question about the results? about calc

[nmerrill67]

Hello. I have stored the exact data and readmes containing the command line arguments used in testNet.py in this google drive link. The Alderley dataset has to have the frames aligned in order to work with the code, and they have been in the data in the drive. As for your issues with training, I would suggest cross-validating the snapshotted models from different iterations. I only needed 200,000 iterations to achieve optimal results. Since it is an unsupervised learning method, we care less about the loss and more about the internal representation. Additionally, if you used a small batch size, more iterations may be required. I lowered the batch size from what I used in the released model for this repo since it may be too large for most users. Since I trained on multiple GPUs, I had an effective batch size on the order of 1800 instead of the default 256. You can enable multi-gpu training with nccl with Caffe. With this batch size and number of iterations, the number of epochs is roughly 42, which is the number of times the model sees all of the training data. I hope this helps.