Is there a way to use weights without using gpuArray

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I found this code online and was wondering if there is another way to use it without the gpuArray function. I keep recieving this error when I use it and I am not familiar with gpu:
Error using gpuArray
Failed to load graphics driver. Unable to load library 'nvcuda.dll'. The error was:
The specified module could not be found.
Update or reinstall your graphics driver. For more information on GPU support, see GPU Support by Release.
varSize = 21;
conv1 = convolution2dLayer(5,varSize,'Padding',2,'BiasLearnRateFactor',2);
conv1.Weights = gpuArray(single(randn([5 5 3 varSize])*0.0001));
fc1 = fullyConnectedLayer(64,'BiasLearnRateFactor',2);
fc1.Weights = gpuArray(single(randn([64 576])*0.1));
fc2 = fullyConnectedLayer(4,'BiasLearnRateFactor',2);
fc2.Weights = gpuArray(single(randn([4 64])*0.1));
i want to be able to use convulation neural network and be able to use the weights because I know they help the progrma run faster. so my question is: Is there a way to use weights without using gpuArray?
Thank you
  4 Comments
Walter Roberson
Walter Roberson on 25 Jul 2021
varSize = 21;
gpuArray = @(x) x;
conv1 = convolution2dLayer(5,varSize,'Padding',2,'BiasLearnRateFactor',2);
conv1.Weights = gpuArray(single(randn([5 5 3 varSize])*0.0001));
fc1 = fullyConnectedLayer(576,'BiasLearnRateFactor',2);
fc1.Weights = gpuArray(single(randn([576, 64])*0.1));
fc2 = fullyConnectedLayer(4,'BiasLearnRateFactor',2);
fc2.Weights = gpuArray(single(randn([4 64])*0.1))
fc2 =
FullyConnectedLayer with properties: Name: '' Hyperparameters InputSize: 64 OutputSize: 4 Learnable Parameters Weights: [4×64 single] Bias: [] Show all properties
fc1
fc1 =
FullyConnectedLayer with properties: Name: '' Hyperparameters InputSize: 64 OutputSize: 576 Learnable Parameters Weights: [576×64 single] Bias: [] Show all properties
fc2
fc2 =
FullyConnectedLayer with properties: Name: '' Hyperparameters InputSize: 64 OutputSize: 4 Learnable Parameters Weights: [4×64 single] Bias: [] Show all properties

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