I am a newbie in Deep Learning libraries and thus decided to go with Keras. While implementing a NN model, I saw the
batch_size parameter in
Now, I was wondering if I use the
SGD optimizer, and then set the
batch_size = 1,
m = no. of training examples and 1 <
m, then I would be actually implementing Stochastic, Batch and Mini-Batch Gradient Descent respectively. However, on the other hand, I felt using SGD as the optimizer would by default ignore the
batch_size parameter, since SGD stands for Stochastic Gradient Descent and it should always use a batch_size of 1 (i.e use a single data point for each iteration of gradient descent).
I would be grateful if someone could clarify as to which of the above two cases is true.