Working with Batch and Stochastic Training
While TensorFlow updates our model variables according to the prior described back propagation, it can operate on anywhere from one datum observation to a large group of data at once. Operating on one training example can make for a very erratic learning process, while using a too large batch can be computationally expensive. Choosing the right type of training is crucial to getting our machine learning algorithms to converge to a solution.
In order for TensorFlow to compute the variable gradients for back propagation to work, we have to measure the loss on a sample or multiple samples. Stochastic training is only putting through one randomly sampled data-target pair at a time, just like we ...