Importance of number of steps in an epoch for LSTM model training in Keras
An epoch consists of going through all your training samples once. And one step/iteration refers to training over a single minibatch. So if you have 1,000,000 training samples and use a batch size of 100, one epoch will be equivalent to 10,000 steps, with 100 samples per step.
A high-level neural network framework may let you set either the number of epochs or total number of training steps. But you can't set them both since one directly determines the value of the other.
Effect of Batch Size on Model Behavior: Small batch results generally in rapid learning but a volatile learning process with higher variance. Larger batch sizes slow down the learning process but the final stages result in a convergence to a more stable model exemplified by lower variance.
Whats the difference between two LSTM models A and B that are trained on same data, but the batches are shuffled randomly for each epoch, that A has 14 steps per epoch and B has 132 steps per epoch?
Which one will perform better in validation?