Default training not converging

Running just the train_softmax.py command in the example run.sh script with the sample data doesn’t seem to converge, even at 50 epochs.

Command:

python -u ./code/1-development/train_softmax.py --num_epochs=50 --batch_size=3 --development_dataset_path=data/development_sample_dataset_speaker.hdf5 --train_dir=results/TRAIN_CNN_3D/train_logs

Output:

Loss:

Learning rate:

[astorfi]

Section VI

The output of the last layer (FC5) will be fed to the softmax layer which has the cardinality of N = 511, where N is the number of speakers in the development phase. For the enrollment and evaluation stages, 100 subjects have been used and the speaker utterances are split into two equal parts for two aforementioned phases.

However, I accept it could be explained in a better way in the paper.

About the number of samples per speaker, In my experiments, I had more than 50 per speaker. Perhaps if you take 50 for development and 50 for enrollment/evaluation it can demonstrate a fair experiment. However, your background model may not be that strong for generalization. In any case, you can give it a shot!

[skanderm]

Oops, I might have had an old version of the paper:

I’ll have a look at the newest version and re-open if there’s an issue. Thanks for clarifying!