Build your own music critic

First, click New project to create a project. Name it something like Song mood, so you know what kind of project it is. Naming is important.

Please note that by working with this dataset, you accept the author’s license in the Dataset licenses section of the Knowledge center.

This is a big dataset, so it will take some time to download and upload the dataset.

Waiting is normal when handling big datasets.

All samples in the dataset are by default split into 10% validation, 10% test, and 80% training subsets. Usually, the default settings are used to train a deployed model for real-world use.
However, this dataset is so large and it’s always a good idea to test your model first. You will therefore start by using only 8% of the dataset for training and 2% for validation.

In the Experiment wizard, name the experiment, then in the:

Since you used the magic Experiment wizard, everything is set up and ready to be trained. So just click Run to start to train the model.

While the first experiment runs you can build and run a concurrent experiment to find out if you can improve the experiment.

Note: If you’re on the Free plan you can run 1 experiment at a time. All other plans can run concurrent experiments.

Duplicate the experiment and then use the left side section to add blocks and change settings in the new model. As long as you keep the same loss function (in this case, binary crossentropy) you can compare the models' results and see which one is the best in the Evaluation view.

Example:
If there is a large discrepancy between training and validation losses, try to introduce Dropout and or Batch normalization blocks to improve generalization. If the training loss is very high, the model is not learning well enough. After you have adjusted your model you can compare the two different results in the Evaluation view, to see which one performed the best.

Navigate to the Evaluation view. As the training of the models advances, epoch by epoch, the training and validation performance metrics are visualized in the evaluation graphs.

Often an experiment is early stopped. Early stopping monitors the evolution of the given metric after every epoch. If the metric has not improved for Patience epochs in a row, the experiment is early stopped. You don’t want to run an experiment for too long to prevent overfitting, and there is no point to continue to train if the model doesn’t improve.

In this tutorial, we’ve kept things small just to test out our ideas. We’ve only used 5 out of 46 moods plus used the 8%-training and 2%-validation subsets. Now when you’re satisfied with your model, you should train it on the complete dataset with all moods to see what happens. This will take a much longer time though, since we’re using all data.

To do this, navigate to the Datasets view and click on Go to draft to create a new editable version of the dataset. In this version, create a new feature set with all 46 moods.
Click on Save version to make it version 2 of the dataset.

Then navigate to the Modeling view and click on Duplicate to create a fresh copy of the model.

In the project Settings, under Dataset, select #2 as the Version, and Default split as split.

Finally, you will need to update the model to work with all the new moods.
Click on the Target block and set the target feature to the one that includes all 46 moods. Now change the number of Nodes in the last Dense block to 46 to match the number of moods. Then Run the experiment again to train using the full dataset.

If you want to learn how to deploy your model and put it to use, you’ll find all info in the Deployment view section.

Congratulations! You have built a multi-label classification model that labels songs according to mood. It took time, but you’ve just built something fundamentally cool. Achievement unlocked!

Think of other dense data types you can represent as a mel spectrogram. Or other datasets that you can now classify into multiple classes.