Error using activations
Expected layer to be one of these types:
numeric
Instead its type was nnet.cnn.layer.Layer.
Error in nnet.internal.cnn.util.validateNetworkLayerNameOrIndex (line 26)
validateattributes(layerNameOrIndex, {'numeric'},...
Error in DAGNetwork/activationsSeries (line 263)
layerID = nnet.internal.cnn.util.validateNetworkLayerNameOrIndex(layerID, this.Layers, 'activations');
Error in SeriesNetwork/activations (line 779)
Y = this.UnderlyingDAGNetwork.activationsSeries(X, layerID, varargin{:});
AnisotropyDatasetPath = fullfile(matlabroot,'Training', 'Anisotropy');
IsotropyDatasetPath = fullfile(matlabroot,'Training', 'Isotropy');
FillerDatasetPath = fullfile(matlabroot,'Training', 'Filler');
TrainingDatasetPath = fullfile(matlabroot,'Training');
cropDatasetPath = fullfile('C:\Users\ezxtg4\Downloads\JPEG pics', 'crops');
imds = imageDatastore(TrainingDatasetPath, 'IncludeSubfolders',true,...
'LabelSource','foldernames');
labelCount = countEachLabel(imds)
numTrainFiles = 999;
[imdsTrain,imdsValidation] = splitEachLabel(imds,numTrainFiles,'randomize');
layers = [
imageInputLayer([227 227 3])
convolution2dLayer(3,8,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,16,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,32,'Padding','same')
batchNormalizationLayer
reluLayer
fullyConnectedLayer(3)
softmaxLayer
classificationLayer];
options = trainingOptions('sgdm', ...
'InitialLearnRate',0.01, ...
'MaxEpochs',4, ...
'Shuffle','every-epoch', ...
'ValidationData',imdsValidation, ...
'ValidationFrequency',30, ...
'Verbose',false, ...
'Plots','training-progress');
net = trainNetwork(imdsTrain,layers,options);
YPred = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;
accuracy = sum(YPred == YValidation)/numel(YValidation)
testImage = imread('C:\Users\ezxtg4\Downloads\JPEG pics\crops\crop 1.jpeg');
testLabel = imdsValidation.Labels(1)
ds = augmentedImageDatastore([227 227 3], testImage, 'ColorPreprocessing', 'gray2rgb');
imageFeatures = activations(net, ds, layers, 'OutputAs', 'columns');
predictedLabel = predict(classifier, imageFeatures, 'ObservationsIn', 'columns')
