Working with batch and stochastic training

While TensorFlow updates our model variables according to back propagation, it can operate on anything from one-datum observation to a large batch of data at once. Operating on one training example can make for a very erratic learning process, while using too large a batch can be computationally expensive. Choosing the right type of training is crucial for getting our machine learning algorithms to converge to a solution.

Getting ready

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 only works on one randomly sampled data-target pair at a time, just as we did in the previous recipe. Another option is to put a larger portion of the training examples in at a time and average the loss for the gradient calculation. The sizes of the training batch can vary, up to and including the whole dataset at once. Here, we will show how to extend...