What is epoch and mini batch?
Epoch means one pass over the full training set. Batch means that you use all your data to compute the gradient during one iteration. Mini-batch means you only take a subset of all your data during one iteration.
Is Mini batch faster than batch?
Mini Batch Gradient Descent SGD can be used when the dataset is large. Batch Gradient Descent converges directly to minima. SGD converges faster for larger datasets. We use a batch of a fixed number of training examples which is less than the actual dataset and call it a mini-batch.
How are epochs used in Batch Gradient descent?
An epoch corresponds to one whole training dataset sweep. This sweep can be performed in several ways. Batch mode: Gradient of loss over the whole training dataset is used to update model weights. One optimisation iteration corresponds to one epoch. Stochastic mode: Gradient of loss over one training dataset point is used to update model weights.
Why do you use mini batch in SGD?
This way, you get a way higher update rate. Mini Batching: Since the frequent updates of SGD are quite costly (updating the gradients is kind of tedious to perform), and can lead to worse results in certain circumstances, it is helpful to aggregate multiple (but not all) examples into one update.
What is the difference between 1 epoch and 1 epoch?
From the definitions I know, 1 epoch = going through all training examples once to do one weight update. batch_size is used in optimizer that divide the training examples into mini batches. Each mini batch is of size batch_size. I am not familiar with adam optimization, but I believe it is a variation of the GD or Mini batch GD.
Why is the graph of cost vs epochs smooth?
The graph of cost vs epochs is also quite smooth because we are averaging over all the gradients of training data for a single step. The cost keeps on decreasing over the epochs.