The batch size defines the number of samples that will be propagated through the network. For instance, let's say you have 1050 training samples and you want to set up a batch_size equal to 100. The algorithm takes the first 100 samples (from 1st to 100th) from the training dataset and trains the network. Next it takes the second 100 samples (from 101st to 200th) and trains the network again. We can keep doing this procedure until we have propagated through all samples of the network. A problem usually happens with the last set of samples. In our example we've used 1050 which is not divisible by 100 without remainder. The simplest solution is just to get the final 50 samples and train the network.

Advantages of using a batch size < number of all samples: It requires less memory. Since you train the network using fewer samples, the overall training procedure requires less memory. That's especially important if you are not able to fit the whole dataset in your machine's memory. Typically networks train faster with mini-batches. That's because we update the weights after each propagation. In our example we've propagated 11 batches (10 of them had 100 samples and 1 had 50 samples) and after each of them we've updated our network's parameters. If we used all samples during propagation we would make only 1 update for the network's parameter.

Disadvantages of using a batch size < number of all samples: The smaller the batch the less accurate the estimate of the gradient will be.

An epoch usually means one iteration over all of the training data. For instance if you have 20,000 images and a batch size of 100 then the epoch should contain 20,000 / 100 = 200 steps. However I usually just set a fixed number of steps like 1000 per epoch even though I have a much larger data set. At the end of the epoch I check the average cost and if it improved I save a checkpoint. There is no difference between steps from one epoch to another. I just treat them as checkpoints.

People often shuffle around the data set between epochs. I prefer to use the random.sample function to choose the data to process in my epochs. So say I want to do 1000 steps with a batch size of 32. I will just randomly pick 32,000 samples from the pool of training data.

Iterations is the number of batches needed to complete one epoch. The number of batches is equal to number of iterations for one epoch.

Let’s say we have 2000 training examples that we are going to use . We can divide the dataset of 2000 examples into batches of 500 then it will take 4 iterations to complete 1 epoch. Where Batch Size is 500 and Iterations is 4, for 1 complete epoch.