It’s my first post here, I finished the first part of the book and I’ve wrote a simple image classifier.
I recently went to the Uffizi Museum in Florence and there I toke some pictures of paints and statues, so I wrote down a neural network to identify if an image contains a paint or a statue.
It works very well despite the small number of data for training, I’m amazed, it even classified a paint that portray a statue as a paint with a 99% of confidence!
While writing the code for the NN I noticed that if I use a batch size
bs of 5 the
train_loss increased after epoch 3 (on a total of 4)
dls = datablock.dataloaders('/content/images', bs=5) learn = cnn_learner(dls, resnet18, metrics=error_rate) learn.fine_tune(4)
instead if I use a batch size of 10 the
train_loss decreases from epoch 1 to epoch 4 as I expected.
Do you know why using a small batch size the
train_loss at a certain epoch starts to increase?