I'm working with Caffe and am interested in comparing my training and test errors in order to determine if my network is overfitting or underfitting. However, I can't seem to figure out how to have Caffe report training error. It will show training loss (the value of the loss function computed over the batch), but this is not useful in determining if the network is overfitting/underfitting. Is there a straightforward way to do this?
I'm using the Python interface to Caffe (pycaffe). If I could get access to the raw training set somehow, I could just put batches through with forward passes and evaluate the results. But, I can't seem to figure out how to access more than the currently-processing batch of training data. Is this possible? My data is in a LMDB format.
In the train_val.prototxt file change the source in the TEST phase to point to the training LMDB database (by default it points to the validation LMDB database) and then run this command:
$ ./build/tools/caffe test -solver models/bvlc_reference_caffenet/solver.prototxt -weights models/bvlc_reference_caffenet/<caffenet_train_iter>.caffemodel -gpu 0