Error in helperModClassTrainingOptions (line 29) 'CheckpointPath',checkpointPath,...
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john karli on 16 Feb 2022
Commented: Joss Knight on 18 Feb 2022
I want to train the model using following link
I want to save every epochs but when i run the following section
checkpointPath = pwd;
maxEpochs = 20;
miniBatchSize = 128;
options = helperModClassTrainingOptions(maxEpochs,miniBatchSize,...
trainedNettime = trainNetwork(rxTrainFrames,rxTrainLabels,lgraph_1 ,options);
I got the error
Unrecognized function or variable 'checkpointPath'.
Error in helperModClassTrainingOptions (line 29)
my helperModClassTrainingOptions function is
function options = helperModClassTrainingOptions(maxEpochs,miniBatchSize,...
%helperModClassTrainingOptions Modulation classification training options
% OPT = helperModClassTrainingOptions(MAXE,MINIBATCH,NTRAIN,Y,YLABEL)
% returns the training options, OPT, for the modulation classification
% CNN, where MAXE is the maximum number of epochs, MINIBATCH is the mini
% batch size, NTRAIN is the number of training frames, Y is the
% validation frames and YLABEL is the labels.
% This function configures the training options to use an SGDM solver.
% By default, the 'ExecutionEnvironment' property is set to 'auto', where
% the trainNetwork function uses a GPU if one is available or uses the
% CPU, if not. To use the GPU, you must have a Parallel Computing Toolbox
% license. Set the initial learning rate to 2e-2. Reduce the learning
% rate by a factor of 10 every 9 epochs. Set 'Plots' to
% 'training-progress' to plot the training progress.
% See also ModulationClassificationWithDeepLearningExample.
% Copyright 2019 The MathWorks, Inc.
validationFrequency = floor(trainingSize/miniBatchSize);
options = trainingOptions('sgdm', ...
'LearnRateSchedule', 'piecewise', ...
'LearnRateDropPeriod', 9, ...
Joss Knight on 16 Feb 2022
You need to pass the checkpointPath variable to your function.
Joss Knight on 18 Feb 2022
The final validation is computed after a final epoch to compute the batch normalization statistics. Some networks are particularly sensitive to the difference between the mini-batch statistics and those of the whole dataset. Make sure your dataset is shuffled and your minibatch size is as large as possible. To avoid this (at a small additional performance cost), using moving averages (see BatchNormalizationStatistics training option).
I can't explain why it's not checkpointing the network every epoch.
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