Why mini-batch size dosn't make any diferance in trining speed of my neural network?

I have created a neural network just to do some benchmarks and it seems that the training time is not effected by the MiniBatchSize option.

I have tried the following code with batch=32 and batch=1000.

I have 1491 data sequences .

the length is 10 for each sequence of the training data.

I am getting 60 sec for training.(for 500 epochs)

I have tried the same architecture in python using keras with tensorflow. my results there have changed significantly when I have changed the batch size. 17 sec for batch=1000 compared to 140 sec with batch=32.

I am also getting the same training time in matlab regardless of training algorithm ('sgdm' / 'rmsprop' / 'adam')

Why is it happening?

am I doing something wrong?

inputSize = 10;
lstm_neurons=100;
maxEpochs = 500;
batch=32;

layers = [ ...
  sequenceInputLayer(finaal_inputSize)
  fullyConnectedLayer(finaal_inputSize)
  lstmLayer(lstm_neurons,'OutputMode','sequence')
  lstmLayer(lstm_neurons,'OutputMode','sequence')
  fullyConnectedLayer(1)
  regressionLayer];

 options = trainingOptions('adam', ...
    'ExecutionEnvironment','gpu', ...
    'MaxEpochs',maxEpochs, ...
    'Verbose',0, ... 
    'InitialLearnRate' ,learningrate , ...
    'MiniBatchSize',batch);

 temp_nets= trainNetwork(x_train,y_train,layers,options);

[Tran Vinh]

Hi J R,

Did you find a solution yet? I also got this issue. If you or anyone found the solutions, pls share :D

Thank you