I am medical student with only a very basic background in CNN's and TensorFlow, so I appreciate any advice.
I implemented a CNN with ~20K black and white images that are 32x32 pixels. I chose to partition my dataset into ~19K images as training data and ~1K images as testing data. Using Tensorflow and TFLearn, I classified my images into one of three classes and plotted accuracy over time with TensorBoard.
My questions are:
- Why does the accuracy suddenly spike down randomly into the mid 90 percents, then shoot back up?
- How can I tell if I am "overfitting" my data set?
- Is my breakdown of training vs. testing data optimal? (i.e. I chose to use 5% of my images to test and 95% of my images to train. Is there a better combination?)