Is there a way to use weights without using gpuArray

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

[Walter Roberson]

What happens if at the beginning of that section of code, you assign

gpuArray = @(x) x;

which makes gpuArray effectively do nothing, as if the call were not there ? That would tell you whether weights can still be used without gpuArray (you might also have to change an option or two to turn off GPU)

[Marina Ghobrial]

yup, that worked, thank you so much for your help!

One more question, I am not sure if you can answer it but do you know how I can correct this error. They are talking about "fc1 = fullyConnectedLayer(576,'BiasLearnRateFactor',2);":

Error using nnet.cnn.layer.FullyConnectedLayer/set.Weights (line 164)

Expected input to be of size 576xM, but it is of size 64x576.

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([64 576])*0.1));
fc2 = fullyConnectedLayer(4,'BiasLearnRateFactor',2);
 fc2.Weights = gpuArray(single(randn([4 64])*0.1));

[Walter Roberson]

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

[Marina Ghobrial]

It worked! Thank you so much for all your help!