Retraining YAMNet for audio classification returns channel mismatch error in “deep.internal.train.Trainer/train”
I am retraining YAMNet for a binary classification task, operating on spectrograms of audio signals. My training audio has two classes, positive and negative. Audio is preprocessed & features extracted using yamnetPreprocess(). When training the network, trainnet() produces the following error:
Error using deep.internal.train.Trainer/train (line 74)
Number of channels in predictions (2) must match the number of
channels in the targets (3).
Error in deep.internal.train.ParallelTrainer>iTrainWithSplitCommunicator (line 227)
remoteNetwork = train(remoteTrainer, remoteNetwork, workerMbq);
Error in deep.internal.train.ParallelTrainer/computeTraining (line 127)
spmd
Error in deep.internal.train.Trainer/train (line 59)
net = computeTraining(trainer, net, mbq);
Error in deep.internal.train.trainnet (line 54)
net = train(trainer, net, mbq);
Error in trainnet (line 42)
[net,info] = deep.internal.train.trainnet(mbq, net, loss, options, …
Error in train_DenseNet_detector_from_semi_synthetic_dataset (line 192)
[trained_network, train_info] = trainnet(trainFeatures, trainLabels’, net, "crossentropy", options);
My understanding of this error is that it indicates a mismatch between the number of classes the network expects, and the number of classes in the dataset. I do not see how this can be possible, considering the number of classes in the network is explicitly set by the number of classes in the datastore:
classNames = unique(ads.Labels);
numClasses = numel(classNames);
net = audioPretrainedNetwork("yamnet", NumClasses=numClasses);
My script is based on this MATLAB tutorial: audioPretrainedNetwork and there are no functional differences in the way I’m building datastores or preprocessing the data. The training options and the call to trainnet() are configured as follows:
options = trainingOptions(‘adam’, …
InitialLearnRate = initial_learn_rate, …
MaxEpochs = max_epochs, …
MiniBatchSize = mini_batch_size, …
Shuffle = "every-epoch", …
Plots = "training-progress", …
Metrics = "accuracy", …
Verbose = 1, …
ValidationData = {single(validationFeatures), validationLabels’}, …
ValidationFrequency = validationFrequency,…
ExecutionEnvironment="parallel-auto");
[trained_network, train_info] = trainnet(trainFeatures, trainLabels’, net, "crossentropy", options);
Relevant variable dimensions are as follows:
>> unique(ads.Labels)
ans =
2×1 categorical array
negative
positiveNoisy
>> size(trainLabels)
ans =
1 16240
>> size(trainFeatures)
ans =
96 64 1 16240
>> size(validationLabels)
ans =
1 6960
>> size(validationFeatures)
ans =
96 64 1 6960
The only real differences between my script and the MATLAB tutorial are that I’m using parallel execution in the training solver, and the datastore outputEnvironment is set to "gpu" . If I set ExecutionEnvironment = "auto" instead of "parallel-auto" and set ads.OutputEnvironment = ‘cpu’ the error stack is shorter, but the problem is the same:
Error using trainnet (line 46)
Number of channels in predictions (2) must match the number of channels in
the targets (3).
Error in train_DenseNet_detector_from_semi_synthetic_dataset (line 189)
[trained_network, train_info] = trainnet(trainFeatures, trainLabels’, net, "crossentropy", options);
Please could someone give me some advice? The root cause of this is buried in the deep learning toolbox, and it’s a little beyond me right now.
Thanks,
BenI am retraining YAMNet for a binary classification task, operating on spectrograms of audio signals. My training audio has two classes, positive and negative. Audio is preprocessed & features extracted using yamnetPreprocess(). When training the network, trainnet() produces the following error:
Error using deep.internal.train.Trainer/train (line 74)
Number of channels in predictions (2) must match the number of
channels in the targets (3).
Error in deep.internal.train.ParallelTrainer>iTrainWithSplitCommunicator (line 227)
remoteNetwork = train(remoteTrainer, remoteNetwork, workerMbq);
Error in deep.internal.train.ParallelTrainer/computeTraining (line 127)
spmd
Error in deep.internal.train.Trainer/train (line 59)
net = computeTraining(trainer, net, mbq);
Error in deep.internal.train.trainnet (line 54)
net = train(trainer, net, mbq);
Error in trainnet (line 42)
[net,info] = deep.internal.train.trainnet(mbq, net, loss, options, …
Error in train_DenseNet_detector_from_semi_synthetic_dataset (line 192)
[trained_network, train_info] = trainnet(trainFeatures, trainLabels’, net, "crossentropy", options);
My understanding of this error is that it indicates a mismatch between the number of classes the network expects, and the number of classes in the dataset. I do not see how this can be possible, considering the number of classes in the network is explicitly set by the number of classes in the datastore:
classNames = unique(ads.Labels);
numClasses = numel(classNames);
net = audioPretrainedNetwork("yamnet", NumClasses=numClasses);
My script is based on this MATLAB tutorial: audioPretrainedNetwork and there are no functional differences in the way I’m building datastores or preprocessing the data. The training options and the call to trainnet() are configured as follows:
options = trainingOptions(‘adam’, …
InitialLearnRate = initial_learn_rate, …
MaxEpochs = max_epochs, …
MiniBatchSize = mini_batch_size, …
Shuffle = "every-epoch", …
Plots = "training-progress", …
Metrics = "accuracy", …
Verbose = 1, …
ValidationData = {single(validationFeatures), validationLabels’}, …
ValidationFrequency = validationFrequency,…
ExecutionEnvironment="parallel-auto");
[trained_network, train_info] = trainnet(trainFeatures, trainLabels’, net, "crossentropy", options);
Relevant variable dimensions are as follows:
>> unique(ads.Labels)
ans =
2×1 categorical array
negative
positiveNoisy
>> size(trainLabels)
ans =
1 16240
>> size(trainFeatures)
ans =
96 64 1 16240
>> size(validationLabels)
ans =
1 6960
>> size(validationFeatures)
ans =
96 64 1 6960
The only real differences between my script and the MATLAB tutorial are that I’m using parallel execution in the training solver, and the datastore outputEnvironment is set to "gpu" . If I set ExecutionEnvironment = "auto" instead of "parallel-auto" and set ads.OutputEnvironment = ‘cpu’ the error stack is shorter, but the problem is the same:
Error using trainnet (line 46)
Number of channels in predictions (2) must match the number of channels in
the targets (3).
Error in train_DenseNet_detector_from_semi_synthetic_dataset (line 189)
[trained_network, train_info] = trainnet(trainFeatures, trainLabels’, net, "crossentropy", options);
Please could someone give me some advice? The root cause of this is buried in the deep learning toolbox, and it’s a little beyond me right now.
Thanks,
Ben I am retraining YAMNet for a binary classification task, operating on spectrograms of audio signals. My training audio has two classes, positive and negative. Audio is preprocessed & features extracted using yamnetPreprocess(). When training the network, trainnet() produces the following error:
Error using deep.internal.train.Trainer/train (line 74)
Number of channels in predictions (2) must match the number of
channels in the targets (3).
Error in deep.internal.train.ParallelTrainer>iTrainWithSplitCommunicator (line 227)
remoteNetwork = train(remoteTrainer, remoteNetwork, workerMbq);
Error in deep.internal.train.ParallelTrainer/computeTraining (line 127)
spmd
Error in deep.internal.train.Trainer/train (line 59)
net = computeTraining(trainer, net, mbq);
Error in deep.internal.train.trainnet (line 54)
net = train(trainer, net, mbq);
Error in trainnet (line 42)
[net,info] = deep.internal.train.trainnet(mbq, net, loss, options, …
Error in train_DenseNet_detector_from_semi_synthetic_dataset (line 192)
[trained_network, train_info] = trainnet(trainFeatures, trainLabels’, net, "crossentropy", options);
My understanding of this error is that it indicates a mismatch between the number of classes the network expects, and the number of classes in the dataset. I do not see how this can be possible, considering the number of classes in the network is explicitly set by the number of classes in the datastore:
classNames = unique(ads.Labels);
numClasses = numel(classNames);
net = audioPretrainedNetwork("yamnet", NumClasses=numClasses);
My script is based on this MATLAB tutorial: audioPretrainedNetwork and there are no functional differences in the way I’m building datastores or preprocessing the data. The training options and the call to trainnet() are configured as follows:
options = trainingOptions(‘adam’, …
InitialLearnRate = initial_learn_rate, …
MaxEpochs = max_epochs, …
MiniBatchSize = mini_batch_size, …
Shuffle = "every-epoch", …
Plots = "training-progress", …
Metrics = "accuracy", …
Verbose = 1, …
ValidationData = {single(validationFeatures), validationLabels’}, …
ValidationFrequency = validationFrequency,…
ExecutionEnvironment="parallel-auto");
[trained_network, train_info] = trainnet(trainFeatures, trainLabels’, net, "crossentropy", options);
Relevant variable dimensions are as follows:
>> unique(ads.Labels)
ans =
2×1 categorical array
negative
positiveNoisy
>> size(trainLabels)
ans =
1 16240
>> size(trainFeatures)
ans =
96 64 1 16240
>> size(validationLabels)
ans =
1 6960
>> size(validationFeatures)
ans =
96 64 1 6960
The only real differences between my script and the MATLAB tutorial are that I’m using parallel execution in the training solver, and the datastore outputEnvironment is set to "gpu" . If I set ExecutionEnvironment = "auto" instead of "parallel-auto" and set ads.OutputEnvironment = ‘cpu’ the error stack is shorter, but the problem is the same:
Error using trainnet (line 46)
Number of channels in predictions (2) must match the number of channels in
the targets (3).
Error in train_DenseNet_detector_from_semi_synthetic_dataset (line 189)
[trained_network, train_info] = trainnet(trainFeatures, trainLabels’, net, "crossentropy", options);
Please could someone give me some advice? The root cause of this is buried in the deep learning toolbox, and it’s a little beyond me right now.
Thanks,
Ben yammnet, audio, deep learning, trainnet, classification, signal processing, machine learning, image processing MATLAB Answers — New Questions
