU CPU
ZerosLike: GPU CPU
Select: GPU CPU
StackPop: GPU CPU
AddN: GPU CPU
RefEnter: GPU CPU
Stack: GPU CPU
My Model looks like this:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
masking_1 (Masking) (None, None, 3) 0
_________________________________________________________________
simple_rnn_1 (SimpleRNN) (None, None, 50) 2700
_________________________________________________________________
time_distributed_1 (TimeDist (None, None, 11) 561
_________________________________________________________________
activation_1 (Activation) (None, None, 11) 0
=================================================================
Total params: 3,261
Trainable params: 3,261
EDIT: When i switch the backend to theano, the same net runs much faster on the GPU, i think there is a problem with "tile" on GPU in tensorflow
If you are using ReLU, try use a Tanh loss.
I was using ReLU for a LSTM-DNN model at 100 epochs. Each Epoch went from about 14 second to 2
hopefully this solves your problem - I was running on GPU too so it could just be your processor unfortunately - use google Colab if you are bottlenecked - you get access to free GPU usage
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