Feedforward Nueral Networks Training

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Felicia Ojinji
Felicia Ojinji on 2 Dec 2020
Answered: Swetha Polemoni on 7 Dec 2020
I have trained a feedward neural network. I used the tr function to get the netwrok parameters shown below. I would like to know the time elapsed in training the network as this would help me make a better comparison while using other training functions. However, I do not understand what " time: [1×82 double]" means.
Please can someone tell me how i can find out the time taken to train the network in seconds?
>> tr
tr =
struct with fields:
trainFcn: 'trainlm'
trainParam: [1×1 struct]
performFcn: 'mse'
performParam: [1×1 struct]
derivFcn: 'defaultderiv'
divideFcn: 'dividerand'
divideMode: 'sample'
divideParam: [1×1 struct]
trainInd: [1×6132 double]
valInd: [1×1314 double]
testInd: [1×1314 double]
stop: 'Validation stop.'
num_epochs: 81
trainMask: {[1×8760 double]}
valMask: {[1×8760 double]}
testMask: {[1×8760 double]}
best_epoch: 71
goal: 0
states: {'epoch' 'time' 'perf' 'vperf' 'tperf' 'mu' 'gradient' 'val_fail'}
epoch: [1×82 double]
time: [1×82 double]
perf: [1×82 double]
vperf: [1×82 double]
tperf: [1×82 double]
mu: [1×82 double]
gradient: [1×82 double]
val_fail: [1×82 double]
best_perf: 2.0020e+06
best_vperf: 1.8338e+06
best_tperf: 2.1547e+06
Thank you

Answers (1)

Swetha Polemoni
Swetha Polemoni on 7 Dec 2020
Hi Felicia Ojinji,
  • It is my understanding that you want to calculate how much time has been taken by your network to train. You may find "tic" and "toc" functions useful. Below code
tic
A = rand(12000,4400);
B = rand(12000,4400);
toc
The above code gives the elapsed time, in seconds ,between tic and toc. In other words time taken to generate matrices A and B. You may place your code between tic and toc to get the time taken by those lines of code to run.
  • You can also use "trainingOptions" for plotting training progress, where information about elapsed time is also given. To plot training progress during training, specify 'training-progress' as the 'Plots' value in trainingOptions. Here is an example that shows how to plot training progress.

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