Manually Plotting the graph from R-CNN training parameters

Question

Abdussalam Elhanashi 2019-11-29

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/493867-manually-plotting-the-graph-from-r-cnn-training-parameters

评论： Raunak Gupta 2019-12-5

采纳的回答： Raunak Gupta

在 MATLAB Online 中打开

Hi Guys

I have a problem when i want to make a graph for the training phase like between Epoch & Mini-batch Loss

Herein the code

load('gTruth.mat')
smokedetection = selectLabels(gTruth,'car');
if isfolder(fullfile('TrainingData'))
    cd TrainingData
else
    mkdir TrainingData
end 
addpath('TrainingData');
options = trainingOptions('sgdm', ...
  'MiniBatchSize', 32, ...
  'InitialLearnRate', 1e-6, ...
  'MaxEpochs', 10);
layers = [
    imageInputLayer([32 32 3],"Name","imageinput")
    convolution2dLayer([5 5],32,"Name","conv","BiasLearnRateFactor",2,"Padding",[2 2 2 2],"WeightsInitializer","narrow-normal")
    maxPooling2dLayer([3 3],"Name","maxpool","Stride",[2 2])
    reluLayer("Name","relu")
    averagePooling2dLayer([3 3],"Name","avgpool","Stride",[2 2])
    fullyConnectedLayer(2,"Name","fc_rcnn","BiasL2Factor",1,"BiasLearnRateFactor",10,"WeightLearnRateFactor",20,"WeightsInitializer","narrow-normal")
    softmaxLayer("Name","softmax")
    classificationLayer("Name","classoutput")];
trainingData = objectDetectorTrainingData(smokedetection,'SamplingFactor',1,...
'WriteLocation','TrainingData');
detector = trainRCNNObjectDetector(trainingData, layers, options,  ...
        'NegativeOverlapRange', [0 0.3]);
save('Detector.mat','detector');
[detector,info] = trainRCNNObjectDetector('Epoch','Mini-batch Loss','Training Accuracy','Base Learning Rate','Mini-batch Accuracy');  
x = ('Epoch');
y = ('Mini-batch Loss');
figure
plot(x,y)
title('Training Phase')
xlabel('Number of Epochs')
ylabel('Training Loss')

Error

Error using trainRCNNObjectDetector
Expected input number 1, trainingData, to be one of these types:
table
Error in vision.internal.cnn.validation.checkGroundTruth (line 2)
validateattributes(gt, {'table'},{'nonempty'}, name, 'trainingData',1);
Error in trainRCNNObjectDetector>parseInputs (line 311)
vision.internal.cnn.validation.checkGroundTruth(trainingData, fname);
Error in trainRCNNObjectDetector (line 248)
[network, params] = parseInputs(trainingData, network, options, mfilename, varargin{:});
Error in TrainingSmokeDetectionwithRCNN (line 29)
[detector,info] = trainRCNNObjectDetector('Epoch','Mini-batch Loss','Training Accuracy','Base
Learning Rate','Mini-batch Accuracy');

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Answer 1

Raunak Gupta 2019-12-2

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/493867-manually-plotting-the-graph-from-r-cnn-training-parameters#answer_404268

在 MATLAB Online 中打开

Hi,

The trainingOptions for trainRCNNObjectDetector does not support plotting the train-progress while the training is going on, but the training parameters are returned at the end of training if trainRCNNObjectDetector is used with following arguments.

[detector,info] = trainRCNNObjectDetector(___)

In the script from the question following command looks incorrect,

[detector,info] = trainRCNNObjectDetector('Epoch','Mini-batch Loss','Training Accuracy','Base Learning Rate','Mini-batch Accuracy'); 

instead the above can be replaced by

[detector,info] = trainRCNNObjectDetector(trainingData, layers, options, 'NegativeOverlapRange', [0 0.3]); 

Here info will be returned as a structure which contains the ‘Training Accuracy’, ‘Training Loss’ and ‘Base Learning Rate’ for each iteration. These can be then plotted as per required. The error was there because correct input arguments weren’t passed.

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Abdussalam Elhanashi 2019-12-2

在 MATLAB Online 中打开

Hi Raunak Gupta

I made this following code but i am still getting error

load('gTruth.mat')
smokedetection = selectLabels(gTruth,'car');
if isfolder(fullfile('TrainingData'))
    cd TrainingData
else
    mkdir TrainingData
end 
addpath('TrainingData');
options = trainingOptions('sgdm', ...
  'MiniBatchSize', 32, ...
  'InitialLearnRate', 1e-6, ...
  'MaxEpochs', 10);
layers = [
    imageInputLayer([32 32 3],"Name","imageinput")
    convolution2dLayer([5 5],32,"Name","conv","BiasLearnRateFactor",2,"Padding",[2 2 2 2],"WeightsInitializer","narrow-normal")
    maxPooling2dLayer([3 3],"Name","maxpool","Stride",[2 2])
    reluLayer("Name","relu")
    averagePooling2dLayer([3 3],"Name","avgpool","Stride",[2 2])
    fullyConnectedLayer(2,"Name","fc_rcnn","BiasL2Factor",1,"BiasLearnRateFactor",10,"WeightLearnRateFactor",20,"WeightsInitializer","narrow-normal")
    softmaxLayer("Name","softmax")
    classificationLayer("Name","classoutput")];
trainingData = objectDetectorTrainingData(smokedetection,'SamplingFactor',1,...
'WriteLocation','TrainingData');
[detector,info] = trainRCNNObjectDetector(trainingData, layers, options,  ...
        'NegativeOverlapRange', [0 0.3]);
save('Detector.mat','detector');
x = ('Epoch');
y = ('Mini-batch Loss');
figure
plot(x,y)
title('Training Phase')
xlabel('Number of Epochs')
ylabel('Training Loss')
 
Error using plot
Invalid first data argument.
Error in TrainingSmokeDetectionwithRCNN (line 33)
plot(x,y)
 

Abdussalam Elhanashi 2019-12-5

Hi Raunak Gupta

reference to this issue i already plot many graphs even for the example using the RCNN for stop sign by Matlab but i got fluactuation AS SHOWN IN IMAGE is it possible to improve it for the given code?